Search Results (28)

The emergence of antimicrobial resistance is a significant challenge in global healthcare, necessitating innovative techniques to address multidrug-resistant pathogens. Multidrug-resistant pathogens like Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa pose significant public health threats, as they are increasingly resistant to common antibiotics, leading to more severe and difficult-to-treat infections. These pathogens are part of the ESKAPE group, which includes Enterococcus faecium, Staphylococcus aureus, and Enterobacter species. Animal venoms, derived from a wide range of species such as snakes, scorpions, spiders, bees, wasps, and ants, represent a rich source of bioactive peptides. Venoms have been a valuable source for drug discovery, providing unique compounds with therapeutic potential. Venom-derived drugs are known for their increased bioactivity, specificity, and stability compared to synthetic alternatives. These compounds are being investigated for various conditions, including treatments for diabetes, pain relief, cancer, and infections, showcasing their remarkable antimicrobial efficacy. In this review, we provide a comprehensive investigation into the potential of venom-derived compounds for developing new antimicrobial agents, including antibacterial, antifungal, antiviral, and antiparasitic therapeutics. Key venom components, including melittin from bee venom, phospholipase A₂ from snake venom, and chlorotoxin from scorpion venom, exhibit potent antimicrobial effects through mechanisms such as membrane disruption, enzymatic inhibition, and immune modulation. We also explore the challenges related to the development and clinical use of venom-derived antimicrobials, including toxicity, stability, and delivery mechanisms. These compounds hold immense promise as transformative tools against resistant pathogens, offering a unique avenue for groundbreaking advancements in antimicrobial research and therapeutic development. Full article

In this systematic review, we look to the long-established medical relationship between humans and stingless bees to support the notion that health and conservation research needs to look differently at examples of the relationship between human health and biodiversity. Through the PRISMA statement, we synthesized 1128 Web of Science references between 2000 and 2024 regarding the clinical or experimental therapeutic applications of stingless bee bioproducts (honey and propolis) for human health. We aligned this trend with 2023’s leading morbidities in Mexico and people’s perceptions of healing experiences using stingless bee bioproducts. We found that the honey and propolis of 28 stingless bee species can aid in treating 8 out of the 19 most prevalent diseases in Mexico, primarily cancer, type-2 diabetes, obesity, and COVID-19. Although there is limited evidence from studies regarding the therapeutic applications of stingless bee bioproducts in the Americas, people can actively contribute to conservation as stewards of biodiversity by recognizing and appreciating the health benefits these bioproducts offer. We conclude that traditional meliponiculture systems safeguard knowledge that can be used to improve socio-ecosystem health. This is significant for strengthening locally based healthcare systems while fostering collaborative tropical landscape conservation. Full article

Honey, a natural product with a rich history of medicinal use, has gained increasing recognition for its potent antimicrobial properties, particularly against antibiotic-resistant pathogens. This review focuses on the antimicrobial mechanisms of honey, including its efficacy against resistant bacteria, such as Methicillin-resistant Staphylococcus aureus and Pseudomonas aeruginosa. The antimicrobial action of honey is multifactorial, involving hydrogen peroxide production, phenolic compounds, high sugar concentrations, and the presence of bee defensin-1. The composition of honey varies based on its floral source, which can influence its antimicrobial strength. Certain types, such as Manuka honey, are particularly effective in clinical applications due to their higher levels of bioactive compounds. Honey has also been shown to disrupt bacterial biofilms, a major factor in antibiotic resistance, enhancing its therapeutic potential in treating chronic wounds and infections, especially in patients with compromised immune systems. Moreover, honey’s ability to improve wound healing, reduce inflammation, and promote tissue regeneration highlights its broad therapeutic profile. As antibiotic resistance continues to challenge modern healthcare, honey offers a promising complementary treatment in antimicrobial therapy. Research into its specific bioactive components and potential synergistic effects with other natural agents, like ginger and propolis, could expand its applications. Standardizing honey products for medical use and establishing clinical guidelines are essential for optimizing its therapeutic benefits. As scientific understanding of honey’s antimicrobial mechanisms deepens, its integration into healthcare systems as an adjunct therapy is expected to increase, offering a natural and effective alternative in the fight against infectious diseases. Full article

The field of indoor localization is fast developing and has important ramifications for a number of areas, such as smart infrastructure development, healthcare settings, industrial automation, and military operations. Advances in a range of technologies, each suited to certain use cases and objectives, have been fueled by the capacity to precisely locate objects or people inside places. Prominent indoor localization technologies like Bluetooth, Wi-Fi, ultra-wideband (UWB), ZigBee, and RFID-based systems are examined in this review, along with hybrid solutions that combine several technologies to get around their individual drawbacks and enhance system performance. The field still faces several obstacles in spite of these developments. Widespread acceptance is hampered by persistent problems such as signal interference, high energy consumption, and restricted scalability. The deployment of these systems is further complicated by elements like cost-effectiveness, privacy issues, and compatibility in a variety of situations. This study also examines potential avenues for future research to improve the precision, dependability, and versatility of indoor localization technology in order to overcome these obstacles. Designing systems with increased resilience to environmental changes, utilizing edge computing for real-time processing, and integrating artificial intelligence for predictive modeling are all promising areas of emphasis. This study attempts to help academics and practitioners navigate the changing terrain of indoor localization by offering a comprehensive picture of the field’s present status and future directions. Full article

In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach, namely integrating vehicle and unmanned aerial vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework which employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm for efficiently clustering demand zones unreachable by conventional vehicles, thereby identifying areas requiring UAV delivery. Furthermore, we categorize the demand for medical supplies into two distinct sets based on vehicle accessibility, optimizing distribution routes via both UAVs and vehicles. Through comparative analysis, our findings reveal that the artificial bee colony (ABC) algorithm significantly outperforms the genetic algorithm in terms of solving efficiency, iteration counts, and delivery speed. However, the ABC algorithm’s tendency toward early local optimization and rapid convergence leads to potential stagnation in local optima. To mitigate this issue, we incorporate a simulated annealing technique into the ABC framework, culminating in a refined optimization approach which successfully overcomes the limitations of premature local optima convergence. The experimental results validate the efficacy of our enhanced algorithm, demonstrating reduced iteration counts, shorter computation times, and substantially improved solution quality over traditional logistic models. The proposed method holds promise for significantly improving the operational efficiency and service quality of the healthcare system’s logistics during critical situations. Full article

The rapid development of IoT technologies leads to their wide use in various spheres of life such as healthcare, agriculture, automotive, etc. IoT devices generate a large amount of data, which, if accessed without authorization, can lead to problems for both organizations and individuals. Therefore, security in IoT networks is a critical aspect. Due to the limited computing resources and memory of IoT devices, cryptographic algorithms are often used for their efficiency. One of the widely used cryptographic algorithms in IoT is the symmetric AES algorithm. It is the main encryption algorithm in ZigBee IoT networks. This paper presents the realization of a simulation environment that enables the investigation of the secure connection process of end nodes in a ZigBee wireless IoT network. The developed environment makes it possible to simulate the process of the secure connection of an end device in a ZigBee network. The simulation makes it possible to trace the process of obtaining an encrypted network key from the trust center based on ZigBee specifications. It is also possible to simulate the process of denying access of an end device to the network. The simulation environment implements network key encryption using the install code or a well-known key defined by the ZigBee Alliance. The encryption functionality in the simulator is realized through the implementation of the AES cryptographic algorithm. Full article

Camellia bee pollen (CBP) is a major kind of bee product which is collected by honeybees from tea tree (Camellia sinensis L.) flowers and agglutinated into pellets via oral secretion. Due to its special healthcare value, the authenticity of its botanical origin is of great interest. This study aimed at distinguishing CBP from other bee pollen, including rose, apricot, lotus, rape, and wuweizi bee pollen, based on a non-targeted metabolomics approach using ultra-high performance liquid chromatography–mass spectrometry. Among the bee pollen groups, 54 differential compounds were identified, including flavonol glycosides and flavone glycosides, catechins, amino acids, and organic acids. A clear separation between CBP and all other samples was observed in the score plots of the principal component analysis, indicating distinctive metabolic profiles of CBP. Notably, L-theanine (864.83–2204.26 mg/kg) and epicatechin gallate (94.08–401.82 mg/kg) were identified exclusively in all CBP and were proposed as marker compounds of CBP. Our study unravels the distinctive metabolic profiles of CBP and provides specific and quantified metabolite indicators for the assessment of authentic CBP. Full article

The field of biomedical engineering highly demands technological improvements to allow the successful engraftment of biomaterials requested for healing damaged host tissues, tissue regeneration, and drug delivery. Polymeric materials, particularly natural polymers, are one of the primary suitable materials employed and functionalized to enhance their biocompatibility and thus confer advantageous features after graft implantation. Incorporating bioactive substances from nature is a good technique for expanding or increasing the functionality of biomaterial scaffolds, which may additionally encourage tissue healing. Our ecosystem provides natural resources, like honeybee products, comprising a rich blend of phytochemicals with interesting bioactive properties, which, when functionally coupled with biomedical biomaterials, result in the biomaterial exhibiting anti-inflammatory, antimicrobial, and antioxidant effects. Bee pollen is a sustainable product recently discovered as a new functionalizing agent for biomaterials. This review aims to articulate the general idea of using honeybee products for biomaterial engineering, mainly focusing on describing recent literature on experimental studies on biomaterials functionalized with bee pollen. We have also described the underlying mechanism of the bioactive attributes of bee pollen and shared our perspective on how future biomedical research will benefit from the fabrication of such functionalized biomaterials. Full article

The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions. Full article

Pesticides impair honeybee health in many ways. Imidacloprid (IMD) is a pesticide used worldwide. No information exists on how IMD impact the bees’ body bioelement balance, which is essential for bee health. We hypothesized that IMD disturbs this balance and fed the bees (in field conditions) with diets containing 0 ppb (control), 5 ppb (sublethal considered field-relevant), and 200 ppb (adverse) doses of IMD. IMD severely reduced the levels of K, Na, Ca, and Mg (electrolytic) and of Fe, Mo, Mn, Co, Cu, Ni, Se, and Zn, while those of Sn, V, and Cr (enzymatic) were increased. Levels of P, S, Ti, Al, Li, and Sr were also decreased, while only the B content (physiologically essential) was increased. The increase in Tl, Pb, and As levels (toxic) was alarming. Generally, IMD, even in sublethal doses, unexpectedly led to severe bioelement malnutrition in 69% of bioelements and to a stoichiometric mismatch in the remaining ones. This points to the IMD-dependent bioelement disturbance as another, yet unaccounted for, essential metabolic element which can interfere with apian health. Consequently, there is a need for developing methods of bioelement supplementation of the honey bee diet for better preventing bee colony decline and protecting apian health status when faced with pesticides. Full article

A subset of Wireless Sensor Networks, Wireless Body Area Networks (WBAN) is an emerging technology. WBAN is a collection of tiny pieces of wireless body sensors with small computational capability, communicating short distances using ZigBee or Bluetooth, an application mainly in the healthcare industry like remote patient monitoring. The small piece of sensor monitors health factors like body temperature, pulse rate, ECG, heart rate, etc., and communicates to the base station or central coordinator for aggregation or data computation. The final data is communicated to remote monitoring devices through the internet or cloud service providers. The main challenge for this technology is energy consumption and secure communication within the network and the possibility of attacks executed by malicious nodes, creating problems for the network. This system proposes a suitable trust model for secure communication in WBAN based on node trust and data trust. Node trust is calculated using direct trust calculation and node behaviours. The data trust is calculated using consistent data success and data aging. The performance is compared with an existing protocol like Trust Evaluation (TE)-WBAN and Body Area Network (BAN)-Trust which is not a cryptographic technique. The protocol is lightweight and has low overhead. The performance is rated best for Throughput, Packet Delivery Ratio, and Minimum delay. With extensive simulation on-off attacks, Selfishness attacks, sleeper attacks, and Message suppression attacks were prevented. Full article

Heart disease is one of the lethal diseases causing millions of fatalities every year. The Internet of Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection of disease. The biomedical data collected using IoMT contains personalized information about the patient and this data has serious privacy concerns. To overcome data privacy issues, several data protection laws are proposed internationally. These privacy laws created a huge problem for techniques used in traditional machine learning. We propose a framework based on federated matched averaging with a modified Artificial Bee Colony (M-ABC) optimization algorithm to overcome privacy issues and to improve the diagnosis method for the prediction of heart disease in this paper. The proposed technique improves the prediction accuracy, classification error, and communication efficiency as compared to the state-of-the-art federated learning algorithms on the real-world heart disease dataset. Full article

The nurse scheduling problem (NSP) is an NP-Hard combinatorial optimization scheduling problem that allocates a set of shifts to the group of nurses concerning the schedule period subject to the constraints. The objective of the NSP is to create a schedule that satisfies both hard and soft constraints suggested by the healthcare management. This work explores the meta-heuristic approach to an artificial bee colony algorithm with the Nelder–Mead method (NM-ABC) to perform efficient nurse scheduling. Nelder–Mead (NM) method is used as a local search in the onlooker bee phase of ABC to enhance the intensification process of ABC. Thus, the author proposed an improvised solution strategy at the onlooker bee phase with the benefits of the NM method. The proposed algorithm NM-ABC is evaluated using the standard dataset NSPLib, and the experiments are performed on various-sized NSP instances. The performance of the NM-ABC is measured using eight performance metrics: best time, standard deviation, least error rate, success percentage, cost reduction, gap, and feasibility analysis. The results of our experiment reveal that the proposed NM-ABC algorithm attains highly significant achievements compared to other existing algorithms. The cost of our algorithm is reduced by 0.66%, and the gap percentage to move towards the optimum value is 94.30%. Instances have been successfully solved to obtain the best deal with the known optimal value recorded in NSPLib. Full article

The Internet of Things (IoT) technology has revolutionized the healthcare industry by enabling a new paradigm for healthcare delivery. This paradigm is known as the Internet of Medical Things (IoMT). IoMT devices are typically connected via a wide range of wireless communication technologies, such as Bluetooth, radio-frequency identification (RFID), ZigBee, Wi-Fi, and cellular networks. The ZigBee protocol is considered to be an ideal protocol for IoMT communication due to its low cost, low power usage, easy implementation, and appropriate level of security. However, maintaining ZigBee’s high reliability is a major challenge due to multi-path fading and interference from coexisting wireless networks. This has increased the demand for more efficient channel coding schemes that can achieve a more reliable transmission of vital patient data for ZigBee-based IoMT communications. To meet this demand, a novel coding scheme called inter-multilevel super-orthogonal space–time coding (IM-SOSTC) can be implemented by combining the multilevel coding and set partitioning of super-orthogonal space–time block codes based on the coding gain distance (CGD) criterion. The proposed IM-SOSTC utilizes a technique that provides inter-level dependency between adjacent multilevel coded blocks to facilitate high spectral efficiency, which has been compromised previously by the high coding gain due to the multilevel outer code. In this paper, the performance of IM-SOSTC is compared to other related schemes via a computer simulation that utilizes the quasi-static Rayleigh fading channel. The simulation results show that IM-SOSTC outperforms other related coding schemes and is capable of providing the optimal trade-off between coding gain and spectral efficiency whilst guaranteeing full diversity and low complexity. Full article

Propolis, also known as bee-glue, is a resinous substance produced by honeybees from materials collected from plants they visit. It contains mixtures of wax and bee enzymes and is used by bees as a building material in their hives and by humans for different purposes in traditional healthcare practices. Although the composition of propolis has been shown to depend on its geographic location, climatic zone, and local flora; two largely studied types of propolis: (i) New Zealand and (ii) Brazilian green propolis have been shown to possess Caffeic Acid Phenethyl Ester (CAPE) and Artepillin C (ARC) as the main bioactive constituents, respectively. We have earlier reported that CAPE and ARC possess anticancer activities, mediated by abrogation of mortalin-p53 complex and reactivation of p53 tumor suppressor function. Like CAPE, Artepillin C (ARC) and the supercritical extract of green propolis (GPSE) showed potent anticancer activity. In this study, we recruited low doses of GPSE and ARC (that did not affect either cancer cell proliferation or migration) to investigate their antistress potential using in vitro cell based assays. We report that both GPSE and ARC have the capability to disaggregate metal- and heat-induced aggregated proteins. Metal-induced aggregation of GFP was reduced by fourfold in GPSE- as well as ARC-treated cells. Similarly, whereas heat-induced misfolding of luciferase protein showed 80% loss of activity, the cells treated with either GPSE or ARC showed 60–80% recovery. Furthermore, we demonstrate their pro-hypoxia (marked by the upregulation of HIF-1α) and neuro-differentiation (marked by differentiation morphology and upregulation of expression of GFAP, β-tubulin III, and MAP2). Both GPSE and ARC also offered significant protection against oxidative stress and, hence, may be useful in the treatment of old age-related brain pathologies. Full article