Showing papers by "Yuyin Zhou published in 2023"

Utilization of, satisfaction toward, and challenges for Internet-based healthcare services provided by primary health institutions: Evidence from China

Abstract: Background The rapid development of “Internet plus healthcare” in China has provided new ways for the innovative development of primary healthcare. In addition, a series of favorable policies have been issued to promote Internet-based healthcare services in primary health institutions. Objective The aim of this study was to describe the utilization of, satisfaction toward, and challenges faced by Internet-based healthcare services provided by primary health institutions in China. Methods A self-designed structured questionnaire was employed to collect related data in January 2022 through Credamo. The questionnaire mainly included sociodemographic characteristics, health-related information, utilization of, satisfaction toward, and challenges faced by Internet-based healthcare services provided by primary health institutions. Descriptive analysis was used to describe the sociodemographic characteristics, utilization, satisfaction, and challenges by subgroups. The Wilcoxon rank-sum test was carried out to examine the differences in satisfaction with Internet-based healthcare services between participants who ever received these services and those who did not. A multiple logistic regression model was also used to examine the factors influencing the utilization of Internet-based healthcare services provided by primary health institutions. Results A total of 10,600 residents were included in the final analysis, of whom 5,754 (54.3%) were women. Overall, 51.3% (5,434) of the total participants ever used Internet-based healthcare services provided by primary health institutions. Among those who used Internet-based healthcare services, the most widely used services were procedure-related consultation services (63.7%). The satisfaction among those who ever used it was significantly higher than that among those who did not (84.7 vs. 45.4%; p-value < 0.001). One of the biggest challenges (69.3%) expressed by the residents was that it was difficult for the elderly to use Internet-based services, followed by community doctors with low capacity of providing primary care online (49.0%) and residents were worried about the information security and privacy protection (48.5%). Younger people, people with lower education levels, and people with chronic diseases were significantly more likely to use Internet-based healthcare services provided by primary health institutions (P < 0.05). Conclusion Among 10,600 residents surveyed in China in 2022, more than half of the people used Internet-based healthcare services provided by primary health institutions, and most of them were satisfied, although subgroups significant differences existed. The most common use was procedure-related (e.g., online registration and result query), and several challenges of using Internet-based healthcare services exist (e.g., information safety and usage among elderly people). Therefore, it is important to further improve Internet-based primary healthcare services based on the population perception of achieving healthy China in 2030.

Migration experiences and reported commercial and non-commercial sexual behaviors among newly diagnosed HIV infections in China: a cross-sectional study

Abstract: Migration is known to influence human health. China has a high migration rate and a significant number of people who are HIV-positive, but little is known about how these factors intersect in sexual risk behaviors.This study aimed to explore sexual risk behaviors between migrants and non-migrants among newly diagnosed HIV infections, and assess the changes of sexual risk behaviors with length of stay in the current city of migrants.A cross-sectional questionnaire was conducted among people newly diagnosed with HIV from July 2018 to December 2020 who lived in Zhejiang Province. In the study, sexual risk behaviors included having multiple sexual partners and unprotected sexual behaviors (in commercial sexual behaviors, non-commercial sexual behaviors, heterosexual behaviors, and homosexual behaviors). Binary logistic regression models were employed to explore the influencing factors of sexual risk behaviors, measured by multiple sexual partners and unprotected sexual partners.A total of 836 people newly diagnosed with HIV/AIDS were incorporated in the study and 65.31% (546) were migrants. The percentages of non-commercial sexual behaviors among migrants were statistically higher than those of non-migrants. Commercial heterosexual behavior was higher among non-migrants compared with migrants. The proportion of study participants having unprotected sexual behaviors and multiple sexual partners with commercial/non-commercial partners was both higher among migrants compared with non-migrants. Among migrants, the likelihood of sexual risk behaviors in both commercial and non-commercial sex increased in the first 3 years and reduced after 10 years. Compared with non-migrants, migrants were statistically associated with multiple sexual partners [P = .007, odds ratio (OR) = 1.942]. However, migrants did not exhibit a significant difference in unprotected sexual behaviors compared with non-migrants. In addition, migrants aged between 18 and 45 years who relocated to the current city in the past 2-3 years tended to have multiple sexual partners (P < .05).People newly diagnosed with HIV engaged in different sexual risk behaviors among migrants and non-migrants and more attention should be paid to migrants. For non-migrants, it is urgent to promote the prevention of commercial sexual behaviors. For migrants, prevention of non-commercial sexual behaviors and universal access to health care especially for new arrivals who migrated to the current city for 2-3 years are needed. Moreover, sexual health education and early HIV diagnosis are necessary for the entire population.

Patient-specific Auto-segmentation on Daily kVCT Images for Adaptive Radiotherapy.

Abstract: Purpose This study explored deep-learning-based patient-specific auto-segmentation using transfer learning on daily RefleXion kilovoltage computed tomography (kVCT) images to facilitate adaptive radiation therapy, based on data from the first group of patients treated with the innovative RefleXion system. Methods and Materials For head and neck (HaN) and pelvic cancers, a deep convolutional segmentation network was initially trained on a population data set that contained 67 and 56 patient cases, respectively. Then the pretrained population network was adapted to the specific RefleXion patient by fine-tuning the network weights with a transfer learning method. For each of the 6 collected RefleXion HaN cases and 4 pelvic cases, initial planning computed tomography (CT) scans and 5 to 26 sets of daily kVCT images were used for the patient-specific learning and evaluation separately. The performance of the patient-specific network was compared with the population network and the clinical rigid registration method and evaluated by the Dice similarity coefficient (DSC) with manual contours being the reference. The corresponding dosimetric effects resulting from different auto-segmentation and registration methods were also investigated. Results The proposed patient-specific network achieved mean DSC results of 0.88 for 3 HaN organs at risk (OARs) of interest and 0.90 for 8 pelvic target and OARs, outperforming the population network (0.70 and 0.63) and the registration method (0.72 and 0.72). The DSC of the patient-specific network gradually increased with the increment of longitudinal training cases and approached saturation with more than 6 training cases. Compared with using the registration contour, the target and OAR mean doses and dose-volume histograms obtained using the patient-specific auto-segmentation were closer to the results using the manual contour. Conclusions Auto-segmentation of RefleXion kVCT images based on the patient-specific transfer learning could achieve higher accuracy, outperforming a common population network and clinical registration-based method. This approach shows promise in improving dose evaluation accuracy in RefleXion adaptive radiation therapy. This study explored deep-learning-based patient-specific auto-segmentation using transfer learning on daily RefleXion kilovoltage computed tomography (kVCT) images to facilitate adaptive radiation therapy, based on data from the first group of patients treated with the innovative RefleXion system. For head and neck (HaN) and pelvic cancers, a deep convolutional segmentation network was initially trained on a population data set that contained 67 and 56 patient cases, respectively. Then the pretrained population network was adapted to the specific RefleXion patient by fine-tuning the network weights with a transfer learning method. For each of the 6 collected RefleXion HaN cases and 4 pelvic cases, initial planning computed tomography (CT) scans and 5 to 26 sets of daily kVCT images were used for the patient-specific learning and evaluation separately. The performance of the patient-specific network was compared with the population network and the clinical rigid registration method and evaluated by the Dice similarity coefficient (DSC) with manual contours being the reference. The corresponding dosimetric effects resulting from different auto-segmentation and registration methods were also investigated. The proposed patient-specific network achieved mean DSC results of 0.88 for 3 HaN organs at risk (OARs) of interest and 0.90 for 8 pelvic target and OARs, outperforming the population network (0.70 and 0.63) and the registration method (0.72 and 0.72). The DSC of the patient-specific network gradually increased with the increment of longitudinal training cases and approached saturation with more than 6 training cases. Compared with using the registration contour, the target and OAR mean doses and dose-volume histograms obtained using the patient-specific auto-segmentation were closer to the results using the manual contour. Auto-segmentation of RefleXion kVCT images based on the patient-specific transfer learning could achieve higher accuracy, outperforming a common population network and clinical registration-based method. This approach shows promise in improving dose evaluation accuracy in RefleXion adaptive radiation therapy.

Vulnerability to HIV Infection Among International Immigrants in China: Cross-sectional Web-Based Survey

Abstract: Background The rising number of migrants worldwide, including in China given its recent rapid economic development, poses a challenge for the public health system to prevent infectious diseases, including sexually transmitted infections (STIs) caused by risky sexual behaviors. Objective The aim of this study was to explore the risky sexual behaviors of international immigrants living in China to provide evidence for establishment of a localized public health service system. Methods Risky sexual behaviors were divided into multiple sexual partners and unprotected sexual behaviors. Basic characteristics, sexual knowledge, and behaviors of international immigrants were summarized with descriptive statistics. Multivariate logistic regression analyses were used to identify factors associated with risky sexual behaviors, and the associations of demographic characteristics and risk behaviors with HIV testing and intention to test for HIV. Results In total, 1433 international immigrants were included in the study, 61.76% (n=885) of whom had never heard of STIs, and the mean HIV knowledge score was 5.42 (SD 2.138). Overall, 8.23% (118/1433) of the participants had been diagnosed with an STI. Among the 1433 international immigrants, 292 indicated that they never use a condom for homosexual sex, followed by sex with a stable partner (n=252), commercial sex (n=236), group sex (n=175), and casual sex (n=137). In addition, 119 of the international immigrants had more than three sex partners. Individuals aged 31-40 years were more likely to have multiple sexual partners (adjusted odds ratio [AOR] 2.364, 95% CI 1.149-4.862). Married participants were more likely to have unprotected sexual behaviors (AOR 3.096, 95% CI –1.705 to 5.620), whereas Asians were less likely to have multiple sexual partners (AOR 0.446, 95% CI 0.328-0.607) and unprotected sexual behaviors (AOR 0.328, 95% CI 0.219-0.492). Women were more likely to have taken an HIV test than men (AOR 1.413, 95% CI 1.085-1.841). Those who were married (AOR 0.577, 95% CI 0.372-0.894), with an annual disposable income >150,000 yuan (~US $22,000; AOR 0.661, 95% CI 0.439-0.995), considered it impossible to become infected with HIV (AOR 0.564, 95% CI 0.327-0.972), and of Asian ethnicity (AOR 0.330, 95% CI 0.261-0.417) were less likely to have an HIV test. People who had multiple sexual partners were more likely to have taken an HIV test (AOR 2.041, 95% CI 1.442-2.890) and had greater intention to test for HIV (AOR 1.651, 95% CI 1.208-2.258). Conclusions International immigrants in China exhibit risky sexual behaviors, especially those aged over 30 years. In addition, the level of HIV-related knowledge is generally low. Therefore, health interventions such as targeted, tailored programming including education and testing are urgently needed to prevent new HIV infections and transmission among international immigrants and the local population.

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery

Abstract: Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. The available methods, including state-of-the-art deep learning techniques, are incapable of imputing the gene expressions reliably because of the lack of a mechanism to explicitly consider the underlying biological knowledge of the system. Here we tackle the problem in two steps to exploit the gene-gene interactions of the system: (i) we reposition the genes in such a way that their spatial configuration reflects their interactive relationships; and (ii) we use a self-supervised 2D convolutional neural network to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.