Digital Strategies Supporting Social Network Approaches to HIV Testing: A Scoping Review.

PURPOSE OF REVIEW: This review captured how digital strategies support social network approaches to promote HIV testing. RECENT FINDING: Overall, 29 studies were identified by searching PubMed and Embase for studies published up to June 2023. Existing studies revealed three types of digital strategies (social media (n = 28), online information channels (n = 4), and multifunctional digital platforms (n = 4)) split into four major modes of digital strategy-supported social-network-based HIV testing promotion: 1) Online outreach and recruiting, 2) gathering and identifying key populations for HIV testing, 3) communicating and disseminating online HIV testing health interventions, and 4) assisting and facilitating HIV testing uptake and distribution. Social network approaches supported by digital strategies yielded advantages in HIV testing education and distribution, which increases HIV testing coverage among key populations. Studies are needed on how to facilitate the use of digital strategies for social network-based HIV testing, as well as how to integrate them with existing HIV testing approaches.

Analysis of gait characteristics and related factors in patients with Parkinson's disease based on wearable devices.

BACKGROUND: Postural instability and gait disorder dominant (PIGD) is one of the most common disabling symptoms of Parkinson's disease (PD), which seriously affects patients' quality of life. Therefore, it is essential to identify PIGD and develop targeted interventions to reduce the risk of PIGD in PD patients. AIM: This study aimed to investigate the gait characteristics of PD patients based on wearable devices and to establish a predictive model for their related influencing factors. METHODS: The retrospective medical records of patients from January 2020 to September 2023 were collected, including 159 patients with PD (divided into PIGD [n = 73] and non-PIGD [n = 86] groups) and 200 healthy patients (as the healthy control group). Information from social demographic data, a blood test, scale scores, gait analysis based on wearable devices, white matter lesions, and the Fazekas scale was extracted and analyzed. RESULTS: Compared with the healthy control group, the mean step length, mean rate, mean angular velocity, and step length were lower in the PD group, while the mean steps were higher in the turning test. The incidence of PIGD was 46% in PD patients, and PD patients with the non-tremor onset mode were more likely to develop PIGD than those with the tremor onset mode. Compared to the non-PIGD group, the PIGD group showed more serious gait problems in different experimental tasks and had a higher Hoehn and Yahr (H-Y) stage, Hamilton Anxiety Scale (HAMA) score, Hamilton Depression Scale score, periventricular white matter (PVWM) score, deep white matter score, and Fazekas scale score, but they had lower hemoglobin levels, D-dimer levels, Tinetti Balance scores, Tinetti Gait scores, Berg Balance Scale scores, and Mini-Mental State Examination (MMSE) scores. Logistic regression analysis showed that the MMSE score was negatively correlated with the occurrence of PIGD, while the HAMA score, H-Y stage, PVWM score, and non-tremor form of onset were positively correlated with the occurrence of PIGD CONCLUSION: The incidence of gait disorder in PD patients is higher than that in the normal population. Moreover, cognitive dysfunction, anxiety state, H-Y stage, PVWM score, and the non-tremor mode of onset can be considered independent risk factors for PIGD.

The quality of telemedicine consultations for sexually transmitted infections in China.

The burden of sexually transmitted infections (STIs) continues to increase in developing countries like China, but the access to STI care is often limited. The emergence of direct-to-consumer (DTC) telemedicine offers unique opportunities for patients to directly access health services when needed. However, the quality of STI care provided by telemedicine platforms remains unknown. After systemically identifying the universe of DTC telemedicine platforms providing on-demand consultations in China in 2019, we evaluated their quality using the method of unannounced standardized patients (SPs). SPs presented routine cases of syphilis and herpes. Of the 110 SP visits conducted, physicians made a correct diagnosis in 44.5% (95% CI: 35.1% to 54.0%) of SP visits, and correctly managed 10.9% (95% CI: 5.0% to 16.8%). Low rates of correct management were primarily attributable to the failure of physicians to refer patients for STI testing. Controlling for other factors, videoconference (vs SMS-based) consultation mode and the availability of public physician ratings were associated with higher-quality care. Our findings suggest a need for further research on the causal determinants of care quality on DTC telemedicine platforms and effective policy approaches to promote their potential to expand access to STI care in developing countries while limiting potential unintended consequences for patients.

Identification of Key Influencers for Secondary Distribution of HIV Self-Testing Kits Among Chinese Men Who Have Sex With Men: Development of an Ensemble Machine Learning Approach.

BACKGROUND: HIV self-testing (HIVST) has been rapidly scaled up and additional strategies further expand testing uptake. Secondary distribution involves people (defined as "indexes") applying for multiple kits and subsequently sharing them with people (defined as "alters") in their social networks. However, identifying key influencers is difficult. OBJECTIVE: This study aimed to develop an innovative ensemble machine learning approach to identify key influencers among Chinese men who have sex with men (MSM) for secondary distribution of HIVST kits. METHODS: We defined three types of key influencers: (1) key distributors who can distribute more kits, (2) key promoters who can contribute to finding first-time testing alters, and (3) key detectors who can help to find positive alters. Four machine learning models (logistic regression, support vector machine, decision tree, and random forest) were trained to identify key influencers. An ensemble learning algorithm was adopted to combine these 4 models. For comparison with our machine learning models, self-evaluated leadership scales were used as the human identification approach. Four metrics for performance evaluation, including accuracy, precision, recall, and F1-score, were used to evaluate the machine learning models and the human identification approach. Simulation experiments were carried out to validate our approach. RESULTS: We included 309 indexes (our sample size) who were eligible and applied for multiple test kits; they distributed these kits to 269 alters. We compared the performance of the machine learning classification and ensemble learning models with that of the human identification approach based on leadership self-evaluated scales in terms of the 2 nearest cutoffs. Our approach outperformed human identification (based on the cutoff of the self-reported scales), exceeding by an average accuracy of 11.0%, could distribute 18.2% (95% CI 9.9%-26.5%) more kits, and find 13.6% (95% CI 1.9%-25.3%) more first-time testing alters and 12.0% (95% CI -14.7% to 38.7%) more positive-testing alters. Our approach could also increase the simulated intervention's efficiency by 17.7% (95% CI -3.5% to 38.8%) compared to that of human identification. CONCLUSIONS: We built machine learning models to identify key influencers among Chinese MSM who were more likely to engage in secondary distribution of HIVST kits. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR) ChiCTR1900025433; https://www.chictr.org.cn/showproj.html?proj=42001.

The construction and operational models of internet hospitals in China: a hospital-based survey study.

BACKGROUND: China has empowered and continues to empower internet hospitals, which saw an increase in their development due to the pandemic, to fight against COVID-19. The construction and operational models of internet hospitals can be categorized as self-constructed and self-managed models, self-constructed and enterprise-run models, hospital and enterprise joint-owned models, and hosted by a third-party platform. Despite the growing importance of internet hospitals, there have been few systematic summaries of their construction and operational models. The primary purpose of the study was to understand the construction and operational models of internet hospitals in China. METHODS: Data was collected from 39 internet hospitals and 356 medical staff between September 2020 and April 2021, via internet hospital and hospital staff surveys. T-tests were used to compare the continuous variables, while Chi-square tests were employed to compare the proportions of categorical variables. The self-perception of the internet hospitals' services was assessed using a 5-point Likert scale on 16 aspects and a root cause analysis was conducted to identify the root causes and influencing factors of current deficiencies experienced by internet hospitals. RESULTS: Among the 39 internet hospitals, 22 (56.4%) were self-constructed and self-managed. Compared to other models of Internet hospitals, self-constructed and self-managed hospitals had lower percentages of professionals providing online services (P = 0.006), numbers of doctors outside of the entity (P = 0.006), numbers of online nurses (P = 0.004), and the ratio of online nurses to offline doctors (P < 0.001). Of the 16 aspects evaluated with regards to the medical staff's self-perception of the internet hospital services, the highest scores were given for fee transparency, fee rationality, travel cost capital, patience and responsibility, and consultation behaviors. The root causes included five aspects: human, channels, prices, services, and time. CONCLUSIONS: While the self-constructed and self-managed model was found to be the most prevalent form of internet hospital in China, the different models of internet hospitals can have an impact on both the quantity and quality of online healthcare services. This study contributes to the existing literature on internet hospitals' construction and operational models, offering additional policy implications for telemedicine management.

Developing and validating a parser-based suicidality detection model in text-based mental health services.

BACKGROUND: Advances in text-mining can potentially aid online text-based mental health services in detecting suicidality. However, false positive remains a challenge. METHODS: Data of a free 24/7 online text-based counseling service in Hong Kong were used to develop a novel parser-based algorithm (PBSD) to detect suicidal ideation while minimizing false alarms. Sessions containing keywords related to suicidality were extracted (N = 1267). PBSD first applies a sentence parser to work out the grammatical structure of each sentence, including subject, object, dependent and modifier. Then a set of syntax rules were applied to judge if a flagged sentence is a true or false positive. Half of the sessions were randomly selected to train PBSD, the remaining were used as the test set. A standard keywords matching model was adopted as the baseline comparison. Accuracy and recall were reported to demonstrate models' performance. RESULTS: Of the 1267 sessions, 585 (46.2 %) were false alarms. The false alarms were categorized into four types: negation-induced false alarms (NIFA; 14 %), subject-induced false alarms (SIFA; 19 %), tense-induced false alarms (TIFA; 30 %), and other types of false alarms (OTFA; 37 %). PBSD significantly outperforms the baseline keywords matching model on accuracy (0.68 vs 0.53, 28.3 %). It successfully amended 36.8 % (105/297) lexicon matching-caused false alarms. The reduction on recall was marginal (1 vs 0.96, 4 %). CONCLUSIONS: The proposed model significantly improves the use of lexicon-based method by reducing false alarms and improving the accuracy of suicidality detection. It can potentially reduce unnecessary panic and distraction caused by false alarms among frontline service-providers.

IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity.

CD8+ T cells can recognize peptides presented by class I human leukocyte antigen (HLA-I) of nucleated cells. Exploring this immune mechanism is essential for identifying T-cell vaccine targets in cancer immunotherapy. Over the past decade, the wealth of data generated by experiments has spawned many computational approaches for predicting HLA-I binding, antigen presentation and T-cell immune responses. Nevertheless, existing HLA-I binding and antigen presentation prediction approaches suffer from low precision due to the absence of T-cell receptor (TCR) recognition. Direct modeling of T-cell immune responses is less effective as TCR recognition's mechanism still remains underexplored. Therefore, directly applying these existing methods to screen cancer neoantigens is still challenging. Here, we propose a novel immune epitope prediction method termed IEPAPI by effectively incorporating antigen presentation and immunogenicity. First, IEPAPI employs a transformer-based feature extraction block to acquire representations of peptides and HLA-I proteins. Second, IEPAPI integrates the prediction of antigen presentation prediction into the input of immunogenicity prediction branch to simulate the connection between the biological processes in the T-cell immune response. Quantitative comparison results on an independent antigen presentation test dataset exhibit that IEPAPI outperformed the current state-of-the-art approaches NetMHCpan4.1 and mhcflurry2.0 on 100 (25/25) and 76% (19/25) of the HLA subtypes, respectively. Furthermore, IEPAPI demonstrates the best precision on two independent neoantigen datasets when compared with existing approaches, suggesting that IEPAPI provides a vital tool for T-cell vaccine design.

Social, lifestyle, and health status characteristics as a proxy for occupational burnout identification: A network approach analysis.

Background: Occupational burnout is a type of psychological syndrome. It can lead to serious mental and physical disorders if not treated in time. However, individuals tend to conceal their genuine feelings of occupational burnout because such disclosures may elicit bias from superiors. This study aims to explore a novel method for estimating occupational burnout by elucidating its links with social, lifestyle, and health status factors. Methods: In this study 5,794 participants were included. Associations between occupational burnout and a set of features from a survey was analyzed using Chi-squared test and Wilcoxon rank sum test. Variables that are significantly related to occupational burnout were grouped into four categories: demographic, work-related, health status, and lifestyle. Then, from a network science perspective, we inferred the colleague's social network of all participants based on these variables. In this inferred social network, an exponential random graph model (ERGM) was used to analyze how occupational burnout may affect the edge in the network. Results: For demographic variables, age (p < 0.01) and educational background (p < 0.01) were significantly associated with occupational burnout. For work-related variables, type of position (p < 0.01) was a significant factor as well. For health and chronic diseases variables, self-rated health status, hospitalization history in the last 3 years, arthritis, cardiovascular diseases, high blood lipid, breast diseases, and other chronic diseases were all associated with occupational burnout significantly (p < 0.01). Breakfast frequency, dairy consumption, salt-limiting tool usage, oil-limiting tool usage, vegetable consumption, pedometer (step counter) usage, consuming various types of food (in the previous year), fresh fruit and vegetable consumption (in the previous year), physical exercise participation (in the previous year), limit salt consumption, limit oil consumption, and maintain weight were also significant factors (p < 0.01). Based on the inferred social network among all airport workers, ERGM showed that if two employees were both in the same occupational burnout status, they were more likely to share an edge (p < 0.0001). Limitation: The major limitation of this work is that the social network for occupational burnout ERGM analysis was inferred based on associated factors, such as demographics, work-related conditions, health and chronic diseases, and behaviors. Though these factors have been proven to be associated with occupational burnout, the results inferred by this social network cannot be warranted for accuracy. Conclusion: This work demonstrated the feasibility of identifying people at risk of occupational burnout through an inferred colleague's social network. Encouraging staff with lower occupational burnout status to communicate with others may reduce the risk of burnout for other staff in the network.

Influenza vaccine uptake among children and older adults in China: a secondary analysis of a quasi-experimental study.

BACKGROUND: Influenza vaccination is the key to prevent influenza-related disease, especially among high-risk populations. However, influenza vaccine uptake in China is low. This secondary analysis of a quasi-experimental trial aimed to understand factors associated with influenza vaccine uptake among children and older people stratified by funding context. METHODS: A total of 225 children (aged 0.5-8 years) and 225 older people (aged 60 years or above) were recruited from three clinics (rural, suburban and urban) in Guangdong Province. Participants were allocated into two groups based on funding contexts: a self-paid group (N = 150, 75 children and 75 older adults) in which participants paid full price for their vaccination; and a subsidized group (N = 300, 150 children and 150 older adults) in which varying levels of financial support was provided. Univariate and multivariable logistic regressions were conducted stratified by funding contexts. RESULTS: Overall, 75.0% (225/300) of participants in the subsidized group and 36.7% (55/150) in the self-paid group got vaccinated. Older adults had lower vaccination rates than children in both funding groups, while both age groups showed much higher uptake in the subsidized group than in the self-paid group (aOR = 5.96, 95% CI: 3.77-9.42, p = 0.001). In the self-paid group, having prior influenza vaccination history of children (aOR:2.61, 95%CI: 1.06-6.42) or older people (aOR:4.76, 95%CI: 1.08-20.90) was associated with increased influenza vaccine uptake compared to those who had no prior vaccination experiences in the family. While in the subsidized group, participants who got married or lived with partners (aOR = 0.32, 0.10-0.98) had lower vaccination uptake than single ones. Trust in providers' advice (aOR = 4.95, 95%CI:1.99, 12.43), perceived effectiveness of the vaccine (aOR: 12.18, 95%CI: 5.21-28.50), and experienced influenza-like illnesses in the family in the past year (aOR = 46.52, 4.10, 533.78) were associated with higher vaccine uptake. CONCLUSIONS: Older people had suboptimal vaccine uptake compared to children in both contexts and need more attention to enhance influenza vaccination. Tailoring interventions to different vaccine funding contexts may help improve influenza vaccination: In self-paid context, motivating people to accept their first ever influenza vaccination may be a promising strategy. In subsidized context, improving public confidence in vaccine effectiveness and providers' advice would be useful.

Prevalence of and Factors Associated With Intimate Partner Violence Victimhood Among Men Who Have Sex With Men in Guangzhou, China.

BACKGROUND: Intimate partner violence (IPV) has been a concern among men who have sex with men (MSM), but less attention has been paid to the factors associated with this population in China. AIMS: We investigated the prevalence of and factors associated with IPV victimhood among MSM in Guangzhou, China. METHODS: Men who have sex with men were recruited from May to November 2017, and data were collected using an anonymous electronic questionnaire. χ2 Tests and nonconditional logistic regressions were used to explore the factors associated with IPV victimhood. RESULTS: A total of 129 in 976 MSM (13.22%) reported experiencing IPV victimhood. Multivariable logistic regression analysis showed that individuals who had condomless anal intercourse (CAI; adjusted odds ratio [aOR], 1.47; 95% confidence interval [CI], 1.00-2.17) or had sex with a female partner (aOR, 1.81; 95% CI, 1.15-2.83) in the past 6 months were at a higher risk of IPV victimhood. Participants who had ever experienced child sexual abuse (CSA) were more likely to experience IPV (aOR, 1.97; 95% CI, 1.32-2.94). Individuals who used rush poppers before sex had a higher risk of IPV (aOR, 1.79; 95% CI, 1.21-2.63). In addition, ever having sex with a female sex partner (aOR, 1.65; 95% CI, 1.04-2.60), ever having used rush poppers before sex (aOR, 1.79; 95% CI, 1.22-2.64) in the past 6 months, and ever having experienced CSA (aOR, 2.01; 95% CI, 1.35-3.01) were associated with experiencing more types of IPV. CONCLUSIONS: Intimate partner violence victimhood was relatively common among MSM in Guangzhou, particularly among those who had CAI, experienced CSA, had sex with a female partner, used rush poppers before sex, and with less education.

Future trajectory of respiratory infections following the COVID-19 pandemic in Hong Kong.

The accumulation of susceptible populations for respiratory infectious diseases (RIDs) when COVID-19-targeted non-pharmaceutical interventions (NPIs) were in place might pose a greater risk of future RID outbreaks. We examined the timing and magnitude of RID resurgence after lifting COVID-19-targeted NPIs and assessed the burdens on the health system. We proposed the Threshold-based Control Method (TCM) to identify data-driven solutions to maintain the resilience of the health system by re-introducing NPIs when the number of severe infections reaches a threshold. There will be outbreaks of all RIDs with staggered peak times after lifting COVID-19-targeted NPIs. Such a large-scale resurgence of RID patients will impose a significant risk of overwhelming the health system. With a strict NPI strategy, a TCM-initiated threshold of 600 severe infections can ensure a sufficient supply of hospital beds for all hospitalized severely infected patients. The proposed TCM identifies effective dynamic NPIs, which facilitate future NPI relaxation policymaking.

Improving cascade outcomes for active TB: A global systematic review and meta-analysis of TB interventions.

BACKGROUND: To inform policy and implementation that can enhance prevention and improve tuberculosis (TB) care cascade outcomes, this review aimed to summarize the impact of various interventions on care cascade outcomes for active TB. METHODS AND FINDINGS: In this systematic review and meta-analysis, we retrieved English articles with comparator arms (like randomized controlled trials (RCTs) and before and after intervention studies) that evaluated TB interventions published from January 1970 to September 30, 2022, from Embase, CINAHL, PubMed, and the Cochrane library. Commentaries, qualitative studies, conference abstracts, studies without standard of care comparator arms, and studies that did not report quantitative results for TB care cascade outcomes were excluded. Data from studies with similar comparator arms were pooled in a random effects model, and outcomes were reported as odds ratio (OR) with 95% confidence interval (CI) and number of studies (k). The quality of evidence was appraised using GRADE, and the study was registered on PROSPERO (CRD42018103331). Of 21,548 deduplicated studies, 144 eligible studies were included. Of 144 studies, 128 were from low/middle-income countries, 84 were RCTs, and 25 integrated TB and HIV care. Counselling and education was significantly associated with testing (OR = 8.82, 95% CI:1.71 to 45.43; I2 = 99.9%, k = 7), diagnosis (OR = 1.44, 95% CI:1.08 to 1.92; I2 = 97.6%, k = 9), linkage to care (OR = 3.10, 95% CI = 1.97 to 4.86; I2 = 0%, k = 1), cure (OR = 2.08, 95% CI:1.11 to 3.88; I2 = 76.7%, k = 4), treatment completion (OR = 1.48, 95% CI: 1.07 to 2.03; I2 = 73.1%, k = 8), and treatment success (OR = 3.24, 95% CI: 1.88 to 5.55; I2 = 75.9%, k = 5) outcomes compared to standard-of-care. Incentives, multisector collaborations, and community-based interventions were associated with at least three TB care cascade outcomes; digital interventions and mixed interventions were associated with an increased likelihood of two cascade outcomes each. These findings remained salient when studies were limited to RCTs only. Also, our study does not cover the entire care cascade as we did not measure gaps in pre-testing, pretreatment, and post-treatment outcomes (like loss to follow-up and TB recurrence). CONCLUSIONS: Among TB interventions, education and counseling, incentives, community-based interventions, and mixed interventions were associated with multiple active TB care cascade outcomes. However, cost-effectiveness and local-setting contexts should be considered when choosing such strategies due to their high heterogeneity.

Predicting Acute Onset of Heart Failure Complicating Acute Coronary Syndrome: An Explainable Machine Learning Approach.

Patients with acute coronary syndrome (ACS) are at high risk of heart failure (HF). Early prediction and management of HF among ACS patients are essential to provide timely and cost-effective care. The aim of this study is to train and evaluate a machine learning model to predict the acute onset of HF subsequent to ACS. A total of 1,028 patients with ACS admitted to Guangdong Second Provincial General Hospital between October 2019 and May 2022 were included in this study. 128 clinical features were ranked using Shapley additive exPlanations (SHAP) values and the top 20% of features were selected for building a balanced random forest (BRF) model. We compared the discriminatory capability of BRF with linear logistic regression (LLR). In the hold-out test set, the BRF model predicted subsequent HF with areas under the curve (AUC) of 0.76 (95% CI: 0.75-0.77), sensitivity of 0.97 (95% CI: 0.96-0.97), positive predictive value (PPV) of 0.73 (95% CI: 0.72-0.74), negative predictive value (NPV) of 0.63 (95% CI: 0.60-0.66), and accuracy of 0.73 (95% CI: 0.72-0.73), respectively. BRF outperforms linear logistic regression by 15.6% in AUC, 3.0% in sensitivity, and 60.8% in NPV. End-to-end machine learning approaches can predict the acute onset of HF following ACS with high prediction accuracy. This proof-of-concept study has the potential to substantially advance the management of ACS patients by utilizing the machine learning model as a triage tool to automatically identify clinically significant patients allowing for prioritization of interventions.

Factors Influencing Adoption and Use of Telemedicine Services in Rural Areas of China: Mixed Methods Study.

BACKGROUND: The shortage of medical resources in rural China reflects the health inequity in resource-limited settings, whereas telemedicine could provide opportunities to fill this gap. However, evidence of patient acceptance of telemedicine services from low- and middle-income countries is still lacking. OBJECTIVE: We aimed to understand the profile of patient end-user telemedicine use and identify factors influencing telemedicine service use in rural China. METHODS: Our study followed a mixed methods approach, with a quantitative cross-sectional survey followed by in-depth semistructured interviews to describe telemedicine use and its associated factors among rural residents in Guangdong Province, China. In the quantitative analysis, explanatory variables included environmental and context factors, household-level factors, individual sociodemographic factors, access to digital health care, and health needs and demand factors. We conducted univariate and multivariate analyses using Firth logistic regression to examine the correlations of telemedicine uptake. A thematic approach was used, guided by the Social Cognitive Theory for the qualitative analysis. RESULTS: A total of 2101 households were recruited for the quantitative survey. With a mean age of 61.4 (SD 14.41) years, >70% (1364/2101, 72.94%) of the household respondents were male. Less than 1% (14/2101, 0.67%) of the respondents reported experience of using telemedicine. The quantitative results supported that villagers living with family members who had a fever in the past 2 weeks (adjusted odds ratio 6.96, 95% CI 2.20-21.98; P=.001) or having smartphones or computers (adjusted odds ratio 3.71, 95% CI 0.64-21.32; P=.14) had marginally higher telemedicine uptake, whereas the qualitative results endorse these findings. The results of qualitative interviews (n=27) also supplemented the potential barriers to telemedicine use from the lack of knowledge, trust, demand, low self-efficacy, and sufficient physical and social support. CONCLUSIONS: This study found extremely low use of telemedicine in rural China and identified potential factors affecting telemedicine uptake. The main barriers to telemedicine adoption among rural residents were found, including lack of knowledge, trust, demand as well as low self-efficacy, and insufficient physical and social support. Our study also suggests strategies to facilitate telemedicine engagement in low-resource settings: improving digital literacy and self-efficacy, building trust, and strengthening telemedicine infrastructure support.

Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis.

Tumor-derived circulating cell-free DNA (cfDNA) provides critical clues for cancer early diagnosis, yet it often suffers from low sensitivity. Here, we present a cancer early diagnosis approach using tumor fractions deciphered from circulating cfDNA methylation signatures. We show that the estimated fractions of tumor-derived cfDNA from cancer patients increase significantly as cancer progresses in two independent datasets. Employing the predicted tumor fractions, we establish a Bayesian diagnostic model in which training samples are only derived from late-stage patients and healthy individuals. When validated on early-stage patients and healthy individuals, this model exhibits a sensitivity of 86.1% for cancer early detection and an average accuracy of 76.9% for tumor localization at a specificity of 94.7%. By highlighting the potential of tumor fractions on cancer early diagnosis, our approach can be further applied to cancer screening and tumor progression monitoring.

Evaluation of a village-based digital health kiosks program: A protocol for a cluster randomized clinical trial.

Background: To address disparities in healthcare quality and access between rural and urban areas in China, reforms emphasize strengthening primary care and digital health utilization. Yet, evidence on digital health approaches in rural areas is lacking. Objective: This study will evaluate the effectiveness of Guangdong Second Provincial General Hospital's Digital Health Kiosk program, which uses the Dingbei telemedicine platform to connect rural clinicians to physicians in upper-level health facilities and provide access to artificial intelligence-enabled diagnostic support. We hypothesize that our interventions will increase healthcare utilization and patient satisfaction, decrease out-of-pocket costs, and improve health outcomes. Methods: This cluster randomized control trial will enroll clinics according to a partial factorial design. Clinics will be randomized to either a control arm with clinician medical training, a second arm additionally receiving Dingbei telemedicine training, or a third arm with monetary incentives for patient visits conducted through Dingbei plus all prior interventions. Clinics in the second and third arm will then be orthogonally randomized to a social marketing arm that targets villager awareness of the kiosk program. We will use surveys and Dingbei administrative data to evaluate clinic utilization, revenue, and clinician competency, as well as patient satisfaction and expenses. Results: We have received ethical approval from Guangdong Second Provincial General Hospital (IRB approval number: GD2H-KY IRB-AF-SC.07-01.1), Peking University (IRB00001052-21007), and the University of North Carolina at Chapel Hill (323385). Study enrollment began April 2022. Conclusions: This study has the potential to inform future telemedicine approaches and assess telemedicine as a method to address disparities in healthcare access.Trial registration number: ChiCTR2100053872.

Age-related changes in the risk of high blood pressure.

Background and aims: Understanding the age-related trend of risk in high blood pressure (BP) is important for preventing heart failure and cardiovascular diseases. But such a trend is still underexplored. This study aims to (a) depict the relationship of BP patterns with age, and (b) understand the trend of high BP prevalence over time in different age groups. Materials and methods: Health check-up data with an observational period of 8 years (January 1, 2011, to December 31, 2018) was used as the data source. A total of 71,468 participants aged over 18 years old with complete information on weight, height, age, gender, glucose, triglyceride, total cholesterol, systolic (SBP), and diastolic blood pressure (DBP) were included for analysis. Generalized additive models were adopted to explore the relationship between the risk of high BP and age. Variance analysis was conducted by testing the trend of high BP prevalence in age groups over time. Results: Risk of high SBP showed a continuous rise from age 35 to 79 years and a concurrent early increase in the risk of high DBP; after age 50-65 years, high DBP risk declined. The risk of SBP rises linearly with age for men, whereas increases non-linearly for women. In addition, a significant increasing trend of high SBP risk among middle-aged people was found during the past decade, men experienced a later but longer period of increase in high SBP than women. Conclusion: The high SBP risk progresses more rapidly in the early lifetime in women, compared to the lifetime thereafter. Thresholds of increasing trend of SBP suggest a possible need for hypertension screening in China after the age of 40.

Correction: Expanding syphilis test uptake using rapid dual self-testing for syphilis and HIV among men who have sex with men in China: A multiarm randomized controlled trial.

[This corrects the article DOI: 10.1371/journal.pmed.1003930.].