Xiang Guo

Background: College mental health screening relies almost exclusively on self-report questionnaires, which are vulnerable to recall bias, impression management, and administrative burden. Digital phenotyping, the measurement of behavior through human-computer interaction, offers complementary, passively collected signals, yet few systems integrate it with validated screening scales in a privacy-conscious way. Because the required sensors, the computer mouse and the webcam, are already standard on screening workstations, objective behavioral measurement can be added without any new peripherals such as EEG headsets or wristbands, at near-zero hardware cost. We present PsyTrace, a privacy-aware, local-first platform that integrates a multi-scale self-report battery with unobtrusive behavioral sensing to support counseling-center screening workflows. Methods: PsyTrace is implemented as a Tauri 2 (Rust + WebView) desktop application with a Next.js 14 / TypeScript user interface. Assessment methodology comprises built-in validated scales (PHQ-9, GAD-7, C-SSRS, PSS-10, PSQI, SIAS-6, ASLEC) plus locally uploaded custom scales, a four-tier risk taxonomy with per-scale adjustable thresholds. Behavioral sensing captures mouse trajectories throughout the questionnaire (14 kinematic features, 5 heuristic signals), and a camera feed at 320*240. Results: The full screening workflow, informed consent, silent sensing, four-tier risk classification, per-scale risk dashboards, exploratory correlation analytics, and privacy-aware report export, is demonstrated end-to-end. Engineering verification includes successful type-checking, a production build, and 26 DOM-level smoke tests. Conclusions: PsyTrace demonstrates a feasible engineering foundation for multimodal, privacy-preserving college mental health screening. Running on ubiquitous hardware with no additional peripherals, it is also a low-cost, easily promotable screening infrastructure. It is a research prototype, not a diagnostic device: calibration of heuristic thresholds and clinical validation with longitudinal cohorts are required next steps. The software is fully open source, and the complete source code is available on GitHub (github.com/GuoXiang9399/PsyPlat).

Ovarian cancer (OC) exhibits significant ethnic and geographic disparities, with rising incidence in Asia contrasting global declines. While non-modifiable

Dengue fever, an acute Aedes ‐borne infectious disease caused by the dengue virus (DENV), is a substantial global health challenge, necessitating the development of swift and effective surveillance methodologies. We have developed a Recombinase Aided Amplification (RAA)–Cas12a assay that exhibits superior analytical sensitivity compared with real‐time PCR while maintaining comparable specificity. In clinical specimen testing, the assay demonstrated a sensitivity of 92.3% and a specificity of 100%, using a signal‐to‐noise ratio of ≥2.0 as the positivity threshold. Furthermore, the RAA–Cas12a assay displayed remarkable sensitivity in detecting DENV in both adult and larval mosquitoes, with field data from mosquito surveillance aligning closely with the emergence of local dengue cases. Characterized by its simplicity, rapidity, and precision, the RAA–Cas12a approach emerges as a highly promising technology for monitoring virus carriage in field‐collected mosquitoes, thereby functioning as both an early warning system and a crucial indicator for effective dengue outbreak management. The practicality and cost‐effectiveness of this methodology provide a robust foundation for the establishment of an efficient global collaborative surveillance and risk assessment platform to mitigate the rapid spread of dengue.

Mosquitoes, as vectors of deadly diseases like malaria and dengue, exhibit complex and dynamic global distribution patterns influenced by climate change and human activity. However, gaps in species-level data and biases in existing databases hinder accurate risk assessments. Here, we present mMap (v1.01), a comprehensive and curated mosquito distribution database integrating multi-source records from literature (36,242 entries across 1,809 studies), museum collections, and field reports in 6 languages. Systematic data harmonization and validation workflows address taxonomic and spatial uncertainties, enabling family-level analyses previously constrained by fragmented data. Our results reveal significant range expansions for 28 invasive species (e.g., Aedes albopictus in Europe) and corrections to historical distributions (e.g., Ae. vexans in 22 new countries). The interactive mMap platform (accessible at https://mos-map.vercel.app/) provides open-access tools for visualizing spatial-temporal trends, automated SDM forecasting, and expert-curated species profiles. By unifying dispersed data into a scalable framework, mMap advances research on mosquito biodiversity, invasion ecology, and disease risk mapping, offering a critical resource for global health strategies in an era of rapid environmental change.

Background: The RNA virosphere's extensive diversity and its role in emerging infectious diseases underscore the importance of non-targeted sequencing for identifying unknown or rare pathogens, including co-infections. However, enriching low-abundance viral sequences in RNA metaviromics, particularly in the preparation of cDNA libraries and their compatibility with next-generation sequencing (NGS) and third-generation sequencing (TGS), remains challenging. Therefore, our objective is to develop and systematically assess a practical RNA metavirome methodology specifically tailored for the enrichment of low-abundance viral sequences within samples. Methods: We developed the SMART-RNA-Metavirome platform, integrating SMART-9n library preparation with NGS and TGS technologies. Total RNA was extracted from two field-collected wild Aedes albopictus pools, along with one laboratory-infected Ae. albopictus pool harboring dengue virus (DENV). This RNA was subjected to reverse transcription using both this optimized protocol and random primer-based methods, followed by high-throughput sequencing on Illumina, Oxford Nanopore, and QitanTech Nanopore technologies. Welch's t-test was employed for comparative analysis of the subsequent RNA metavirome data, specifically to evaluate differences in viral species composition and abundance of viral reads between experimental groups. Furthermore, the effectiveness of this platform was systematically validated via RT-qPCR and SMART-RNA-Metavirome-based Oxford Nanopore sequencing across multiple sample types, including mosquito specimens from DENV-infected Ae. albopictus, serum samples from dengue patients and viral isolates of Japanese encephalitis virus (JEV) and Zika virus (ZIKV). Results: The SMART-RNA-Metavirome platform has been systematically validated to excel in enriching the composition and diversity of the RNA virome (P = 0.04), providing sufficient coverage for the complete reconstruction of viral genomes. When employed in the detection of DENV-infected Ae. albopictus, clinical serum samples, and viral isolates of JEV and ZIKV, this technique exhibits a robust correlation with RT-qPCR (r ² > 0.95). Notably, it demonstrates exceptional sensitivity, ensuring sufficient coverage even in samples of DENV-infected Ae. albopictus with a Ct-value of 35.3, attaining an impressive 99.88% genome coverage. Furthermore, this platform possesses the capability to identify virus species and determine their serotypes. Conclusions: In our study, the SMART-RNA-Metavirome platform outperforms traditional methods, enriching RNA virome composition and diversity, enabling practical compatibility with both NGS and TGS technologies. It demonstrates significant proficiency in detecting both known and unknown arboviruses, even in low-titer samples such as those from wild mosquitoes and clinical sera. This platform facilitates comprehensive monitoring, risk assessment, and early warning of RNA virus transmissions, enhancing our understanding of RNA virome diversity and ecological patterns. Graphical

GISDD, the Global Integrated Sequence and Genotyping Database for Dengue Virus (DENV), provides an integrated online analysis platform encompassing tools such as GISDDrlearn, GISDDprimer, and GISDDref, enabling swift identification and tracing of well‐established DENV genotypes, subgenotypes, and clades, thereby facilitating a stratified global coordinated surveillance strategy.

Dengue, an acute febrile disease transmitted by Aedes mosquitoes, is caused by the dengue virus (DENV), presenting a formidable challenge to global public health. By examining clues from ancient Chinese books and conducting a comprehensive review, this study elucidates the characteristics of potential dengue epidemics in China prior to 1978. This evidence indicates that China may not have experience dengue epidemics before 1840. During 1840–1949, however, it experienced a noticeable dengue occurrence and prevalence in the 1870s, 1920s, and 1940s. Then from 1949 to 1978, only sporadic reports were accounted. The disparity in the frequency of dengue occurrences across three time periods suggests that the persistent characteristic of dengue epidemics in China primarily arises from imported cases resulting from international exchanges, subsequently leading to local outbreaks influenced by global epidemic trend. This research offers a novel perspective on retrospectively examining the historical trajectory of dengue epidemics and provides valuable insights into exploration of DENV epidemic patterns. Graphical Abstract

Background: The strong invasiveness and rapid expansion of dengue virus (DENV) pose a great challenge to global public health. However, dengue epidemic patterns and mechanisms at a genetic scale, particularly in term of cross-border transmissions, remain poorly understood. Importation is considered as the primary driver of dengue outbreaks in China, and since 1990 a frequent occurrence of large outbreaks has been triggered by the imported cases and subsequently spread to the western and northern parts of China. Therefore, this study aims to systematically reveal the invasion and diffusion patterns of DENV-1 in Guangdong, China from 1990 to 2019. Methods: These analyses were performed on 179 newly assembled genomes from indigenous dengue cases in Guangdong, China and 5152 E gene complete sequences recorded in Chinese mainland. The genetic population structure and epidemic patterns of DENV-1 circulating in Chinese mainland were characterized by phylogenetics, phylogeography, phylodynamics based on DENV-1 E-gene-based globally unified genotyping framework. Results: Multiple serotypes of DENV were co-circulating in Chinese mainland, particularly in Guangdong and Yunnan provinces. A total of 189 transmission clusters in 38 clades belonging to 22 subgenotypes of genotype I, IV and V of DENV-1 were identified, with 7 Clades of Concern (COCs) responsible for the large outbreaks since 1990. The epidemic periodicity was inferred from the data to be approximately 3 years. Dengue transmission events mainly occurred from Great Mekong Subregion-China (GMS-China), Southeast Asia (SEA), South Asia Subcontinent (SASC), and Oceania (OCE) to coastal and land border cities respectively in southeastern and southwestern China. Specially, Guangzhou was found to be the most dominant receipting hub, where DENV-1 diffused to other cities within the province and even other parts of the country. Genome phylogeny combined with epidemiological investigation demonstrated a clear local consecutive transmission process of a 5C1 transmission cluster (5C1-CN4) of DENV-1 in Guangzhou from 2013 to 2015, while the two provinces of Guangdong and Yunnan played key roles in ongoing transition of dengue epidemic patterns. In contextualizing within Invasion Biology theories, we have proposed a derived three-stage model encompassing the stages of invasion, colonization, and dissemination, which is supposed to enhance our understanding of dengue spreading patterns. Conclusions: This study demonstrates the invasion and diffusion process of DENV-1 in Chinese mainland within a global genotyping framework, characterizing the genetic diversities of viral populations, multiple sources of importation, and periodic dynamics of the epidemic. These findings highlight the potential ongoing transition trends from epidemic to endemic status offering a valuable insight into early warning, prevention and control of rapid spreading of dengue both in China and worldwide. Graphical

Background: More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005. Methods: Based on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R₀) using the classical Ross-Macdonald framework combined with a linear mixed-effects model. Results: During the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R₀ estimated from the daily ADI (ADI_D) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) ADI_D and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R₀. MOI-based R₀ of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R₀ model, especially from August to October, with the predicted R₀ > 1. Meanwhile, from December to March, the estimates of MOI-based R₀ were < 1. Conclusions: The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R₀ of dengue using a statistical model. The MOI-based R₀ model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system. Graphical

Background: Aedes albopictus is an invasive vector of serious Aedes-borne diseases of global concern. Habitat management remains a critical factor for establishing a cost-effective systematic strategy for sustainable vector control. However, the community-based characteristics of Ae. albopictus habitats in complex urbanization ecosystems are still not well understood. Methods: A large-scale investigation of aquatic habitats, involving 12 sites selected as representative of four land use categories at three urbanization levels, was performed in Guangzhou, China during 2015–2017. The characteristics and dynamics of these Ae. albopictus habitats were assessed using habitat-type composition, habitat preference, diversity indexes and the Route index (RI), and the temporal patterns of these indexes were evaluated by locally weighted scatterplot smoothing models. The associations of RI with urbanization levels, land use categories and climatic variables were inferred using generalized additive mixed models. Results: A total of 1994 potential habitats and 474 Ae. albopictus-positive habitats were inspected. The majority of these habitats were container-type habitats, with Ae. albopictus showing a particularly higher habitat preference for plastic containers, metal containers and ceramic vessels. Unexpectedly, some non-container-type habitats, especially ornamental ponds and surface water, were found to have fairly high Ae. albopictus positivity rates. Regarding habitats, the land use category residential and rural in Jiangpu (Conghua District, Guangzhou) had the highest number of Ae. albopictus habitats with the highest positive rates. The type diversity of total habitats (H-total) showed a quick increase from February to April and peaked in April, while the H-total of positive habitats (H-positive) and RIs peaked in May. RIs mainly increased with the monthly average daily mean temperature and monthly cumulative rainfall. We also observed the accumulation of diapause eggs in the winter and diapause termination in the following March. Conclusions: Ecological heterogeneity of habitat preferences of Ae. albopictus was demonstrated in four land use categories at three urbanization levels. The results reveal diversified habitat-type compositions and significant seasonal variations, indicating an ongoing adaptation of Ae. albopictus to the urbanization ecosystem. H-positivity and RIs were inferred as affected by climatic variables and diapause behavior of Ae. albopictus, suggesting that an effective control of overwintering diapause eggs is crucial. Our findings lay a foundation for establishing a stratified systematic management strategy of Ae. albopictus habitats in cities that is expected to complement and improve community-based interventions and sustainable vector management. Graphical