3 research outputs found

    Crononutrició. Estat nutricional i de salut del treballador a torns

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    Treballs Finals de Grau de Nutrició Humana i Dietètica, Facultat de Farmàcia i Ciències de l'Alimentació, Campus de l'Alimentació de Torribera, Universitat de Barcelona. Curs: 2020-2021. Tutor: Trinitat Cambras Riu[cat] La cronobiologia és la ciència que estudia els ritmes biològics dels ésser vius. Un complex sistema de rellotges interns controla les accions per ajustar-se als cicles de llum/foscor. Actualment moltes malalties s’associen amb un desajust del sistema circadiari. Una de les principals causes de desajust és treballar a torns i es relaciona amb obesitat, diabetis tipus 2, càncer, malalties cardiovasculars i metabòliques. Pocs treballs estudien la relació del patró circadiari amb els aspectes nutricionals d’aquest col·lectiu. Per aquest motiu, es va realitzar un estudi pilot amb treballadors sanitaris per analitzar la relació dels ritmes circadiaris, son i ingesta amb la dieta. Els resultats conclouen que l’estabilitat del ritme circadiari de temperatura tendeix a disminuir amb l’edat (p = 0,074) i que el ritme del patró de la ingesta correlaciona positivament amb la ingesta proteica (p = 0,057) i negativament amb la de carbohidrats (p = - 0,024). El càlcul de l’estabilitat rítmica amb R de Rayleigh pot ser una eina important en l’estudi dels patrons com a indicador del grau de desajust circadiari i alteracions de la salut. L’estudi ha permès la validació de la mesura del ritme de temperatura perifèrica al turmell i per últim, establir unes recomanacions nutricionals.[eng] Chronobiology is the science that studies the biological rhythms of living organisms. A complex system if internal clocks control the adjustment actions to the cycles of light/darkness. Many diseases are now associated with a dislocation of the circadian system. One of the main causes of mismatch is shift work and is related to obesity, type 2 diabetes, cancer as well as cardiovascular and metabolic diseases. Only a few research papers study the relationship between the circadian pattern and de nutritional aspects of this group. For this reason, a pilot study was conducted with health workers to analyze the relationship of circadian rhythms, sleep and diet. The results conclude that the stability of the circadian temperature rhythm tends to decrease with age (p=0.074) and the rhythm of the intake pattern correlates positively with protein intake (p=0.057) and negatively with that of carbohydrates (p= -0.024). Calculating rhythmic stability with Rayleigh R can be an important tool in the study of patterns as an indicator of the degree of circadian mismatch and health alterations. The study enable the validation of the ankle peripheral temperature rhythm measurement and the establishment of nutritional recommendations

    Microorganisms resistant to conventional antimicrobials in acute exacerbations of chronic obstructive pulmonary disease

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    BACKGROUND: Antimicrobial treatment for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) remains controversial. In some cases AECOPD are caused by microorganisms that are resistant to treatments recommended by guidelines. Our aims were: 1) identify the risk factors associated with infection by microorganisms resistant to conventional treatment (MRCT), 2) Compare the clinical characteristics and outcomes of patients with AECOPD resulting from MRCT against those with AECOPD from other causes. METHODS: We prospective analysed a cohort of patients admitted with severe AECOPD (2009 to 2015) who were assigned to three groups: patients with MRCT (those patients with germs resistant to antibiotics recommended in guidelines), patients with microorganisms sensitive to conventional antimicrobial treatment (MSCT), and patients with negative microbiology results who had not previously received antibiotics. Multinomial logistic regression analyses were used to examine the associations between microbial aetiology groups and risk factors. The association between LOS and risk factors was also tested in simple and multiple analyses, and similar inclusion criteria were applied for the linear regression analysis. RESULTS: Of the 451 patients admitted, 195 patients (43%) were included. Respiratory cultures were positive in 86(44%) and negative in 109(56%). MRCT were isolated in 34 cases (40%) and MSCT in 52 (60%). Patients with MRCT had more AECOPD in the previous year, received more antibiotic treatment in the previous three months, had more severe disease, higher dyspnoea and a positive respiratory culture in the previous year (mainly for Pseudomonas aeruginosa). The following conditions were independent factors for MRCT isolation: non-current smoker (odds ratio [OR] 4.19 [95% confidence interval [CI] 1.29-13.67], p = 0.017), ≥ 2 AECOPD or ≥ 1 admission for AECOPD in the previous year (OR 4.13 [95% CI 1.52-11.17], p = 0.005), C-reactive protein < 5 mg/dL; (OR 3.58 [95% CI 1.41-9.07], p = 0.007). Mortality rates were comparable at 30-days, one year and 3 years; however, patients in the MRCT group had longer hospital stays. CONCLUSION: In conclusion, there are risk factors for resistant germs in AECOPD; however, the presence of these germs does not increase mortality. Patients with isolation of MRCT had longer length of stay

    Additional file 1: of Microorganisms resistant to conventional antimicrobials in acute exacerbations of chronic obstructive pulmonary disease

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    Table S1. Microbiological Isolations. Table S2. Internal Validation of the Multivariate Logistic Regression Model using Bootstrap Method. Table S3. Multinomial Logistic Regression Model for Microorganisms Resistant to Conventional Treatment (with Pseudomonas aeruginosa) or Microorganisms Sensitive to Conventional Treatment Relative to Negative Microbiology. Table S4. Internal Validation of the Multinomial Logistic Regression Model for Microorganisms Resistant to Conventional Treatment (with Pseudomonas aeruginosa) or Microorganisms Sensitive to Conventional Treatment using Bootstrap Method. Table S5. Outcomes according to Appropriateness of Empiric Treatment. Table S6. Comparison of Outcomes between Patients with Pseudomonas Aeruginosa and Patients without Pseudomonas Aeruginosa in Microorganisms Resistant to Conventional Treatment Group. Table S7. Comparison between Pseudomonas Aeruginosa MDR/XDR Isolation with other Microorganism Isolated in Microorganisms Resistant to Conventional Treatment Group. Table S8. Internal Validation of Risk of Length of Hospital Stay Using Bootstrap Technique. Figure S1. Receiver Operating Characteristic Curve for Multinomial Logistic Regression Model to Pseudomonas aeruginosa. Figure S2. Kaplan–Meier Analysis of the Effect of Microbial Aetiology Groups on Time to Death. Figure S3. Kaplan–Meier Analysis of the Effect of Microbial Aetiology Groups on Time to Death. A) Patients with < 2 AECOPD and none admission by AECOPD in the previous year; B) Patients with ≥ 2 AECOPD or 1 admission by AECOPD in the previous year. (DOC 265 kb
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