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.

Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning.

Field-derived metrics are critical for effective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million people yearly. One key metric is entomological inoculation rate, a direct measure of transmission intensities, computed as a product of human biting rates and prevalence of Plasmodium sporozoites in mosquitoes. Unfortunately, current methods for identifying infectious mosquitoes are laborious, time-consuming, and may require expensive reagents that are not always readily available. Here, we demonstrate the first field-application of mid-infrared spectroscopy and machine learning (MIRS-ML) to swiftly and accurately detect Plasmodium falciparum sporozoites in wild-caught Anopheles funestus, a major Afro-tropical malaria vector, without requiring any laboratory reagents. We collected 7178 female An. funestus from rural Tanzanian households using CDC-light traps, then desiccated and scanned their heads and thoraces using an FT-IR spectrometer. The sporozoite infections were confirmed using enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), to establish references for training supervised algorithms. The XGBoost model was used to detect sporozoite-infectious specimen, accurately predicting ELISA and PCR outcomes with 92% and 93% accuracies respectively. These findings suggest that MIRS-ML can rapidly detect P. falciparum in field-collected mosquitoes, with potential for enhancing surveillance in malaria-endemic regions. The technique is both fast, scanning 60-100 mosquitoes per hour, and cost-efficient, requiring no biochemical reactions and therefore no reagents. Given its previously proven capability in monitoring key entomological indicators like mosquito age, human blood index, and identities of vector species, we conclude that MIRS-ML could constitute a low-cost multi-functional toolkit for monitoring malaria risk and evaluating interventions.

Lifting Hofmeister's Curse: Impact of Cations on Diffusion, Hydrogen Bonding, and Clustering of Water.

Water plays a role in the stability, reactivity, and dynamics of the solutes that it contains. The presence of ions alters this capacity by changing the dynamics and structure of water. However, our understanding of how and to what extent this occurs is still incomplete. Here, a study of the low-frequency Raman spectra of aqueous solutions of various cations by using optical Kerr-effect spectroscopy is presented. This technique allows for the measurement of the changes that ions cause in both the diffusive dynamics and the vibrations of the hydrogen-bond structure of water. It is found that when salts are added, some of the water molecules become part of the ion solvation layers, while the rest retain the same diffusional properties as those of pure water. The slowing of the dynamics of the water molecules in the solvation shell of each ion was found to depend on its charge density at infinite dilution conditions and on its position in the Hofmeister series at higher concentrations. It is also observed that all cations weaken the hydrogen-bond structure of the solution and that this weakening depends only on the size of the cation. Finally, evidence is found that ions tend to form amorphous aggregates, even at very dilute concentrations. This work provides a novel approach to water dynamics that can be used to better study the mechanisms of solute nucleation and crystallization, the structural stability of biomolecules, and the dynamic properties of complex solutions, such as water-in-salt electrolytes.

Frecuencia del síndrome metabólico y factores de riesgo en adultos con y sin diabetes mellitus e hipertensión arterial / Frequency of metabolic syndrome and risk factors in adults with and without diabetes mellitus and arterial hypertension

RESUMEN Objetivo Determinar la frecuencia y factores de riesgo para Síndrome Metabólico (SM) en adultos con Diabetes mellitus, hipertensión arterial y sin diabetes-hipertensión. Material y Métodos Se realizó un estudio transversal analítico en derechohabientes de ambos sexos y mayores de 20 años de los servicios de consulta externa del HGZ No. 1 IMSS Colima, México. Las variables estudiadas fueron edad, IMC, diámetro de la cintura, grado de escolaridad, estado socioeconómico, grado de actividad física, tabaquismo, antecedentes familiares para diabetes e hipertensión arterial (HTA) y parámetros bioquímicos como glucosa, colesterol HDL, triglicéridos. Resultados Se estudiaron 417 pacientes (170 hombres y 247 mujeres), con un promedio de edad 53,2 ± 13,4 años (intervalo 20 a 86 años). La frecuencia global del SM fue del 52,3 % (56 % mujeres y 46,4 % hombres). Mientras que la frecuencia del SM fue de 50 % en DM2, 42% en HTA, 80 % DM2 + HTA y 28,2 % sin DM o HTA. La frecuencia del tabaquismo fue del 27,8 % y fue un factor de riesgo importante para la totalidad de pacientes con SM, en DM2 y en DM2+HAT. Conclusiones La frecuencia del SM en adultos fue del 52,3 %, las mujeres fueron más afectadas y el tabaquismo fue el factor de riesgo más importante.(AU)

ABSTRACT Objective Determinate the frequency and the risk factors for Metabolic Syndrome in adults with diabetes mellitus, Hypertension and without Diabetes- Hypertension. Materials and Methods We realized a cross-sectional study in patients of both sexes and older than 20 years of the "Hospital General de Zona 1 IMSS" in Colima, Mexico. The variables studied were: age, BMI, waist circumference, cigarette smoking, and family history of diabetes and hypertension, and biochemical parameters, such as glucose, HDL cholesterol and triglycerides. Results A total of 417 persons were enrolled (170 men and 247 women), with an age average of 53.2 ± 13.4 years (age range, 20 to 86 years). The global frequency of the metabolic syndrome was 52.3 % (56 % in women and 46.4 % in men). While the MS frequency was 50 % in DM2, 42 % in hypertension, 80 % in DM2+hypertension and 28.2 % without DM2 and hypertension. The cigarette smoking frequency was 27.8 %, and it was an important risk factor for the totally of patients with MS, in DM2 and in DM2+hypertension. Conclusions The frequency of MS in adults was 52.3 %, women were the most affected, and cigarette smoking was the most important risk factor.(AU)