Influence of the Gold Nanoparticle Size on the Colorimetric Detection of Histamine.

Histamine is a well-known biogenic amine (BA) that is often associated with allergic reactions and is a significant cause of foodborne illnesses resulting from the consumption of spoiled food. Detecting histamine is essential for maintaining food safety standards and preserving the quality. In this work, we developed a simple, low-cost, and rapid colorimetric method for detecting histamine.

Facile Synthesis of Band Gap-Tunable Kappa-Carrageenan-Mediated C,S-Doped TiO Nanoparticles for Enhanced Dye Degradation.

Semiconducting nanoparticles (SNPs) have garnered significant attention for their role in photocatalysis technology, offering a cost-effective and highly efficient method for breaking down organic dyes. Of particular significance within SNP-based photocatalysis are tunable band gap TiO nanoparticles (NPs), which demonstrate remarkable enhancement in photocatalytic efficiency. In the present work, we introduce an approach for the synthesis of TiO NPs using kappa-carrageenan (κ-carrageenan), not just as a reducing and stabilizing agent but as a dopant for the resulting TiO NPs.

Development of High-Performance Coconut Oil-Based Rigid Polyurethane-Urea Foam: A Novel Sequential Amidation and Prepolymerization Process.

The utilization of coconut diethanolamide (-CDEA) as a substitute polyol for petroleum-based polyol in fully biobased rigid polyurethane-urea foam (RPUAF) faces challenges due to its short chain and limited cross-linking capability. This leads to compromised cell wall resistance during foam expansion, resulting in significant ruptured cells and adverse effects on mechanical and thermal properties. To address this, a novel sequential amidation-prepolymerization route was employed on coconut oil, yielding a hydroxyl-terminated poly(urethane-urea) prepolymer polyol (COPUAP).

Valorization of Agricultural Rice Straw as a Sustainable Feedstock for Rigid Polyurethane/Polyisocyanurate Foam Production.

Agricultural rice straw (RS), often discarded as waste in farmlands, represents a vast and underutilized resource. This study explores the valorization of RS as a potential feedstock for rigid polyurethane/polyisocyanurate foam (RPUF) production. The process begins with the liquefaction of RS to create an RS-based polyol, which is then used in a modified foam formulation to prepare RPUFs.

Quantification of ProTectis in MRS Broth Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy and Chemometrics.

There is an increasing global demand for probiotics because of their numerous health benefits. However, a significant percentage of commercially available probiotic products have microbial quantities that are not in accordance with their product labels. In quantifying bacteria, the viable plate count is the standard method but is considered laborious and time-consuming.

Producing polyglycerol polyester polyol for thermoplastic polyurethane application: A novel valorization of glycerol, a by-product of biodiesel production.

The production of biodiesel generates glycerol as a by-product that needs valorization. Glycerol, when converted to polyglycerol, is a potential polyol for bio-based thermoplastic polyurethane (TPU) production. In this study, a novel polyglycerol polyester polyol (PPP) was developed from refined glycerol and coconut oil-based polyester polyol.

Production of Bio-Based Polyol from Coconut Fatty Acid Distillate (CFAD) and Crude Glycerol for Rigid Polyurethane Foam Applications.

This study propounds a sustainable alternative to petroleum-based polyurethane (PU) foams, aiming to curtail this nonrenewable resource's continued and uncontrolled use. Coconut fatty acid distillate (CFAD) and crude glycerol (CG), both wastes generated from vegetable oil processes, were utilized for bio-based polyol production for rigid PU foam application. The raw materials were subjected to catalyzed glycerolysis with alkaline-alcohol neutralization and bleaching.

Coconut power: a sustainable approach for the removal of Cr ions using a new coconut-based polyurethane foam/activated carbon composite in a fixed-bed column.

To attain efficient removal of hexavalent chromium (Cr) from aqueous solutions, a novel polyurethane foam-activated carbon (PUAC) adsorbent composite was developed. The composite material was synthesized by the binding of coconut shell-based activated carbon (AC) onto a coconut oil-based polyurethane matrix. To thoroughly characterize the physicochemical properties of the newly developed material, various analytical techniques including FTIR spectroscopy, SEM, XRD, BET, and TGA analyses were conducted.

A novel reaction mechanism for the synthesis of coconut oil-derived biopolyol for rigid poly(urethane-urea) hybrid foam application.

Coconut oil (CO) has become one of the most important renewable raw materials for polyol synthesis due to its abundance and low price. However, the saturated chemical structure of CO limits its capability for functionalization. In this study, a novel reaction mechanism the sequential glycerolysis and amidation of CO triglycerides produced an amine-based polyol (p-CDEA).

Biomarker Discovery for Meta-Classification of Melanoma Metastatic Progression Using Transfer Learning.

Melanoma is considered to be the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in its prognosis. Herein, we developed a transfer learning-based biomarker discovery model that could aid in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results revealed that the genes found were consistent with those found using other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set.

Are phenome-wide association studies feasible in a developing country?

Phenome-wide association studies (PheWASs), a powerful approach that examines phenotypes associated with a genetic marker, have been used extensively in highly developed countries. Although there may be a clear need for PheWAS in a developing country such as the Philippines, limitations related to resources and practicality would make conducting them a challenge.

Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory.

Facile, robust, and accurate analyses of honey adulterants are required in the honey industry to assess its purity for commercialization purposes. A stacked regression ensemble approach using Fourier transform infrared spectroscopic method was developed for the quantitative determination of corn, cane, beet, and rice syrup adulterants in honey. A training set (n=81) was used to predict the percent adulterant composition of the aforementioned constituents in an independent test set (n=32).

The Isorhamnetin-Containing Fraction of Philippine Honey Produced by the Stingless Bee Is an Antibiotic against Multidrug-Resistant .

Honey exhibits antibacterial and antioxidant activities that are ascribed to its diverse secondary metabolites. In the Philippines, the antibacterial and antioxidant activities, as well as the bioactive metabolite contents of the honey, have not been thoroughly described. In this report, we investigated the in vitro antibacterial and antioxidant activities of honey from and , identified the compound responsible for the antibacterial activity, and compared the observed bioactivities and metabolite profiles to that of Manuka honey, which is recognized for its antibacterial and antioxidant properties.

Current Developments in Machine Learning Techniques in Biological Data Mining.

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques.

Development of a Direct Spectrophotometric and Chemometric Method for Determining Food Dye Concentrations.

An ultraviolet visible (UV-Vis) spectrophotometric and partial least squares (PLS) chemometric method was developed for the simultaneous determination of erythrosine B (red), Brilliant Blue, and tartrazine (yellow) dyes. A training set (n = 64) was generated using a full factorial design and its accuracy was tested in a test set (n = 13) using a Box-Behnken design. The test set garnered a root mean square error (RMSE) of 1.

Castor Oil: Properties, Uses, and Optimization of Processing Parameters in Commercial Production.

Castor oil, produced from castor beans, has long been considered to be of important commercial value primarily for the manufacturing of soaps, lubricants, and coatings, among others. Global castor oil production is concentrated primarily in a small geographic region of Gujarat in Western India. This region is favorable due to its labor-intensive cultivation method and subtropical climate conditions.

Development and Application of a Genetic Algorithm for Variable Optimization and Predictive Modeling of Five-Year Mortality Using Questionnaire Data.

The problem of selecting important variables for predictive modeling of a specific outcome of interest using questionnaire data has rarely been addressed in clinical settings. In this study, we implemented a genetic algorithm (GA) technique to select optimal variables from questionnaire data for predicting a five-year mortality. We examined 123 questions (variables) answered by 5,444 individuals in the National Health and Nutrition Examination Survey.

Development and Validation of a Clinical Risk-Assessment Tool Predictive of All-Cause Mortality.

In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient's health and overall function of multiple organ systems. We have developed a statistical procedure that condenses the information from a variety of health biomarkers into a composite index, which could be used as a risk score for predicting all-cause mortality.