Raspberry leaf 113mg tablets

Abstract Background Skin aging has many manifestations such as wrinkles, uneven skin tone, and dryness. Both intrinsic and extrinsic factors, especially ultraviolet light‐induced oxidative radicals, contribute to the etiology of aging. Human skin requires both water‐ and lipid‐soluble nutrient components, including hydrophilic and lipophilic antioxidants. Vitamins C and E have important protective effects in the aging process and require exogenous supply. Raspberry leaf extracts contain botanical actives that have the potential to hydrating and moisturizing skin. Topical products with these ingredients may therefore combine to provide improved anti‐aging effects over single ingredients. Objectives To evaluate the anti‐aging and brightening effects of an encapsulated serum containing vitamin C (20% w/w), vitamin E, and European raspberry ( Rubus idaeus ) leaf cell culture extract. Methods Fifty female volunteers aged 30‐65 years were allocated one capsule of serum for topical application on one side of the face for 2 months, in addition to self‐use of facial skin products. Both test (treated) and contralateral (untreated) sides were dermatologically assessed after 4 and 8 weeks. Skin color (melanin index), elasticity, radiance, moisture, and water evaporation were measured by Mexameter MX18 ® , Cutometer ® , Glossymeter GL200 ® , Corneometer CM825 ® , and Tewameter TM300 ® instruments, respectively (Courage + Khazaka Electronic GmbH). Skin microtopography parameters, smoothness (SEsm), roughness (SEr), scaliness (SEsc), and wrinkles (SEw), were measured by Visioscan ® VC98 USB (Courage + Khazaka Electronic GmbH), and gross lifting effects were measured by VECTRA ® H1 (Canfield Scientific), and adverse reactions and satisfaction were also assessed. Results Skin color, elasticity, and radiance were significantly improved. The smoothness, scaliness, and wrinkles were also revealed significant improvement. Mild adverse reactions were tingling and tightness. Conclusions The vitamin C, vitamin E, and raspberry leaf cell culture extract serum has anti‐aging and brightening effects of skin.

Abstract licence: CC BY-NC

BACKGROUND: Raspberry leaf use during pregnancy in Australia is widespread. There has been little research exploring the potential beneficial or harmful effects of raspberry leaf on pregnancy, labour, and birth. More research is needed to appropriately inform childbearing women and maternity healthcare professionals on the effects of raspberry leaf so that women can make informed choices. METHODS: This study aimed to determine associations between raspberry leaf use in pregnancy and augmentation of labour and other secondary outcomes. Data was derived from questionnaires which captured demographic information and herbal use in pregnancy. Clinical outcomes were accessed from the maternity services' clinical database. Data analysis was conducted in R via package 'brms' an implementation for Bayesian regression models. RESULTS: A total of 91 completed records were obtained, 44 exposed to raspberry leaf and 47, not exposed. A smaller proportion of women in the raspberry leaf cohort had augmentation of labour, epidural anaesthesia, instrumental births, caesarean section, and postpartum haemorrhage. A larger proportion had vaginal birth and length of all phases of labour were shorter. Under these conditions the use of raspberry leaf was strongly predictive of women not having their labours medically augmented. CONCLUSIONS: While our study demonstrated that raspberry leaf was strongly predictive of women not having their labours medically augmented, the results cannot be relied on or generalised to the wider population of pregnant women. While there were no safety concerns observed in our study, this should not be taken as evidence that raspberry leaf is safe. A randomised controlled trial is urgently needed to provide women and healthcare providers with robust evidence on which to base practice.

Abstract licence: CC BY

Background: Raspberry leaf (RL; Rubus idaeus) is a by-product of raspberry cultivation and has been proposed to be a rich source of micronutrients and potential bioactive components, including polyphenols. However, the precise chemical composition of the non-nutrient (poly)phenols in RL has not been as extensively studied. Objective: To evaluate the (poly)phenolic content of six RL samples from different geographical locations and to explore the impact of brewing duration on the levels of phenolic compounds available for absorption following consumption. Methods: A total of 52 polyphenolic constituents were investigated in the RL samples using Liquid Chromatography–Mass Spectrometry (LC-MS), and RL tea samples were analysed for ellagitannins, flavonoids, and phenolic acids. Tea samples were extracted using 80:20 (v/v) methanol/acidified water (0.1% formic acid) to maximise polyphenol recovery, with two sonication steps (30 and 25 min), followed by centrifugation, filtration, and storage at −18 °C. Extractions were performed in triplicate for comprehensive profiling. Additionally, raspberry leaf tea (2 g) was brewed in 200 mL of boiling water at various times (0.5–20 min) to simulate standard consumption practices; this was also performed in triplicate. This approach aimed to quantify polyphenols in the brew and identify optimal steeping times for maximum polyphenol release. Results: Raspberry leaf (RL) samples from six geographical sources were analysed, with 37 compounds identified in methanol and 37 in water out of the 52 targeted compounds, with only 7 compounds not detected in either methanol or water extracts. The analysis indicated that the total measured polyphenol content across the six samples from various sources ranged between 358.66 and 601.65 mg/100 g (p < 0.001). Ellagitannins were identified as the predominant polyphenolic compound in all RL samples, ranging from 155.27 to 394.22 mg/100 g. The phenolic acid and flavonoid concentrations in these samples exhibited a relatively narrow range, with the phenolic acids spanning from 38.87 to 119.03 mg/100 g and the flavonoids ranging from 125.03 to 156.73 mg/100 g. When brewing the tea, the 5 min extraction time was observed to yield the highest level of polyphenols (505.65 mg/100 g) (p< 0.001), which was significantly higher than that with shorter (409.84 mg/100g) and longer extraction times (429.28 mg/100 g). Notably, ellagic acid levels were highest at 5 min (380.29 mg/100 g), while phenolic acid peaked at 15 min (50.96 mg/100 g). The flavonoid content was shown to be highest at 4 min (82.58 mg/100 g). Conclusions: RL contains a relatively high level of polyphenols, particularly ellagic acid; thus, its consumption may contribute to the daily intake of these health-beneficial non-nutrient components.

Abstract licence: CC BY

The utilization of deep learning-based models for automatic plant leaf disease detection has been established for many years. Such methods have been successfully integrated in the agriculture domain, aiding the swift and accurate identification of various diseases. However, the unavailability of annotated data, the variability of systems, and the lack of an efficient model for real-time use remain unresolved challenges. The goal of this work was to develop a deep learning-based model for crop disease detection and recognition system for real-field scenarios. For this, we trained lightweight versions of the YOLOv5, YOLOv7, YOLOv8 and compared their detection performance. Experiments were carried out on a self-collected dataset containing 3136 real-field images of apples ( healthy and diseased ) and 567 images of PlantDoc dataset. Results revealed that the prediction accuracy of YOLOv8 was superior to others on AdamW optimizer. The results were further validated by deploying it on Raspberry Pi 4.

Abstract licence: CC BY

• Raspberry Pi Powered IoT : Affordable and versatile platform enabling IoT solutions with low power consumption and seamless integration. • Precision-Aware Learning : Focuses on optimizing model performance by balancing accuracy and computational efficiency. • Convolutional Neural Network (CNN) : Deep learning architecture designed for processing structured grid data, widely used in image and video analysis. • Real-Time Data Acquisition : Captures and processes live data streams with minimal latency for immediate analysis and decision-making. In this scenario, the rising prevalence of leaf diseases in finger millet poses serious threats to yield and hence food security. Most conventional methods have a serious limitation to precision, scalability, and adaptability; hence, accuracy in diagnosis and inefficiency in disease management are not rare. This work proposes a new Raspberry Pi-based IoT-enabled real-time data acquisition and a machine learning-driven framework that shows immense promise to improve substantially the accuracy and reliability of the leaf diseases in finger millet. Our proposed framework embeds the features of various advanced models and algorithms to compensate for the pitfalls of earlier contributions. The Multimodal Data Acquisition Model utilises both RGB and infrared cameras to capture holistic images of the leaf in real-time delays. This is further refined by the Adaptive Data Filtering Algorithm, which weeds out a lot of noise and irrelevant information from the data but keeps all critical features pertaining to diseases intact. Then, there is a convolutional feature extraction model, powered by a deep convolutional neural network that captures intricate details of leaf texture and lesions with a selective attention mechanism in the service of paying attention to disease-specific patterns to enhance the precision of extracted features. We propose a precision-aware convolutional neural network, P-CNN, specifically designed for the phase of classification with a handcrafted loss function that differentially penalizes misclassifications according to their agricultural impact sets. Further enrichment is provided by residual learning and precision calibration for sophisticated patterns of diseases and optimization in decision boundaries. Incremental Learning with Precision Feedback will ensure it adapts to new data, aided by Bayesian Inference in order to make confident decisions. Finally, post-processing will be done using an Error Correction with Precision Assurance model that refines the results of classification to give maximum accuracy and a Disease Severity Estimation Model, which assesses and prioritizes diseases interventions during the process.

Abstract licence: CC BY-NC-ND

Over the last 10 years, global raspberry production has increased by 47.89%, based mainly on the red raspberry species (Rubus idaeus). However, the black raspberry (Rubus occidentalis), although less consumed, is resistant to one of the most important diseases for the crop, the late leaf rust caused by Acculeastrum americanum fungus. In this context, genetic resistance is the most sustainable way to control the disease, mainly because there are no registered fungicides for late leaf rust in Brazil. Therefore, the aim was to understand the genetic architecture that controls resistance to late leaf rust in raspberries. For that, we used an interspecific multiparental population using the species mentioned above as parents, 2 different statistical approaches to associate the phenotypes with markers [GWAS (genome-wide association studies) and copula graphical models], and 2 phenotyping methodologies from the first to the 17th day after inoculation (high-throughput phenotyping with a multispectral camera and traditional phenotyping by disease severity scores). Our findings indicate that a locus of higher effect, at position 13.3 Mb on chromosome 5, possibly controls late leaf rust resistance, as both GWAS and the network suggested the same marker. Of the 12 genes flanking its region, 4 were possible receptors, 3 were likely defense executors, 1 gene was likely part of signaling cascades, and 4 were classified as nondefense related. Although the network and GWAS indicated the same higher effect genomic region, the network identified other different candidate regions, potentially complementing the genetic control comprehension.

Abstract licence: CC BY

Light-emitting diodes (LEDs) have become an alternative light source to the fluorescent lamp (FL) for the maintenance of plant tissue cultures due to their low heat emission, low power consumption and the ability to fine-tune the light spectrum. In this study, the effect of LEDs on the growth and leaf stomata features of in vitro cultivated raspberries (Rubus idaeus L. 'Lloyd George') was examined. The plantlets were grown in vitro under a lighting system based on the Philips GreenPower LED research module. Four groups of LEDs emitting white (W), red (R), blue (B), and mixed (W:R:B: far red = 1:1:1:1) lights and FL (control) were used. As a second control (marked as EV), plants grown in the multiplication and rooting stage under fluorescent lamps and then acclimatized ex vitro in a greenhouse for 90 days in natural light were included. Relative growth rate (RGR), protein content as well as stomata morphology and density of the plantlets were analysed after three passages under corresponding light treatment. The results show that different LEDs specifically affect the growth and size of leaf stomata and density of in vitro cultured raspberry plants and can be applied as an effective modulator of morphogenesis during micropropagation. The combination of white, blue, red and far red LED stimulated the accumulation of biomass and proteins, as well as the formation of a higher number of stomata on the lower surface of the leaves, which would be a prerequisite for more effective control of water loss from plantlets during the ex vitro acclimatization.

Abstract licence: CC BY-ND