Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites.

BACKGROUND: The spread of mosquito-transmitted diseases such as dengue is a major public health issue worldwide. The Aedes aegypti mosquito, a primary vector for dengue, thrives in urban environments and breeds mainly in artificial or natural water containers. While the relationship between urban landscapes and potential breeding sites remains poorly understood, such a knowledge could help mitigate the risks associated with these diseases. This study aimed to analyze the relationships between urban landscape characteristics and potential breeding site abundance and type in cities of French Guiana (South America), and to evaluate the potential of such variables to be used in predictive models. METHODS: We use Multifactorial Analysis to explore the relationship between urban landscape characteristics derived from very high resolution satellite imagery, and potential breeding sites recorded from in-situ surveys. We then applied Random Forest models with different sets of urban variables to predict the number of potential breeding sites where entomological data are not available. RESULTS: Landscape analyses applied to satellite images showed that urban types can be clearly identified using texture indices. The Multiple Factor Analysis helped identify variables related to the distribution of potential breeding sites, such as buildings class area, landscape shape index, building number, and the first component of texture indices. Models predicting the number of potential breeding sites using the entire dataset provided an R² of 0.90, possibly influenced by overfitting, but allowing the prediction over all the study sites. Predictions of potential breeding sites varied highly depending on their type, with better results on breeding sites types commonly found in urban landscapes, such as containers of less than 200 L, large volumes and barrels. The study also outlined the limitation offered by the entomological data, whose sampling was not specifically designed for this study. Model outputs could be used as input to a mosquito dynamics model when no accurate field data are available. CONCLUSION: This study offers a first use of routinely collected data on potential breeding sites in a research study. It highlights the potential benefits of including satellite-based characterizations of the urban environment to improve vector control strategies.

Cooperation for malaria control and elimination in the Guiana Shield.

The Guiana Shield, a small region of South America, is currently one of the main hotspots of malaria transmission on the continent. This Amazonian area is characterised by remarkable socioeconomic, cultural, health, and political heterogeneity and a high degree of regional and cross-border population mobility, which has contributed to the increase of malaria in the region in the past few years. In this context, regional cooperation to control malaria represents both a challenge and an indispensable initiative. This Viewpoint advocates for the creation of a regional cooperative mechanism for the elimination of malaria in the Guiana Shield. This strategy would help address operational and political obstacles to successful technical cooperation in the region and could contribute to reversing the regional upsurge in malaria incidence through creating a functional international control and elimination partnership.

Pulmonary inflammation decreases with ultra-protective ventilation in experimental ARDS under VV-ECMO: a positron emission tomography study.

Background: Experimentally, ultra-protective ventilation (UPV, tidal volumes [VT] < 4 mL.kg-1) strategies in conjunction with veno-venous extracorporeal membrane oxygenation (VV-ECMO) are associated with lesser ventilator-induced lung injuries (VILI) during acute respiratory distress syndrome (ARDS). However, whether these strategies reduce lung inflammation more effectively than protective ventilation (PV) remains unclear. We aimed to demonstrate that a UPV strategy decreases acute lung inflammation in comparison with PV in an experimental swine model of ARDS. Methods: ARDS was induced by tracheal instillation of chlorhydric acid in sedated and paralyzed animals under mechanical ventilation. Animals were randomized to receive either UPV (VT 1 mL.kg-1, positive end-expiration pressure [PEEP] set to obtain plateau pressure between 20 and 25 cmH2O and respiratory rate [RR] at 5 min-1 under VV-ECMO) or PV (VT 6 mL.kg-1, PEEP set to obtain plateau pressure between 28 and 30 cmH2O and RR at 25 min-1) during 4 h. After 4 h, a positron emission tomography with [11C](R)-PK11195 (ligand to TSPO-bearing macrophages) injection was realized, coupled with quantitative computerized tomography (CT). Pharmacokinetic multicompartment models were used to quantify regional [11C](R)-PK11195 lung uptake. [11C](R)-PK11195 lung uptake and CT-derived respiratory variables were studied regionally across eight lung regions distributed along the antero-posterior axis. Results: Five pigs were randomized to each study group. Arterial O2 partial pressure to inspired O2 fraction were not significantly different between study groups after experimental ARDS induction (75 [68-80] mmHg in a PV group vs. 87 [69-133] mmHg in a UPV group, p = 0.20). Compared to PV animals, UPV animals exhibited a significant decrease in the regional non-aerated compartment in the posterior lung levels, in mechanical power, and in regional dynamic strain and no statistical difference in tidal hyperinflation after 4 h. UPV animals had a significantly lower [11C](R)-PK11195 uptake, compared to PV animals (non-displaceable binding potential 0.35 [IQR, 0.20-0.59] in UPV animals and 1.01 [IQR, 0.75-1.59] in PV animals, p = 0.01). Regional [11C](R)-PK11195 uptake was independently associated with the interaction of regional tidal hyperinflation and regional lung compliance. Conclusion: In an experimental model of ARDS, 4 h of UPV strategy significantly decreased lung inflammation, in relation to the control of VT-derived determinants of VILI.

Understanding the impact of mobility on Plasmodium spp. carriage in an Amazon cross-border area with low transmission rate.

Despite the large reduction in malaria incidence in the last decade, the last kilometre to elimination is often the hardest, especially in international border areas. This study investigated the impact of mobility on Plasmodium spp. carriage in people living in a cross-border area in Amazonia with a low malaria transmission rate. We implemented a longitudinal ancillary study in the French Guiana town of St. Georges de l'Oyapock, which is located on the border with Brazil. It was based on data from two transversal surveys performed in October 2017 and October 2018. Data were collected on peri-domestic mobility for food-producing activities, and longer-distance mobility in high-risk areas. Participants were screened for Plasmodium spp. carriage using PCR tests, and treated if positive. Vector density around a participant's home was estimated using a previously published model based on remote sensing and meteorological data. The association between Plasmodium spp. carriage and mobility was analysed using a generalized additive mixed model. A total of 1,192 inhabitants, aged between 0 and 92 years old, were included. Median age was 18 years in 2017 (IQR [8;35]). Plasmodium spp. prevalence in the study population was 7% in 2017 (n = 89) and 3% in 2018 (n = 35). Plasmodium spp. carriage was independently associated with i) travel to the adjoining Oiapoque Indigenous Territories in Brazil (OR = 1.76, p = 0.023), ii) the estimated vector density around a participant's home (High versus Low risk OR = 4.11, p<0.001), iii) slash-and-burn farming (OR = 1.96, p = 0.013), and iv) age (p = 0.032). Specific surveillance systems and interventions which take into account different types of mobility are needed in cross-border areas to achieve and maintain malaria elimination (e.g., reactive case detection and treatment in the places visited).

Complete Complementary Coded Excitation Scheme for SNR Improvement of 2D Sparse Array Ultrasound Imaging.

Driving the numerous elements of 2D matrix arrays for 3D ultrasound imaging is very challenging in terms of cable size, wiring and data rate. The sparse array approach tackles this problem by optimally distributing a reduced number of elements over a 2D aperture while preserving a decent image quality and beam steering capabilities. Unfortunately, reducing the number of elements significantly reduces the active probe footprint reducing as a consequence the sensitivity and at the end the signal-to-noise ratio. Here we propose a new coded excitation scheme based on complete complementary codes to increase the signal-to-noise ratio in 3D ultrasound imaging with sparse arrays. These codes are known for their ideal auto-correlation and cross-correlation properties and have been widely used in Code-Division Multiple Access systems (CDMA). An algorithm for generating such codes is presented as well as the adopted imaging sequence. The proposed method has been compared in simulations to other coded excitation schemes and showed significant increase in the signal-to-noise ratio of sparse arrays with no correlation artifacts and no frame rate reduction. The gain in signal-to-noise ratio compared to the case where no coded excitation is used was around [Formula: see text] and the contrast was also improved by [Formula: see text] while the resolution was unchanged.

Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS.

BACKGROUND: Assessing measurement error in alveolar recruitment on computed tomography (CT) is of paramount importance to select a reliable threshold identifying patients with high potential for alveolar recruitment and to rationalize positive end-expiratory pressure (PEEP) setting in acute respiratory distress syndrome (ARDS). The aim of this study was to assess both intra- and inter-observer smallest real difference (SRD) exceeding measurement error of recruitment using both human and machine learning-made lung segmentation (i.e., delineation) on CT. This single-center observational study was performed on adult ARDS patients. CT were acquired at end-expiration and end-inspiration at the PEEP level selected by clinicians, and at end-expiration at PEEP 5 and 15 cmH2O. Two human observers and a machine learning algorithm performed lung segmentation. Recruitment was computed as the weight change of the non-aerated compartment on CT between PEEP 5 and 15 cmH2O. RESULTS: Thirteen patients were included, of whom 11 (85%) presented a severe ARDS. Intra- and inter-observer measurements of recruitment were virtually unbiased, with 95% confidence intervals (CI95%) encompassing zero. The intra-observer SRD of recruitment amounted to 3.5 [CI95% 2.4-5.2]% of lung weight. The human-human inter-observer SRD of recruitment was slightly higher amounting to 5.7 [CI95% 4.0-8.0]% of lung weight, as was the human-machine SRD (5.9 [CI95% 4.3-7.8]% of lung weight). Regarding other CT measurements, both intra-observer and inter-observer SRD were close to zero for the CT-measurements focusing on aerated lung (end-expiratory lung volume, hyperinflation), and higher for the CT-measurements relying on accurate segmentation of the non-aerated lung (lung weight, tidal recruitment…). The average symmetric surface distance between lung segmentation masks was significatively lower in intra-observer comparisons (0.8 mm [interquartile range (IQR) 0.6-0.9]) as compared to human-human (1.0 mm [IQR 0.8-1.3] and human-machine inter-observer comparisons (1.1 mm [IQR 0.9-1.3]). CONCLUSIONS: The SRD exceeding intra-observer experimental error in the measurement of alveolar recruitment may be conservatively set to 5% (i.e., the upper value of the CI95%). Human-machine and human-human inter-observer measurement errors with CT are of similar magnitude, suggesting that machine learning segmentation algorithms are credible alternative to humans for quantifying alveolar recruitment on CT.

Prone position decreases acute lung inflammation measured by [11C](R)-PK11195 positron emission tomography in experimental acute respiratory distress syndrome.

Whether prone positioning (PP) modulates acute lung inflammation by the modulation of biomechanical forces of ventilator-induced lung injuries (VILIs) remains unclear. We aimed to demonstrate that PP decreases acute lung inflammation in animals with experimental acute respiratory distress syndrome (ARDS). Animals were under general anesthesia and protective ventilation (tidal volume 6 mL·kg-1, PEEP 5 cmH2O). ARDS was induced by intratracheal instillation of chlorohydric acid. Animals were then randomized to PP, or to supine position (SP). After 4 h, a positron emission tomography (PET) acquisition with [11C](R)-PK11195 was performed coupled with computerized tomography (CT) acquisitions, allowing the CT quantification of VILI-associated parameters. [11C](R)-PK11195 lung uptake was quantified using pharmacokinetic multicompartment models. Analyses were performed on eight lung sections distributed along the antero-posterior dimension. Six animals were randomized to PP, five to SP (median [Formula: see text]/[Formula: see text] [interquartile range]: 164 [102-269] mmHg). The normally aerated compartment was significantly redistributed to the posterior lung regions of animals in PP, compared with SP. Dynamic strain was significantly increased in posterior regions of SP animals, compared with PP. After 4 h, animals in PP had a significantly lower uptake of [11C](R)-PK11195, compared with SP. [11C](R)-PK11195 regional uptake was independently associated with the study group, dynamic strain, tidal hyperinflation, and regional respiratory system compliance in multivariate analysis. In an experimental model of ARDS, 4 h of PP significantly decreased acute lung inflammation assessed with PET. The beneficial impact of PP on acute lung inflammation was consecutive to the combination of decreased biomechanical forces and changes in the respiratory system mechanics.NEW & NOTEWORTHY Prone position decreases acute lung macrophage inflammation quantified in vivo with [11C](R)-PK11195 positron emission tomography in an experimental acute respiratory distress syndrome. Regional macrophage inflammation is maximal in the most anterior and posterior lung section of supine animals, in relation with increased regional tidal strain and hyperinflation, and reduced regional lung compliance.

Determining the spatial distribution of environmental and socio-economic suitability for human leptospirosis in the face of limited epidemiological data.

BACKGROUND: Leptospirosis is among the leading zoonotic causes of morbidity and mortality worldwide. Knowledge about spatial patterns of diseases and their underlying processes have the potential to guide intervention efforts. However, leptospirosis is often an underreported and misdiagnosed disease and consequently, spatial patterns of the disease remain unclear. In the absence of accurate epidemiological data in the urban agglomeration of Santa Fe, we used a knowledge-based index and cluster analysis to identify spatial patterns of environmental and socioeconomic suitability for the disease and potential underlying processes that shape them. METHODS: We geocoded human leptospirosis cases derived from the Argentinian surveillance system during the period 2010 to 2019. Environmental and socioeconomic databases were obtained from satellite images and publicly available platforms on the web. Two sets of human leptospirosis determinants were considered according to the level of their support by the literature and expert knowledge. We used the Zonation algorithm to build a knowledge-based index and a clustering approach to identify distinct potential sets of determinants. Spatial similarity and correlations between index, clusters, and incidence rates were evaluated. RESULTS: We were able to geocode 56.36% of the human leptospirosis cases reported in the national epidemiological database. The knowledge-based index showed the suitability for human leptospirosis in the UA Santa Fe increased from downtown areas of the largest cities towards peri-urban and suburban areas. Cluster analysis revealed downtown areas were characterized by higher levels of socioeconomic conditions. Peri-urban and suburban areas encompassed two clusters which differed in terms of environmental determinants. The highest incidence rates overlapped areas with the highest suitability scores, the strength of association was low though (CSc r = 0.21, P < 0.001 and ESc r = 0.19, P < 0.001). CONCLUSIONS: We present a method to analyze the environmental and socioeconomic suitability for human leptospirosis based on literature and expert knowledge. The methodology can be thought as an evolutive and perfectible scheme as more studies are performed in the area and novel information regarding determinants of the disease become available. Our approach can be a valuable tool for decision-makers since it can serve as a baseline to plan intervention measures.

Impact of COVID-19 on Subclinical Placental Thrombosis and Maternal Thrombotic Factors.

BACKGROUND: In the context of the SARS-CoV-2 pandemic, our interest was to evaluate the effect of COVID-19 during pregnancy on placenta and coagulation factors. METHODS: a prospective cohort study between January and July 2021 of 55 pregnant women stratified into: Group O, 16 patients with ongoing SARS-CoV-2 infection at delivery; Group R, 21 patients with a history of SARS-CoV-2 infection during pregnancy but who recovered prior to delivery; Group C, 18 control patients with no infection at any time. All women had nasopharyngeal SARS-CoV-2 RT-PCR tests performed within 72 h of delivery. Obstetrical complications were recorded and two physiological inhibitors of coagulation, protein Z (PZ) and dependent protease inhibitor (ZPI), were analyzed in maternal and cord blood. All placentae were analyzed by a pathologist for vascular malperfusion. RESULTS: No patient in any group had a severe COVID-19 infection. More obstetrical complications were observed in Group O (O: n = 6/16 (37%), R: n = 2/21 (10%), C: n = 1/18 (6%), p = 0.03). The incidence of placental vascular malperfusion was similar among the groups (O: n = 9/16 (56%), R: n = 8/21 (42%), C: n = 8/18 (44%), p = 0.68). No PZ or ZPI deficiency was associated with COVID-19. However, an increased ZPI/PZ ratio was observed in neonates of Group R (O: 82.6 (min 41.3-max 743.6), R: 120.7 (29.8-203.5), C: 66.8 (28.2-2043.5), p = 0.04). CONCLUSION: COVID-19 was associated with more obstetrical complications, but not an increased incidence of placental lesions or PZ and ZPI abnormalities.

Neutralising antibody responses to SARS-CoV-2 omicron among elderly nursing home residents following a booster dose of BNT162b2 vaccine: A community-based, prospective, longitudinal cohort study.

Background: The protective immunity against omicron following a BNT162b2 Pfizer booster dose among elderly individuals (ie, those aged >65 years) is not well characterised. Methods: In a community-based, prospective, longitudinal cohort study taking place in France in which 75 residents from three nursing homes were enrolled, we selected 38 residents who had received a two-dose regimen of mRNA vaccine and a booster dose of Pfizer BNT162b2 vaccine. We excluded individuals that did not receive three vaccine doses or did not have available sera samples. We measured anti-S IgG antibodies and neutralisation capacity in sera taken 56 (28-68) and 55 (48-64) days (median (range)) after the 2nd and 3rd vaccine doses, respectively. Antibodies targeting the SARS-CoV-2 Spike protein were measured with the S-Flow assay as binding antibody units per milliliter (BAU/mL). Neutralising activities in sera were measured as effective dilution 50% (ED50) with the S-Fuse assay using authentic isolates of delta and omicron BA.1. Findings: Among the 38 elderly individuals recruited to the cohort study between November 23rd, 2020 and April 29th, 2021, with median age of 88 (range 72-101) years, 30 (78.95%) had been previously infected with SARS-CoV-2. After three vaccine doses, serum neutralising activity was lower against omicron BA.1 (median ED50 of 774.5, range 15.0-34660.0) than the delta variant (median ED50 of 4972.0, range 213.7-66340.0), and higher among previously infected (ie, convalescent; median ED50 against omicron: 1088.0, range 32.6-34660.0) compared with infection-naive residents (median ED50 against omicron: 188.4, range 15.0-8918.0). During the French omicron wave in December 2021-January 2022, 75% (6/8) of naive residents were infected, compared to 25% (7/30) of convalescent residents (P=0.0114). Anti-Spike antibody levels and neutralising activity against omicron BA.1 after a third BNT162b2 booster dose were lower in those with breakthrough BA.1 infection (n=13) compared with those without (n=25), with a median of 1429.9 (range 670.9-3818.3) BAU/mL vs 2528.3 (range 695.4-8832.0) BAU/mL (P=0.029) and a median ED50 of 281.1 (range 15.0-2136.0) vs 1376.0 (range 32.6-34660.0) (P=0.0013), respectively. Interpretation: This study shows that elderly individuals who received three vaccine doses elicit neutralising antibodies against the omicron BA.1 variant of SARS-CoV-2. Elderly individuals who had also been previously infected showed higher neutralising activity compared with naive individuals. Yet, breakthrough infections with omicron occurred. Individuals with breakthrough infections had significantly lower neutralising titers compared to individuals without breakthrough infection. Thus, a fourth dose of vaccine may be useful in the elderly population to increase the level of neutralising antibodies and compensate for waning immunity. Funding: Institut Pasteur, Fondation pour la Recherche Médicale (FRM), European Health Emergency Preparedness and Response Authority (HERA), Agence nationale de recherches sur le sida et les hépatites virales - Maladies Infectieuses Emergentes (ANRS-MIE), Agence nationale de la recherche (ANR), Assistance Publique des Hôpitaux de Paris (AP-HP) and Fondation de France.

Response to PEEP in COVID-19 ARDS patients with and without extracorporeal membrane oxygenation. A multicenter case-control computed tomography study.

BACKGROUND: PEEP selection in severe COVID-19 patients under extracorporeal membrane oxygenation (ECMO) is challenging as no study has assessed the alveolar recruitability in this setting. The aim of the study was to compare lung recruitability and the impact of PEEP on lung aeration in moderate and severe ARDS patients with or without ECMO, using computed tomography (CT). METHODS: We conducted a two-center prospective observational case-control study in adult COVID-19-related patients who had an indication for CT within 72 h of ARDS onset in non-ECMO patients or within 72  h after ECMO onset. Ninety-nine patients were included, of whom 24 had severe ARDS under ECMO, 59 severe ARDS without ECMO and 16 moderate ARDS. RESULTS: Non-inflated lung at PEEP 5 cmH2O was significantly greater in ECMO than in non-ECMO patients. Recruitment induced by increasing PEEP from 5 to 15 cmH2O was not significantly different between ECMO and non-ECMO patients, while PEEP-induced hyperinflation was significantly lower in the ECMO group and virtually nonexistent. The median [IQR] fraction of recruitable lung mass between PEEP 5 and 15 cmH2O was 6 [4-10]%. Total superimposed pressure at PEEP 5 cmH2O was significantly higher in ECMO patients and amounted to 12 [11-13] cmH2O. The hyperinflation-to-recruitment ratio (i.e., a trade-off index of the adverse effects and benefits of PEEP) was significantly lower in ECMO patients and was lower than one in 23 (96%) ECMO patients, 41 (69%) severe non-ECMO patients and 8 (50%) moderate ARDS patients. Compliance of the aerated lung at PEEP 5 cmH2O corrected for PEEP-induced recruitment (CBABY LUNG) was significantly lower in ECMO patients than in non-ECMO patients and was linearly related to the logarithm of the hyperinflation-to-recruitment ratio. CONCLUSIONS: Lung recruitability of COVID-19 pneumonia is not significantly different between ECMO and non-ECMO patients, with substantial interindividual variations. The balance between hyperinflation and recruitment induced by PEEP increase from 5 to 15 cmH2O appears favorable in virtually all ECMO patients, while this PEEP level is required to counteract compressive forces leading to lung collapse. CBABY LUNG is significantly lower in ECMO patients, independently of lung recruitability.

Semi-automatic data annotation based on feature-space projection and local quality metrics: An application to cerebral emboli characterization.

We propose a semi-supervised learning approach to annotate a dataset with reduced requirements for manual annotation and with controlled annotation error. The method is based on feature-space projection and label propagation using local quality metrics. First, an auto-encoder extracts the features of the samples in an unsupervised manner. Then, the extracted features are projected by a t-distributed stochastic neighbor embedding algorithm into a two-dimensional (2D) space. A selection of the best 2D projection is introduced based on the silhouette score. The expert annotator uses the obtained 2D representation to manually label samples. Finally, the labels of the labeled samples are propagated to the unlabeled samples using a K-nearest neighbor strategy and local quality metrics. We compare our method against semi-supervised optimum-path forest and K-nearest neighbor label propagation (without considering local quality metrics). Our method achieves state-of-the-art results on three different datasets by labeling more than 96% of the samples with an annotation error from 7% to 17%. Additionally, our method allows to control the trade-off between annotation error and number of labeled samples. Moreover, we combine our method with robust loss functions to compensate for the label noise introduced by automatic label propagation. Our method allows to achieve similar, and even better, classification performances compared to those obtained using a fully manually labeled dataset, with up to 6% in terms of classification accuracy.

Design, Implementation, and Medical Applications of 2-D Ultrasound Sparse Arrays.

An ultrasound sparse array consists of a sparse distribution of elements over a 2-D aperture. Such an array is typically characterized by a limited number of elements, which in most cases is compatible with the channel number of the available scanners. Sparse arrays represent an attractive alternative to full 2-D arrays that may require the control of thousands of elements through expensive application-specific integrated circuits (ASICs). However, their massive use is hindered by two main drawbacks: the possible beam profile deterioration, which may worsen the image contrast, and the limited signal-to-noise ratio (SNR), which may result too low for some applications. This article reviews the work done for three decades on 2-D ultrasound sparse arrays for medical applications. First, random, optimized, and deterministic design methods are reviewed together with their main influencing factors. Then, experimental 2-D sparse array implementations based on piezoelectric and capacitive micromachined ultrasonic transducer (CMUT) technologies are presented. Sample applications to 3-D (Doppler) imaging, super-resolution imaging, photo-acoustic imaging, and therapy are reported. The final sections discuss the main shortcomings associated with the use of sparse arrays, the related countermeasures, and the next steps envisaged in the development of innovative arrays.

Improving motion-mask segmentation in thoracic CT with multiplanar U-nets.

PURPOSE: Motion-mask segmentation from thoracic computed tomography (CT) images is the process of extracting the region that encompasses lungs and viscera, where large displacements occur during breathing. It has been shown to help image registration between different respiratory phases. This registration step is, for example, useful for radiotherapy planning or calculating local lung ventilation. Knowing the location of motion discontinuity, that is, sliding motion near the pleura, allows a better control of the registration preventing unrealistic estimates. Nevertheless, existing methods for motion-mask segmentation are not robust enough to be used in clinical routine. This article shows that it is feasible to overcome this lack of robustness by using a lightweight deep-learning approach usable on a standard computer, and this even without data augmentation or advanced model design. METHODS: A convolutional neural-network architecture with three 2D U-nets for the three main orientations (sagittal, coronal, axial) was proposed. Predictions generated by the three U-nets were combined by majority voting to provide a single 3D segmentation of the motion mask. The networks were trained on a database of nonsmall cell lung cancer 4D CT images of 43 patients. Training and evaluation were done with a K-fold cross-validation strategy. Evaluation was based on a visual grading by two experts according to the appropriateness of the segmented motion mask for the registration task, and on a comparison with motion masks obtained by a baseline method using level sets. A second database (76 CT images of patients with early-stage COVID-19), unseen during training, was used to assess the generalizability of the trained neural network. RESULTS: The proposed approach outperformed the baseline method in terms of quality and robustness: the success rate increased from 53 % to 79 % without producing any failure. It also achieved a speed-up factor of 60 with GPU, or 17 with CPU. The memory footprint was low: less than 5 GB GPU RAM for training and less than 1 GB GPU RAM for inference. When evaluated on a dataset with images differing by several characteristics (CT device, pathology, and field of view), the proposed method improved the success rate from 53 % to 83 % . CONCLUSION: With 5-s processing time on a mid-range GPU and success rates around 80 % , the proposed approach seems fast and robust enough to be routinely used in clinical practice. The success rate can be further improved by incorporating more diversity in training data via data augmentation and additional annotated images from different scanners and diseases. The code and trained model are publicly available.

Ecology, evolution, and epidemiology of zoonotic and vector-borne infectious diseases in French Guiana: Transdisciplinarity does matter to tackle new emerging threats.

French Guiana is a European ultraperipheric region located on the northern Atlantic coast of South America. It constitutes an important forested region for biological conservation in the Neotropics. Although very sparsely populated, with its inhabitants mainly concentrated on the Atlantic coastal strip and along the two main rivers, it is marked by the presence and development of old and new epidemic disease outbreaks, both research and health priorities. In this review paper, we synthetize 15 years of multidisciplinary and integrative research at the interface between wildlife, ecosystem modification, human activities and sociodemographic development, and human health. This study reveals a complex epidemiological landscape marked by important transitional changes, facilitated by increased interconnections between wildlife, land-use change and human occupation and activity, human and trade transportation, demography with substantial immigration, and identified vector and parasite pharmacological resistance. Among other French Guianese characteristics, we demonstrate herein the existence of more complex multi-host disease life cycles than previously described for several disease systems in Central and South America, which clearly indicates that today the greater promiscuity between wildlife and humans due to demographic and economic pressures may offer novel settings for microbes and their hosts to circulate and spread. French Guiana is a microcosm that crystallizes all the current global environmental, demographic and socioeconomic change conditions, which may favor the development of ancient and future infectious diseases.

Spatio-Temporal Dynamics of Plasmodium falciparum and Plasmodium vivax in French Guiana: 2005-2019.

Aims: This study examines the dynamics of malaria as influenced by meteorological factors in French Guiana from 2005 to 2019. It explores spatial hotspots of malaria transmission and aims to determine the factors associated with variation of hotspots with time. Methods: Data for individual malaria cases came from the surveillance system of the Delocalized Centers for Prevention and Care (CDPS) (n = 17) from 2005-2019. Meteorological data was acquired from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) database. The Box-Jenkins autoregressive integrated moving average (ARIMA) model tested stationarity of the time series, and the impact of meteorological indices (issued from principal component analysis-PCA) on malaria incidence was determined with a general additive model. Hotspot characterization was performed using spatial scan statistics. Results: The current sample includes 7050 eligible Plasmodium vivax (n = 4111) and Plasmodium falciparum (n = 2939) cases from health centers across French Guiana. The first and second PCA-derived meteorological components (maximum/minimum temperature/minimum humidity and maximum humidity, respectively) were significantly negatively correlated with total malaria incidence with a lag of one week and 10 days, respectively. Overall malaria incidence decreased across the time series until 2017 when incidence began to trend upwards. Hotspot characterization revealed a few health centers that exhibited spatial stability across the entire time series: Saint Georges de l'Oyapock and Antecume Pata for P. falciparum, and Saint Georges de l'Oyapock, Antecume Pata, Régina and Camopi for P. vivax. Conclusions: This study highlighted changing malaria incidence in French Guiana and the influences of meteorological factors on transmission. Many health centers showed spatial stability in transmission, albeit not temporal. Knowledge of the areas of high transmission as well as how and why transmission has changed over time can inform strategies to reduce the transmission of malaria in French Guiana. Hotspots should be further investigated to understand other influences on local transmission, which will help to facilitate elimination.

Kdr genotyping in Aedes aegypti from Brazil on a nation-wide scale from 2017 to 2018.

Insecticide resistance is currently a threat to the control of Aedes agypti, the main vector of arboviruses in urban centers. Mutations in the voltage gated sodium channel (NaV), known as kdr (knockdown resistance), constitute an important selection mechanism for resistance against pyrethroids. In the present study, we investigated the kdr distribution for the Val1016Ile and Phe1534Cys alterations in Ae. aegypti from 123 Brazilian municipalities, based on SNP genotyping assays in over 5,500 mosquitoes. The alleles NaVS (1016Val+ + 1534Phe+), NaVR1 (1016Val+ + 1534Cyskdr) and NaVR2 (1016Ilekdr + 1534Cyskdr) were consistently observed, whereas kdr alleles have rapidly spread and increased in frequency. NaVS was the less frequent allele, mostly found in Northeastern populations. The highest allelic frequencies were observed for NaVR1, especially in the North, which was fixed in one Amazonian population. The double kdr NaVR2 was more prevalent in the Central-west and South-eastern populations. We introduce the 'kdr index', which revealed significant spatial patterns highlighting two to three distinct Brazilian regions. The 410L kdr mutation was additionally evaluated in 25 localities, evidencing that it generally occurs in the NaVR2 allele. This nationwide screening of a genetic mechanism for insecticide resistance is an important indication on how pyrethroid resistance in Ae. aegypti is evolving in Brazil.

Contributing to Elimination of Cross-Border Malaria Through a Standardized Solution for Case Surveillance, Data Sharing, and Data Interpretation: Development of a Cross-Border Monitoring System.

BACKGROUND: Cross-border malaria is a significant obstacle to achieving malaria control and elimination worldwide. OBJECTIVE: This study aimed to build a cross-border surveillance system that can make comparable and qualified data available to all parties involved in malaria control between French Guiana and Brazil. METHODS: Data reconciliation rules based on expert knowledge were defined and applied to the heterogeneous data provided by the existing malaria surveillance systems of both countries. Visualization dashboards were designed to facilitate progressive data exploration, analysis, and interpretation. Dedicated advanced open source and robust software solutions were chosen to facilitate solution sharing and reuse. RESULTS: A database gathering the harmonized data on cross-border malaria epidemiology is updated monthly with new individual malaria cases from both countries. Online dashboards permit a progressive and user-friendly visualization of raw data and epidemiological indicators, in the form of time series, maps, and data quality indexes. The monitoring system was shown to be able to identify changes in time series that are related to control actions, as well as differentiated changes according to space and to population subgroups. CONCLUSIONS: This cross-border monitoring tool could help produce new scientific evidence on cross-border malaria dynamics, implementing cross-border cooperation for malaria control and elimination, and can be quickly adapted to other cross-border contexts.

Resurgence risk for malaria, and the characterization of a recent outbreak in an Amazonian border area between French Guiana and Brazil.

BACKGROUND: In 2017, inhabitants along the border between French Guiana and Brazil were affected by a malaria outbreak primarily due to Plasmodium vivax (Pv). While malaria cases have steadily declined between 2005 and 2016 in this Amazonian region, a resurgence was observed in 2017. METHODS: Two investigations were performed according to different spatial scales and information details: (1) a local study on the French Guiana border, which enabled a thorough investigation of malaria cases treated at a local village health center and the entomological circumstances in the most affected neighborhood, and (2) a regional and cross-border study, which enabled exploration of the regional spatiotemporal epidemic dynamic. Number and location of malaria cases were estimated using French and Brazilian surveillance systems. RESULTS: On the French Guianese side of the border in Saint-Georges de l'Oyapock, the attack rate was 5.5% (n = 4000), reaching 51.4% (n = 175) in one Indigenous neighborhood. Entomological findings suggest a peak of Anopheles darlingi density in August and September. Two female An. darlingi (n = 1104, 0.18%) were found to be Pv-positive during this peak. During the same period, aggregated data from passive surveillance conducted by Brazilian and French Guianese border health centers identified 1566 cases of Pv infection. Temporal distribution during the 2007-2018 period displayed seasonal patterns with a peak in November 2017. Four clusters were identified among epidemic profiles of cross-border area localities. All localities of the first two clusters were Brazilian. The localization of the first cluster suggests an onset of the outbreak in an Indigenous reservation, subsequently expanding to French Indigenous neighborhoods and non-Native communities. CONCLUSIONS: The current findings demonstrate a potential increase in malaria cases in an area with otherwise declining numbers. This is a transborder region where human mobility and remote populations challenge malaria control programs. This investigation illustrates the importance of international border surveillance and collaboration for malaria control, particularly in Indigenous villages and mobile populations.

Complementarity of empirical and process-based approaches to modelling mosquito population dynamics with Aedes albopictus as an example-Application to the development of an operational mapping tool of vector populations.

Mosquitoes are responsible for the transmission of major pathogens worldwide. Modelling their population dynamics and mapping their distribution can contribute effectively to disease surveillance and control systems. Two main approaches are classically used to understand and predict mosquito abundance in space and time, namely empirical (or statistical) and process-based models. In this work, we used both approaches to model the population dynamics in Reunion Island of the 'Tiger mosquito', Aedes albopictus, a vector of dengue and chikungunya viruses, using rainfall and temperature data. We aimed to i) evaluate and compare the two types of models, and ii) develop an operational tool that could be used by public health authorities and vector control services. Our results showed that Ae. albopictus dynamics in Reunion Island are driven by both rainfall and temperature with a non-linear relationship. The predictions of the two approaches were consistent with the observed abundances of Ae. albopictus aquatic stages. An operational tool with a user-friendly interface was developed, allowing the creation of maps of Ae. albopictus densities over the whole territory using meteorological data collected from a network of weather stations. It is now routinely used by the services in charge of vector control in Reunion Island.