The role of local inflammation in complications associated with intubation in pediatric patients: A narrative review.

Although the most important primary local inflammatory response factor to intubation is not yet clear, it is known that it may be directly attributed to the presence of trauma during intubation or the response of oral bacterial flora present in the trachea. It is known that prolonged intubation is associated with worse outcomes, but other underlying systemic issues, such as sepsis and trauma, are also associated with this result. Likewise, patients who require advanced airway management and excessive manipulation are more likely to experience complications. There are various inflammatory mediators that are generated during orotracheal intubation, many of which can be considered targets for therapies to help reduce inflammation caused by intubation. However, there is little evidence on the management of the inflammatory response induced by orotracheal intubation in pediatric patients. Therefore, the aim of this narrative review is to highlight the intubation associated complications that can arise from poorly controlled inflammation in intubated pediatric patients, review the proposed pathophysiology behind this, and discuss the current treatments that exist. Finally, taking into account the discussion on pathophysiology, we describe the current therapies being developed and future directions that can be taken in order to create more treatment options within this patient population.

Glycemic variability of acute stroke patients and clinical outcomes: a continuous glucose monitoring study.

INTRODUCTION: Glycemic variability (GV) has been associated with worse prognosis in critically ill patients. We sought to evaluate the potential association between GV indices and clinical outcomes in acute stroke patients. METHODS: Consecutive diabetic and nondiabetic, acute ischemic or hemorrhagic stroke patients underwent regular, standard-of-care finger-prick measurements and continuous glucose monitoring (CGM) for up to 96 h. Thirteen GV indices were obtained from CGM data. Clinical outcomes during hospitalization and follow-up period (90 days) were recorded. Hypoglycemic episodes disclosed by CGM but missed by finger-prick measurements were also documented. RESULTS: A total of 62 acute stroke patients [48 ischemic and 14 hemorrhagic, median NIHSS score: 9 (IQR: 3-16) points, mean age: 65 ± 10 years, women: 47%, nondiabetic: 79%] were enrolled. GV expressed by higher mean absolute glucose (MAG) values was associated with a lower likelihood of neurological improvement during hospitalization before and after adjusting for potential confounders (OR: 0.135, 95% CI: 0.024-0.751, p = 0.022). There was no association of GV indices with 3-month clinical outcomes. During CGM recording, 32 hypoglycemic episodes were detected in 17 nondiabetic patients. None of these episodes were identified by the periodic blood glucose measurements and therefore they were not treated. CONCLUSIONS: Greater GV of acute stroke patients may be related to lower odds of neurological improvement during hospitalization. No association was disclosed between GV indices and 3-month clinical outcomes.

Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients.

Accurate prediction of blood glucose variations in type 2 diabetes (T2D) will facilitate better glycemic control and decrease the occurrence of hypoglycemic episodes as well as the morbidity and mortality associated with T2D, hence increasing the quality of life of patients. Owing to the complexity of the blood glucose dynamics, it is difficult to design accurate predictive models in every circumstance, i.e., hypo/normo/hyperglycemic events. We developed deep-learning methods to predict patient-specific blood glucose during various time horizons in the immediate future using patient-specific every 30-min long glucose measurements by the continuous glucose monitoring (CGM) to predict future glucose levels in 5 min to 1 h. In general, the major challenges to address are (1) the dataset of each patient is often too small to train a patient-specific deep-learning model, and (2) the dataset is usually highly imbalanced given that hypo- and hyperglycemic episodes are usually much less common than normoglycemia. We tackle these two challenges using transfer learning and data augmentation, respectively. We systematically examined three neural network architectures, different loss functions, four transfer-learning strategies, and four data augmentation techniques, including mixup and generative models. Taken together, utilizing these methodologies we achieved over 95% prediction accuracy and 90% sensitivity for a time period within the clinically useful 1 h prediction horizon that would allow a patient to react and correct either hypoglycemia and/or hyperglycemia. We have also demonstrated that the same network architecture and transfer-learning methods perform well for the type 1 diabetes OhioT1DM public dataset.

Enhancing Phenol Conversion Rates in Saline Anaerobic Membrane Bioreactor Using Acetate and Butyrate as Additional Carbon and Energy Sources.

Phenolic industrial wastewater, such as those from coal gasification, are considered a challenge for conventional anaerobic wastewater treatment systems because of its extreme characteristics such as presence of recalcitrant compounds, high toxicity, and salinity. However, anaerobic membrane bioreactors (AnMBRs) are considered of potential interest since they retain all micro-organism that are required for conversion of the complex organics. In this study, the degradation of phenol as main carbon and energy source (CES) in AnMBRs at high salinity (8.0 g Na+⋅L-1) was evaluated, as well as the effect of acetate and an acetate-butyrate mixture as additional CES on the specific phenol conversion rate and microbial community structure. Three different experiments in two lab-scale (6.5 L) AnMBRs (35°C) were conducted. The first reactor (R1) was fed with phenol as the main CES, the second reactor was fed with phenol and either acetate [2 g COD⋅L-1], or a 2:1 acetate-butyrate [2 g COD⋅L-1] mixture as additional CES. Results showed that phenol conversion could not be sustained when phenol was the sole CES. In contrast, when the reactor was fed with acetate or an acetate-butyrate mixture, specific phenol conversion rates of 115 and 210 mgPh⋅gVSS-1 d-1, were found, respectively. The syntrophic phenol degrader Syntrophorhabdus sp. and the acetoclastic methanogen Methanosaeta sp. were the dominant bacteria and archaea, respectively, with corresponding relative abundances of up to 63 and 26%. The findings showed that dosage of additional CES allowed the development of a highly active phenol-degrading biomass, potentially improving the treatment of industrial and chemical wastewaters.

A crash-course in cultural competence.

America is a multi-cultural society. Yet, there are cultural dimensions to the clinician-patient relationship that have not been systematically addressed in medical education or in clinical practice. Lack of diversity and lack of cross-cultural skills in the medical profession may contribute to health disparities in America. Cultural competence for the medical profession represents a core set of skills that can be learned to respectfully and effectively communicate healthcare information with diverse patient populations. The authors blended their extensive literature review with the knowledge and experience of a culturally diverse medical team to develop the CRASH-Course in Cultural Competency training program for medical professionals. CRASH is a mnemonic for the following essential components of culturally competent health care--consider Culture, show Respect, Assess/Affirm differences, show Sensitivity and Self-awareness, and do it all with Humility. The goal of the CRASH-Course in Cultural Competency is to build confidence and competence in the clinician's ability to communicate effectively with diverse patient populations.