Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning.

Cannabis is cultivated for therapeutic and recreational purposes where delta-9 tetrahydrocannabinol (THC) is a main target for its therapeutic effects. As the global cannabis industry and research into cannabinoids expands, more efficient and cost-effective analysis methods for determining cannabinoid concentrations will be beneficial to increase efficiencies and maximize productivity. The utilization of machine learning tools to develop near-infrared (NIR) spectroscopy-based prediction models, which have been validated from accurate and sensitive chemical analysis, such as gas chromatography (GC) or liquid chromatography mass spectroscopy (LCMS), is essential. Previous research on cannabinoid prediction models targeted decarboxylated cannabinoids, such as THC, rather than the naturally occurring precursor, tetrahydrocannabinolic acid (THCA), and utilize finely ground cannabis inflorescence. The current study focuses on building prediction models for THCA concentrations in whole cannabis inflorescences prior to harvest, by employing non-destructive screening techniques so cultivators may rapidly characterize high-performing cultivars for chemotype in real time, thus facilitating targeted optimization of crossbreeding efforts. Using NIR spectroscopy and LCMS to create prediction models we can differentiate between high-THCA and even ratio classes with 100% prediction accuracy. We have also developed prediction models for THCA concentration with a R2 = 0.78 with a prediction error average of 13%. This study demonstrates the viability of a portable handheld NIR device to predict THCA concentrations on whole cannabis samples before harvest, allowing the evaluation of cannabinoid profiles to be made earlier, therefore increasing high-throughput and rapid capabilities.

Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa.

Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography-mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II-Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4-100% accuracy prediction), the handheld device also performed well (83.1-100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.

A Simple High-Throughput Method for the Analysis of Vicine and Convicine in Faba Bean.

The faba bean is one of the earliest domesticated crops, with both economic and environmental benefits. Like most legumes, faba beans are high in protein, and can be used to contribute to a balanced diet, or as a meat substitute. However, they also produce the anti-nutritional compounds, vicine and convicine (v-c), that when enzymatically degraded into reactive aglycones can potentially lead to hemolytic anemia or favism. Current methods of analysis use LC-UV, but are only suitable at high concentrations, and thus lack the selectivity and sensitivity to accurately quantitate the low-v-c genotypes currently being developed. We have developed and fully validated a rapid high-throughput LC-MS method for the analysis of v-c in faba beans by optimizing the extraction protocol and assessing the method of linearity, limit of detection, limit of quantitation, accuracy, precision and matrix effects. This method uses 10-times less starting material; removes the use of buffers, acids and organic chemicals; and improves precision and accuracy when compared to current methods.

High-Throughput Quantitation of Cannabinoids by Liquid Chromatography Triple-Quadrupole Mass Spectrometry.

The high-throughput quantitation of cannabinoids is important for the cannabis industry. As medicinal products increase, and research into compounds that have pharmacological benefits increase, and the need to quantitate more than just the main cannabinoids becomes more important. This study aims to provide a rapid, high-throughput method for cannabinoid quantitation using a liquid chromatography triple-quadrupole mass spectrometer (LC-QQQ-MS) with an ultraviolet diode array detector (UV-DAD) for 16 cannabinoids: CBDVA, CBDV, CBDA, CBGA, CBG, CBD, THCV, THCVA, CBN, CBNA, THC, Δ8-THC, CBL, CBC, THCA-A and CBCA. Linearity, limit of detection (LOD), limit of quantitation (LOQ), accuracy, precision, recovery and matrix effect were all evaluated. The validated method was used to determine the cannabinoid concentration of four different Cannabis sativa strains and a low THC strain, all of which have different cannabinoid profiles. All cannabinoids eluted within five minutes with a total analysis time of eight minutes, including column re-equilibration. This was twice as fast as published LC-QQQ-MS methods mentioned in the literature, whilst also covering a wide range of cannabinoid compounds.

High-Throughput Analysis of Amino Acids for Protein Quantification in Plant and Animal-Derived Samples Using High Resolution Mass Spectrometry.

Current methods for measuring the abundance of proteogenic amino acids in plants require derivatisation, extended run times, very sensitive pH adjustments of the protein hydrolysates, and the use of buffers in the chromatographic phases. Here, we describe a fast liquid chromatography-mass spectrometry (LC-MS) method for the determination of amino acids that requires only three steps: hydrolysis, neutralisation, and sample dilution with a borate buffer solution for pH and retention time stability. The method shows excellent repeatability (repeated consecutive injections) and reproducibility (repeated hydrolysis) in the amino acid content, peak area, and retention time for all the standard amino acids. The chromatographic run time is 20 min with a reproducibility and repeatability of <1% for the retention time and <11% for the peak area of the BSA and quality control (QC) lentil samples. The reproducibility of the total protein levels in the hydrolysis batches 1-4 was <12% for the BSA and the lentil samples. The level of detection on column was below 0.1 µM for most amino acids (mean 0.017 µM).

Identification and Distribution of Novel Metabolites of Lolitrem B in Mice by High-Resolution Mass Spectrometry.

Lolitrem B is the most potent indole-diterpene mycotoxin produced by Epichloë festucae var. lolii (termed LpTG-1), with severe intoxication cases reported in livestock. To date, there are no in vivo metabolism studies conducted for the mycotoxin. A mouse model assay established for assessing toxicity of indole-diterpenes was used to investigate metabolic products of lolitrem B. Mice were administered lolitrem B at 0.5 and 2.0 mg/kg body weight (b.wt) intraperitoneally before body and brain tissues were collected at 6 h and 24 h post-treatment. Samples were cryoground and subjected to a biphasic or monophasic extraction. The aqueous and lipophilic phases were analysed using liquid chromatography high-resolution mass spectrometry (LC-HRMS); data analysis was performed with Compound Discoverer™ software. A total of 10 novel phase I metabolic products were identified in the lipophilic phase and their distribution in the liver, kidney and various brain regions are described. The biotransformation products of lolitrem B were found to be present in low levels in the brain. Based on structure-activity postulations, six of these may contribute towards the protracted tremors exhibited by lolitrem B-exposed animals.

Genetic modification of asexual Epichloë endophytes with the perA gene for peramine biosynthesis.

Development of grass-endophyte associations with minimal or no detrimental effects in combination with beneficial characteristics is important for pastoral agriculture. The feasibility of enhancing production of an endophyte-derived beneficial alkaloid through introduction of an additional gene copy was assessed in a proof-of-concept study. Sexual and asexual Epichloë species that form symbiotic associations with cool-season grasses of the Poaceae sub-family Pooideae produce bioactive alkaloids that confer resistance to herbivory by a number of organisms. Of these, peramine is thought to be crucial for protection of perennial ryegrass (Lolium perenne L.) from the Argentinian stem weevil, an economically important exotic pest in New Zealand, contributing significantly to pasture persistence. A single gene (perA) has been identified as solely responsible for peramine biosynthesis and is distributed widely across Epichloë taxa. In the present study, a functional copy of the perA gene was introduced into three recipient endophyte genomes by Agrobacterium tumefaciens-mediated transformation. The target strains included some that do not produce peramine, and others containing different perA gene copies. Mitotically stable transformants generated from all three endophyte strains were able to produce peramine in culture and in planta at variable levels. In summary, this study provides an insight into the potential for artificial combinations of alkaloid biosynthesis in a single endophyte strain through transgenesis, as well as the possibility of using novel genome editing techniques to edit the perA gene of non-peramine producing strains.

Tools for patient-centred family planning counselling: A scoping review.

Background: The focus of family planning counselling is gradually shifting from the tiered-effectiveness model to patient-centred counselling. Although tools exist that aim to make family planning counselling more patient-oriented without increasing the provider's workload, they are not widely used. This scoping review aims to address this by identifying key tools to make family planning care more patient-centred, reviewing the domains of patient-centred care they address, and identifying gaps in the evidence base. Methods: We systematically searched PubMed and SCOPUS for documents on 'patient-centred family planning counselling or support' published between 2013 and 2022. Eligibility criteria included discussion of: 1) strategies for providing patient-centred care; 2) interventions using a patient-centred approach; or 3) the impact of patient-centred approaches. We identified tools for patient-centred care, and mapped them against an existing framework of the main domains of patient-centred care. We reported the available evidence of the impact on those tools. Results: Our scoping review is based on 33 documents. We identified six tools for increasing the patient-centeredness of family planning counselling. None of the tools addressed all domains of patient-centred care. Evidence about the impact of these tools remains scarce. Although there is some evidence about the acceptability of the tools, key evidence gaps include the effect of the tools on quality of care and family planning outcomes. Conclusions: Family planning implementers should be aware that existing tools differ in the extent to which they address key domains of patient-centred family planning counselling. There is a need for further research on factors that may deter providers from adopting these tools. A larger evidence base is needed to permit a future systematic review to determine the effect of these tools on family planning outcomes, such as method adoption and continuation.

The Cannabis Plant as a Complex System: Interrelationships between Cannabinoid Compositions, Morphological, Physiological and Phenological Traits.

Maintaining specific and reproducible cannabinoid compositions (type and quantity) is essential for the production of cannabis-based remedies that are therapeutically effective. The current study investigates factors that determine the plant's cannabinoid profile and examines interrelationships between plant features (growth rate, phenology and biomass), inflorescence morphology (size, shape and distribution) and cannabinoid content. An examination of differences in cannabinoid profile within genotypes revealed that across the cultivation facility, cannabinoids' qualitative traits (ratios between cannabinoid quantities) remain fairly stable, while quantitative traits (the absolute amount of Δ9-tetrahydrocannabinol (THC), cannabidiol (CBD), cannabichromene (CBC), cannabigerol (CBG), Δ9-tetrahydrocannabivarin (THCV) and cannabidivarin (CBDV)) can significantly vary. The calculated broad-sense heritability values imply that cannabinoid composition will have a strong response to selection in comparison to the morphological and phenological traits of the plant and its inflorescences. Moreover, it is proposed that selection in favour of a vigorous growth rate, high-stature plants and wide inflorescences is expected to increase overall cannabinoid production. Finally, a range of physiological and phenological features was utilised for generating a successful model for the prediction of cannabinoid production. The holistic approach presented in the current study provides a better understanding of the interaction between the key features of the cannabis plant and facilitates the production of advanced plant-based medicinal substances.

Antioxidant activity and polyphenol composition of sugarcane molasses extract.

The antioxidant activity of sugarcane molasses ethanol extract (ME) and its fraction (ME-RBF) was evaluated using ABTS, ORAC 6.0 and CAA assays and ME-RBF demonstrated 26-fold, 12-fold and 2-fold higher values, respectively than ME. Likewise, total polyphenol and flavonoid concentration in ME-RBF are more than 10-fold higher than ME, that suggested antioxidant activity is correlated with polyphenol composition. Quantitative analysis of 13 polyphenols (chlorogenic acid, caffeic acid, sinapic acid, syringic acid, vanillin, homoorientin, orientin, vitexin, swertisin, diosmin, apigenin, tricin and diosmetin) was carried out by LCMS. MS/MS analysis allowed the tentative identification of seven apigenin-C-glycosides, three methoxyluteolin-C-glycosides and three tricin-O-glycosides some of which have not been reported in sugarcane before to the best of our knowledge. The results demonstrated that sugarcane molasses can be used as potential source of polyphenols that can be beneficial to health.

Utilisation of Design of Experiments Approach to Optimise Supercritical Fluid Extraction of Medicinal Cannabis.

Carbon dioxide supercritical fluid extraction (CO2 SFE) is a clean and cost-effective method of extracting cannabinoids from cannabis. Using design of experiment methodologies an optimised protocol for extraction of medicinal cannabis bud material (population of mixed plants, combined THC:CBD approximately 1:1.5) was developed at a scale of one kg per extraction. Key variables investigated were CO2 flow rate, extraction time and extraction pressure. A total of 15 batches were analysed for process development using a two-level, full factorial design of experiments for three variable factors over eleven batches. The initial eleven batches demonstrated that CO2 flow rate has the most influence on the overall yield and recovery of the key cannabinoids, particularly CBD. The additional four batches were conducted as replicated runs at high flow rates to determine reproducibility. The highest extraction weight of 71 g (7.1%) was obtained under high flow rate (150 g/min), with long extraction time (600 min) at high pressure (320 bar). This method also gave the best recoveries of THC and CBD. This is the first study to report the repeated extraction of large amounts of cannabis (total 15 kg) to optimise the CO2 SFE extraction process for a pharmaceutical product.

Use of Large and Diverse Datasets for 1H NMR Serum Metabolic Profiling of Early Lactation Dairy Cows.

Most livestock metabolomic studies involve relatively small, homogenous populations of animals. However, livestock farming systems are non-homogenous, and large and more diverse datasets are required to ensure that biomarkers are robust. The aims of this study were therefore to (1) investigate the feasibility of using a large and diverse dataset for untargeted proton nuclear magnetic resonance (1H NMR) serum metabolomic profiling, and (2) investigate the impact of fixed effects (farm of origin, parity and stage of lactation) on the serum metabolome of early-lactation dairy cows. First, we used multiple linear regression to correct a large spectral dataset (707 cows from 13 farms) for fixed effects prior to multivariate statistical analysis with principal component analysis (PCA). Results showed that farm of origin accounted for up to 57% of overall spectral variation, and nearly 80% of variation for some individual metabolite concentrations. Parity and week of lactation had much smaller effects on both the spectra as a whole and individual metabolites (< 3% and < 20%, respectively). In order to assess the effect of fixed effects on prediction accuracy and biomarker discovery, we used orthogonal partial least squares (OPLS) regression to quantify the relationship between NMR spectra and concentrations of the current gold standard serum biomarker of energy balance, ß-hydroxybutyrate (BHBA). Models constructed using data from multiple farms provided reasonably robust predictions of serum BHBA concentration (0.05 ≤ RMSE ≤ 0.18). Fixed effects influenced the results biomarker discovery; however, these impacts could be controlled using the proposed method of linear regression spectral correction.

A Simple GC-MS/MS Method for Determination of Smoke Taint-Related Volatile Phenols in Grapes.

Volatile phenols (VPs) derived from smoke-exposed grapes are known to confer a smoky flavor to wine. Current methods for determination of VPs in grape berries either involve complex sample purification/derivatization steps or employ two analytical platforms for free and bound VP fractions. We report here a simple gas chromatography-tandem mass spectrometry (GC-MS/MS) method for quantification of both free and bound VPs in grapes, based on optimized (1) GC-MS/MS parameters, (2) an analyte extraction procedure, and (3) phenol glycoside hydrolysis conditions. Requiring neither sample cleanup nor a derivatization step, this method is sensitive (LOD ≤ 1 ng/g berries) and reproducible (RSD < 12% for repeated analyses) and is expected to significantly reduce the sample turnover time for smoke taint detection in vineyards.

Development of a validated method for the qualitative and quantitative analysis of cannabinoids in plant biomass and medicinal cannabis resin extracts obtained by super-critical fluid extraction.

The social push for the therapeutic use of cannabis extracts has increased significantly over recent years. Cannabis is being used for treatment for conditions such as epilepsy, cancer and pain management. There are a range of medicinal cannabis products available, but the use of cannabis resin obtained by super critical fluid extraction, often diluted in oil, is becoming increasingly more prominent. Much of the research on cannabis has focused on plant biomass or the final therapeutic product with a concerning lack of information on the intermediate resin. This study aims to bridge the gap between current methods of analysis for biomass and the final therapeutic product by describing a fully developed and validated ultra-high-performance-liquid-chromatography method with diode array detection (UHPLC-DAD) for the qualification and quantification of the cannabinoids CBDA, CBD, CBN, THC, CBC and THCA, in medicinal cannabis biomass and resin obtained by super-critical fluid extraction (SFE). The method was validated for specificity, linearity, limit of detection (LOD), limit of quantitation (LOQ), precision, accuracy, robustness, spike recovery and stability in accordance with the Validation of Analytical Procedures: Text and Methodology Q2 to meet the requirements of the International Council for Harmonisation (ICH), Therapeutic Goods Authority (TGA) and the Food and Drug Administration (FDA) test method validation regulations.

A physical therapy decision-making tool for stratifying persons with Parkinson's disease into community exercise classes.

Aim: Physical therapy and exercise are considered essential components in the management of Parkinson's disease (PD). Using our retrospective data and years of experience in assigning persons with PD to multilevel group classes we propose a two-part physical therapy decision-making tool consisting of participant and exercise program considerations. Methods: Retrospective medical record review and therapist consensus identified evaluation considerations determined to aide clinical decision making. The ability of these variables (i.e., demographics, clinical characteristics, clinical measures cut-offs) to predict the class assignment decision of PD-specialized physical therapists was evaluated using discriminant function analysis. Results: Therapist-assigned groups differed significantly on all clinical measures (p < 0.001) which provided the categorical data required for discriminant analysis. Using all variables, the discriminant function analysis predicted class assignment of the therapists with 79% agreement. Conclusion: This proposed tool provides a framework that may guide the process for increasing access to multilevel group classes.

A Simple LC-MS Method for the Quantitation of Alkaloids in Endophyte-Infected Perennial Ryegrass.

The rapid identification and quantitation of alkaloids produced by Epichloë endophyte-infected pasture grass is important for the agricultural industry. Beneficial alkaloids, such as peramine, provide the grass with enhanced insect protection. Conversely, ergovaline and lolitrem B can negatively impact livestock. Currently, a single validated method to measure these combined alkaloids in planta does not exist. Here, a simple two-step extraction method was developed for Epichloë-infected perennial ryegrass (Lolium perenne L.). Peramine, ergovaline and lolitrem B were quantified using liquid chromatography-mass spectrometry (LC-MS). Alkaloid linearity, limit of detection (LOD), limit of quantitation (LOQ), accuracy, precision, selectivity, recovery, matrix effect and robustness were all established. The validated method was applied to eight different ryegrass-endophyte symbiota. Robustness was established by comparing quantitation results across two additional instruments; a triple quadruple mass spectrometer (QQQ MS) and by fluorescence detection (FLD). Quantitation results were similar across all three instruments, indicating good reproducibility. LOQ values ranged from 0.8 ng/mL to 6 ng/mL, approximately one hundred times lower than those established by previous work using FLD (for ergovaline and lolitrem B), and LC-MS (for peramine). This work provides the first highly sensitive quantitative LC-MS method for the accurate and reproducible quantitation of important endophyte-derived alkaloids.

Quantitation and Identification of Intact Major Milk Proteins for High-Throughput LC-ESI-Q-TOF MS Analyses.

Cow's milk is an important source of proteins in human nutrition. On average, cow's milk contains 3.5% protein. The most abundant proteins in bovine milk are caseins and some of the whey proteins, namely beta-lactoglobulin, alpha-lactalbumin, and serum albumin. A number of allelic variants and post-translationally modified forms of these proteins have been identified. Their occurrence varies with breed, individuality, stage of lactation, and health and nutritional status of the animal. It is therefore essential to have reliable methods of detection and quantitation of these proteins. Traditionally, major milk proteins are quantified using liquid chromatography (LC) and ultra violet detection method. However, as these protein variants co-elute to some degree, another dimension of separation is beneficial to accurately measure their amounts. Mass spectrometry (MS) offers such a tool. In this study, we tested several RP-HPLC and MS parameters to optimise the analysis of intact bovine proteins from milk. From our tests, we developed an optimum method that includes a 20-28-40% phase B gradient with 0.02% TFA in both mobile phases, at 0.2 mL/min flow rate, using 75°C for the C8 column temperature, scanning every 3 sec over a 600-3000 m/z window. The optimisations were performed using external standards commercially purchased for which ionisation efficiency, linearity of calibration, LOD, LOQ, sensitivity, selectivity, precision, reproducibility, and mass accuracy were demonstrated. From the MS analysis, we can use extracted ion chromatograms (EICs) of specific ion series of known proteins and integrate peaks at defined retention time (RT) window for quantitation purposes. This optimum quantitative method was successfully applied to two bulk milk samples from different breeds, Holstein-Friesian and Jersey, to assess differences in protein variant levels.

The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset.

Pain-related emotions are a major barrier to effective self rehabilitation in chronic pain. Automated coaching systems capable of detecting these emotions are a potential solution. This paper lays the foundation for the development of such systems by making three contributions. First, through literature reviews, an overview of how pain is expressed in chronic pain and the motivation for detecting it in physical rehabilitation is provided. Second, a fully labelled multimodal dataset (named 'EmoPain') containing high resolution multiple-view face videos, head mounted and room audio signals, full body 3D motion capture and electromyographic signals from back muscles is supplied. Natural unconstrained pain related facial expressions and body movement behaviours were elicited from people with chronic pain carrying out physical exercises. Both instructed and non-instructed exercises were considered to reflect traditional scenarios of physiotherapist directed therapy and home-based self-directed therapy. Two sets of labels were assigned: level of pain from facial expressions annotated by eight raters and the occurrence of six pain-related body behaviours segmented by four experts. Third, through exploratory experiments grounded in the data, the factors and challenges in the automated recognition of such expressions and behaviour are described, the paper concludes by discussing potential avenues in the context of these findings also highlighting differences for the two exercise scenarios addressed.

Milk Bottom-Up Proteomics: Method Optimization.

Milk is a complex fluid whose proteome displays a diverse set of proteins of high abundance such as caseins and medium to low abundance whey proteins such as ß-lactoglobulin, lactoferrin, immunoglobulins, glycoproteins, peptide hormones, and enzymes. A sample preparation method that enables high reproducibility and throughput is key in reliably identifying proteins present or proteins responding to conditions such as a diet, health or genetics. Using skim milk samples from Jersey and Holstein-Friesian cows, we compared three extraction procedures which have not previously been applied to samples of cows' milk. Method A (urea) involved a simple dilution of the milk in a urea-based buffer, method B (TCA/acetone) involved a trichloroacetic acid (TCA)/acetone precipitation, and method C (methanol/chloroform) involved a tri-phasic partition method in chloroform/methanol solution. Protein assays, SDS-PAGE profiling, and trypsin digestion followed by nanoHPLC-electrospray ionization-tandem mass spectrometry (nLC-ESI-MS/MS) analyses were performed to assess their efficiency. Replicates were used at each analytical step (extraction, digestion, injection) to assess reproducibility. Mass spectrometry (MS) data are available via ProteomeXchange with identifier PXD002529. Overall 186 unique accessions, major and minor proteins, were identified with a combination of methods. Method C (methanol/chloroform) yielded the best resolved SDS-patterns and highest protein recovery rates, method A (urea) yielded the greatest number of accessions, and, of the three procedures, method B (TCA/acetone) was the least compatible of all with a wide range of downstream analytical procedures. Our results also highlighted breed differences between the proteins in milk of Jersey and Holstein-Friesian cows.