A low-cost, portable device for the study of the malaria parasite's growth inhibition via microwave exposure.

Recently, a novel method for the growth inhibition of malaria parasites using microwaves was proposed. However, the apparatuses used to demonstrate this method are high-cost and immovable, hindering the progression in this field of research, which is still in its early stages. This paper presents the redesign, construction, and validation of an equivalent system, converting it into a portable and low-cost system, capable of replacing the existing one. The proposed system is mainly composed of an RF generator (MAX2870), an RF amplifier (SKYWORKS 66292-11) and a graphical user interface. Likewise, the RF applicator proposed by the original study was redesigned, resulting in a five-fold improvement in return loss. The obtained results indicate that the proposed system achieves 90% parasite growth inhibition, matching the performance of its counterpart at less than 1% of its cost. These results represent a breakthrough for the creation of smaller, enhanced devices that open new possibilities for an alternative treatment to combat this devastating disease.

Machine Learning-Based Prediction of Changes in the Clinical Condition of Patients With Complex Chronic Diseases: 2-Phase Pilot Prospective Single-Center Observational Study.

BACKGROUND: Functional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases. A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic actions that stop the exacerbation of the disease. OBJECTIVE: This study aimed to predict alterations in the clinical condition of patients with complex chronic diseases by predicting the Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence model and data collected through an internet of things mobility device. METHODS: A 2-phase pilot prospective single-center observational study was designed. During both phases, patients were recruited, and a wearable activity tracker was allocated to gather physical activity data. Patients were categorized into class A (BI≤20; total dependence), class B (2060; moderate or mild dependence, or independent). Data preprocessing and machine learning techniques were used to analyze mobility data. A decision tree was used to achieve a robust and interpretable model. To assess the quality of the predictions, several metrics including the mean absolute error, median absolute error, and root mean squared error were considered. Statistical analysis was performed using SPSS and Python for the machine learning modeling. RESULTS: Overall, 90 patients with complex chronic diseases were included: 50 during phase 1 (class A: n=10; class B: n=20; and class C: n=20) and 40 during phase 2 (class B: n=20 and class C: n=20). Most patients (n=85, 94%) had a caregiver. The mean value of the BI was 58.31 (SD 24.5). Concerning mobility aids, 60% (n=52) of patients required no aids, whereas the others required walkers (n=18, 20%), wheelchairs (n=15, 17%), canes (n=4, 7%), and crutches (n=1, 1%). Regarding clinical complexity, 85% (n=76) met patient with polypathology criteria with a mean of 2.7 (SD 1.25) categories, 69% (n=61) met the frailty criteria, and 21% (n=19) met the patients with complex chronic diseases criteria. The most characteristic symptoms were dyspnea (n=73, 82%), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78) of patients. The most important variables for predicting the BI were identified as the maximum step count during evening and morning periods and the absence of a mobility device. The model exhibited consistency in the median prediction error with a median absolute error close to 5 in the training, validation, and production-like test sets. The model accuracy for identifying the BI class was 91%, 88%, and 90% in the training, validation, and test sets, respectively. CONCLUSIONS: Using commercially available mobility recording devices makes it possible to identify different mobility patterns and relate them to functional capacity in patients with polypathology according to the BI without using clinical parameters.

Microwaves can kill malaria parasites non-thermally.

Malaria, which infected more than 240 million people and killed around six hundred thousand only in 2021, has reclaimed territory after the SARS-CoV-2 pandemic. Together with parasite resistance and a not-yet-optimal vaccine, the need for new approaches has become critical. While earlier, limited, studies have suggested that malaria parasites are affected by electromagnetic energy, the outcomes of this affectation vary and there has not been a study that looks into the mechanism of action behind these responses. In this study, through development and implementation of custom applicators for in vitro experimentation, conditions were generated in which microwave energy (MW) killed more than 90% of the parasites, not by a thermal effect but via a MW energy-induced programmed cell death that does not seem to affect mammalian cell lines. Transmission electron microscopy points to the involvement of the haemozoin-containing food vacuole, which becomes destroyed; while several other experimental approaches demonstrate the involvement of calcium signaling pathways in the resulting effects of exposure to MW. Furthermore, parasites were protected from the effects of MW by calcium channel blockers calmodulin and phosphoinositol. The findings presented here offer a molecular insight into the elusive interactions of oscillating electromagnetic fields with P. falciparum, prove that they are not related to temperature, and present an alternative technology to combat this devastating disease.

A Low-Cost Automatic Vehicle Identification Sensor for Traffic Networks Analysis.

In recent years, different techniques to address the problem of observability in traffic networks have been proposed in multiple research projects, being the technique based on the installation of automatic vehicle identification sensors (AVI), one of the most successful in terms of theoretical results, but complex in terms of its practical application to real studies. Indeed, a very limited number of studies consider the possibility of installing a series of non-definitive plate scanning sensors in the elements of a network, which allow technicians to obtain a better conclusions when they deal with traffic network analysis such as urbans mobility plans that involve the estimation of traffic flows for different scenarios. With these antecedents, the contributions of this paper are (1) an architecture to deploy low-cost sensors network able to be temporarily installed on the city streets as an alternative of rubber hoses commonly used in the elaboration of urban mobility plans; (2) a design of the low-cost, low energy sensor itself, and (3) a sensor location model able to establish the best set of links of a network given both the study objectives and of the sensor needs of installation. A case of study with the installation of as set of proposed devices is presented, to demonstrate its viability.

A Novel Passive Method for the Assessment of Skin-Electrode Contact Impedance in Intraoperative Neurophysiological Monitoring Systems.

Intraoperative Neurophysiological Monitoring is a set of monitoring techniques consisting of reading electrical activity generated by the nervous system structures during surgeries. In order to guarantee signal quality, contact impedance between the sensing electrodes and the patient's skin needs to be as low as possible. Hence, monitoring this impedance while signals are measured is an important feature of current medical devices. The most commonly used technique involves injection of a known current and measurement of the voltage drop in the contact interface. This method poses several problems, such as power consumption (critical in battery-powered systems), frequency dependency and regulation issues, which are overcome by using a passive method. The fundamentals of the method proposed in this paper are based on the utilization of the variation suffered by the input random signal when a known resistance is connected in parallel to the input terminals of the low-noise amplifier (LNA) of the analog front-end of the acquisition system. Controlling the connection of the resistors and computing the root mean square of the LNA output voltage has been proved to be a useful tool to assess that the contact impedance is suitably low, allowing the user to know if the neural measurements obtained are valid.

Identification of a novel risk locus for multiple sclerosis at 13q31.3 by a pooled genome-wide scan of 500,000 single nucleotide polymorphisms.

Multiple sclerosis is a chronic inflammatory demyelinating disease of the central nervous system with an important genetic component and strongest association driven by the HLA genes. We performed a pooling-based genome-wide association study of 500,000 SNPs in order to find new loci associated with the disease. After applying several criteria, 320 SNPs were selected from the microarrays and individually genotyped in a first and independent Spanish Caucasian replication cohort. The 8 most significant SNPs validated in this cohort were also genotyped in a second US Caucasian replication cohort for confirmation. The most significant association was obtained for SNP rs3129934, which neighbors the HLA-DRB/DQA loci and validates our pooling-based strategy. The second strongest association signal was found for SNP rs1327328, which resides in an unannotated region of chromosome 13 but is in linkage disequilibrium with nearby functional elements that may play important roles in disease susceptibility. This region of chromosome 13 has not been previously identified in MS linkage genome screens and represents a novel risk locus for the disease.

Evidence for association of chromosome 10 open reading frame (C10orf27) gene polymorphisms and multiple sclerosis.

A recent association study has provided evidence that chromosome 10q22.1 may contain candidate genes for multiple sclerosis (MS). We analysed two intronic and a non-synonymous single nucleotide polymorphism (SNP) of the C10orf27 gene in 571 patients with MS (relapsing remitting and primary progressive) and healthy controls. Adjusted comparisons revealed significant association with disease susceptibility for one intronic SNP in RRMS individuals and the amino acid modifying SNP for PPMS cases; the latter may also contribute to faster disease progression. Transcript expression in brain lesions from MS patients was increased. These findings suggest C10orf27 as a candidate gene for MS susceptibility and pathogenesis.