Method for Reading Sensors and Controlling Actuators Using Audio Interfaces of Mobile Devices

This article presents a novel closed loop control architecture based on audio channels of several types of computing devices, such as mobile phones and tablet computers, but not restricted to them. The communication is based on an audio interface that relies on the exchange of audio tones, allowing sensors to be read and actuators to be controlled. As an application example, the presented technique is used to build a low cost mobile robot, but the system can also be used in a variety of mechatronics applications and sensor networks, where smartphones are the basic building blocks

An essential bifunctional enzyme in Mycobacterium tuberculosis for itaconate dissimilation and leucine catabolism

Mycobacterium tuberculosis (Mtb) is the etiological agent of tuberculosis. One-fourth of the global population is estimated to be infected with Mtb, accounting for ∼1.3 million deaths in 2017. As part of the immune response to Mtb infection, macrophages produce metabolites with the purpose of inhibiting or killing the bacterial cell. Itaconate is an abundant host metabolite thought to be both an antimicrobial agent and a modulator of the host inflammatory response. However, the exact mode of action of itaconate remains unclear. Here, we show that Mtb has an itaconate dissimilation pathway and that the last enzyme in this pathway, Rv2498c, also participates in L-leucine catabolism. Our results from phylogenetic analysis, in vitro enzymatic assays, X-ray crystallography, and in vivo Mtb experiments, identified Mtb Rv2498c as a bifunctional β-hydroxyacyl-CoA lyase and that deletion of the rv2498c gene from the Mtb genome resulted in attenuation in a mouse infection model. Altogether, this report describes an itaconate resistance mechanism in Mtb and an L-leucine catabolic pathway that proceeds via an unprecedented (R)-3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) stereospecific route in nature

Biochemical and genetic analysis of butyrylcholinesterase (BChE) in a family, due to prolonged neuromuscular blockade after the use of succinylcholine

Butyrylcholinesterase (BChE) is a plasma enzyme that catalyzes the hydrolysis of choline esters, including the muscle-relaxant succinylcholine and mivacurium. Patients who present sustained neuromuscular blockade after using succinylcholine usually carry BChE variants with reduced enzyme activity or an acquired BChE deficiency. We report here the molecular basis of the BCHE gene underlying the slow catabolism of succinylcholine in a patient who underwent endoscopic nasal surgery. We measured the enzyme activity of BChE and extracted genomic DNA in order to study the promoter region and all exons of the BCHE gene of the patient, her parents and siblings. PCR products were sequenced and compared with reference sequences from GenBank. We detected that the patient and one of her brothers have two homozygous mutations: nt1615 GCA > ACA (Ala539Thr), responsible for the K variant, and nt209 GAT > GGT (Asp70Gly), which produces the atypical variant A. Her parents and two of her brothers were found to be heterozygous for the AK allele, and another brother is homozygous for the normal allele. Sequence analysis of exon 1 including 5′UTR showed that the proband and her brother are homozygous for –116GG. The AK/AK genotype is considered the most frequent in hereditary hypocholinesterasemia (44%). This work demonstrates the importance of defining the phenotype and genotype of the BCHE gene in patients who are subjected to neuromuscular block by succinylcholine, because of the risk of prolonged neuromuscular paralysis

Stroke outcome measurements from electronic medical records : cross-sectional study on the effectiveness of neural and nonneural classifiers

Background: With the rapid adoption of electronic medical records (EMRs), there is an ever-increasing opportunity to collect data and extract knowledge from EMRs to support patient-centered stroke management. Objective: This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of clinical patient outcomes and the extraction of patient characteristics from EMRs. Methods: Our study addressed the computational problems of information extraction and automatic text classification. We identified essential tasks to be considered in an ischemic stroke value-based program. The 30 selected tasks were classified (manually labeled by specialists) according to the following value agenda: tier 1 (achieved health care status), tier 2 (recovery process), care related (clinical management and risk scores), and baseline characteristics. The analyzed data set was retrospectively extracted from the EMRs of patients with stroke from a private Brazilian hospital between 2018 and 2019. A total of 44,206 sentences from free-text medical records in Portuguese were used to train and develop 10 supervised computational machine learning methods, including state-of-the-art neural and nonneural methods, along with ontological rules. As an experimental protocol, we used a 5-fold cross-validation procedure repeated 6 times, along with subject-wise sampling. A heatmap was used to display comparative result analyses according to the best algorithmic effectiveness (F1 score), supported by statistical significance tests. A feature importance analysis was conducted to provide insights into the results. Results: The top-performing models were support vector machines trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR textual representations. The support vector machine models produced statistically superior results in 71% (17/24) of tasks, with an F1 score >80% regarding care-related tasks (patient treatment location, fall risk, thrombolytic therapy, and pressure ulcer risk), the process of recovery (ability to feed orally or ambulate and communicate), health care status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional nonneural methods, given the characteristics of the data set. Ontological rules were also effective in tasks such as baseline characteristics (alcoholism, atrial fibrillation, and coronary artery disease) and the Rankin scale. The complementarity in effectiveness among models suggests that a combination of models could enhance the results and cover more tasks in the future. Conclusions: Advances in information technology capacity are essential for scalability and agility in measuring health status outcomes. This study allowed us to measure effectiveness and identify opportunities for automating the classification of outcomes of specific tasks related to clinical conditions of stroke victims, and thus ultimately assess the possibility of proactively using these machine learning techniques in real-world situations

Arousals are frequent and associated with exacerbated blood pressure response in patients with primary hypertension

Background\ud Spontaneous arousals are relatively common during sleep, and induce\ud hemodynamic responses. We sought to investigate the frequency and\ud magnitude of blood pressure (BP) increases triggered by spontaneous\ud arousals in patients with primary hypertension.\ud Methods\ud We conducted a study in which we divided 18 nonobese, sedentary\ud adults without sleep-disordered breathing into two groups, consisting\ud of: (i) hypertensive (HT, n = 8) patients; and (ii) normotensive (NT, n = 10)\ud controls. The groups were matched for age and body mass index. All\ud subjects underwent full polysomnography with simultaneous monitoring\ud of heart rate (HR) and beat-by-beat BP. Each subject’s BP and HR\ud were analyzed immediately before BP peaks triggered by spontaneous\ud arousals during stage 2 of nonrapid eye movement sleep.\ud Results\ud The total sleep time, sleep efficiency, and sleep structure in the two\ud study groups were similar. In contrast, the number of arousals was\ud significantly higher in the HT than in the NT group, at 25 ± 5 vs. 12 ± 3\ud events/h, respectively (P < 0.05). The HR of the HT and NT groups was\ud similar before arousal (65 ± 3 bpm vs. 67 ± 3 bpm, respectively, P < 0.01)\ud and increased significantly and similarly in the two groups upon arousal\ud (to 79 ± 6 bpm vs. 74 ± 4 bpm, respectively, P < 0.01). Systolic and diastolic\ud BPs were significantly higher throughout sleep in the HT than\ud in the NT group. During spontaneous arousals, BP increased in both\ud groups (P < 0.05). However, the magnitude of the increase in systolic BP\ud was significantly greater in the HT than in the NT group (22 ± 3 mm Hg\ud vs. 15 ± 3 mm Hg, P < 0.05).\ud Conclusions\ud Patients with hypertension who do not have sleep-disordered breathing\ud have an increased cardiovascular burden during sleep, which may\ud be due to the greater number of arousals and exacerbated systolic BP\ud response that they experience during sleep. These novel findings may\ud have cardiovascular implications in patients with hypertensionFAPESP 2010/18183–6Conselho Nacional de Pesquisa (CNPq) 301867/2010-0Fundação Zerbin