Dynamic evolution and spatial difference of public health service supply in economically developed provinces of China: typical evidence from Guangdong Province.

OBJECTIVE: The outbreak of the COVID-19 pandemic has drawn attention from all sectors of society to the level of public health services. This study aims to investigate the level of public health service supply in the four major regions of Guangdong Province, providing a basis for optimizing health resource allocation. METHODS: This article uses the entropy method and panel data of 21 prefecture-level cities in Guangdong Province from 2005 to 2021 to construct the evaluation index system of public health service supply and calculate its supply index. On this basis, the standard deviation ellipse method, kernel density estimation, and Markov chain are used to analyze the spatiotemporal evolution trend of the public health service supply level in Guangdong Province. The Dagum Gini coefficient and panel regression model are further used to analyze the relative differences and the key influencing factors of difference formation. Finally, the threshold effect model is used to explore the action mechanism of the key factors. RESULTS: Overall, the level of public health service supply in Guangdong Province is on an upward trend. Among them, polarization and gradient effects are observed in the Pearl River Delta and Eastern Guangdong regions; the balance of public health service supply in Western Guangdong and Northern Mountainous areas has improved. During the observation period, the level of public health services in Guangdong Province shifted towards a higher level with a smaller probability of leapfrogging transition, and regions with a high level of supply demonstrated a positive spillover effect. The overall difference, intra-regional difference and inter-regional difference in the level of public health service supply in Guangdong Province during the observation period showed different evolutionary trends, and spatial differences still exist. These differences are more significantly positively affected by factors such as the level of regional economic development, the degree of fiscal decentralization, and the urbanization rate. Under different economic development threshold values, the degree of fiscal decentralization and urbanization rate both have a double threshold effect on the role of public health service supply level. CONCLUSION: The overall level of public health service supply in Guangdong Province has improved, but spatial differences still exist. Key factors influencing these differences include the level of regional economic development, the degree of fiscal decentralization, and the urbanization rate, all of which exhibit threshold effects. It is suggested that, in view of the actual situation of each region, efforts should be made to build and maintain their own advantages, enhance the spatial linkage of public health service supply, and consider the threshold effects of key factors in order to optimize the allocation of health resources.

Impact of digital economic development and environmental pollution on residents' health: an empirical analysis based on 279 prefecture-level cities in China.

BACKGROUND: The digital economy based on the internet and IT is developing rapidly in China, which makes a profound impact on urban environmental quality and residents' health activities. Thus, this study introduces environmental pollution as a mediating variable based on Grossman's health production function to explore the impact of digital economic development on the health of the population and its influence path. METHODS: Based on the panel data of 279 prefecture-level cities in China from 2011 to 2017, this paper investigates the acting mechanism of digital economic development on residents' health by employing a combination of mediating effects model and spatial Durbin model. RESULTS: The development of digital economy makes direct improvement on residents' health condition, which is also obtained indirectly by means of environmental pollution mitigation. Besides, from the perspective of spatial spillover effect, the development of digital economy also has a significant promoting effect on the health of adjacent urban residents, and further analysis reveals that the promoting effect in the central and western regions of China is more pronounced than that in the eastern region. CONCLUSIONS: Digital economy can have a direct promoting effect on the health of residents, and environmental pollution has an intermediary effect between digital economy and residents' health; At the same time, there is also a regional heterogeneity among the three relationships. Therefore, this paper believes that the government should continue to formulate and implement scientific digital economy development policies at the macro and micro levels to narrow the regional digital divide, improve environmental quality and enhance the health level of residents.

The 501st case: elevated blood glucose, chronic pancreatitis, and post- pancreatoduodenectomy malnutrition / 中华内科杂志

A 50-year-old man with a 15-year history of elevated blood glucose and an approximately 2-year history of diarrhea was admitted to the Peking Union Medical College Hospital. The initial diagnosis was type 2 diabetes. After repeated pancreatitis and pancreatoduodenectomy, severe pancreatic endocrine and exocrine dysfunction including alternating high and low blood glucose and fat diarrhea occurred. Tests for type 1 diabetes-related antibodies were all negative, C-peptide levels were substantially reduced, fat-soluble vitamin levels were reduced, and there was no obvious insulin resistance. Therefore, a diagnosis of pancreatic diabetes was clear. The patient was given small doses of insulin and supplementary pancreatin and micronutrients. Diarrhea was relieved and blood glucose was controlled. The purpose of this article is to raise clinicians' awareness of the possibility of pancreatic diabetes after pancreatitis or pancreatic surgery. Timely intervention and monitoring may reduce the occurrence of complications.

Serum Uric Acid Level and Its Influencing Factors in Patients with Diabetes Insipidus / 中国医学科学院学报

Objective To investigate the level of serum uric acid in patients with diabetes insipidus (DI),summarize the clinical characteristics of central diabetes insipidus (CDI) patients with hyperuricemia (HUA),and analyze the factors affecting the level of serum uric acid in the patients with CDI. Methods The clinical data of DI patients admitted to Peking Union Medical College Hospital from 2018 to 2021 were retrospectively analyzed.The patients were assigned into a child and adolescent group (≤ 18 years old) and an adult group (>18 years old) according to their ages.The demographic and biochemical data between two groups of patients with and without HUA were compared.Spearman correlation analysis and multiple linear regression analysis were performed to analyze the correlations between serum uric acid level and other factors. Results Among the 420 DI patients,411 patients had CDI (97.9%),including 189 patients with HUA (46.0%).Thirteen (6.9%) out of the 189 CDI patients with HUA presented the disappearance of thirst.The prevalence of HUA in children and adolescents was higher than that in adults (χ2=4.193,P=0.041).The level of serum uric acid in the CDI patients with HUA and disappearance of thirst was higher than those without disappearance of thirst (U=2.593,P=0.010).The multiple linear regression predicted serum creatinine (β=0.472,95%CI=2.451-4.381,P<0.001) and body mass index (β=0.387,95%CI=6.18-12.874,P<0.001) as the independent risk factors of serum uric acid level increment in children and adolescents,while serum creatinine (β=0.361,95%CI=1.016-1.785,P<0.001),body mass index (β=0.208,95%CI=2.321-6.702,P<0.001),triglyceride (β=0.268,95%CI=12.936-28.840,P<0.001),and total cholesterol (β=0.129,95%CI=2.708-22.250,P=0.013) were the independent risk factors in adults. Conclusions The patients with CDI were more likely to have HUA,and the prevalence of HUA in children and adolescents was higher than that in adults.Body mass index,serum creatinine,triglyceride,total cholesterol,and disappearance of thirst were the risk factors for the increased level of serum uric acid in CDI patients.

Spatio-temporal evolution and trend prediction of the incidence of Class B notifiable infectious diseases in China: a sample of statistical data from 2007 to 2020.

BACKGROUND: With the accelerated global integration and the impact of climatic, ecological and social environmental changes, China will continue to face the challenge of the outbreak and spread of emerging infectious diseases and traditional ones. This study aims to explore the spatial and temporal evolutionary characteristics of the incidence of Class B notifiable infectious diseases in China from 2007 to 2020, and to forecast the trend of it as well. Hopefully, it will provide a reference for the formulation of infectious disease prevention and control strategies. METHODS: Data on the incidence rates of Class B notifiable infectious diseases in 31 provinces, municipalities and autonomous regions of China from 2007 to 2020 were collected for the prediction of the spatio-temporal evolution and spatial correlation as well as the incidence of Class B notifiable infectious diseases in China based on global spatial autocorrelation and Autoregressive Integrated Moving Average (ARIMA). RESULTS: From 2007 to 2020, the national incidence rate of Class B notifiable infectious diseases (from 272.37 per 100,000 in 2007 to 190.35 per 100,000 in 2020) decreases year by year, and the spatial distribution shows an "east-central-west" stepwise increase. From 2007 to 2020, the spatial clustering of the incidence of Class B notifiable infectious diseases is significant and increasing year by year (Moran's I index values range from 0.189 to 0.332, p < 0.05). The forecasted incidence rates of Class B notifiable infectious diseases nationwide from 2021 to 2024 (205.26/100,000, 199.95/100,000, 194.74/100,000 and 189.62/100,000) as well as the forecasted values for most regions show a downward trend, with only some regions (Guangdong, Hunan, Hainan, Tibet, Guangxi and Guizhou) showing an increasing trend year by year. CONCLUSIONS: The current study found that since there were significant regional disparities in the prevention and control of infectious diseases in China between 2007 and 2020, the reduction of the incidence of Class B notifiable infectious diseases requires the joint efforts of the surrounding provinces. Besides, special attention should be paid to provinces with an increasing trend in the incidence of Class B notifiable infectious diseases to prevent the re-emergence of certain traditional infectious diseases in a particular province or even the whole country, as well as the outbreak and spread of emerging infectious diseases.

Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images.

Novel coronavirus (COVID-19) has been endangering human health and life since 2019. The timely quarantine, diagnosis, and treatment of infected people are the most necessary and important work. The most widely used method of detecting COVID-19 is real-time polymerase chain reaction (RT-PCR). Along with RT-PCR, computed tomography (CT) has become a vital technique in diagnosing and managing COVID-19 patients. COVID-19 reveals a number of radiological signatures that can be easily recognized through chest CT. These signatures must be analyzed by radiologists. It is, however, an error-prone and time-consuming process. Deep Learning-based methods can be used to perform automatic chest CT analysis, which may shorten the analysis time. The aim of this study is to design a robust and rapid medical recognition system to identify positive cases in chest CT images using three Ensemble Learning-based models. There are several techniques in Deep Learning for developing a detection system. In this paper, we employed Transfer Learning. With this technique, we can apply the knowledge obtained from a pre-trained Convolutional Neural Network (CNN) to a different but related task. In order to ensure the robustness of the proposed system for identifying positive cases in chest CT images, we used two Ensemble Learning methods namely Stacking and Weighted Average Ensemble (WAE) to combine the performances of three fine-tuned Base-Learners (VGG19, ResNet50, and DenseNet201). For Stacking, we explored 2-Levels and 3-Levels Stacking. The three generated Ensemble Learning-based models were trained on two chest CT datasets. A variety of common evaluation measures (accuracy, recall, precision, and F1-score) are used to perform a comparative analysis of each method. The experimental results show that the WAE method provides the most reliable performance, achieving a high recall value which is a desirable outcome in medical applications as it poses a greater risk if a true infected patient is not identified.

A study of self-precaution against the background of the COVID-19 pandemic from the perspective of risk perception attitude theory and social support.

BACKGROUND: In this research, the factors that influence the self-precautionary behavior during the pandemic are explored with the combination of social support and a risk perception attitude framework. METHODS: An online survey was conducted among 429 members to collect information on demographic data, social support, perceptions of outbreak risk, health self-efficacy, and self-precautionary behaviors with the guide of the Social Support Scale, the COVID-19 Risk Perception Scale, the Health Self-Efficacy Scale and the Self-precautionary Behavior Scale. RESULTS: The research shows that among the three dimensions of social support, both objective support and support utilization negatively predict risk perception, while subjective support positively predicts health self-efficacy; health self-efficacy and risk perception significantly predict self-precautionary behavior; the relationship between risk perception and self-precautionary behavior is significantly moderated by health self-efficacy. CONCLUSIONS: The combined influence of social capital and risk perception attitudinal frameworks on self-precautionary behavior is highlighted in this study, with the relationship between the public's risk perception, health self-efficacy, and self-precautionary behavior intentions examined against the background of coronavirus disease 2019 (COVID-19). These findings contribute to understanding the impact of social capital factors on risk perception and health self-efficacy, which provides insight into the current status and influencing factors of the public's precautionary behavior and facilitates early intervention during a pandemic.

[In vivo Study of siRNA Silencing XIAP Gene to Reverse Taxol-resistance in Human Ovarian Cancer Cells].

OBJECTIVE: To study the relationship between down-regulated expression of X linked inhibitor of apoptosis protein (XIAP) gene and the reversal effect of taxol-resistance by using siRNA interference technology in the taxol-resistant ovarian cancer. METHODS: Randomly assigned the nude mice into six groups (6 in each group) . Group A: normal saline; Group B: taxol; Group C: siRNA-NC+normal saline; Group D: siRNA-NC+taxol; Group E: siRNA XIAP+normal saline; Group F: siRNA XIAP+taxol. Each group was dealt with the corresponding processing depending on the agreed protocol and the transplanted tumors had a multi-point injection with reagents related siRNA, one time every 3 days, 9 times (27 d) in total. Taxol (2 mg/kg) was used in the intraperitoneal injection, 0.2 mL every time, once a week, for four weeks. After 27 d of siRNA treatment, xenograft volumes and qualities were measured and the inhibitory rate was calculated; RNA expression levels and protein levels of XIAP gene in xenografts were detected respectively by real-time fluorescent quantitative PCR and Western blot. Apoptosis of the transplanted tumor cells was examined by TUNEL method. RESULTS: Among the six groups, the proliferation of transplanted tumor in Group F was the slowest, and the tumor inhibition rate was the highest compared with control Group A, followed by Group E, and the tumor inhibition rate was the lowest in Group C. Group F and E expressed the lowest XIAP mRNA and protein expressions ( P<0.05, vs. the other 4 groups) .The apoptosis rate was highest in Group F, followed by Group E, and lowest in Group A and C ( P<0.05). CONCLUSION: XIAP siRNA has synergy with taxol in taxol-resistant ovarian cancer cells.

Experimental Simultaneous Learning of Multiple Nonclassical Correlations.

Nonclassical correlations can be regarded as resources for quantum information processing. However, the classification problem of nonclassical correlations for quantum states remains a challenge, even for finite-size systems. Although there exists a set of criteria for determining individual nonclassical correlations, a unified framework that is capable of simultaneously classifying multiple correlations is still missing. In this Letter, we experimentally explored the possibility of applying machine-learning methods for simultaneously identifying nonclassical correlations. Specifically, by using partial information, we applied an artificial neural network, support vector machine, and decision tree for learning entanglement, quantum steering, and nonlocality. Overall, we found that, for a family of quantum states, all three approaches can achieve high accuracy for the classification problem. Moreover, the run time of the machine-learning methods to output the state label is experimentally found to be significantly less than that of state tomography.

[Study on the Relationship Between XIAP Gene and Resistance of Taxol in Ovarian Cancer].

OBJECTIVE: To research the expression of X-linked inhibitor of apoptosis protein gene (XIAP) on paclitaxel resistance in ovarian cancer. METHODS: A2780 and A2780/T cells were treated with paclitaxel respectively at the concentrations of 5 ng/mL,10 ng/mL,20 ng/mL , 40 ng/mL,80 ng/mL,160 ng/mL,320 ng/mL,then the inhibition rate of cells were detected by MTT assay. The expression of XIAP mRNA and protein among the A2780 and A2780/T cells treated respectively with paclitaxel at the concentration of 100 ng/mL was detected by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) and Western blot. The A2780/T cells were divided into blank group,empty group,small interfering RNA (siRNA) XIAP group and siRNA-non-specific group. The expression of XIAP mRNA and protein of four groups were detected by RT-qPCR and Western blot. Apoptotic rate of these groups with addition of paclitaxel at the concentrations of 0 ng/mL,1 000 ng/mL,1 500 ng/mL,2 000 ng/mL and 2 500 ng/mL were detected by flow cytometry. RESULTS: After the treatments on A2780 and A2780/T cells with the different concentrations of paclitaxel,the inhibition rate of A2780 cells were gradually increased with the increased paclitaxel concentrations (P<0.05),while there were no obvious differences in A2780/T cells (P>0.05). After the treatment on these cells with paclitaxel at the concentration of 100 ng/mL,the expression of XIAP mRNA was lower than that non-treatment with paclitaxelin A2780 cells (P<0.05),and the expression of XIAP mRNA in the A2780/T cells were no statistical significance between the treatment group and non-treatment group with paclitaxel (P>0.05). However,the expression of A2780/T cells'XIAP mRNA and protein treated with paclitaxel were higher than A2780 cells' (P<0.05). The expression of XIAP mRNA and protein in siRNA-XIAP group was lower than those of other groups (P<0.05). The apoptotic rate of siRNA-XIAP group was higher than those of other groups treated with the paclitaxel at concentrations of 2 000 ng/mL and 2 500 ng/mL (P<0.05). CONCLUSION: XIAP's high expression on mRNA and protein was correlated with ovarian cancer paclitaxel-resistance,specific siRNA can promote cell apoptosis by reducing the expression of XIAP,and increase the sensitivity of drug-resistant cancer cells to paclitaxel.

Experimental Machine Learning of Quantum States.

Quantum information technologies provide promising applications in communication and computation, while machine learning has become a powerful technique for extracting meaningful structures in "big data." A crossover between quantum information and machine learning represents a new interdisciplinary area stimulating progress in both fields. Traditionally, a quantum state is characterized by quantum-state tomography, which is a resource-consuming process when scaled up. Here we experimentally demonstrate a machine-learning approach to construct a quantum-state classifier for identifying the separability of quantum states. We show that it is possible to experimentally train an artificial neural network to efficiently learn and classify quantum states, without the need of obtaining the full information of the states. We also show how adding a hidden layer of neurons to the neural network can significantly boost the performance of the state classifier. These results shed new light on how classification of quantum states can be achieved with limited resources, and represent a step towards machine-learning-based applications in quantum information processing.

Simulation of molecular spectroscopy with circuit quantum electrodynamics.

Spectroscopy is a crucial laboratory technique for understanding quantum systems through their interactions with the electromagnetic radiation. Particularly, spectroscopy is capable of revealing the physical structure of molecules, leading to the development of the maser-the forerunner of the laser. However, real-world applications of molecular spectroscopy are mostly confined to equilibrium states, due to computational and technological constraints; a potential breakthrough can be achieved by utilizing the emerging technology of quantum simulation. Here we experimentally demonstrate through a toy model, a superconducting quantum simulator capable of generating molecular spectra for both equilibrium and non-equilibrium states, reliably producing the vibronic structure of diatomic molecules. Furthermore, our quantum simulator is applicable not only to molecules with a wide range of electronic-vibronic coupling strength, characterized by the Huang-Rhys parameter, but also to molecular spectra not readily accessible under normal laboratory conditions. These results point to a new direction for predicting and understanding molecular spectroscopy, exploiting the power of quantum simulation.

Combined therapy with thioridazine decreases plasma levels of quetiapine inTaiwanese schizophrenic patients.

BACKGROUND: In this study, the authors studied the effect of thioridazine (TDZ) on the pharmacokinetic profile of quetiapine (QTP) in Taiwanese patients with schizophrenia. METHODS: Sixteen subjects with schizophrenia were recruited for this study. The authors pretreated 8 patients with TDZ 50 mg daily continuously given until the end of the study. QTP was administered to all the participants, and their doses were escalated to 300 mg once daily over a 7-day period and maintained for another week. On day 15, blood samples were collected at 12 time points within an 8-hour interval. The authors assayed the plasma levels of QTP with a high-performance liquid chromatography system coupled with ultraviolet detector. RESULTS: Significantly decreased plasma levels of QTP after oral administration were observed in patients comedicated with TDZ compared with the QTP monotherapy group at 1.5, 2, and 2.5 hours, and the P values were 0.046, 0.001, and 0.005, respectively. The Cmax of QTP was significantly lower in the group comedicated with TDZ (776.9 ± 175.2 versus 1452.3 ± 707.5 ng/mL; P = 0.002). The oral clearance of QTP was significantly higher in the combined group than in the monothreapy group (123.3 ± 66.8 versus 60.3 ± 28.5 L/h; P = 0.03). Other pharmacokinetic parameters were not significantly different. CONCLUSIONS: The coadministration of TDZ significantly decreased plasma QTP level and significantly increased the oral clearance of QTP. Although TDZ is switched to QTP, choosing larger doses of QTP for titration may be necessary to avoid the emergence of psychotic symptoms among schizophrenic patients.