The underexploited biotechnology of overexploited Origanum species: Status, knowledge gaps, prospects and potential

The genus Origanum is a group of phytochemically diverse, aromatic species. Distributed mainly in Eurasia and Mediterranean, they are used in traditional medicine, cosmetics and for culinary purposes. They possess antiproliferative, antioxidant, antiviral, antiseptic, anti-inflammatory, anti-hypertensive properties. The major constituents of Origanum spp. are carvacrol and/or thymol together with ?-terpinene, p-cymene, linalool, terpinene-4-ol and sabinene hydrate. Several flavonoids and glycosides are also found. Although the Origanum spp. can either be cultivated or sourced from nature, high demand has necessitated increased production. Overexploitation from natural habitat has threatened these species. Also, due to its poor viability and small-sized seeds, cross-pollination abilities, less productive vegetative propagation, climate-dependent conventional propagation, its genetic improvement has been limited and thus scientific management of available germplasms through biotechnological approach is necessary. For in-vitro propagation, the literature review showed significant differences in culture protocols, genotypes and their success rates. Studies reported cell culture-based production of secondary metabolites or isolation of active compounds in different species of Origanum, which show antiproliferative activity in cancerous cell lines. However, significant knowledge gaps exist. The urgent need is to use advance technologies in enhancing either plant propagation thus the production of source material for active constituents or for genetic improvement of Origanum germplasms for contents, as well as to validate the therapeutic potential of Origanum constituents. This review critically appraises the status of mostly underexploited biotechnological know-how and research on highly valued medicinal herbs, Origanum and throws light on prospects and potential

Evaluation of serum paraoxonase, 1 and its association with serum cholinesterase as a cause of congenital anomalies

Background: Birth defects are conditions of prenatal origin that are present at birth, potentially impacting an infant's health, development, and/or survival. Several environmental toxins affect the growth of the fetus during the intrauterine period by affecting various cellular components. Pesticides and industrial chemicals are known toxins that can hinder the developmental process. In this study, authors are evaluating the relation of cholinesterase and paraoxonase-1 with visible congenital anomalies.Methods: Sixty babies delivered in the labor room were selected for the study. They were divided into two groups. Thirty newborns with visible congenital anomalies were included in Group I. Only babies with visible congenital anomalies were taken as inclusion criteria for this group. This group was compared with Group II, which were taken as controls and consisted of 30 healthy newborns without any congenital anomalies. Serum cholinesterase and serum paraoxonase-1 were estimated and statistical tests were applied.Results: Serum cholinesterase and serum paraoxonase-1 were significantly low in the babies with visible congenital anomalies. Serum cholinesterase levels showed a statistically significant positive correlation with serum paraoxonase 1 level in both the groups.Conclusions: Decrease in acetylcholinesterase by various environmental toxins and the associated decrease in serum paraoxonase level imposes significant oxidant stress and the resultant risk of developing congenital anomalies

A review on distribution, properties, genetic organization, immobilisation and applications of urease

Urease, a nickel-containing metalloenzyme is getting remarkable attention due to a diverse range of applications for mankind. Urease plays a magnificent role in various field like agriculture, analytical, geological phenomena, beverage industry and is an important diagnostic tool. Urease is mainly present in bacteria, fungi, plants and invertebrates and its manifestation in specific genera may open new vistas for its taxonomic position. Various qualitative and quantitative assays are also reported for the estimation of urease enzyme. Urease based biosensors utilizing green synthesis on nanoparticles are also trending. Recently developed inhibitors against urease were discussed in the review. Inhibitory mechanisms involving the structural similarity of the substrate through modification or derivatization can also help in rational drug design by two possible competitive ways either by mimicking monodentate urea binding or binding as a tetrahedral intermediate. Immobilisation of urease through gel entrapment, using non-covalent and covalent protein tags, cross linkage, covalent bonding, using composite films, Teflon, co-precipitation and coating on nanoparticles is also reported. This review also comprised of various application of urease including enhancement of fertility in the soil, cell to cell organization, protection to predators, treatment of various bladder related diseases and infections, analysis of urea and heavy metal ions, biocementation, pollution control by bioleaching of heavy metals and making beverages urea and ethyl carbamate free. As researchers have a keen interest in urease enzyme at present, most of its aspects were incorporated in the article to make it helpful to the scientific community for further research related to the development of new inhibitors and add on applications of urease for the upliftment of the human as well as environment.

Efficacy of mHealth aided 12-week meditation and breath intervention on change in burnout and professional quality of life among health care providers of a tertiary care hospital in north India: a randomized waitlist-controlled trial

IntroductionBurnout is “Chronic workplace stress that has not been successfully managed.” Professional quality of life (PQL) includes work related experiences of compassion satisfaction and compassion fatigue. Healthcare providers (HCPs) are highly susceptible to burnout and compassion fatigue due to their demanding work, which lowers PQL. Burnout leads to poor care, medical errors, and patient safety across healthcare disciplines. Yoga has been shown to improve resilience, reduce stress, and increase self-compassion and psycho-physiological coherence. This study compared HCPs in a mHealth-aided 12-week yoga-based meditation and breath intervention to waitlist controls for HCP burnout and PQL at a north Indian tertiary care hospital.MethodsThis was randomized waitlist-controlled trial. Total 98 HCPs (62 males and 36 females) with an average age of 28.26 ± 3.547 years were enrolled consecutively from March 2021 to November 2022. Randomization was done with opaque sealed envelopes numbered in a computer-generated sequence. The experimental group (n = 49) received 12 online weekly yoga sessions and performed daily home practice (6 days a week). The waitlisted control group (n = 49) continued their daily routine. Maslach’s burnout inventory (MBI), professional quality of life (PQL) and anthropometric measurements were assessed at baseline and after 12 weeks.ResultsAfter 12 weeks, the MBI outcomes of emotional exhaustion, depersonalization, and personal accomplishment showed a highly significant difference between the two groups (p < 0.001). PQL outcomes of compassion satisfaction, burnout, and secondary trauma also differed significantly (p < 0.001). Within group analysis showed that MBI and PQL outcomes improved significantly (p < 0.001) for the experimental group after 12 weeks.ConclusionThe current study contributes to the existing evidence on the effectiveness of Yoga in managing stress and developing resilience among doctors, nurses, and other medical professionals. Integrating yoga into healthcare settings is crucial for addressing the detrimental impact of burnout on decision-making and promoting positive patient outcomes. mHealth technologies have the potential to enhance the user-friendliness of yoga-based interventions by personalizing the practice space and time. Yoga-based interventions and mHealth technologies can effectively address physician burnout, in a simple and implementable manner

Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies

Aim: We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT). Materials & methods: QUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were classified into complete and partial responders. Results: Single-feature naive-Bayes classification performed best with a prediction accuracy of 80, 86 and 85% at 24 h, week 1 and 4, respectively. Conclusion: QUS-radiomics can predict RT response at 3 months as early as 24 h with reasonable accuracy, which further improves into 1 week of treatment

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Inverse Discrete Cosine Transform by Bit Parallel Implementation and Power Comparision

The goal of this project was to implement and compare Invere Discrete Cosine Transform using three methods i.e. by bit parallel, digit serial and bit serial. This application describes a one dimensional Discrete Cosine Transform by bit prallel method and has been implemented by 0.35 ìm technology. When implementing a design, there are several considerations like word length etc. were taken into account. The code was implemented using WHDL and some of the calculations were done in MATLAB. The VHDL code was the synthesized using Design Analyzer of Synopsis; power was calculated and the results were compared