1H-Nuclear Magnetic Resonance Analysis of Urine as Diagnostic Tool for Organic Acidemias and Aminoacidopathies.

The utility of low-resolution 1H-NMR analysis for the identification of biomarkers provided evidence for rapid biochemical diagnoses of organic acidemia and aminoacidopathy. 1H-NMR, with a sensitivity expected for a field strength of 400 MHz at 64 scans was used to establish the metabolomic urine sample profiles of an infant population diagnosed with small molecule Inborn Errors of Metabolism (smIEM) compared to unaffected individuals. A qualitative differentiation of the 1H-NMR spectral profiles of urine samples obtained from individuals affected by different organic acidemias and aminoacidopathies was achieved in combination with GC-MS. The smIEM disorders investigated in this study included phenylalanine metabolism; isovaleric, propionic, 3-methylglutaconicm and glutaric type I acidemia; and deficiencies in medium chain acyl-coenzyme and holocarboxylase synthase. The observed metabolites were comparable and similar to those reported in the literature, as well as to those detected with higher-resolution NMR. In this study, diagnostic marker metabolites were identified for the smIEM disorders. In some cases, changes in metabolite profiles differentiated post-treatments and follow-ups while allowing for the establishment of different clinical states of a biochemical disorder. In addition, for the first time, a 1H-NMR-based biomarker profile was established for holocarboxylase synthase deficiency spectrum.

Efficacy of Human Recombinant Epidermal Growth Factors vs Conventional Therapy for the Treatment of Chronic Venous Ulcers: A Retrospective Case Series.

INTRODUCTION: Venous ulcers are the terminal phase of chronic venous insufficiency, the result of induced skin disorders and maintained by persistent venous hypertension. Affecting a large part of the adult population, they drain economic resources and greatly impact patient quality of life. OBJECTIVE: The objective of this descriptive, retrospective case series was to determine the efficacy of recombinant human epidermal growth factor (rhEGF) plus compression therapy vs standard of care in 48 patients with active ulcers resulting from chronic venous insufficiency. MATERIALS AND METHODS: In this descriptive, retrospective case series, 24 patients (mean age, 62.4 years) received rhEGF by intralesional and perilesional infiltration with compression therapy, and 24 patients (mean age, 69.4 years) received treatment with topical hydrocolloid gels and compression therapy. In 62.5% of patients, the ulcers were located in the internal malleoli. Ulcer progression time, ulcer size, Wollina score index, number of conventional cures, rhEGF vials used, and time to epithelialization were documented. RESULTS: Epithelialization of the active ulcer was reached in 100% of intervened patients. In the 24 patients receiving rhEGF, 71% achieved wound closure in 8 weeks or less, and the remaining percentage achieved closure within 9 and 12 weeks. In the conventional therapy group, patients achieved closure in an average of 29.5 weeks, with a minimum of 13 weeks and a maximum of 46 weeks. CONCLUSIONS: Although conventional therapy with the use of hydrocolloids and compression achieved adequate epithelialization of venous ulcers, the use of rhEGF significantly decreased healing time and could be a beneficial therapy to these patients who have few therapeutic options.

Prediction of dry matter content of recently harvested 'Hass' avocado fruits using hyperspectral imaging.

BACKGROUND: 'Hass' avocado consumption is increasing due to its organoleptic properties, so it is necessary to develop new technologies to guarantee export quality. Avocado fruits do not ripen on the tree, and the visual classification of its maturity is not accurate. The most commonly used fruit maturity indicator is the percentage of dry matter (DM). The aim of this research was to investigate a non-destructive method with hyperspectral images to predict the percentage of DM of fruits across the spectral range of 400-1000 nm. RESULTS: No correlation between fruit weight and color with the percentage of DM was found in the study area. Cross-validation efficiency of different data sources, including the spectrum extraction zone (the center, a line from the peduncle to the base, and the whole fruit) and the average of one or two fruit faces, was compared. Four linear regression models were compared. Data of the whole fruit and average of both sides per fruit using a support vector machine regression were selected for the prediction test. Following the cross-validation concept, five sets of calibration and test data were selected and optimized for calibration. The best test prediction set comprised an R2 = 0.9, a root-mean-square error of 2.6 g kg-1 DM, a Pearson correlation of 0.95, and a ratio of prediction to deviation of 3.2. CONCLUSIONS: The results of the study indicate that hyperspectral images allow classifying export fruits and making harvesting decisions. © 2020 Society of Chemical Industry.

Solar Panel Detection within Complex Backgrounds Using Thermal Images Acquired by UAVs.

The installation of solar plants everywhere in the world increases year by year. Automated diagnostic methods are needed to inspect the solar plants and to identify anomalies within these photovoltaic panels. The inspection is usually carried out by unmanned aerial vehicles (UAVs) using thermal imaging sensors. The first step in the whole process is to detect the solar panels in those images. However, standard image processing techniques fail in case of low-contrast images or images with complex backgrounds. Moreover, the shades of power lines or structures similar to solar panels impede the automated detection process. In this research, two self-developed methods are compared for the detection of panels in this context, one based on classical techniques and another one based on deep learning, both with a common post-processing step. The first method is based on edge detection and classification, in contrast to the second method is based on training a region based convolutional neural networks to identify a panel. The first method corrects for the low contrast of the thermal image using several preprocessing techniques. Subsequently, edge detection, segmentation and segment classification are applied. The latter is done using a support vector machine trained with an optimized texture descriptor vector. The second method is based on deep learning trained with images that have been subjected to three different pre-processing operations. The postprocessing use the detected panels to infer the location of panels that were not detected. This step selects contours from detected panels based on the panel area and the angle of rotation. Then new panels are determined by the extrapolation of these contours. The panels in 100 random images taken from eleven UAV flights over three solar plants are labeled and used to evaluate the detection methods. The metrics for the new method based on classical techniques reaches a precision of 0.997, a recall of 0.970 and a F1 score of 0.983. The metrics for the method of deep learning reaches a precision of 0.996, a recall of 0.981 and a F1 score of 0.989. The two panel detection methods are highly effective in the presence of complex backgrounds.

Trombosis venosa profunda en un paciente con hipertiroidismo de novo. Presentación de caso / A case of deep venous thrombosis in a patient with de novo hyperthyroidism

La trombosis venosa es una condición clínica en la cual un trombo se genera y ocluye la luz de un vaso venoso con el consecuente riesgo de embolia pulmonar. La fisiopatología se relaciona con estados de hipercoagulabilidad, alteraciones del flujo sanguíneo y lesiones vasculares endoteliales, conocida como la triada de Virchow. Dentro de los factores de riesgo se encuentran el cáncer, la estancia hospitalaria prolongada, la cirugía mayor, el trauma mayor y las trombofilias; sin embargo en el 25-50% de los casos es imposible identificar el factor etiológico. Presentamos el caso de un hombre con clínica y diagnóstico de trombosis venosa profunda en manejo extrainstitucional con cumínicos, que acude al Hospital Infantil Universitario de San José por incremento de los signos inflamatorios en el miembro inferior derecho. Al ingreso hay aumento de la extensión de la trombosis venosa y niveles subterapéuticos de INR, a pesar de dosis óptimas de cumínicos. La evolución trascurrió con taquicardia y episodios de diaforesis. Se documenta y realiza el diagnóstico de hipertiroidismo asociado con elevación del factor viii de coagulación. Conclusión: Se considera que la trombosis venosa profunda está favorecida por un estado de hipercoagulación determinado por hipertiroidismo.

Venous thrombosis is a clinical condition that occurs when a thrombus forms and obstructs the lumen of a vein constituting a risk for pulmonary embolism. Pathophysiology features Virchow's triad, that is, hypercoagulability, blood flow alterations and vascular endothelial damage. Risk factors include, cancer, prolonged length of hospital stay, major surgery, major trauma and thrombophilias, although, identifying the etiological factor is impossible in 25 to 50% of all cases. A case is presented in a man with clinical presentation and diagnosis of deep venous thrombosis who had been managed with coumarin before consulting to our institution. He came to Hospital Infantil Universitario de San José for presenting an increase of the inflammatory signs on his right leg. At admission, a greater areawas affected by venous thrombosis and subtherapeutic INR values were evidenced despite patient had received optimal doses of coumarin derivatives. He presented tachycardia and episodes of diaphoresis during his hospital stay. A diagnosis of hyperthyroidism associated with high concentrations of coagulation factor VIII was documented. Conclusion: It is considered that DVT is favored by hypercoagulable states determined by hyperthyroidism.

Geometric characterization of cell membrane of mouse oocytes for ICSI.

Intracytoplasmic sperm injection (ICSI) is a broadly utilized assisted reproductive technology. A number of technologies for this procedure have evolved lately, such as the most commonly utilized piezo-assisted ICSI technique (P-ICSI). An important problem with this technique, however, is that it requires a small amount of mercury to stabilize the tip of the penetration micropipette. A completely different and mercury-free injection technology, called the rotationally oscillating drill (Ros-Drill) (RD-ICSI), was recently developed. It uses microprocessor-controlled rotational oscillations of a spiked micropipette after the pipette deforms the membrane to a certain tension level. Inappropriate selection of this initiation instant typically results in cell damage, which ultimately leads to unsuccessful ICSI. During earlier manual clinical tests of Ros-Drill, the technicians' expertise determined this instant in an ad hoc fashion. In this paper, we introduce a computer-vision-based tool to mechanize this process with the objective of maintaining the repeatability and introducing potential automation. Computer images are used for monitoring the membrane deformations and curvature variations as the basis for decision making. The main contribution of this paper is in the specifics of the computer logic to perform the monitoring. These new tools are expected to provide a practicable means for automating the Ros-Drill-assisted ICSI operation.

Structural variety of alkali metal compounds containing P-E-M (E = S, Se; M = Li, Na, K) units derived from nitrogen rich heterocycles.

The preparation of novel alkali metal chalcogenides supported by multidentate nitrogen rich ligands is reported. Treatment of the ligand precursors [H{(4,5-(P(E)Ph(2))(2)tz}] (E = S (1a), Se (1b)) with organolithium reagents or elemental sodium and potassium in tetrahydrofuran (THF) leads to the isolation of 2-7 in high yields. These compounds were characterized by elemental analysis, IR spectroscopy, mass spectrometry, solution and solid-state multinuclear NMR spectroscopy, and single crystal X-ray diffraction analysis. In the solid state, 2, 4, and 5 are dimers that contain bimetallic six-membered (M(2)N(4)) rings (M = Li, Na). In 3, the discrete monomer [Li{4,5-(P(Se)Ph(2))(2)tz}(thf)(2)] (tz = 1,2,3-triazole) contains a five-membered CPSeLiN ring which adopts an envelope conformation. The polymeric arrangement [K{4,5-(P(S)Ph(2))(2)}tz](infinity) in 6 displays different bonding modes based on the hapticity of the ligand upon binding to the potassium atom. In compounds 2-6, the presence of secondary bonding features the alkali metal chalcogen bonds.