In Vitro Glucose Measurement from NIR and MIR Spectroscopy: Comprehensive Benchmark of Machine Learning and Filtering Chemometrics.

The quantitative analysis of glucose using spectroscopy is a topic of great significance and interest in science and industry. One conundrum in this area is deploying appropriate preprocessing and regression tools. To contribute to addressing this challenge, in this study, we conducted a comprehensive and novel comparative analysis of various machine learning and preprocessing filtering techniques applied to near-infrared, mid-infrared, and a combination of near-infrared and mid-infrared spectroscopy for glucose assay. Our objective was to evaluate the effectiveness of these techniques in accurately predicting glucose levels and to determine which approach was most optimal. Our investigation involved the acquisition of spectral data from samples of glucose solutions using the three aforementioned spectroscopy techniques. The data was subjected to several preprocessing filtering methods, including convolutional moving average, Savitzky-Golay, multiplicative scatter correction, and normalisation. We then applied representative machine learning algorithms from three categories: linear modelling, traditional nonlinear modelling, and artificial neural networks. The evaluation results revealed that linear models exhibited higher predictive accuracy than nonlinear models, whereas artificial neural network models demonstrated comparable performance. Additionally, the comparative analysis of various filtering methods demonstrated that the convolutional moving average and Savitzky-Golay filters yielded the most precise outcomes overall. In conclusion, our study provides valuable insights into the efficacy of different machine learning techniques for glucose measurement and highlights the importance of applying appropriate filtering methods in enhancing predictive accuracy. These findings have important implications for the development of new and improved glucose quantification technologies.

Preferred surface orientation for CO oxidation on SnO2 surfaces.

In the present study, we perform a comparative study on the oxidation mechanism of CO gas molecules on SnO2 (110), (101), and (100) surfaces. The optimized adsorption configurations show that the adsorption of CO molecules could occur similarly on the three SnO2 surfaces via two adsorption modes, physisorption of CO on the Sn5c site that is considered as the first step for CO oxidation, followed by CO chemisorption on the O2c site resulting in the formation of CO2 species. Based on the calculated adsorption energies and CO molecule diffusion on SnO2 surfaces, CO molecule adsorption on the (101) surface exhibits the highest adsorption energy and the lowest reaction barrier for CO oxidation compared to the widely considered (110) surface or the (100) surface. These findings are expected to have a major impact on improving sensing properties toward toxic gas by means of surface-orientation engineering.

Serological evidence of Rift Valley fever viral infection among camels imported into Southern Algeria.

Rift Valley fever (RVF) is a mosquito-borne viral zoonosis caused by the Rift Valley fever virus (RVFV). The present work aims to investigate the epidemiological status and identify the risk factors associated with RVFV infection in dromedary camels (Camelus dromedarius) from southern Algeria. A total of 269 sera of apparently healthy camels was collected and tested using a competitive Enzyme-Linked Immunosorbent Assay (ELISA). Overall, 72 camels (26.7 %, 95 % CI: 21.4-32) were seropositive to RVFV. IgG antibodies were found to be most prevalent in camels from south-western areas, particularly in Tindouf wilaya (52.38 %, p < 0.0001), and in camels introduced from bordering Sahelian countries (35.8 %) (OR = 8.75, 95 %CI: 2.14-35.81). No anti-RVFV antibodies were detected in sera collected from local camels (0 %). Adult (5-10 years) and aged (>10 years) camels have a significantly higher risk of being infected by RVFV (OR = 2.15; 95 %CI = 1.21-3.81, OR = 2.05; 95 %CI = 1.03-4.11, respectively). This report indicated that dromedaries imported to the south-western areas are exposed to RVFV and may contribute to its spread in Algerian territories.

Corrigendum: Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study.

[This corrects the article DOI: 10.3389/fcdhc.2023.1095859.].

Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study.

Background: Hypoglycemia is the most common adverse consequence of treating diabetes, and is often due to suboptimal patient self-care. Behavioral interventions by health professionals and self-care education helps avoid recurrent hypoglycemic episodes by targeting problematic patient behaviors. This relies on time-consuming investigation of reasons behind the observed episodes, which involves manual interpretation of personal diabetes diaries and communication with patients. Therefore, there is a clear motivation to automate this process using a supervised machine learning paradigm. This manuscript presents a feasibility study of automatic identification of hypoglycemia causes. Methods: Reasons for 1885 hypoglycemia events were labeled by 54 participants with type 1 diabetes over a 21 months period. A broad range of possible predictors were extracted describing a hypoglycemic episode and the subject's general self-care from participants' routinely collected data on the Glucollector, their diabetes management platform. Thereafter, the possible hypoglycemia reasons were categorized for two major analysis sections - statistical analysis of relationships between the data features of self-care and hypoglycemia reasons, and classification analysis investigating the design of an automated system to determine the reason for hypoglycemia. Results: Physical activity contributed to 45% of hypoglycemia reasons on the real world collected data. The statistical analysis provided a number of interpretable predictors of different hypoglycemia reasons based on self-care behaviors. The classification analysis showed the performance of a reasoning system in practical settings with different objectives under F1-score, recall and precision metrics. Conclusion: The data acquisition characterized the incidence distribution of the various hypoglycemia reasons. The analyses highlighted many interpretable predictors of the various hypoglycemia types. Also, the feasibility study presented a number of concerns valuable in the design of the decision support system for automatic hypoglycemia reason classification. Therefore, automating the identification of the causes of hypoglycemia may help objectively to target behavioral and therapeutic changes in patients' care.

Ethnobotanical survey of the traditional antiparasitic use of medicinal plants in humans and animals in Laghouat (Southern Algeria).

Background and Aim: An ethnobotanical survey was carried out among the inhabitants of the Aflou region of Laghouat (Southern Algeria). This study was considered as a first step toward the identification of new bioactive antiparasitic molecules. The preservation and documentation of this traditional knowledge will ensure its continuity and transmission from one generation to another, especially because of the emergence of resistant parasites and the lack of references caused by the lack of work in this area; therefore, we intended to inventory and collect the maximum amount of information on medicinal plants that are traditionally used by the local population as antiparasitic in humans and animals (small ruminants, cattle, and livestock). Materials and Methods: The information was collected using open interviews; the ethnobotanical survey was carried out in the area mentioned above from April to July 2021 using a semi-structured questionnaire and a global sample of 200 respondents. The data were analyzed using the System Package for the Social Sciences software and Microsoft Excel 2010 using the following quantitative indices: Relative frequency of citation (RFC), family importance value (FIV), fidelity level, and informant consensus factor (ICF). Results: The investigation uncovered the antiparasitic use of 58 plant species belonging to 30 families. The family Asteraceae had the highest FIV (FIV = 0.23). The pathology with the highest degree of agreement among the informants was genitourinary parasitosis (ICF = 0.930). The species that was most commonly cited by the local population was Artemisia herba-alba Asso (RFC = 1), and the foliage was the most commonly used part (46.4%). Infusion (38.8%) was the most-used preparation for remedies. Conclusion: This investigation revealed a rich ethnopharmacological knowledge in southern Algeria; therefore, the data gathered in this survey may be utilized to create novel antiparasitic compounds with activity in humans and animals.

Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion.

Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications. One conundrum in developing time-series forecasting models for blood glucose level prediction is to determine an appropriate length for look-back windows. On the one hand, studying short histories foists the risk of information incompletion. On the other hand, analysing long histories might induce information redundancy due to the data shift phenomenon. Additionally, optimal lag lengths are inconsistent across individuals because of the domain shift occurrence. Therefore, in bespoke analysis, either optimal lag values should be found for each individual separately or a globally suboptimal lag value should be used for all. The former approach degenerates the analysis's congruency and imposes extra perplexity. With the latter, the fine-tunned lag is not necessarily the optimum option for all individuals. To cope with this challenge, this work suggests an interconnected lag fusion framework based on nested meta-learning analysis that improves the accuracy and precision of predictions for personalised blood glucose level forecasting. The proposed framework is leveraged to generate blood glucose prediction models for patients with type 1 diabetes by scrutinising two well-established publicly available Ohio type 1 diabetes datasets. The models developed undergo vigorous evaluation and statistical analysis from mathematical and clinical perspectives. The results achieved underpin the efficacy of the proposed method in blood glucose level time-series prediction analysis.

Causality analysis in type 1 diabetes mellitus with application to blood glucose level prediction.

Effective control of blood glucose level (BGL) is the key factor in the management of type 1 diabetes mellitus (T1D). BGL prediction is an important tool to help maximise the time BGL is in the target range and thus minimise both acute and chronic diabetes-related complications. To predict future BGL, histories of variables known to affect BGL, such as carbohydrate intake, injected bolus insulin, and physical activity, are utilised. Due to these identified cause and effect relationships, T1D management can be examined via the causality context. In this respect, this work initially investigates these relations and quantifies the causality strengths of each variable with BGL using the convergent cross mapping method (CCM). Then, considering the extended CCM, the causality strengths of each variable for different lags are quantified. After that, the optimal time lag for each variable is determined according to the quantified causality effects. Subsequently, the feasibility of leveraging causality information as prior knowledge for BGL prediction is investigated by proposing two approaches. In the first approach, causality strengths are used as weights for relevant affecting variables. In the second approach, the optimal causal lags and the corresponding causality strengths are considered the shifts and weights for the variables, respectively. Overall, the evaluation criteria and statistical analysis used for comparing results show the effectiveness of using causality analysis in T1D management.

Analysis of real-world capillary blood glucose data to help reduce HbA1c and hypoglycaemia in type 1 diabetes: Evidence in favour of using the percentage of readings in target and coefficient of variation.

AIMS: To examine real-world capillary blood glucose (CBG) data according to HbA1c to define proportions of CBG readings at different HbA1c levels, and evaluate patterns in CBG measurements to suggest areas to focus on with regard to self-management. METHODS: A retrospective analysis stratified 682 adults with type 1 diabetes split into quartiles based on their HbA1c . The proportions of results in different CBG ranges and associations with HbA1c were evaluated. Patterns in readings following episodes of hyperglycaemia and hypoglycaemia were examined, using glucose to next glucose reading table (G2G). RESULTS: CBG readings in the target range (3.9-10 mmol/L) increase by ~10% across each CBG quartile (31% in the highest versus 63% in the lowest quartile, p < 0.05). The novel G2G table helps the treatment-based interpretation of data. Hypoglycaemia is often preceded by hyperglycaemia, and vice-versa, and is twice as likely in the highest HbA1c quartile. Re-testing within 30 min of hypoglycaemia is associated with less hypoglycaemia, 1.6% versus 7.2%, p < 0.001, and also reduces subsequent hyperglycaemia and further hypoglycaemia in the proceeding 24 h. The coefficient of variation, but not standard deviation, is highly associated with hypoglycaemia, r = 0.71, and a CV ≤ 36% equates to 3.3% of CBG readings in the hypoglycaemic range. CONCLUSIONS: HbA1c <58 mmol/mol (7.5%) is achievable even when only ~60% of CBG readings are between 3.9-10 mmol/L. Examining readings subsequent to out-of-range readings suggests useful behaviours which people with type 1 diabetes could be supported to adhere to, both in a clinic and structured education programmes, thereby decreasing the risk of hypoglycaemia whilst also reducing hyperglycaemia and improving HbA1c .

Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay.

People with diabetes mellitus (DM) are at elevated risk of in-hospital mortality from coronavirus disease-2019 (COVID-19). This vulnerability has spurred efforts to pinpoint distinctive characteristics of COVID-19 patients with DM. In this context, the present article develops ML models equipped with interpretation modules for inpatient mortality risk assessments of COVID-19 patients with DM. To this end, a cohort of 156 hospitalised COVID-19 patients with pre-existing DM is studied. For creating risk assessment platforms, this work explores a pool of historical, on-admission, and during-admission data that are DM-related or, according to preliminary investigations, are exclusively attributed to the COVID-19 susceptibility of DM patients. First, a set of careful pre-modelling steps are executed on the clinical data, including cleaning, pre-processing, subdivision, and feature elimination. Subsequently, standard machine learning (ML) modelling analysis is performed on the cured data. Initially, a classifier is tasked with forecasting COVID-19 fatality from selected features. The model undergoes thorough evaluation analysis. The results achieved substantiate the efficacy of the undertaken data curation and modelling steps. Afterwards, SHapley Additive exPlanations (SHAP) technique is assigned to interpret the generated mortality risk prediction model by rating the predictors' global and local influence on the model's outputs. These interpretations advance the comprehensibility of the analysis by explaining the formation of outcomes and, in this way, foster the adoption of the proposed methodologies. Next, a clustering algorithm demarcates patients into four separate groups based on their SHAP values, providing a practical risk stratification method. Finally, a re-evaluation analysis is performed to verify the robustness of the proposed framework.

COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation framework.

This research develops machine learning models equipped with interpretation modules for mortality risk prediction and stratification in cohorts of hospitalised coronavirus disease-2019 (COVID-19) patients with and without diabetes mellitus (DM). To this end, routinely collected clinical data from 156 COVID-19 patients with DM and 349 COVID-19 patients without DM were scrutinised. First, a random forest classifier forecasted in-hospital COVID-19 fatality utilising admission data for each cohort. For the DM cohort, the model predicted mortality risk with the accuracy of 82%, area under the receiver operating characteristic curve (AUC) of 80%, sensitivity of 80%, and specificity of 56%. For the non-DM cohort, the achieved accuracy, AUC, sensitivity, and specificity were 80%, 84%, 91%, and 56%, respectively. The models were then interpreted using SHapley Additive exPlanations (SHAP), which explained predictors' global and local influences on model outputs. Finally, the k-means algorithm was applied to cluster patients on their SHAP values. The algorithm demarcated patients into three clusters. Average mortality rates within the generated clusters were 8%, 20%, and 76% for the DM cohort, 2.7%, 28%, and 41.9% for the non-DM cohort, providing a functional method of risk stratification.

Signal fragmentation based feature vector generation in a model agnostic framework with application to glucose quantification using absorption spectroscopy.

This paper proposes feature vector generation based on signal fragmentation equipped with a model interpretation module to enhance glucose quantification from absorption spectroscopy signals. For this purpose, near-infrared (NIR) and mid-infrared (MIR) spectra collected from experimental samples of varying glucose concentrations are scrutinised. Initially, a given spectrum is optimally dissected into several fragments. A base-learner then studies the obtained fragments individually to estimate the reference glucose concentration from each fragment. Subsequently, the resultant estimates from all fragments are stacked, forming a feature vector for the original spectrum. Afterwards, a meta-learner studies the generated feature vector to yield a final estimation of the reference glucose concentration pertaining to the entire original spectrum. The reliability of the proposed approach is reviewed under a set of circumstances encompassing modelling upon NIR or MIR signals alone and combinations of NIR and MIR signals at different fusion levels. In addition, the compatibility of the proposed approach with an underlying preprocessing technique in spectroscopy is assessed. The results obtained substantiate the utility of incorporating the designed feature vector generator into standard benchmarked modelling procedures under all considered scenarios. Finally, to promote the transparency and adoption of the propositions, SHapley additive exPlanations (SHAP) is leveraged to interpret the quantification outcomes.

Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach.

Optimal and sustainable control of blood glucose levels (BGLs) is the aim of type-1 diabetes management. The automated prediction of BGL using machine learning (ML) algorithms is considered as a promising tool that can support this aim. In this context, this paper proposes new advanced ML architectures to predict BGL leveraging deep learning and ensemble learning. The deep-ensemble models are developed with novel meta-learning approaches, where the feasibility of changing the dimension of a univariate time series forecasting task is investigated. The models are evaluated regression-wise and clinical-wise. The performance of the proposed ensemble models are compared with benchmark non-ensemble models. The results show the superior performance of the developed ensemble models over developed non-ensemble benchmark models and also show the efficacy of the proposed meta-learning approaches.

Follicular fluid and serum biochemical and hormonal profiles of normal and cystic dromedary camel breeds.

Background and Aim: Ovarian cysts (OC) in female dromedary camels have been described as problematic because they can cause infertility. This study aimed to compare the hormone concentrations and biochemical contents present in serum and follicular fluid of normal and cystic she-dromedaries of the two most common Algerian camel breeds (Sahraoui and Targui) to gain a better understanding of biological differences that may yield insights into preventing or treating this ovarian abnormality. Materials and Methods: At an abattoir in southeastern Algeria, 100 pairs of the same females' ovaries and blood samples were taken immediately after the slaughter of clinically healthy, non-pregnant females (8-15 years old) over two consecutive breeding seasons (November 2017-April 2018 and November 2018-April 2019). The concentrations of glucose, cholesterol, protein, urea, creatinine, triglyceride, gamma-glutamyl transferase, alanine aminotransferase, and aspartate aminotransferase were determined using commercial diagnostic kits and standard analytical procedures. Electrochemiluminescence immunoassay was used to measure progesterone (P4) and insulin concentrations. Results: The concentrations of glucose, insulin, cholesterol, and P4 in sera and follicular fluid (regardless of ovarian follicle diameter) were different (p < 0.001), but there was no significant difference in the other parameters studied. Glucose, insulin, cholesterol, urea, and P4 levels in blood serum differed significantly from pre-ovulatory follicles. None of the biochemical and hormonal components measured differed significantly between the pre-ovulatory and cystic fluids of the she-dromedaries studied. The breed did not affect the biochemical and hormonal composition of she-dromedary cystic and follicular fluids. Conclusion: Ovarian cysts appear to form in a metabolic milieu distinct from follicular fluid and blood serum, with no influence from camel breeds. It is suggested that further research on the blood-follicle barrier be conducted to gain a better understanding of the OC development process in she-dromedaries.

Sammon's mapping regression for the quantitative analysis of glucose from both mid infrared and near infrared spectra.

This paper proposes a novel regression method based on Sammon's mapping dimensionality reduction technique for the quantification of glucose from both near infrared and mid infrared spectra. The proposed regression model was validated to determine the concentration of glucose from the spectra of aqueous mixtures consisting of human serum albumin and glucose in phosphate buffer solution from both near infrared (NIR) and mid infrared (MIR) regions. The performance of the proposed prediction model has been analysed with traditional regression methods principal component regression (PCR) and partial least squares regression (PLSR) models. The results indicate that the proposed model yields improved prediction performance compared to PCR and PLSR methods. In detail, the proposed Sammon's mapping regression (SMR) model provides better prediction ability by reducing the root mean square error of prediction (RMSEP) from 35.74 mg dL-1 for PCR and 31.39 mg dL-1 for PLSR to 21.89 mg dL-1 for the proposed regression model in the MIR region and the RMSEP has been reduced from 38.15 mg dL-1 for the PCR model and 37.5 mg dL-1 for the PLSR model to 29.74 mg dL-1 for the SMR model in the NIR region.

Ethnobotanical and ethnomedicinal analysis of wild medicinal plants traditionally used in Naâma, southwest Algeria.

Algerian people largely rely on traditional medicine practices as part of a community's identity. This first ethnobotanical study aimed to quantify and document the wild medicinal plant taxa from four family and the related traditional knowledge in Naâma province, Algeria. The survey was carried out between 2018 and 2020. The socio-demographic data and the use of medicinal species were recorded and collected randomly from 84 indigenous people using pre-prepared questionnaire. The result was evaluated using quantitative indices. A total of 27 medicinal plant species belonging to 21 genera used in the community were mostly recorded. The most represented families were Lamiaceae and Asteraceae (12 species for each of them). The aerial parts were the most frequently used plant part (73 %), while a decoction (34 %), and infusion (31 %) were the major modes of remedy preparation. The species with high UV were Rosmarinus officinalis L. (0.80), Artemisia herba-alba Asso (0.76), and Juniperus phoenicea L. subsp. phoenicea (0.75). Species with highest FL were: Ephedra alata subsp. alenda (Stapf) Trab (100 %), Teucrium polium L. (60 %), and Ballota hirsuta Benth (57.14.5 %). Atractylis caespitosa Desf and Nepeta nepetella subsp.amethystina (Poir.) Briq were newly cited as medicinal plants and have not been recorded previously in Algeria. Artemisia herba-alba Asso and Thymus algeriensis Boiss. & Reut were reported to treat COVID-19 symptoms. The results obtained indicate the richness of the area with medicinal plants as well as knowledge of alternative medicine. The most cited plants could be contained molecules that can be tested for therapeutic uses.

First serological evidence of BHV-1 virus in Algerian dromedary camels: Seroprevalence and associated risk factors.

Infectious bovine rhinotracheitis (IBR), caused by bovine herpesvirus-1 (BHV-1), is a major livestock health concern in many countries of the world. The objectives of this cross-sectional study were (i) to estimate the seroprevalence of BHV-1 infection and (ii) to assess risk factors associated with this disease in dromedary camels in four districts of Algeria. Blood samples were taken from 865 camels from 84 randomly selected herds, and serum was analyzed for presence of antibodies against BHV-1 by indirect enzyme linked immunosorbent assay (ELISA). Logistic regression was used to determine associations between seroprevalence and potential risk factors (collected using a questionnaire). Antibodies against BHV-1 were detected in 3.7 % (32/865) of samples. Eighteen of 84 camel herds had at least one BHV-1 seropositive camel, giving a herd seroprevalence of 21.4 %. Based on univariate analysis, the introduction of purchased animals and contact with others animal herds appeared as major risk factors. By using multivariate analysis, the only important risk factor was introduction of new animals. This study provided, for the first time, evidence of BHV-1 infection in dromedary camels in Algeria; it also provided estimates of seroprevalence of this disease and suggests that camels may serve as a reservoir of BHV-1 for spread to other species.

New Haplotypes of Trypanosoma evansi Identified in Dromedary Camels from Algeria.

PURPOSE: Surra is a zoonotic disease caused by Trypanosoma evansi (Trypanozoon), a salivary trypanosome native to Africa which affects a wide range of mammals worldwide and causes mortality and significant economic loss. The present study was devoted to the molecular characterization of T. evansi derived from naturally infected dromedary camels in Algeria. METHODS: A total of 148 blood samples were collected from mixed age camels living in one of four geographic regions (Ouargla, El Oued, Biskra and Ghardaia) of Algeria. Samples underwent PCR amplification and sequencing of the internal transcribed spacer 1 (ITS1) complete sequence. RESULTS: DNA of Trypanosoma spp. was found in 19 camels (12.84%). Trypanosoma spp. molecular positivity was not affected by sex (p = 0.50), age (p = 0.08), or geographic location (p = 0.12). Based on multiple sequence alignment of the obtained DNA sequences with representative T. evansi ITS1 sequences available globally, the Algerian sequences were grouped within four different haplotypes including two which were original. CONCLUSION: Results of this study provide preliminary data on which future studies of genetic diversity and molecular epidemiology of T. evansi can be based.

Protocol for a cluster randomised controlled trial of the DAFNEplus (Dose Adjustment For Normal Eating) intervention compared with 5x1 DAFNE: a lifelong approach to promote effective self-management in adults with type 1 diabetes.

INTRODUCTION: The successful treatment of type 1 diabetes (T1D) requires those affected to employ insulin therapy to maintain their blood glucose levels as close to normal to avoid complications in the long-term. The Dose Adjustment For Normal Eating (DAFNE) intervention is a group education course designed to help adults with T1D develop and sustain the complex self-management skills needed to adjust insulin in everyday life. It leads to improved glucose levels in the short term (manifest by falls in glycated haemoglobin, HbA1c), reduced rates of hypoglycaemia and sustained improvements in quality of life but overall glucose levels remain well above national targets. The DAFNEplus intervention is a development of DAFNE designed to incorporate behavioural change techniques, technology and longer-term structured support from healthcare professionals (HCPs). METHODS AND ANALYSIS: A pragmatic cluster randomised controlled trial in adults with T1D, delivered in diabetes centres in National Health Service secondary care hospitals in the UK. Centres will be randomised on a 1:1 basis to standard DAFNE or DAFNEplus. Primary clinical outcome is the change in HbA1c and the primary endpoint is HbA1c at 12 months, in those entering the trial with HbA1c >7.5% (58 mmol/mol), and HbA1c at 6 months is the secondary endpoint. Sample size is 662 participants (approximately 47 per centre); 92% power to detect a 0.5% difference in the primary outcome of HbA1c between treatment groups. The trial also measures rates of hypoglycaemia, psychological outcomes, an economic evaluation and process evaluation. ETHICS AND DISSEMINATION: Ethics approval was granted by South West-Exeter Research Ethics Committee (REC ref: 18/SW/0100) on 14 May 2018. The results of the trial will be published in a National Institute for Health Research monograph and relevant high-impact journals. TRIAL REGISTRATION NUMBER: ISRCTN42908016.

Seroprevalence and risk factors for Trypanosoma evansi, the causative agent of surra, in the dromedary camel (Camelus dromedarius) population in Southeastern Algeria.

Surra, caused by Trypanosoma evansi, is a re-emerging animal trypanosomosis, which is of special concern for camel-rearing regions of Africa and Asia. Surra decreases milk yield, lessens animal body condition score and reduces market value of exported animals resulting in substantial economic losses. A cross-sectional seroprevalence study of dromedary camels was conducted in Algeria, and major risk factors associated with infection were identified by collecting data on animal characteristics and herd management practices. The seroprevalence of T. evansi infection was determined in sera of 865 camels from 82 herds located in eastern Algeria using an antibody test (card agglutination test for Trypanosomiasis - CATT/T. evansi). Individual and herd seroprevalence were 49.5% and 73.2%, respectively, indicating substantial exposure of camels to T. evansi in the four districts studied. Five significant risk factors for T. evansi hemoparasite infection were identified: geographical area, herd size, husbandry system, accessibility to natural water sources and type of watering. There was no association between breed, sex or age with T. evansi infection. Results of this study provide baseline information that will be useful for launching control programmes in the region and potentially elsewhere.