Assessing Outcomes of Patients Subject to Intensive Care to Facilitate Organ Donation: A Spanish Multicenter Prospective Study.

Intensive Care to facilitate Organ Donation (ICOD) consists of the initiation or continuation of intensive care measures in patients with a devastating brain injury (DBI) in whom curative treatment is deemed futile and death by neurological criteria (DNC) is foreseen, to incorporate organ donation into their end-of-life plans. In this study we evaluate the outcomes of patients subject to ICOD and identify radiological and clinical factors associated with progression to DNC. In this first prospective multicenter study we tested by multivariate regression the association of clinical and radiological severity features with progression to DNC. Of the 194 patients, 144 (74.2%) patients fulfilled DNC after a median of 25 h (95% IQR: 17-44) from ICOD onset. Two patients (1%) shifted from ICOD to curative treatment, both were alive at discharge. Factors associated with progression to DNC included: age below 70 years, clinical score consistent with severe brain injury, instability, intracranial hemorrhage, midline shift ≥5 mm and certain types of brain herniation. Overall 151 (77.8%) patients progressed to organ donation. Based on these results, we conclude that ICOD is a beneficial and efficient practice that can contribute to the pool of deceased donors.

Pneumonia versus graft dysfunction as the cause of acute respiratory failure after lung transplant: a 4-year multicentre prospective study in 153 adults requiring intensive care admission.

We aimed to assess the main causes of intensive care unit (ICU) readmissions in lung transplant adults and to identify independent predictors of ICU mortality (primary end-point).This Spanish five-centre prospective cohort study enrolled all lung transplant adults with ICU readmissions after post-transplant ICU discharge between 2012 and 2016. Patients were followed until hospital discharge or death.153 lung transplant recipients presented 174 ICU readmissions at a median (interquartile range) of 6 (2-25) months post-transplant. Chronic lung allograft dysfunction was reported in 39 (25.5%) recipients, 13 of whom (all exitus) had restrictive allograft syndrome (RAS). Acute respiratory failure (ARF) (110 (71.9%)) was the main condition requiring ICU readmission. Graft rejection (six (5.4%) acute) caused only 12 (10.8%) readmissions whereas pneumonia (56 (36.6%)) was the main cause (50 admitted for ARF and six for shock), with Pseudomonas aeruginosa (50% multidrug resistant) being the predominant pathogen. 55 (35.9%) and 69 (45.1%) recipients died in the ICU and the hospital, respectively. Bronchiolitis obliterans syndrome (BOS) stage 2 (adjusted OR (aOR) 7.2 (95% CI 1.0-65.7)), BOS stage 3 (aOR 13.7 (95% CI 2.5-95.3)), RAS (aOR >50) and pneumonia at ICU readmission (aOR 2.5 (95% CI 1.0-7.1)) were identified in multivariate analyses as independent predictors of ICU mortality. Only eight (5.2%) patients had positive donor-specific antibodies prior to ICU readmission and this variable did not affect the model.ARF was the main condition requiring ICU readmission in lung transplant recipients and was associated with high mortality. Pneumonia was the main cause of death and was also an independent predictor. RAS should receive palliative care rather than ICU admission.

Inteligencia artificial en la colonoscopia de tamizaje y la disminución del error.

Artificial Intelligence (AI) has the potential to change many aspects of healthcare practice. Image discrimination and classification has many applications within medicine. Machine learning algorithms and complicated neural networks have been developed to train a computer to differentiate between normal and abnormal areas. Machine learning is a form of AI that allows the platform to improve without being programmed. Computer Assisted Diagnosis (CAD) is based on latency, which is the time between the captured image and when it is displayed on the screen. AI-assisted endoscopy can increase the detection rate by identifying missed lesions. An AI CAD system must be responsive, specific, with easy-to-use interfaces, and provide fast results without substantially prolonging procedures. AI has the potential to help both, trained and trainee endoscopists. Rather than being a substitute for high-quality technique, it should serve as a complement to good practice. AI has been evaluated in three clinical scenarios in colonic neoplasms: the detection of polyps, their characterization (adenomatous vs. non-adenomatous) and the prediction of invasive cancer within a polypoid lesion.

La inteligencia artificial (IA) tiene el potencial de cambiar muchos aspectos de la práctica sanitaria. La discriminación y la clasificación de imágenes tiene muchas aplicaciones dentro de la medicina. Se han desarrollado algoritmos de aprendizaje automático y redes neuronales complicadas para entrenar a una computadora a diferenciar las áreas normales de las anormales. El aprendizaje automático es una forma de IA que permite que la plataforma mejore sin ser programada. El diagnóstico asistido por computadora (CAD) se basa en latencia, que es el tiempo entre la imagen capturada y cuando es mostrada en la pantalla. La endoscopia asistida por IA puede incrementar la tasa de detección al identificar lesiones obviadas. Un sistema CAD de IA debe ser sensible, específico, con interfaces fáciles de usar, y proporcionar resultados rápidos sin prolongar sustancialmente los procedimientos. La IA tiene el potencial de ayudar tanto a endoscopistas entrenados como a los que están en entrenamiento. En vez de ser un sustituto para una técnica de alta calidad, deberá servir como un complemento de las buenas prácticas. La IA ha sido evaluada en tres escenarios clínicos en las neoplasias colónicas: la detección de pólipos, su caracterización (adenomatosos vs. no adenomatosos) y la predicción de cáncer invasor dentro de una lesión polipoide.

Optimized high performance liquid chromatography-ultraviolet detection method using core-shell particles for the therapeutic monitoring of methotrexate.

Methotrexate (MTX) is an antineoplastic drug, and due to its high toxicity, the therapeutic drug monitoring is strictly conducted in the clinical practice. The chemometric optimization and validation of a high performance liquid chromatography (HPLC) method using core-shell particles is presented for the determination of MTX in plasma during therapeutic monitoring. Experimental design and response surface methodology (RSM) were applied for the optimization of the chromatographic system and the analyte extraction step. A Poroshell 120 EC-C18 (3.0 mm×75 mm, 2.7 µm) column was used to obtain a fast and efficient separation in a complete run time of 4 min. The optimum conditions for the chromatographic system resulted in a mobile phase consisting of acetic acid/sodium acetate buffer solution (85.0 mM, pH=4.00) and 11.2% of acetonitrile at a flow rate of 0.4 mL/min. Selectivity, linearity, accuracy and precision were demonstrated in a range of 0.10-6.0 µM of MTX. The application of the optimized method required only 150 µL of patient plasma and a low consumption of solvent to provide rapid results.

A novel ion-pairing chromatographic method for the simultaneous determination of both nicarbazin components in feed additives: chemometric tools for improving the optimization and validation.

The development, optimization and validation of an ion-pairing high performance liquid chromatography method for the simultaneous determination of both nicarbazin (NIC) components: 4,4'-dinitrocarbanilide (DNC) and 2-hydroxy-4,6-dimethylpyrimidine (HDP) in bulk materials and feed additives are described. An experimental design was used for the optimization of the chromatographic system. Four variables, including mobile phase composition and oven temperature, were analyzed through a central composite design exploring their contribution to analyte separation. Five responses: peak resolutions, HDP capacity factor, HDP tailing and analysis time, were modelled by using the response surface methodology and were optimized simultaneously by implementing the desirability function. The optimum conditions resulted in a mobile phase consisting of 10.0 mmol L(-1) of 1-heptanesulfonate, 20.0 mmol L(-1) of sodium acetate, pH=3.30 buffer and acetonitrile in a gradient system at a flow rate of 1.00 mL min(-1). Column was an INERSTIL ODS-3 (4.6 mm×150 mm, 5 µm particle size) at 40.0°C. Detection was performed at 300 nm by a diode array detector. The validation results of the method indicated a high selectivity and good precision characteristics, with RSD less than 1.0% for both components, both in intra and inter-assay precision studies. Linearity was proved for a range of 32.0-50.0 µg mL(-1) of NIC in sample solution. The recovery, studied at three different fortification levels, varied from 98.0 to 101.4 for HDP and from 99.1 to 100.2 for DNC. The applicability of the method was demonstrated by determining DNC and HDP content in raw materials and commercial formulations used for coccidiosis prevention. Assays results on real samples showed that considerable differences in molecular ratio DNC:HDP exist among them.

Development and validation of a simple stability-indicating high performance liquid chromatographic method for the determination of miconazole nitrate in bulk and cream formulations.

A simple and stability-indicating high performance liquid chromatographic method was developed and validated for the determination of miconazole nitrate in bulk and cream preparations. The extraction step for cream samples consisted in a warming, cooling and centrifugation procedure that assures the elimination of the lipophilic matrix component, in order to avoid further precipitation in the chromatographic system. Separation was achieved on a ZORBAX Eclipse XDB - C18 (4.6 mm x 150 mm, 5 microm particle size) column, using a mobile phase consisting of water, methanol and acetonitrile, in a flow and solvent gradient elution for 15 min. The column was maintained at 25 degrees C and 10 microL of solutions were injected. UV detection was performed at 232 nm, although employment of a diode array detector allowed selectivity confirmation by peak purity evaluation. The method was validated reaching satisfactory results for selectivity, precision and accuracy. Degradation products in naturally aged samples could be simultaneously evaluated, without interferences in the quantitative analysis.

Multiple response optimization applied to the development of a capillary electrophoretic method for pharmaceutical analysis.

Multiple response simultaneous optimization by using the desirability function was used for the development of a capillary electrophoresis method for the simultaneous determination of four active ingredients in pharmaceutical preparations: vitamins B(6) and B(12), dexamethasone and lidocaine hydrochloride. Five responses were simultaneously optimized: the three resolutions, the analysis time and the capillary current. This latter response was taken into account in order to improve the quality of the separations. The separation was carried out by using capillary zone electrophoresis (CZE) with a silica capillary and UV detection (240 nm). The optimum conditions were: 57.0 mmol l(-1) sodium phosphate buffer solution, pH 7.0 and voltage=17.2 kV. Good results concerning precision (CV lower than 2%), accuracy (recoveries ranged between 98.5 and 102.6%) and selectivity were obtained in the concentration range studied for the four compounds. These results are comparable to those provided by the reference high performance liquid chromatography (HPLC) technique.

Multilocus sequence typing is a reliable alternative method to DNA fingerprinting for discriminating among strains of Candida albicans.

Multilocus sequence typing (MLST) has emerged as a powerful new DNA-typing tool for the evaluation of intraspecies genetic relatedness. This method relies on DNA sequence analysis of nucleotide polymorphisms in housekeeping genes and has shown a high degree of intraspecies discriminatory power for bacterial and fungal pathogens. However, the results of the MLST scheme for Candida albicans have heretofore never been formally compared to those of other established typing techniques. To assess the value of MLST relative to those of other DNA fingerprinting tools for discriminating among strains of C. albicans, we applied it to a previously well-characterized set of 29 C. albicans isolates evaluated by the random amplified polymorphic DNA (RAPD), multilocus enzyme electrophoresis (MLEE), and Ca3 Southern hybridization probe techniques. MLST identified three clusters of genetically related isolates, with 82.3% direct concordance with MLEE, 82.7% with RAPD analysis, and 86.2% with the Ca3 Southern hybridization technique. When MLST was applied to a subset of 22 isolates of unrelated origins, it identified 21 independent diploid sequence types (DSTs), resulting in a discriminatory power of 99.6%. These DSTs were 96.9, 99.6, and 99.6% concordant with the genotypes identified by RAPD analysis, MLEE, and Ca3 Southern hybridization, respectively. These results demonstrate that MLST is a highly effective technique that performs at least comparably to other established DNA fingerprinting techniques.