Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning.

BACKGROUND: The degree to which Anopheles mosquitoes prefer biting humans over other vertebrate hosts, i.e. the human blood index (HBI), is a crucial parameter for assessing malaria transmission risk. However, existing techniques for identifying mosquito blood meals are demanding in terms of time and effort, involve costly reagents, and are prone to inaccuracies due to factors such as cross-reactivity with other antigens or partially digested blood meals in the mosquito gut. This study demonstrates the first field application of mid-infrared spectroscopy and machine learning (MIRS-ML), to rapidly assess the blood-feeding histories of malaria vectors, with direct comparison to PCR assays. METHODS AND RESULTS: Female Anopheles funestus mosquitoes (N = 1854) were collected from rural Tanzania and desiccated then scanned with an attenuated total reflectance Fourier-transform Infrared (ATR-FTIR) spectrometer. Blood meals were confirmed by PCR, establishing the 'ground truth' for machine learning algorithms. Logistic regression and multi-layer perceptron classifiers were employed to identify blood meal sources, achieving accuracies of 88%-90%, respectively, as well as HBI estimates aligning well with the PCR-based standard HBI. CONCLUSIONS: This research provides evidence of MIRS-ML effectiveness in classifying blood meals in wild Anopheles funestus, as a potential complementary surveillance tool in settings where conventional molecular techniques are impractical. The cost-effectiveness, simplicity, and scalability of MIRS-ML, along with its generalizability, outweigh minor gaps in HBI estimation. Since this approach has already been demonstrated for measuring other entomological and parasitological indicators of malaria, the validation in this study broadens its range of use cases, positioning it as an integrated system for estimating pathogen transmission risk and evaluating the impact of interventions.

Screening of malaria infections in human blood samples with varying parasite densities and anaemic conditions using AI-Powered mid-infrared spectroscopy.

BACKGROUND: Effective testing for malaria, including the detection of infections at very low densities, is vital for the successful elimination of the disease. Unfortunately, existing methods are either inexpensive but poorly sensitive or sensitive but costly. Recent studies have shown that mid-infrared spectroscopy coupled with machine learning (MIRs-ML) has potential for rapidly detecting malaria infections but requires further evaluation on diverse samples representative of natural infections in endemic areas. The aim of this study was, therefore, to demonstrate a simple AI-powered, reagent-free, and user-friendly approach that uses mid-infrared spectra from dried blood spots to accurately detect malaria infections across varying parasite densities and anaemic conditions. METHODS: Plasmodium falciparum strains NF54 and FCR3 were cultured and mixed with blood from 70 malaria-free individuals to create various malaria parasitaemia and anaemic conditions. Blood dilutions produced three haematocrit ratios (50%, 25%, 12.5%) and five parasitaemia levels (6%, 0.1%, 0.002%, 0.00003%, 0%). Dried blood spots were prepared on Whatman™ filter papers and scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) for machine-learning analysis. Three classifiers were trained on an 80%/20% split of 4655 spectra: (I) high contrast (6% parasitaemia vs. negative), (II) low contrast (0.00003% vs. negative) and (III) all concentrations (all positive levels vs. negative). The classifiers were validated with unseen datasets to detect malaria at various parasitaemia levels and anaemic conditions. Additionally, these classifiers were tested on samples from a population survey in malaria-endemic villages of southeastern Tanzania. RESULTS: The AI classifiers attained over 90% accuracy in detecting malaria infections as low as one parasite per microlitre of blood, a sensitivity unattainable by conventional RDTs and microscopy. These laboratory-developed classifiers seamlessly transitioned to field applicability, achieving over 80% accuracy in predicting natural P. falciparum infections in blood samples collected during the field survey. Crucially, the performance remained unaffected by various levels of anaemia, a common complication in malaria patients. CONCLUSION: These findings suggest that the AI-driven mid-infrared spectroscopy approach holds promise as a simplified, sensitive and cost-effective method for malaria screening, consistently performing well despite variations in parasite densities and anaemic conditions. The technique simply involves scanning dried blood spots with a desktop mid-infrared scanner and analysing the spectra using pre-trained AI classifiers, making it readily adaptable to field conditions in low-resource settings. In this study, the approach was successfully adapted to field use, effectively predicting natural malaria infections in blood samples from a population-level survey in Tanzania. With additional field trials and validation, this technique could significantly enhance malaria surveillance and contribute to accelerating malaria elimination efforts.

Distribution and insecticide resistance profile of the major malaria vector Anopheles funestus group across the African continent.

There has been significant progress in malaria control in the last 2 decades, with a decline in mortality and morbidity. However, these gains are jeopardised by insecticide resistance, which negatively impacts the core interventions, such as insecticide-treated nets (ITN) and indoor residual spraying (IRS). While most malaria control and research efforts are still focused on Anopheles gambiae complex mosquitoes, Anopheles funestus remains an important vector in many countries and, in some cases, contributes to most of the local transmission. As countries move towards malaria elimination, it is important to ensure that all dominant vector species, including An. funestus, an important vector in some countries, are targeted. The objective of this review is to compile and discuss information related to A. funestus populations' resistance to insecticides and the mechanisms involved across Africa, emphasising the sibling species and their resistance profiles in relation to malaria elimination goals. Data on insecticide resistance in An. funestus malaria vectors in Africa were extracted from published studies. Online bibliographic databases, including Google Scholar and PubMed, were used to search for relevant studies. Articles published between 2000 and May 2023 reporting resistance of An. funestus to insecticides and associated mechanisms were included. Those reporting only bionomics were excluded. Spatial variation in species distribution and resistance to insecticides was recorded from 174 articles that met the selection criteria. It was found that An. funestus was increasingly resistant to the four classes of insecticides recommended by the World Health Organisation for malaria vector control; however, this varied by country. Insecticide resistance appears to reduce the effectiveness of vector control methods, particularly IRS and ITN. Biochemical resistance due to detoxification enzymes (P450s and glutathione-S-transferases [GSTs]) in An. funestus was widely recorded. However, An. funestus in Africa remains susceptible to other insecticide classes, such as organophosphates and neonicotinoids. This review highlights the increasing insecticide resistance of An. funestus mosquitoes, which are important malaria vectors in Africa, posing a significant challenge to malaria control efforts. While An. funestus has shown resistance to the recommended insecticide classes, notably pyrethroids and, in some cases, organochlorides and carbamates, it remains susceptible to other classes of insecticides such as organophosphates and neonicotinoids, providing potential alternative options for vector control strategies. The study underscores the need for targeted interventions that consider the population structure and geographical distribution of An. funestus, including its sibling species and their insecticide resistance profiles, to effectively achieve malaria elimination goals.

Des progrès importants ont été réalisés dans le contrôle du paludisme au cours des deux dernières décennies, qui se traduisent par une baisse de la mortalité et de la morbidité. Cependant, ces gains sont compromis par la résistance aux insecticides, ce qui a un impact négatif sur les interventions de base, telles que les moustiquaires imprégnées d'insecticides et la pulvérisation intradomicilliare (PID). Alors que la plupart des efforts de contrôle et de recherche sur le paludisme sont toujours axés sur les moustiques du complexes Anopheles gambiae, Anopheles funestus reste un vecteur important dans de nombreux pays et, dans certains cas, contribue à la majeure partie de la transmission locale. Au moment où certains pays se dirigent vers l'élimination du paludisme, il serait important de prendre en considération toutes les espèces vectrices dominantes, y compris An. funestus. L'objectif de cette revue est de compiler et de discuter des informations liées à la résistance des populations d'An. funestus aux insecticides et les mécanismes impliqués à travers l'Afrique, en mettant l'accent sur les sous espèces et leurs profils de résistance en relation avec les objectifs d'élimination du paludisme. Les données sur la résistance aux insecticides chez An. funestus vecteurs du paludisme en Afrique ont été extraites d'études publiées dans des bases de données bibliographiques comme Google Scholar et PubMed. Les articles publiés entre 2000 et mai 2023, rapportant la résistance de An. funestus aux insecticides et les mécanismes associés ont été inclus. Ceux portant uniquement sur la bionomie ont été exclus. Au total 174 articles portant sur la variation spatiale de la résistance des espèces du groupe An. funestus aux insecticides répondaient aux critères de sélection. De ces analyses, il ressort qu'An. funestus était de plus en plus résistant aux quatre classes d'insecticides recommandées par l'Organisation Mondiale de la Santé (OMS) pour le contrôle des vecteurs du paludisme ce qui semble réduire l'efficacité des méthodes de contrôle des vecteurs, en particulier les moustiquaires imprégnées d'insecticide et la pulvérisation intradomiciliaire. avec des variations en fonction des pays. Les mécanismes de résistance aux insecticides de type biochimique liée aux enzymes de détoxification (P450S et GST) ont été largement rapportés chez An. funestus. De nombreux gènes P450 associés à la résistance métabolique ont été mis en évidence chez An. funestus collecté sur le terrain. Cependant, An. funestus en Afrique reste sensible à d'autres classes d'insecticides, telles que les organophosphorés et les néonicotinoïdes. La résistance aux insecticides. Cette revue met en évidence la résistance croissante aux insecticides chez les moustiques du groupe Funestus, un vecteur important du paludisme en Afrique, posant ainsi un défi important aux efforts de contrôle du paludisme. Tandis que An. funestus a montré une résistance aux classes d'insecticide recommandées, notamment les pyréthroïdes et, dans certains cas, les organochlorés et les carbamates, il reste sensible à d'autres classes d'insecticides tels que les organophosphorés et les néonicotinoïdes, offrant des options alternatives potentielles de contrôle des vecteurs. L'étude souligne la nécessité d'interventions ciblées qui considèrent la structure de la population et la distribution géographique d'An. funestus, y compris ses sous espèces et leurs profils de résistance aux insecticides, pour atteindre efficacement les objectifs d'élimination du paludisme.

Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra.

BACKGROUND: Old mosquitoes are more likely to transmit malaria than young ones. Therefore, accurate prediction of mosquito population age can drastically improve the evaluation of mosquito-targeted interventions. However, standard methods for age-grading mosquitoes are laborious and costly. We have shown that Mid-infrared spectroscopy (MIRS) can be used to detect age-specific patterns in mosquito cuticles and thus can be used to train age-grading machine learning models. However, these models tend to transfer poorly across populations. Here, we investigate whether applying dimensionality reduction and transfer learning to MIRS data can improve the transferability of MIRS-based predictions for mosquito ages. METHODS: We reared adults of the malaria vector Anopheles arabiensis in two insectaries. The heads and thoraces of female mosquitoes were scanned using an attenuated total reflection-Fourier transform infrared spectrometer, which were grouped into two different age classes. The dimensionality of the spectra data was reduced using unsupervised principal component analysis or t-distributed stochastic neighbour embedding, and then used to train deep learning and standard machine learning classifiers. Transfer learning was also evaluated to improve transferability of the models when predicting mosquito age classes from new populations. RESULTS: Model accuracies for predicting the age of mosquitoes from the same population as the training samples reached 99% for deep learning and 92% for standard machine learning. However, these models did not generalise to a different population, achieving only 46% and 48% accuracy for deep learning and standard machine learning, respectively. Dimensionality reduction did not improve model generalizability but reduced computational time. Transfer learning by updating pre-trained models with 2% of mosquitoes from the alternate population improved performance to ~ 98% accuracy for predicting mosquito age classes in the alternative population. CONCLUSION: Combining dimensionality reduction and transfer learning can reduce computational costs and improve the transferability of both deep learning and standard machine learning models for predicting the age of mosquitoes. Future studies should investigate the optimal quantities and diversity of training data necessary for transfer learning and the implications for broader generalisability to unseen datasets.

Advances in the genetic characterization of the malaria vector, Anopheles funestus, and implications for improved surveillance and control.

Anopheles mosquitoes present a major public health challenge in sub-Saharan Africa; notably, as vectors of malaria that kill over half a million people annually. In parts of the east and southern Africa region, one species in the Funestus group, Anopheles funestus, has established itself as an exceptionally dominant vector in some areas, it is responsible for more than 90% of all malaria transmission events. However, compared to other malaria vectors, the species is far less studied, partly due to difficulties in laboratory colonization and the unresolved aspects of its taxonomy and systematics. Control of An. funestus is also increasingly difficult because it has developed widespread resistance to public health insecticides. Fortunately, recent advances in molecular techniques are enabling greater insights into species identity, gene flow patterns, population structure, and the spread of resistance in mosquitoes. These advances and their potential applications are reviewed with a focus on four research themes relevant to the biology and control of An. funestus in Africa, namely: (i) the taxonomic characterization of different vector species within the Funestus group and their role in malaria transmission; (ii) insecticide resistance profile; (iii) population genetic diversity and gene flow, and (iv) applications of genetic technologies for surveillance and control. The research gaps and opportunities identified in this review will provide a basis for improving the surveillance and control of An. funestus and malaria transmission in Africa.

Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis.

Studies on the applications of infrared (IR) spectroscopy and machine learning (ML) in public health have increased greatly in recent years. These technologies show enormous potential for measuring key parameters of malaria, a disease that still causes about 250 million cases and 620,000 deaths, annually. Multiple studies have demonstrated that the combination of IR spectroscopy and machine learning (ML) can yield accurate predictions of epidemiologically relevant parameters of malaria in both laboratory and field surveys. Proven applications now include determining the age, species, and blood-feeding histories of mosquito vectors as well as detecting malaria parasite infections in both humans and mosquitoes. As the World Health Organization encourages malaria-endemic countries to improve their surveillance-response strategies, it is crucial to consider whether IR and ML techniques are likely to meet the relevant feasibility and cost-effectiveness requirements-and how best they can be deployed. This paper reviews current applications of IR spectroscopy and ML approaches for investigating malaria indicators in both field surveys and laboratory settings, and identifies key research gaps relevant to these applications. Additionally, the article suggests initial target product profiles (TPPs) that should be considered when developing or testing these technologies for use in low-income settings.

What is the impact of post-COVID-19 syndrome on health-related quality of life and associated factors: a cross-sectional analysis.

BACKGROUND: After the acute phase, symptoms or sequelae related to post-COVID-19 syndrome may persist for months. In a population of patients, previously hospitalized and not, followed up to 12 months after the acute infection, we aim to assess whether and to what extent post-COVID-19 syndrome may have an impact on health-related quality of life (HRQoL) and to investigate influencing factors. METHODS: We present the cross-sectional analysis of a prospective study, including patients referred to the post-COVID-19 service. Questionnaires and scales administered at 3, 6, 12 months were: Short-Form 36-item questionnaire (SF-36); Visual Analogue Scale of the EQ5D (EQ-VAS); in a subgroup, Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI-II) and Pittsburgh Sleep Quality Index (PSQI). Linear regression models were fitted to identify factors associated with HRQoL. RESULTS: We considered the first assessment of each participant (n = 572). The mean scores in SF-36 and in EQ-VAS were significantly lower than the Italian normative values and remained stable over time, except the mental components score (MCS) of the SF-36 and EQ-VAS which resulted in lower ratings at the last observations. Female gender, presence of comorbidities, and corticosteroids treatment during acute COVID-19, were associated with lower scores in SF-36 and EQ-VAS; patients previously hospitalized (54%) reported higher MCS. Alterations in BAI, BDI-II, and PSQI (n = 265)were associated with lower ratings in SF-36 and EQ-VAS. CONCLUSIONS: This study provides evidence of a significantly bad perception of health status among persons with post-COVID-19 syndrome, associated with female gender and, indirectly, with disease severity. In case of anxious-depressive symptoms and sleep disorders, a worse HRQoL was also reported. A systematic monitoring of these aspects is recommended to properly manage the post-COVID-19 period.

Using ecological observations to improve malaria control in areas where Anopheles funestus is the dominant vector.

The most important malaria vectors in sub-Saharan Africa are Anopheles gambiae, Anopheles arabiensis, Anopheles funestus, and Anopheles coluzzii. Of these, An. funestus presently dominates in many settings in east and southern Africa. While research on this vector species has been impeded by difficulties in creating laboratory colonies, available evidence suggests it has certain ecological vulnerabilities that could be strategically exploited to greatly reduce malaria transmission in areas where it dominates. This paper examines the major life-history traits of An. funestus, its aquatic and adult ecologies, and its responsiveness to key interventions. It then outlines a plausible strategy for reducing malaria transmission by the vector and sustaining the gains over the medium to long term. To illustrate the propositions, the article uses data from south-eastern Tanzania where An. funestus mediates over 85% of malaria transmission events and is highly resistant to key public health insecticides, notably pyrethroids. Both male and female An. funestus rest indoors and the females frequently feed on humans indoors, although moderate to high degrees of zoophagy can occur in areas with large livestock populations. There are also a few reports of outdoor-biting by the species, highlighting a broader range of behavioural phenotypes that can be considered when designing new interventions to improve vector control. In comparison to other African malaria vectors, An. funestus distinctively prefers permanent and semi-permanent aquatic habitats, including river streams, ponds, swamps, and spring-fed pools. The species is therefore well-adapted to sustain its populations even during dry months and can support year-round malaria transmission. These ecological features suggest that highly effective control of An. funestus could be achieved primarily through strategic combinations of species-targeted larval source management and high quality insecticide-based methods targeting adult mosquitoes in shelters. If done consistently, such an integrated strategy has the potential to drastically reduce local populations of An. funestus and significantly reduce malaria transmission in areas where this vector species dominates. To sustain the gains, the programmes should be complemented with gradual environmental improvements such as house modification to maintain biting exposure at a bare minimum, as well as continuous engagements of the resident communities and other stakeholders.

An integrated device for fast and sensitive immunosuppressant detection.

The present paper describes a compact point of care (POC) optical device for therapeutic drug monitoring (TDM). The core of the device is a disposable plastic chip where an immunoassay for the determination of immunosuppressants takes place. The chip is designed in order to have ten parallel microchannels allowing the simultaneous detection of more than one analyte with replicate measurements. The device is equipped with a microfluidic system, which provides sample mixing with the necessary chemicals and pumping samples, reagents and buffers into the measurement chip, and with integrated thin film amorphous silicon photodiodes for the fluorescence detection. Submicrometric fluorescent magnetic particles are used as support in the immunoassay in order to improve the efficiency of the assay. In particular, the magnetic feature is used to concentrate the antibody onto the sensing layer leading to a much faster implementation of the assay, while the fluorescent feature is used to increase the optical signal leading to a larger optical dynamic change and consequently a better sensitivity and a lower limit of detection. The design and development of the whole integrated optical device are here illustrated. In addition, detection of mycophenolic acid and cyclosporine A in spiked solutions and in microdialysate samples from patient blood with the implemented device are reported.

Sensitivity Analysis of Sidelobes of the Lowest Order Cladding Mode of Long Period Fiber Gratings at Turn Around Point.

A new methodology to enhance the sensitivity of a long period fiber grating sensor (LPFG) at the Turn Around Point (TAP) is here presented. The LPFG sensor has been fabricated by etching the fiber up to 20.4 µm, until the sidelobes of dispersed LP0,2 cladding mode appeared near TAP in aqueous medium. The dual peak sensitivity of the sidelobes was found to be 16,044 nm/SRIU (surrounding refractive index units) in the RI range from 1.333 to 1.3335.

Immunosuppressant quantification in intravenous microdialysate - towards novel quasi-continuous therapeutic drug monitoring in transplanted patients.

OBJECTIVES: Therapeutic drug monitoring (TDM) plays a crucial role in personalized medicine. It helps clinicians to tailor drug dosage for optimized therapy through understanding the underlying complex pharmacokinetics and pharmacodynamics. Conventional, non-continuous TDM fails to provide real-time information, which is particularly important for the initial phase of immunosuppressant therapy, e.g., with cyclosporine (CsA) and mycophenolic acid (MPA). METHODS: We analyzed the time course over 8 h of total and free of immunosuppressive drug (CsA and MPA) concentrations measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in 16 kidney transplant patients. Besides repeated blood sampling, intravenous microdialysis was used for continuous sampling. Free drug concentrations were determined from ultracentrifuged EDTA-plasma (UC) and compared with the drug concentrations in the respective microdialysate (µD). µDs were additionally analyzed for free CsA using a novel immunosensor chip integrated into a fluorescence detection platform. The potential of microdialysis coupled with an optical immunosensor for the TDM of immunosuppressants was assessed. RESULTS: Using LC-MS/MS, the free concentrations of CsA (fCsA) and MPA (fMPA) were detectable and the time courses of total and free CsA comparable. fCsA and fMPA and area-under-the-curves (AUCs) in µDs correlated well with those determined in UCs (r≥0.79 and r≥0.88, respectively). Moreover, fCsA in µDs measured with the immunosensor correlated clearly with those determined by LC-MS/MS (r=0.82). CONCLUSIONS: The new microdialysis-supported immunosensor allows real-time analysis of immunosuppressants and tailor-made dosing according to the AUC concept. It readily lends itself to future applications as minimally invasive and continuous near-patient TDM.

Ion-exchanged glass microrods as hybrid SERS/fluorescence substrates for molecular beacon-based DNA detection.

Ion-exchange in molten nitrate salts containing metal ions (i.e. silver, copper, etc.) represents a well-established technique able to modify the chemical-physical properties of glass materials. It is widely used not only in the field of integrated optics (IO) but also, more recently, in plasmonics due to the possibility to induce the formation of metal nanoparticles in the glass matrix by an ad hoc thermal post-process. In this work, the application of this technology for the realisation of low-cost and stable surface-enhanced Raman scattering (SERS) active substrates, based on soda-lime glass microrods, is reported. The microrods, with a radius of a few tens of microns, were obtained by cutting the end of an ion-exchanged soda-lime fibre for a length less than 1 cm. As ion source, silver nitrate was selected due to the outstanding SERS properties of silver. The ion-exchange and thermal annealing post-process parameters were tuned to expose the embedded silver nanoparticles on the surface of the glass microrods, avoiding the use of any further chemical etching step. In order to test the combined SERS/fluorescence response of these substrates, labelled molecular beacons (MBs) were immobilised on their surface for deoxyribonucleic acid (DNA) detection. Our experiments confirm that target DNA is attached on the silver nanoparticles and its presence is revealed by both SERS and fluorescence measurements. These results pave the way towards the development of low-cost and stable hybrid fibres, in which SERS and fluorescence interrogation techniques are combined in the same optical device.

Discretized superimposed optical fiber long-period gratings.

A novel technique, to the best of our knowledge, for the inscription of superimposed long-period gratings with arbitrary grating pitches is proposed and experimentally validated. The technique is based on the discretization of an ideal continuous sinusoidal refractive index (RI) pattern with a step function. The RI variation is induced by means of the irradiation of a photosensitive fiber with a 248 nm UV laser beam. The nonlinear relation between the induced RI change and the UV fluence was experimentally derived. Two superimposed long-period grating (LPGs) with different grating pitches have been realized with the discretization technique; the transmission spectrum was compared with that of two superimposed LPGs obtained with the traditional square wave RI modulation. The validity of the proposed technique was demonstrated by the better spectral characteristics of the discretized superimposed LPGs.

Detection of Plasmodium falciparum infected Anopheles gambiae using near-infrared spectroscopy.

BACKGROUND: Large-scale surveillance of mosquito populations is crucial to assess the intensity of vector-borne disease transmission and the impact of control interventions. However, there is a lack of accurate, cost-effective and high-throughput tools for mass-screening of vectors. METHODS: A total of 750 Anopheles gambiae (Keele strain) mosquitoes were fed Plasmodium falciparum NF54 gametocytes through standard membrane feeding assay (SMFA) and afterwards maintained in insectary conditions to allow for oocyst (8 days) and sporozoite development (14 days). Thereupon, each mosquito was scanned using near infra-red spectroscopy (NIRS) and processed by quantitative polymerase chain reaction (qPCR) to determine the presence of infection and infection load. The spectra collected were randomly assigned to either a training dataset, used to develop calibrations for predicting oocyst- or sporozoite-infection through partial least square regressions (PLS); or to a test dataset, used for validating the calibration's prediction accuracy. RESULTS: NIRS detected oocyst- and sporozoite-stage P. falciparum infections with 88% and 95% accuracy, respectively. This study demonstrates proof-of-concept that NIRS is capable of rapidly identifying laboratory strains of human malaria infection in African mosquito vectors. CONCLUSIONS: Accurate, low-cost, reagent-free screening of mosquito populations enabled by NIRS could revolutionize surveillance and elimination strategies for the most important human malaria parasite in its primary African vector species. Further research is needed to evaluate how the method performs in the field following adjustments in the training datasets to include data from wild-caught infected and uninfected mosquitoes.

Correction to: Detection of Plasmodium falciparum infected Anopheles gambiae using near-infrared spectroscopy.

Following publication of the original article [1], it was flagged that the name of the author Lisa Ranford-Cartwright had been (incorrectly) given as 'Lisa-Ranford Cartwright.

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis.

BACKGROUND: The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked immunosorbent assays (ELISA) or polymerase chain reactions (PCR). Here, mid-infrared (MIR) spectroscopy and supervised machine learning are used to accurately distinguish between vertebrate blood meals in guts of malaria mosquitoes, without any molecular techniques. METHODS: Laboratory-reared Anopheles arabiensis females were fed on humans, chickens, goats or bovines, then held for 6 to 8 h, after which they were killed and preserved in silica. The sample size was 2000 mosquitoes (500 per host species). Five individuals of each host species were enrolled to ensure genotype variability, and 100 mosquitoes fed on each. Dried mosquito abdomens were individually scanned using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra (4000 cm-1 to 400 cm-1). The spectral data were cleaned to compensate atmospheric water and CO2 interference bands using Bruker-OPUS software, then transferred to Python™ for supervised machine-learning to predict host species. Seven classification algorithms were trained using 90% of the spectra through several combinations of 75-25% data splits. The best performing model was used to predict identities of the remaining 10% validation spectra, which had not been used for model training or testing. RESULTS: The logistic regression (LR) model achieved the highest accuracy, correctly predicting true vertebrate blood meal sources with overall accuracy of 98.4%. The model correctly identified 96% goat blood meals, 97% of bovine blood meals, 100% of chicken blood meals and 100% of human blood meals. Three percent of bovine blood meals were misclassified as goat, and 2% of goat blood meals misclassified as human. CONCLUSION: Mid-infrared spectroscopy coupled with supervised machine learning can accurately identify multiple vertebrate blood meals in malaria vectors, thus potentially enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities. The technique is cost-effective, fast, simple, and requires no reagents other than desiccants. However, scaling it up will require field validation of the findings and boosting relevant technical capacity in affected countries.

Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis.

BACKGROUND: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. METHODS: Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm-1 to 500 cm-1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. RESULTS: Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. CONCLUSION: These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.

Selective coupling of Whispering Gallery Modes in film coated micro-resonators.

Whispering Gallery Mode (WGM) micro-resonators like microspheres or microtoroids are typically used as high-Q cavity substrate on which a functional film coating is deposited. In order to exploit the coating properties a critical step is the efficient excitation of WGMs mainly contained inside the deposited layer. We developed a simple method able to assess whether or not these modes are selectively excited. The method is based on monitoring the thermal shift of the excited resonance, which uniquely depends on the thermo-optic coefficient and on the thermal expansion coefficient of the material in which the mode is embedded.

Electronic Detection of DNA Hybridization by Coupling Organic Field-Effect Transistor-Based Sensors and Hairpin-Shaped Probes.

In this paper, the electronic transduction of DNA hybridization is presented by coupling organic charge-modulated field-effect transistors (OCMFETs) and hairpin-shaped probes. These probes have shown interesting properties in terms of sensitivity and selectivity in other kinds of assays, in the form of molecular beacons (MBs). Their integration with organic-transistor based sensors, never explored before, paves the way to a new class of low-cost, easy-to-use, and portable genetic sensors with enhanced performances. Thanks to the peculiar characteristics of the employed sensor, measurements can be performed at relatively high ionic strengths, thus optimizing the probes' functionality without affecting the detection ability of the device. A complete electrical characterization of the sensor is reported, including calibration with different target concentrations in the measurement environment and selectivity evaluation. In particular, DNA hybridization detection for target concentration as low as 100 pM is demonstrated.

Long-period fiber grating: a specific design for biosensing applications.

In this paper, a detailed investigation on the modeling of long-period fiber grating (LPFG) sensors is discussed with the aim of providing a more realistic solution for their use in biosensing. Add-layer sensitivity, i.e., sensitivity of the sensor to an additional layer adhered onto the fiber surface, is quantified and a clear and complete analysis about the influence of the average thickness of the deposited biological sensing layers, as well as the change in refractive index of these layers, on the resonant wavelength of the cladding modes of an LPFG is provided. Add-layer sensitivity of LPFG sensors close to mode transition (MT) and also at turn-around point (TAP) are taken into account. Adsorbed layer thicknesses, as estimated from measured wavelength shifts of the LPFG, are found to have a good match with the values obtained through other measurement techniques.