Synthesis and properties of amino acid glucose ester dimeric surfactants

<p>Two series of lysine glucose ester AOT surfactants and glutamic acid glucose ester Bola surfactants were synthesized, using L-lysine, L-glutamic acid, and α-D-methylglucoside as raw materials. The structures of glucose esters were confirmed by ¹H NMR and ESI-MS. Some surface activities were studied, such as cmc, γ_cmc, <i>C</i>₂₀, Γ_max, and <i>A</i>_min. The results showed that cmc decreased, but γ_cmc increased with the lengthening of hydrophobic chain. The thermodynamic parameters of micellization () indicated that the micellization processes of glucose esters were spontaneous and exothermic, driven by entropy.</p

Adsorption and thermodynamic properties of dissymmetric gemini imidazolium surfactants with different spacer length

<p>A series of dissymmetric gemini imidazolium surfactants with different spacer length ([C_<i>m</i>C_<i>s</i>C_<i>n</i>im]Br₂, <i>m</i> + <i>n</i> = 24, <i>m</i> = 12, 14, 16, 18; <i>s</i> = 2, 4, 6) were synthesized and characterized by ¹H NMR and ESI-MS spectroscopy. Their adsorption and thermodynamic properties were investigated by the surface tension and electrical conductivity methods. Consequently, the surface activity parameters (cmc, γ_cmc, <i>π</i>_cmc, p<i>C</i>₂₀, cmc/<i>C</i>₂₀, <i>Γ</i>_max, <i>A</i>_min) and thermodynamic parameters (Δ<i>G</i>_m^θ, Δ<i>H</i>_m^θ, Δ<i>S</i>_m^θ) were obtained. The effects of the dissymmetry (<i>m</i>/<i>n</i>) and the spacer length (<i>s</i>) on the surface activity and micellization process of surfactants have been discussed in detail.</p

Design and Synthesis of Bistetrazole-Based Energetic Salts Bearing the Nitrogen-Rich Fused Ring

A series of bistetrazole-based energetic salts bearing a nitrogen-rich fused ring were designed and synthesized. Among them, compounds 4–10 showed good detonation properties and excellent thermostability. By treating nitrogen-rich fused ring 3 with concentrated hydrochloric acid, a new type of Dimroth rearrangement was observed that afforded compound 12 efficiently. This new transformation herein constitutes a valuable addition to the Dimroth rearrangement

Image_1_The mode and timing of administrating nutritional treatment of critically ill elderly patients in intensive care units: a multicenter prospective study.TIF

IntroductionCritically ill patients are more susceptible to malnutrition due to their severe illness. Moreover, elderly patients who are critically ill lack specific nutrition recommendations, with nutritional care in the intensive care units (ICUs) deplorable for the elderly. This study aims to investigate nutrition treatment and its correlation to mortality in elderly patients who are critically ill in intensive care units.MethodA multiple-center prospective cohort study was conducted in China from 128 intensive care units (ICUs). A total of 1,238 elderly patients were included in the study from 26 April 2017. We analyzed the nutrition characteristics of elderly patients who are critically ill, including initiated timing, route, ways of enteral nutrition (EN), and feeding complications, including the adverse aspects of feeding, acute gastrointestinal injury (AGI), and feeding interruption. Multivariate logistic regression analysis was used to screen out the impact of nutrition treatment on a 28-day survival prognosis of elderly patients in the ICU.ResultA total of 1,238 patients with a median age of 76 (IQR 70–83) were enrolled in the study. The Sequential Organ Failure (SOFA) median score was 7 (interquartile range: IQR 5–10) and the median Acute Physiology and Chronic Health Evaluation (APACHE) II was 21 (IQR 16–25). The all-cause mortality score was 11.6%. The percentage of nutritional treatment initiated 24 h after ICU admission was 58%, with an EN of 34.2% and a parenteral nutrition (PN) of 16.0% in elderly patients who are critically ill. Patients who had gastrointestinal dysfunction with AGI stage from 2 to 4 were 25.2%. Compared to the survivors’ group, the non-survivors group had a lower ratio of EN delivery (57% vs. 71%; p = 0.015), a higher ratio of post-pyloric feeding (9% vs. 2%; p = 0.027), and higher frequency of feeding interrupt (24% vs. 17%, p = 0.048). Multivariable logistics regression analysis showed that patients above 76 years old with OR (odds ratio) 2.576 (95% CI, 1.127–5.889), respiratory rate > 22 beats/min, and ICU admission for 24 h were independent risk predictors of the 28-day mortality study in elderly patients who are critically ill. Similarly, other independent risk predictors of the 28-day mortality study were those with an OR of 2.385 (95%CI, 1.101–5.168), lactate >1.5 mmol/L, and ICU admission for 24 h, those with an OR of 7.004 (95%CI, 2.395–20.717) and early PN delivery within 24 h of ICU admission, and finally those with an OR of 5.401 (95%CI, 1.175–24.821) with EN delivery as reference.ConclusionThis multi-center prospective study describes clinical characteristics, the mode and timing of nutrition treatment, frequency of AGI, and adverse effects of nutrition in elderly ICU patients. According to this survey, ICU patients with early PN delivery, older age, faster respiratory rate, and higher lactate level may experience poor prognosis.</p

Table_1_Machine learning for early prediction of sepsis-associated acute brain injury.DOCX

BackgroundSepsis-associated encephalopathy (SAE) is defined as diffuse brain dysfunction associated with sepsis and leads to a high mortality rate. We aimed to develop and validate an optimal machine-learning model based on clinical features for early predicting sepsis-associated acute brain injury.MethodsWe analyzed adult patients with sepsis from the Medical Information Mart for Intensive Care (MIMIC III) clinical database. Candidate models were trained using random forest, support vector machine (SVM), decision tree classifier, gradients boosting machine (GBM), multiple layer perception (MLP), extreme gradient boosting (XGBoost), light gradients boosting machine (LGBM) and a conventional logistic regression model. These methods were applied to develop and validate the optimal model based on its accuracy and area under curve (AUC).ResultsIn total, 12,460 patients with sepsis met inclusion criteria, and 6,284 (50.4%) patients suffered from sepsis-associated acute brain injury. Compared other models, the LGBM model achieved the best performance. The AUC for both train set and test set indicated excellent validity (Trainset AUC 0.91, Testset AUC 0.87). Feature importance analysis showed that glucose, age, mean arterial pressure, heart rate, hemoglobin, and length of ICU stay were the top 6 important clinical factors to predict occurrence of sepsis-associated acute brain injury.ConclusionAlmost half of patients admitted to ICU with sepsis had sepsis-associated acute brain injury. The LGBM model better identify patients with sepsis-associated acute brain injury than did other machine-learning models. Glucose, age, and mean arterial pressure were the three most important clinical factors to predict occurrence of sepsis-associated acute brain injury.</p

Table_2_Effect of basal metabolic rate on osteoporosis: A Mendelian randomization study.XLSX

PurposeBasal metabolic rate may play a key role in the pathogenesis and progression of osteoporosis. We performed Mendelian random analysis to evaluate the causal relationship between basal metabolic rate and osteoporosis.MethodsInstrumental variables for the basal metabolic rate were selected. We used the inverse variance weighting approach as the main Mendelian random analysis method to estimate causal effects based on the summary-level data for osteoporosis from genome-wide association studies.ResultsA potential causal association was observed between basal metabolic rate and risks of osteoporosis (odds ratio = 0.9923, 95% confidence interval: 0.9898–0.9949; P = 4.005e − 09). The secondary MR also revealed that BMR was causally associated with osteoporosis (odds ratio = 0.9939, 95% confidence interval: 0.9911–0.9966; P = 1.038e − 05). The accuracy and robustness of the findings were confirmed using sensitivity tests.ConclusionBasal metabolic rate may play a causal role in the development of osteoporosis, although the underlying mechanisms require further investigation.</p

Image_3_Effect of basal metabolic rate on osteoporosis: A Mendelian randomization study.TIFF

PurposeBasal metabolic rate may play a key role in the pathogenesis and progression of osteoporosis. We performed Mendelian random analysis to evaluate the causal relationship between basal metabolic rate and osteoporosis.MethodsInstrumental variables for the basal metabolic rate were selected. We used the inverse variance weighting approach as the main Mendelian random analysis method to estimate causal effects based on the summary-level data for osteoporosis from genome-wide association studies.ResultsA potential causal association was observed between basal metabolic rate and risks of osteoporosis (odds ratio = 0.9923, 95% confidence interval: 0.9898–0.9949; P = 4.005e − 09). The secondary MR also revealed that BMR was causally associated with osteoporosis (odds ratio = 0.9939, 95% confidence interval: 0.9911–0.9966; P = 1.038e − 05). The accuracy and robustness of the findings were confirmed using sensitivity tests.ConclusionBasal metabolic rate may play a causal role in the development of osteoporosis, although the underlying mechanisms require further investigation.</p

Image_2_Effect of basal metabolic rate on osteoporosis: A Mendelian randomization study.TIFF

PurposeBasal metabolic rate may play a key role in the pathogenesis and progression of osteoporosis. We performed Mendelian random analysis to evaluate the causal relationship between basal metabolic rate and osteoporosis.MethodsInstrumental variables for the basal metabolic rate were selected. We used the inverse variance weighting approach as the main Mendelian random analysis method to estimate causal effects based on the summary-level data for osteoporosis from genome-wide association studies.ResultsA potential causal association was observed between basal metabolic rate and risks of osteoporosis (odds ratio = 0.9923, 95% confidence interval: 0.9898–0.9949; P = 4.005e − 09). The secondary MR also revealed that BMR was causally associated with osteoporosis (odds ratio = 0.9939, 95% confidence interval: 0.9911–0.9966; P = 1.038e − 05). The accuracy and robustness of the findings were confirmed using sensitivity tests.ConclusionBasal metabolic rate may play a causal role in the development of osteoporosis, although the underlying mechanisms require further investigation.</p

Image_4_Effect of basal metabolic rate on osteoporosis: A Mendelian randomization study.TIFF

PurposeBasal metabolic rate may play a key role in the pathogenesis and progression of osteoporosis. We performed Mendelian random analysis to evaluate the causal relationship between basal metabolic rate and osteoporosis.MethodsInstrumental variables for the basal metabolic rate were selected. We used the inverse variance weighting approach as the main Mendelian random analysis method to estimate causal effects based on the summary-level data for osteoporosis from genome-wide association studies.ResultsA potential causal association was observed between basal metabolic rate and risks of osteoporosis (odds ratio = 0.9923, 95% confidence interval: 0.9898–0.9949; P = 4.005e − 09). The secondary MR also revealed that BMR was causally associated with osteoporosis (odds ratio = 0.9939, 95% confidence interval: 0.9911–0.9966; P = 1.038e − 05). The accuracy and robustness of the findings were confirmed using sensitivity tests.ConclusionBasal metabolic rate may play a causal role in the development of osteoporosis, although the underlying mechanisms require further investigation.</p

Table_3_Effect of basal metabolic rate on osteoporosis: A Mendelian randomization study.XLSX

PurposeBasal metabolic rate may play a key role in the pathogenesis and progression of osteoporosis. We performed Mendelian random analysis to evaluate the causal relationship between basal metabolic rate and osteoporosis.MethodsInstrumental variables for the basal metabolic rate were selected. We used the inverse variance weighting approach as the main Mendelian random analysis method to estimate causal effects based on the summary-level data for osteoporosis from genome-wide association studies.ResultsA potential causal association was observed between basal metabolic rate and risks of osteoporosis (odds ratio = 0.9923, 95% confidence interval: 0.9898–0.9949; P = 4.005e − 09). The secondary MR also revealed that BMR was causally associated with osteoporosis (odds ratio = 0.9939, 95% confidence interval: 0.9911–0.9966; P = 1.038e − 05). The accuracy and robustness of the findings were confirmed using sensitivity tests.ConclusionBasal metabolic rate may play a causal role in the development of osteoporosis, although the underlying mechanisms require further investigation.</p