Background: Immune checkpoint inhibitors (ICIs) are now a therapeutic standard in advanced non-small cell lung cancer (NSCLC), but strong predictive markers for ICIs efficacy are still lacking. We evaluated machine learning models built on simple clinical and biological data to individually predict response to ICIs. Methods: Patients with metastatic NSCLC who received ICI in second line or later were included. We collected clinical and hematological data and studied the association of this data with disease control rate (DCR), progression free survival (PFS) and overall survival (OS). Multiple machine learning (ML) algorithms were assessed for their ability to predict response. Results: Overall, 298 patients were enrolled. The overall response rate and DCR were 15.3% and 53%, respectively. Median PFS and OS were 3.3 and 11.4 months, respectively. In multivariable analysis, DCR was significantly associated with performance status (PS) and hemoglobin level (OR 0.58, p < 0.0001; OR 1.8, p < 0.001). These variables were also associated with PFS and OS and ranked top in random forest-based feature importance. Neutrophil-to-lymphocyte ratio was also associated with DCR, PFS and OS. The best ML algorithm was a random forest. It could predict DCR with satisfactory efficacy based on these three variables. Ten-fold cross-validated performances were: accuracy 0.68 ± 0.04, sensitivity 0.58 ± 0.08; specificity 0.78 ± 0.06; positive predictive value 0.70 ± 0.08; negative predictive value 0.68 ± 0.06; AUC 0.74 ± 0.03. Conclusion: Combination of simple clinical and biological data could accurately predict disease control rate at the individual level.

As insecticide resistance spreads in Africa, next-generation insecticide-treated nets (ITNs) are increasingly being deployed to protect vulnerable populations against malaria. While these nets provide greater entomological efficacy against resistant mosquitoes, their effectiveness against malaria transmission also depends on other factors, such as durability, access, usage, and activity patterns of hosts and vectors. Here, we quantify the impact of two next-generation ITNs, namely Interceptor®G2 (chlorfenapyr-pyrethroid) and Olyset® Plus (piperonyl butoxide-pyrethroid), in a cascade from entomological efficacy to population-level effectiveness. We use a mathematical model that we parameterize with entomological data and validate against results from randomized controlled trials. We found that, beyond entomological factors, operational factors including functional survival, ITN use and in-bed exposure critically impact ITN effectiveness overall and per ITN types. Our results obtained for Tanzania can be extended to other contexts in a dashboard allowing users to explore product selection based on setting-specific factors that influence ITN effectiveness.

Antimicrobial resistance (AMR) is a major public health concern worldwide. International travel is a risk factor for acquiring antibiotic-resistant bacteria (ARB) and antibiotic-resistance genes (ARGs). Therefore, understanding the transmission of ARB and ARGs is instrumental in tackling AMR. This longitudinal study aimed to assess the benefit of wastewater monitoring in Guadeloupe to evaluate the role of tourism in the spread of AMR. A wastewater-based surveillance (WBS) study was conducted to monitor AMR in Guadeloupe in 2022 during dry and wet seasons. We characterized the resistome, microbiome and exposome of water samples collected in wastewater treatment facilities of two cities with different levels of tourism activities, in the content of aircraft toilets, and the pumping station receiving effluents from hotels. The results show that the WBS approach facilitates the differentiation of various untreated effluents concerning exposome, microbiome, and resistome, offering insights into AMR dissemination. Additionally, the findings reveal that microbiome and exposome are comparable across sites and seasons, while resistome characterisation at specific locations may be pertinent for health surveillance. The microbiome of aircraft was predominantly composed of anaerobic bacteria from human intestinal microbiota, whereas the other locations exhibited a blend of human and environmental bacteria. Notably, individuals arriving by air have not introduced clinically significant resistance genes. Exposome compounds have been shown to influence the resistome's variance. Clear differences were seen between the aircraft and the local sampling sites, indicating that the contribution of tourism to the observed resistance in Guadeloupe is not significant.

Wastewater treatment plants (WWTPs) are global hotspots for the dissemination of antibiotic resistance genes (ARGs), primarily driven by different anthropogenic activities. Chemical compounds commonly found in wastewater, including antibiotics, biocides, non-steroidal anti-inflammatory drugs (NSAIDs), and heavy metals, along with environmental factors such as temperature and precipitation, might enhance the selection of antibiotic-resistant bacteria (ARB) even at low concentrations, inadvertently promoting the proliferation of ARGs. Since WWTPs can vary in their removal efficiency and influent compositions, it is important to assess their impact in influencing the relative abundance of ARGs and pollutant concentrations in discharged effluent. We analyzed data collected in Guadeloupe, French Caribbean, using random forest and lasso to investigate changes in the resistome and exposome within untreated wastewater influents and treated effluents from three WWTPs. These WWTPs represented three distinct wastewater continuums—hospital-based, urban non-touristic, and urban touristic—each with unique influent compositions. The study, which spanned four sampling campaigns from September 2021 to January 2023, revealed that the reduction in ARG abundance by the WWTPs was lower than expected. Of the 16 clinically relevant genes and mobile genetic elements examined, the relative abundance of aph(3’)-III, bla _OXA , bla _SHV , bla _TEM , ermB, intI1, qnrS , and tetM was reduced by 59.8% to 89.9% across the three WWTPs. An increasing trend in the relative abundance of the mcr-1 gene was observed, showing a 9.52-fold increase in the treated effluent in the hospital continuum. Of the nine antibiotics tested, trimethoprim levels dropped by 70.8% in the touristic continuum and 78.8% in the non-touristic, while ciprofloxacin saw a significant 64.4% reduction only in the touristic continuum, while no other antibiotics or biocides among the eight tested were effectively removed. Regarding heavy metals, significant reductions were observed only in the non-touristic continuum, with cadmium (Cd), copper (Cu), and mercury (Hg) concentrations reduced by 32.5%, 21.1%, and 36.2%, respectively. Our findings provide insights into the associations between environmental factors and the relative abundance of ARGs in Guadeloupe. Generally, depending on the continuum, biocides, the antibiotic erythromycin, certain heavy metals (As, Cd, Cu, Cr, and Hg) and water temperature were associated with the relative abundance of clinically relevant ARGs. This study was not able to assess the impact of climate. Future research should include longitudinal studies with more frequent sampling to better investigate the evolution of antibiotic resistance in bacterial species and its associated drivers, including pollutants and climate-related factors.

Wastewater is a key reservoir and transmission route for antibiotic resistance genes (ARGs), enabling their spread from influent to effluent and into receiving environments. However, how combined selective pressures (antibiotics, biocides, heavy metals, pharmaceuticals) influence resistant bacteria and ARG persistence over space and time remains poorly understood. Likewise, the role of the wastewater microbiome in ARG dynamics is still unclear, as few studies integrate microbiome shifts with chemical and environmental drivers. Here, we investigated how microbiome dynamics, chemical exposures, and environmental conditions shape clinically relevant ARG dynamics from sewage to receiving environments in Guadeloupe, French Caribbean. We analysed data collected from three wastewater continuums, (hospital-based, domestic, touristic) over four campaigns (September 2021-February 2023). We characterised ARG and microbiome composition spatiotemporal patterns and used a mixed-effect model to investigate ARG associations with potential drivers, including exposome factors, microbiome dissimilarity and environmental factors. Several ARGs were negatively associated with microbiome dissimilarity (Bray-Curtis distances) (aac(6)-Ib, aph(3)-III, blaSHV, blaTEM, intI1, qnrS, sul1 and tetM). Negative associations were also observed between upstream-downstream differences in anti-inflammatory drug concentrations and the abundance of aac(6)-Ib, aph(3)-III, blaCTX-M, ermB, intI1, and tetM. In contrast, ARG relative abundance was positively associated with upstream-downstream differences in antibiotic concentrations, suggesting selection along the continuum. These findings indicate that ARG dissemination along wastewater-to-coastal pathways is shaped by opposing processes, with microbiome turnover potentially limiting ARG persistence while chemical gradients promote specific gene enrichment. The outcome is ARG-specific, with implications for antimicrobial resistance risks associated with recreational waters, seafood consumption, and coastal ecosystem interactions.