Physico-chemical, antimicrobial and antioxidant properties of gelatin-chitosan based films loaded with nanoemulsions encapsulating active compounds

The aim of this research was to develop and characterize gelatin-chitosan (4:1) based films that incorporate nanoemulsions loaded with a range of active compounds; N1: canola oil; N2: α-tocopherol/cinnamaldehyde; N3: α-tocopherol/garlic oil; or N4: a-tocopherol/cinnamaldehyde and garlic oil. Nanoemulsions were prepared in a microfluidizer with pressures ranging from 69 to 100 MPa, and 3 processing cycles. Films were produced by the casting method incorporating 5 g N1,2,3,4/100 g biopolymers and using glycerol as a plasticizer, and subsequently characterized in terms of their physico-chemical, antimicrobial and antioxidant properties. No differences (p > 0.05) were observed for all films in terms of moisture content (18% w/w), and thermal properties. The films' solubility in water and light transmission at 280 nm were considerably reduced as compared to the control, N1 (15% and 60% respectively) because of the nanoemulsion incorporation. The film loaded with N1 showed the greatest (p < 0.05) opacity, elongation at break and stiffness reduction, and was the roughest, whilst the lowest tensile strength and ability to swell were attained by films loaded with N3 and N4, respectively. DSC and X-ray analyses suggested compatibility among the biopolymeric-blend, and a good distribution of nanodroplets embedded into the matrix was confirmed by AFM and SEM analyses. Films loaded with nanoencapsulated active compounds (NAC) were very effective against Pseudomonas aeruginosa, and also showed high antioxidant activity. Overall, the present study offers clear evidence that these active-loaded films have the potential to be utilized as packaging material for enhancing food shelf life

Mendelian randomization for studying the effects of perturbing drug targets [version 1; peer review: awaiting peer review]

Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline

Mendelian randomization for studying the effects of perturbing drug targets [version 2; peer review: 3 approved, 1 approved with reservations]

Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline

Identification and quantification of the antimicrobial components of a citrus essential oil vapor

The anti-bacterial components of a citrus essential oil vapor were identified as linalool, citral and beta-pinene using a bioautography method and quantified by GC-MS. Essential oil vapor release, monitored in real-time with Atmospheric Pressure Chemical Ionization - MS (APCI-MS), showed differences in the vapor release profile oflimonene, beta-pinene and linalool over 24 hours, while Solid Phase Micro-extraction (SPME) GC-MS demonstrated changes in composition of the vapor at 35 degrees C. Fourteen isolates were tested in vitro for their susceptibility to the EO vapor and to linalool, citral and beta-pinene vapors, both separately and in a mixture containing the three components in the amounts at which they occur in the EO vapor. All eleven Gram-positive strains tested were susceptible to the EO vapor, linalool, citral and beta-pinene vapors separately and the mixture with zones of inhibition of 4.34 cm, 5.32 cm, 5.58 cm, 4.86 cm and 4.68 cm, respectively. Of the three Gram-negative strains tested, Pseudomonas aeruginosa 10145 was resistant to all the vapors. When bacteria inoculated onto stainless steel surfaces were exposed to either the EO vapor or a linalool/citral/beta-pinene vapor mixture there was no significant difference in reduction for the Gram-positive isolates, while the Gram-negative isolates were resistant to both EO vapor and the linalool/citral/beta-pinene mixture

Combined Use of Streaming Potential and UV/Vis to Assess Surface Modification of Fabrics via Soil Release Polymers

Polymers have become a widespread part of laundry detergent formulations because of their benefits which are usually delivered via surface modification of fibers. Therefore, there is a growing interest in understanding their deposition on fabrics. In this work, we have used streaming potential to assess changes in surface charge of polyester and knitted cotton after modification via soil release polymers (SRPs). Results identify a relationship between the measured zeta potential for the modified fabrics and the charge of the polymer. The effects of parameters, such as agitation speed and bulk concentration during deposition, have been investigated. Streaming potential data were then correlated to adsorption isotherms from UV absorbance data, and a Langmuir-Freundlich model was proposed to describe the isotherms for polyester. The stain removal index for some common hydrophobic stains was determined via image analysis. A link between SRP deposition efficiency and their effectiveness on greasy soil removal was observed

Cardiometabolic traits, sepsis and severe covid-19 with respiratory failure: a Mendelian randomization investigation

Objectives: To investigate whether there is a causal effect of cardiometabolic traits on risk of sepsis and severe covid-19. Design: Mendelian randomisation analysis. Setting: UK Biobank and HUNT study population-based cohorts for risk of sepsis, and genome-wide association study summary data for risk of severe covid-19 with respiratory failure. Participants: 12,455 sepsis cases (519,885 controls) and 1,610 severe covid-19 with respiratory failure cases (2,205 controls). Exposure: Genetic variants that proxy body mass index (BMI), lipid traits, systolic blood pressure, lifetime smoking score, and type 2 diabetes liability - derived from studies considering between 188,577 to 898,130 participants. Main outcome measures: Risk of sepsis and severe covid-19 with respiratory failure. Results: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64). Higher genetically proxied BMI and lifetime smoking score were also associated with increased risk of severe covid-19, although with wider confidence intervals (BMI: 1.75, 95% CI 1.20 to 2.57; smoking: 3.94, 95% CI 1.13 to 13.75). There was limited evidence to support associations of genetically proxied lipid traits, systolic blood pressure or type 2 diabetes liability with risk of sepsis or severe covid-19. Similar findings were generally obtained when using Mendelian randomization methods that are more robust to the inclusion of pleiotropic variants, although the precision of estimates was reduced. Conclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19. Clinical and public health interventions targeting obesity and smoking are likely to reduce sepsis and covid-19 related morbidity, along with the plethora of other health-related outcomes that these traits adversely affect

Mendelian randomization for studying the effects of perturbing drug targets.

Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline

Mendelian randomization for studying the effects of perturbing drug targets.

Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline