Exploring the Biochemical and Nutra-Pharmaceutical Prospects of Some Thymus Species - A Review.

The Thymus genus includes various medicinal and aromatic species, cultivated worldwide for their unique medicinal and economic value. Besides, their conventional use as a culinary flavoring agent, Thymus species are well-known for their diverse biological effects, such as antioxidant, anti-fungal, anti-bacterial, anti-viral, anti-tumor, anti-inflammatory, anti-cancer, and anti-hypertensive properties. Hence, they are used in the treatment of fever, colds, and digestive and cardiovascular diseases. The pharmaceutical significance of Thymus plants is due to their high levels of bioactive components such as natural terpenoid phenol derivatives (p-cymene, carvacrol, thymol, geraniol), flavonoids, alkaloids, and phenolic acids. This review examines the phytochemicals, biological properties, functional food, and nutraceutical attributes of some important Thymus species, with a specific focus on their potential uses in the nutra-pharmaceutical industries. Furthermore, the review provides an insight into the mechanisms of biological activities of key phytochemicals of Thymus species exploring their potential for the development of novel natural drugs.

Does Behavior Evolve First? Correlated Responses to Selection for Voluntary Wheel-Running Behavior in House Mice.

AbstractHow traits at multiple levels of biological organization evolve in a correlated fashion in response to directional selection is poorly understood, but two popular models are the very general "behavior evolves first" (BEF) hypothesis and the more specific "morphology-performance-behavior-fitness" (MPBF) paradigm. Both acknowledge that selection often acts relatively directly on behavior and that when behavior evolves, other traits will as well but most with some lag. However, this proposition is exceedingly difficult to test in nature. Therefore, we studied correlated responses in the high-runner (HR) mouse selection experiment, in which four replicate lines have been bred for voluntary wheel-running behavior and compared with four nonselected control (C) lines. We analyzed a wide range of traits measured at generations 20-24 (with a focus on new data from generation 22), coinciding with the point at which all HR lines were reaching selection limits (plateaus). Significance levels (226 P values) were compared across trait types by ANOVA, and we used the positive false discovery rate to control for multiple comparisons. This meta-analysis showed that, surprisingly, the measures of performance (including maximal oxygen consumption during forced exercise) showed no evidence of having diverged between the HR and C lines, nor did any of the life history traits (e.g., litter size), whereas body mass had responded (decreased) at least as strongly as wheel running. Overall, results suggest that the HR lines of mice had evolved primarily by changes in motivation rather than performance ability at the time they were reaching selection limits. In addition, neither the BEF model nor the MPBF model of hierarchical evolution provides a particularly good fit to the HR mouse selection experiment.

A comprehensive review of mycotoxins: Toxicology, detection, and effective mitigation approaches.

Mycotoxins, harmful compounds produced by fungal pathogens, pose a severe threat to food safety and consumer health. Some commonly produced mycotoxins such as aflatoxins, ochratoxin A, fumonisins, trichothecenes, zearalenone, and patulin have serious health implications in humans and animals. Mycotoxin contamination is particularly concerning in regions heavily reliant on staple foods like grains, cereals, and nuts. Preventing mycotoxin contamination is crucial for a sustainable food supply. Chromatographic methods like thin layer chromatography (TLC), gas chromatography (GC), high-performance liquid chromatography (HPLC), and liquid chromatography coupled with a mass spectrometer (LC/MS), are commonly used to detect mycotoxins; however, there is a need for on-site, rapid, and cost-effective detection methods. Currently, enzyme-linked immunosorbent assays (ELISA), lateral flow assays (LFAs), and biosensors are becoming popular analytical tools for rapid detection. Meanwhile, preventing mycotoxin contamination is crucial for food safety and a sustainable food supply. Physical, chemical, and biological approaches have been used to inhibit fungal growth and mycotoxin production. However, new strains resistant to conventional methods have led to the exploration of novel strategies like cold atmospheric plasma (CAP) technology, polyphenols and flavonoids, magnetic materials and nanoparticles, and natural essential oils (NEOs). This paper reviews recent scientific research on mycotoxin toxicity, explores advancements in detecting mycotoxins in various foods, and evaluates the effectiveness of innovative mitigation strategies for controlling and detoxifying mycotoxins.

Age-related variation in thyroid function - a narrative review highlighting important implications for research and clinical practice.

BACKGROUND: Thyroid hormones are key determinants of health and well-being. Normal thyroid function is defined according to the standard 95% confidence interval of the disease-free population. Such standard laboratory reference intervals are widely applied in research and clinical practice, irrespective of age. However, thyroid hormones vary with age and current reference intervals may not be appropriate across all age groups. In this review, we summarize the recent literature on age-related variation in thyroid function and discuss important implications of such variation for research and clinical practice. MAIN TEXT: There is now substantial evidence that normal thyroid status changes with age throughout the course of life. Thyroid stimulating hormone (TSH) concentrations are higher at the extremes of life and show a U-shaped longitudinal trend in iodine sufficient Caucasian populations. Free triiodothyronine (FT3) levels fall with age and appear to play a role in pubertal development, during which it shows a strong relationship with fat mass. Furthermore, the aging process exerts differential effects on the health consequences of thyroid hormone variations. Older individuals with declining thyroid function appear to have survival advantages compared to individuals with normal or high-normal thyroid function. In contrast younger or middle-aged individuals with low-normal thyroid function suffer an increased risk of adverse cardiovascular and metabolic outcomes while those with high-normal function have adverse bone outcomes including osteoporosis and fractures. CONCLUSION: Thyroid hormone reference intervals have differential effects across age groups. Current reference ranges could potentially lead to inappropriate treatment in older individuals but on the other hand could result in missed opportunities for risk factor modification in the younger and middle-aged groups. Further studies are now needed to determine the validity of age-appropriate reference intervals and to understand the impact of thyroid hormone variations in younger individuals.

A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things.

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.

An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network.

The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions.

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

3D convolutional neural networks-based multiclass classification of Alzheimer's and Parkinson's diseases using PET and SPECT neuroimaging modalities.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative brain pathology formed due to piling up of amyloid proteins, development of plaques and disappearance of neurons. Another common subtype of dementia like AD, Parkinson's disease (PD) is determined by the disappearance of dopaminergic neurons in the region known as substantia nigra pars compacta located in the midbrain. Both AD and PD target aged population worldwide forming a major chunk of healthcare costs. Hence, there is a need for methods that help in the early diagnosis of these diseases. PD subjects especially those who have confirmed postmortem plaque are a strong candidate for a second AD diagnosis. Modalities such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) can be combined with deep learning methods to diagnose these two diseases for the benefit of clinicians. RESULT: In this work, we deployed a 3D Convolutional Neural Network (CNN) to extract features for multiclass classification of both AD and PD in the frequency and spatial domains using PET and SPECT neuroimaging modalities to differentiate between AD, PD and Normal Control (NC) classes. Discrete Cosine Transform has been deployed as a frequency domain learning method along with random weak Gaussian blurring and random zooming in/out augmentation methods in both frequency and spatial domains. To select the hyperparameters of the 3D-CNN model, we deployed both 5- and 10-fold cross-validation (CV) approaches. The best performing model was found to be AD/NC(SPECT)/PD classification with random weak Gaussian blurred augmentation in the spatial domain using fivefold CV approach while the worst performing model happens to be AD/NC(PET)/PD classification without augmentation in the frequency domain using tenfold CV approach. We also found that spatial domain methods tend to perform better than their frequency domain counterparts. CONCLUSION: The proposed model provides a good performance in discriminating AD and PD subjects due to minimal correlation between these two dementia types on the clinicopathological continuum between AD and PD subjects from a neuroimaging perspective.

Aflatoxin Biosynthesis, Genetic Regulation, Toxicity, and Control Strategies: A Review.

Aflatoxins (AFs) are highly toxic and cancer-causing compounds, predominantly synthesized by the Aspergillus species. AFs biosynthesis is a lengthy process that requires as minimum as 30 genes grouped inside 75 kilobytes (kB) of gene clusters, which are regulated by specific transcription factors, including aflR, aflS, and some general transcription factors. This paper summarizes the status of research on characterizing structural and regulatory genes associated with AF production and their roles in aflatoxigenic fungi, particularly Aspergillus flavus and A. parasiticus, and enhances the current understanding of AFs that adversely affect humans and animals with a great emphasis on toxicity and preventive methods.

Biocontrol of Aflatoxins Using Non-Aflatoxigenic Aspergillus flavus: A Literature Review.

Aflatoxins (AFs) are mycotoxins, predominantly produced by Aspergillus flavus, A. parasiticus, A. nomius, and A. pseudotamarii. AFs are carcinogenic compounds causing liver cancer in humans and animals. Physical and biological factors significantly affect AF production during the pre-and post-harvest time. Several methodologies have been developed to control AF contamination, yet; they are usually expensive and unfriendly to the environment. Consequently, interest in using biocontrol agents has increased, as they are convenient, advanced, and friendly to the environment. Using non-aflatoxigenic strains of A. flavus (AF-) as biocontrol agents is the most promising method to control AFs' contamination in cereal crops. AF- strains cannot produce AFs due to the absence of polyketide synthase genes or genetic mutation. AF- strains competitively exclude the AF+ strains in the field, giving an extra advantage to the stored grains. Several microbiological, molecular, and field-based approaches have been used to select a suitable biocontrol agent. The effectiveness of biocontrol agents in controlling AF contamination could reach up to 99.3%. Optimal inoculum rate and a perfect time of application are critical factors influencing the efficacy of biocontrol agents.

A Lightweight Mutual Authentication and Privacy-preservation Scheme for Intelligent Wearable Devices in Industrial-CPS.

Industry 5.0 is the digitalization, automation and data exchange of industrial processes that involve artificial intelligence, Industrial Internet of Things (IIoT), and Industrial Cyber-Physical Systems (I-CPS). In healthcare, I-CPS enables the intelligent wearable devices to gather data from the real-world and transmit to the virtual world for decision-making. I-CPS makes our lives comfortable with the emergence of innovative healthcare applications. Similar to any other IIoT paradigm, I-CPS capable healthcare applications face numerous challenging issues. The resource-constrained nature of wearable devices and their inability to support complex security mechanisms provide an ideal platform to malevolent entities for launching attacks. To preserve the privacy of wearable devices and their data in an I-CPS environment, we propose a lightweight mutual authentication scheme. Our scheme is based on client-server interaction model that uses symmetric encryption for establishing secured sessions among the communicating entities. After mutual authentication, the privacy risk associated with a patient data is predicted using an AI-enabled Hidden Markov Model (HMM). We analyzed the robustness and security of our scheme using BurrowsAbadiNeedham (BAN) logic. This analysis shows that the use of lightweight security primitives for the exchange of session keys makes the proposed scheme highly resilient in terms of security, efficiency, and robustness. Finally, the proposed scheme incurs nominal overhead in terms of processing, communication and storage and is capable to combat a wide range of adversarial threats.

Real-World Clinical Experience of Semaglutide in Secondary Care Diabetes: A Retrospective Observational Study.

INTRODUCTION: The glucagon-like peptide-1 receptor analogue (GLP-1RA) semaglutide is associated with improvements in glycaemia and cardiovascular risk factors in clinical trials. The aim of this study was to examine the real-world impact of semaglutide administered by injection in people with type 2 diabetes (T2D) across three secondary care sites in Wales. METHODS: A retrospective evaluation of 189 patients with T2D initiated on semaglutide between January 2019 and June 2020 with at least one follow-up visit was undertaken. RESULTS: At baseline, participants had a mean age of 61.1 years, mean glycated haemoglobin (HbA1c) of 77.8 mmol/mol (9.3%) and mean body weight of 101.8 kg. At 6 and 12 months of follow-up, mean HbA1c reductions of 13.3 mmol/mol (1.2%) and 16.4 mmol/mol (1.5%), respectively, were observed, and mean weight loss at 6 months was 3.0 kg (all p < 0.001). At 12 months, there were significant reductions in total cholesterol (0.5 mmol/L) and alanine transaminase (4.8 IU/L). Patients naïve to GLP-1RAs or with higher baseline HbA1c at baseline had greater glycaemic reductions, although clinically significant HbA1c reductions were also observed in those who switched from other GLP-1RAs, whose body mass index was < 35.0 and > 35.0 kg/m2 or who had lower baseline HbA1c. Semaglutide was generally well tolerated, although adverse-effects limited use in 18 patients (9.5%). CONCLUSION: Semaglutide provided clinically and statistically significant reductions in HbA1c, body weight, lipids and liver enzymes.

The Impact of Observation Versus Parathyroidectomy on Bone Mineral Density and Fracture Risk Determined by FRAX Tool in Patients With Primary Hyperparathyroidism.

To study impact of observation (OBV) vs parathyroidectomy (PTX) on biochemistry, bone mineral density (BMD) and fracture risk calculated by Fracture Risk Assessment (FRAX) tool in primary hyperparathyroidism (PHPT). Retrospective study of 60 patients (OBV - 26; PTX - 34 patients). Mean adjusted calcium improved in both groups [OBV - 2.76 ± 0.07 vs 2.51 ± 0.20 mmol/L; p < 0.00001, PTX - 2.87 ± 0.21 vs 2.36 ± 0.12 mmol/L; p < 0.00001]. Mean parathyroid hormone level declined in both but more in PTX group [OBV - 11.4 ± 5.2 vs. 9.7 ± 5.6 pmol/L; p = 0.04, PTX - 14.3 ± 8.2 vs 4.6 ± 2.2 pmol/L; p < 0.00001]. In OBV group, BMD and T scores declined at all sites. Mean percentage change of BMD was -5.8 % at femoral neck (FN), -4.9 % at total hip (TH), -6.2 % at lumbar spine (LS) and -10.0 % at lower 1/3rd radius (LR). PTX led to stabilization of BMD at FN (3.0 %), TH (-0.6 %) and LS (2.2 %) but significant improvement at LR (13.9 %; p = 0.0005). In OBV group, 10 year risk of hip fracture (HF) (7.5 ± 9.0 % vs. 8.6 ± 9.0; p = 0.01) and major osteoporotic fracture (OF) (16.6 ± 10.9 % vs 18.3 ± 10.8 %; p = 0.002) worsened with time whereas in PTX group, risk of both type of fractures remained stable (HF; p = 0.48 and OF; p = 0.43). Comparison between groups showed greater improvement in median % change of fracture risk for both HF and OF in PTX group. OBV in PHPT lead to greater decline in BMD at all skeletal sites and imparted significant risk of HF and major OF. PTX offered stabilization of BMD at most sites but improvement at LR with unchanged fracture risk. FRAX tool should be used more frequently and universally.

Coeliac disease presenting with chorea.

Chorea can be genetic or acquired, and often leads to a challenging diagnostic conundrum. In a significant proportion, there is no specific identifiable cause. Chorea is a rare but potentially reversible neurological manifestation of coeliac disease, usually presenting insidiously and often presumed to be associated with typical gastrointestinal symptoms. We report a patient with rapidly progressive generalised chorea, but without preceding gastrointestinal symptoms, who was subsequently diagnosed with coeliac disease. A gluten-free diet resulted in complete resolution of the chorea.

Optimal Operation of the Hybrid Electricity Generation System Using Multiverse Optimization Algorithm.

The ongoing load-shedding and energy crises due to mismanagement of energy produced by different sources in Pakistan and increasing dependency on those sources which produce energy using expensive fuels have contributed to rise in load shedding and price of energy per kilo watt hour. In this paper, we have presented the linear programming model of 95 energy production systems in Pakistan. An improved multiverse optimizer is implemented to generate a dataset of 100000 different solutions, which are suggesting to fulfill the overall demand of energy in the country ranging from 9587 MW to 27208 MW. We found that, if some of the power-generating systems are down due to some technical problems, still we can get our demand by following another solution from the dataset, which is partially utilizing the particular faulty power system. According to different case studies, taken in the present study, based on the reports about the electricity short falls been published in news from time to time, we have presented our solutions, respectively, for each case. It is interesting to note that it is easy to reduce the load shedding in the country, by following the solutions presented in our dataset. Graphical analysis is presented to further elaborate our findings. By comparing our results with state-of-the-art algorithms, it is interesting to note that an improved multiverse optimizer is better in getting solutions with lower power generation costs.

LCSS-Based Algorithm for Computing Multivariate Data Set Similarity: A Case Study of Real-Time WSN Data.

Multivariate data sets are common in various application areas, such as wireless sensor networks (WSNs) and DNA analysis. A robust mechanism is required to compute their similarity indexes regardless of the environment and problem domain. This study describes the usefulness of a non-metric-based approach (i.e., longest common subsequence) in computing similarity indexes. Several non-metric-based algorithms are available in the literature, the most robust and reliable one is the dynamic programming-based technique. However, dynamic programming-based techniques are considered inefficient, particularly in the context of multivariate data sets. Furthermore, the classical approaches are not powerful enough in scenarios with multivariate data sets, sensor data or when the similarity indexes are extremely high or low. To address this issue, we propose an efficient algorithm to measure the similarity indexes of multivariate data sets using a non-metric-based methodology. The proposed algorithm performs exceptionally well on numerous multivariate data sets compared with the classical dynamic programming-based algorithms. The performance of the algorithms is evaluated on the basis of several benchmark data sets and a dynamic multivariate data set, which is obtained from a WSN deployed in the Ghulam Ishaq Khan (GIK) Institute of Engineering Sciences and Technology. Our evaluation suggests that the proposed algorithm can be approximately 39.9% more efficient than its counterparts for various data sets in terms of computational time.