CFD as a tool for modelling membrane systems

Computational fluid dynamics (CFD) is a computer-based numerical method used to analyse systems that involve fluid flow and/or heat and mass transfer (Versteeg & Malalasekera, 2007). CFD bridges the two different approaches for solving engineering problems before the computer era, theoretical and experimental; it relies on mathematical models while being easy to adapt to almost any realistic condition (Anderson & Wendt, 1995). Another feature of CFD is its versatility, as it allows the analysis of systems for a variety of applications such as chemical reactions (Salehi et al., 2016), aerodynamics (Snel, 2003), dispersion of pollutants (Chu et al., 2005), blood flows (Byun & Rhee, 2004), among many others

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Crystal structure of benzyl (E)-2-(3,4-dimethoxybenzylidene)hydrazine-1-carbodithioate

The title compound, C17H18N2O2S2, synthesized via a condensation reaction between S-benzyl dithiocarbazate and 3,4-dimethoxybenzaldehyde, crystallized with two independent molecules (A and B) in the asymmetric unit. Both molecules have an L-shape but differ in the orientation of the benzyl ring with respect to the 3,4-dimethoxybenzylidine ring, this dihedral angle is 65.59 (8)° in molecule A and 73.10 (8)° in molecule B. In the crystal, the A and B molecules are linked via pairs of N—H...S hydrogen bonds, forming dimers with an R22(8) ring motif. The dimers are linked via pairs of C—H...O hydrogen bonds, giving inversion dimers of dimers. These units are linked by C—H...π interactions, forming ribbons propagating in the [100] direction

Synthesis, Crystal Structure, Antibacterial and In Vitro Anticancer Activity of Novel Macroacyclic Schiff Bases and Their Cu (II) Complexes Derived from S-Methyl and S-Benzyl Dithiocarbazate

A series of novel macroacyclic Schiff base ligands and their Cu (II) complexes were synthesised via reacting dicarbonyls of varying chain lengths with S-methyl dithiocarbazate (SMDTC) and S-benzyl dithiocarbazate (SBDTC) followed by coordination with Cu (II) ions. X-ray crystal structures were obtained for compound 4, an SBDTC-diacetyl analogue, and Cu7, an SMDTC-hexanedione Cu (II) complex. Anticancer evaluation of the compounds showed that Cu1, an SMDTC-glyoxal complex, demonstrated the highest cytotoxic activity against MCF-7 and MDA-MB-231 breast cancer cells with IC50 values of 1.7 µM and 1.4 µM, respectively. There was no clear pattern observed between the effect of chain length and cytotoxic activity; however, SMDTC-derived analogues were more active than SBDTC-derived analogues against MDA-MB-231 cells. The antibacterial assay showed that K. rhizophila was the most susceptible bacteria to the compounds, followed by S. aureus. Compound 4 and the SMDTC-derived analogues 3, 5, Cu7 and Cu9 possessed the highest antibacterial activity. These active analogues were further assessed, whereby 3 possessed the highest antibacterial activity with an MIC of <24.4 µg/mL against K. rhizophila and S. aureus. Further antibacterial studies showed that at least compounds 4 and 5 were bactericidal. Thus, Cu1 and 3 were the most promising anticancer and antibacterial agents, respectively

Specific Macronutrients Exert Unique Influences on the Adipose-Liver Axis to Promote Hepatic Steatosis in Mice

Background &amp; aimsThe factors that distinguish metabolically healthy obesity from metabolically unhealthy obesity are not well understood. Diet has been implicated as a determinant of the unhealthy obesity phenotype, but which aspects of the diet induce dysmetabolism are unknown. The goal of this study was to investigate whether specific macronutrients or macronutrient combinations provoke dysmetabolism in the context of isocaloric, high-energy diets.MethodsMice were fed 4 high-energy diets identical in calorie and nutrient content but different in nutrient composition for 3 weeks to 6 months. The test diets contained 42% carbohydrate (sucrose or starch) and 42% fat (oleate or palmitate). Weight and glucose tolerance were monitored; blood and tissues were collected for histology, gene expression, and immunophenotyping.ResultsMice gained weight on all 4 test diets but differed significantly in other metabolic outcomes. Animals fed the starch-oleate diet developed more severe hepatic steatosis than those on other formulas. Stable isotope incorporation showed that the excess hepatic steatosis in starch-oleate-fed mice derived from exaggerated adipose tissue lipolysis. In these mice, adipose tissue lipolysis coincided with adipocyte necrosis and inflammation. Notably, the liver and adipose tissue abnormalities provoked by starch-oleate feeding were reproduced when mice were fed a mixed-nutrient Western diet with 42% carbohydrate and 42% fat.ConclusionsThe macronutrient composition of the diet exerts a significant influence on metabolic outcome, independent of calories and nutrient proportions. Starch-oleate appears to cause hepatic steatosis by inducing progressive adipose tissue injury. Starch-oleate phenocopies the effect of a Western diet; consequently, it may provide clues to the mechanism whereby specific nutrients cause metabolically unhealthy obesity

Students' participation in collaborative research should be recognised

Letter to the editor

Paediatric COVID-19 mortality: a database analysis of the impact of health resource disparity

Background The impact of the COVID-19 pandemic on paediatric populations varied between high-income countries (HICs) versus low-income to middle-income countries (LMICs). We sought to investigate differences in paediatric clinical outcomes and identify factors contributing to disparity between countries.Methods The International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) COVID-19 database was queried to include children under 19 years of age admitted to hospital from January 2020 to April 2021 with suspected or confirmed COVID-19 diagnosis. Univariate and multivariable analysis of contributing factors for mortality were assessed by country group (HICs vs LMICs) as defined by the World Bank criteria.Results A total of 12 860 children (3819 from 21 HICs and 9041 from 15 LMICs) participated in this study. Of these, 8961 were laboratory-confirmed and 3899 suspected COVID-19 cases. About 52% of LMICs children were black, and more than 40% were infants and adolescent. Overall in-hospital mortality rate (95% CI) was 3.3% [=(3.0% to 3.6%), higher in LMICs than HICs (4.0% (3.6% to 4.4%) and 1.7% (1.3% to 2.1%), respectively). There were significant differences between country income groups in intervention profile, with higher use of antibiotics, antivirals, corticosteroids, prone positioning, high flow nasal cannula, non-invasive and invasive mechanical ventilation in HICs. Out of the 439 mechanically ventilated children, mortality occurred in 106 (24.1%) subjects, which was higher in LMICs than HICs (89 (43.6%) vs 17 (7.2%) respectively). Pre-existing infectious comorbidities (tuberculosis and HIV) and some complications (bacterial pneumonia, acute respiratory distress syndrome and myocarditis) were significantly higher in LMICs compared with HICs. On multivariable analysis, LMIC as country income group was associated with increased risk of mortality (adjusted HR 4.73 (3.16 to 7.10)).Conclusion Mortality and morbidities were higher in LMICs than HICs, and it may be attributable to differences in patient demographics, complications and access to supportive and treatment modalities

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients

Association of Country Income Level With the Characteristics and Outcomes of Critically Ill Patients Hospitalized With Acute Kidney Injury and COVID-19

Introduction: Acute kidney injury (AKI) has been identified as one of the most common and significant problems in hospitalized patients with COVID-19. However, studies examining the relationship between COVID-19 and AKI in low- and low-middle income countries (LLMIC) are lacking. Given that AKI is known to carry a higher mortality rate in these countries, it is important to understand differences in this population. Methods: This prospective, observational study examines the AKI incidence and characteristics of 32,210 patients with COVID-19 from 49 countries across all income levels who were admitted to an intensive care unit during their hospital stay. Results: Among patients with COVID-19 admitted to the intensive care unit, AKI incidence was highest in patients in LLMIC, followed by patients in upper-middle income countries (UMIC) and high-income countries (HIC) (53%, 38%, and 30%, respectively), whereas dialysis rates were lowest among patients with AKI from LLMIC and highest among those from HIC (27% vs. 45%). Patients with AKI in LLMIC had the largest proportion of community-acquired AKI (CA-AKI) and highest rate of in-hospital death (79% vs. 54% in HIC and 66% in UMIC). The association between AKI, being from LLMIC and in-hospital death persisted even after adjusting for disease severity. Conclusions: AKI is a particularly devastating complication of COVID-19 among patients from poorer nations where the gaps in accessibility and quality of healthcare delivery have a major impact on patient outcomes