The mosquito Aedes albopictus is an important vector of dengue, chikungunya, and Zika; and is a globally distributed invasive mosquito with increasing resistance to insecticides, thereby posing a serious risk to global public health. Symbiotic gut bacteria have been shown to be related to insecticide resistance, but knowledge is still limited for A. albopictus. Here, we explored the role of Serratia marcescens, a gut symbiotic bacterium, in the resistance of A. albopictus to the insecticide deltamethrin. Using 16S-rRNA sequencing we found that S. marcescens was significantly enriched in A. albopictus after deltamethrin exposure, and that resistance increased after S. marcescens enrichment. The enzymatic activities of mixed-function oxidase (MFO) and glutathione S-transferase (GST), two important detoxification enzymes, were higher in the bacteria-enriched mosquitoes. The expressions of ABCG4 and GSTD1, two genes related to detoxification metabolism, were up-regulated following S. marcescens infection and after deltamethrin exposure, as assayed using RNA-seq. The up-regulation of these two genes was most significant in midgut and Malpighian tubules. Our results suggest that S. marcescens infection could enhance deltamethrin resistance in A. albopictus by increasing detoxification metabolism; of interest for designing more efficient mosquito control measures.

Insecticide resistance in mosquitoes has become a severe impediment to global vector control and manifests as decreased insecticide effectiveness. The role of target site mutations and detoxification enzymes as resistance markers has been documented in mosquitoes; however, the emergence of complex resistant phenotypes suggest the occurrence of additional mechanisms. Cuticular proteins (CPs) are key constituents of the insect cuticle, and play critical roles in insect development and insecticide resistance. In this study, via electron microscopy we observed that the leg cuticle thickness in deltamethrin-resistant (DR) Anopheles sinensis mosquitoes was significantly greater than that measured in deltamethrin-susceptible (DS) An. sinensis. Transcription analysis revealed that cuticle proteins were enriched in the legs, including members of the CPR, CPAP, and CPF families. Further comparisons revealed the specific overexpression of four CP genes in the legs of DR An. sinensis; whose expression levels increased after treatment with deltamethrin. The RNAi-mediated silencing of one CP gene, AsCPF1, resulted in a significant decrease in the leg cuticle thickness of DR mosquitoes and significantly elevated the mortality rate when exposed to deltamethrin. These findings suggest that alterations in the An. sinensis leg cuticle contribute to the insecticide resistance phenotype. AsCPF1 is thereby a target study molecule for investigation of its mode of action, and broader attention should be paid to the role of mosquito legs in the development of insecticide resistance.

Background: Sustaining the elimination of malaria requires robust surveillance to prevent reintroduction, but standardized frameworks for assessing the surveillance capacity of a country post-elimination are lacking. This study aims to develop a standardized framework for assessing malaria surveillance and response capacity in countries that have eliminated malaria. Methods: We developed a malaria surveillance and response assessment framework through a three-stage process. First, two systematic reviews were conducted to identify indicators used in post-elimination settings worldwide and specifically in China. The candidate indicators were refined through expert panel discussions, which yielded 45 indicators across six domains. Next, a modified two-round Delphi process was conducted, involving 30 experts in epidemiology, disease control, and public health from diverse institutions and administrative levels. The experts rated the importance and feasibility of the indicators using structured questionnaires and then engaged in group discussions to contextualize the findings. Indicator weights were determined using a combined analytic hierarchy process (AHP) and entropy methods. Results: The systematic reviews and expert consultations identified 45 candidate indicators. After two rounds of expert consultation, a framework comprising 34 indicators across six domains for assessing malaria surveillance and response capacity in post-elimination settings was developed. The weights of the six domains are as follows: surveillance system coverage and performance (0.240); the quality and use of the surveillance data (0.3710); the functioning of the information management system (0.0973); the availability and adequacy of resources (0.0375); early diagnosis and treatment (0.1571); and quality control supervision and training (0.0973). The expert authority coefficient ( Cr ) values of the first and second rounds were 0.777 and 0.895 respectively. Of the 34 indicators, the proportion of confirmed cases with completed epidemiological investigations and submitted reports (0.1153) and the interval between the first medical visit and diagnosis (0.1131) had the highest weights. Conclusion: This consensus-based framework provides a standardized tool for evaluating malaria surveillance and response capacity in post-elimination settings. Adoption of the framework could help countries monitor and improve their systems to sustain elimination of the disease, mitigate reintroduction risks, and support global malaria eradication efforts. Graphical

Background: Sustaining malaria elimination requires addressing residual transmission driven by Anopheles sinensis amid rapid environmental change, with a critical knowledge gap in understanding how climate, agricultural practices, and urbanization shape vector ecology. We quantified the One Health drivers of An. sinensis biting activity and population dynamics along urban–rural landscapes in eastern China to inform targeted control. Methods: We conducted longitudinal surveillance (2016–2021) in both rural and urban settings in eastern China. Biting rates and adult densities of An. sinensis were monitored using human-baited double net traps and UV-light traps. We employed a One Health concept-based analytical framework integrating generalized linear mixed models combined with distributed lag non-linear models (GLMM–DLNM) to assess lagged climate effects, regression analyses for socio-demographic, urbanization, and livestock effects, and random forests to determine variable importance. Models were validated using cross-correlation functions with the Modified Chelton method to account for temporal autocorrelation. Results: The six-year surveillance documented 49,970 An. sinensis specimens, confirming its dominance as the primary malaria vector. We found divergent ecological drivers between urban and rural landscapes. Temperature was the dominant predictor, with biting activity and population peaking at 27.45–30.13 °C after a 1-month lag in rural settings. Precipitation exhibited threshold effects, with significant risk peaking elevation observed between 65.57 and 99.66 mm in rural settings. In urban settings, the peak risks associated with temperature and precipitation occurred at the highest levels. In rural settings, a male-biased sex ratio increased biting rates by 12.2–14.0% (IRR = 1.122–1.140). Furthermore, crop cultivation elevated vector risk (IRR = 1.034–1.043), whereas cattle or buffalo presence provided strong zooprophylaxis (IRR = 0.667–0.780). Urbanization rate modestly increased vector density (IRR = 1.053–1.077). We identified a protective urban density paradox, where higher human population density suppressed vector populations (IRR = 0.633–0.682). Cross-correlation function analyses indicated that One Health-based models captured 37.8–90.8% of the spatiotemporal variation across sites. Conclusions: Climatic, zooprophylactic, and anthropogenic factors synergistically shape An. sinensis ecology. Sustaining elimination requires strategies harnessing protective urban factors and optimizing livestock management and agricultural planning in high-risk rural regions. Our One Health framework provides a replicable model for mitigating residual transmission globally.

Imported malaria poses a growing challenge to elimination efforts in non-endemic countries, with migrant workers constituting a high-risk population. Understanding care-seeking behaviours and diagnostic delays is crucial for preventing secondary transmission. This study investigated the systemic determinants of diagnostic delays among returning migrant workers. Using data from the national surveillance system, we retrospectively analyzed a cohort of 2098 imported malaria cases involving migrant workers reported in Jiangsu Province, China, between January 2012 and December 2019. Delayed care-seeking was defined as > 3 days from symptom onset, and diagnostic delay as > 2 days post-consultation. Multivariate regression analyses were used to assess the relationships between demographic characteristics, clinical presentation, healthcare utilization patterns, socioeconomic factors (including destination city GDP), and time-to-diagnosis outcomes. Sub-Saharan Africa accounted for the largest share of infection origins (78.65%, 1650/2098), with Plasmodium falciparum being the leading species (78.41%, 1645/2098). Factors significantly associated with a decreased risk of diagnostic delay included delayed care-seeking > 3 days (OR = 0.62, 95% CI 0.44-0.88), non-severe malaria (OR = 0.57, 95% CI 0.38-0.86), symptom onset > 1 month after-entry (OR = 0.46, 95% CI 0.31-0.69), and initial consultation at municipal (OR = 0.20, 95% CI 0.11-0.37) or provincial (OR = 0.27, 95% CI 0.16-0.52) institutions. Conversely, higher destination city GDP (Second-level: OR = 2.74, 95% CI 1.88-400) were associated with increased diagnostic delays. Our findings highlight the need to increase clinician awareness regarding malaria diagnosis, particularly for patients presenting early with mild symptoms. Enhancing primary healthcare diagnostic capacity and educating migrant workers about promptly seeking medical advice are critical for preventing the re-establishment of malaria.