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The hepatobiliary system is vital for the biotransformation and disposition of endogenous molecules. Any impairment in the normal functioning of the hepatobiliary system leads to a spectrum of hepatobiliary diseases (HBDs), such as liver cirrhosis, fatty liver, biliary dyskinesia, gallbladder cancer, etc. Especially in pregnancy, HBD may result in increased maternal and fetal morbidity and mortality. Maternal HBD is a burden to the fetus’s growth, complicates fetal development, and risks the mother’s life. In fetal programming, the maternal mechanism is significantly disturbed by multiple factors (especially diet) that influence the development of the fetus and increase the frequency of metabolic diseases later in life. Additionally, maternal under-nutrition or over-nutrition (especially in high-fat, high-carbohydrate, or protein-rich diets) lead to dysregulation in gut hormones (CCK, GLP-1, etc.), microbiota metabolite production (SCFA, LPS, TMA, etc.), neurotransmitters (POMC, NPY, etc.), and hepatobiliary signaling (insulin resistance, TNF-a, SREBPs, etc.), which significantly impact fetal programming. Recently, biotherapeutics have provided a new horizon for treating HBD during fetal programming to save the lives of the mother and fetus. This review focuses on how maternal impaired hepatobiliary metabolic signaling leads to disease transmission to the fetus mediated through the gut–brain axis.

The DMP (DOMAIN OF UNKNOWN FUNCTION 679 membrane protein) domain, containing a family of membrane proteins specific to green plants, is involved in numerous biological functions including physiological processes, reproductive development and senescence in Arabidopsis, but their evolutionary relationship and biological function in most crops remains unknown. In this study, we scrutinized phylogenetic relationships, gene structure, conserved domains and motifs, promoter regions, gene loss/duplication events and expression patterns. Overall, 240 DMPs were identified and analyzed in 24 plant species selected from lower plants to angiosperms. Comprehensive evolutionary analysis revealed that these DMPs underwent purifying selection and could be divided into five groups (I–V). DMP gene structure showed that it may have undergone an intron loss event during evolution. The five DMP groups had the same domains, which were distinct from each other in terms of the number of DMPs; group III was the largest, closely followed by group V. The DMP promotor region with various cis-regulatory elements was predicted to have a potential role in development, hormone induction and abiotic stresses. Based on transcriptomic data, expression profiling revealed that DMPs were primarily expressed in reproductive organs and were moderately expressed in other tissues. Evolutionary analysis suggested that gene loss events occurred more frequently than gene duplication events among all groups. Overall, this genome-wide study elucidates the potential function of the DMP gene family in selected plant species, but further research is needed in many crops to validate their biological roles.

A bibliometric analysis of 349 scholarly documents published on the topic plant nativity and endemism from 1991–2022 was carried out using the ‘bibliometrix’ tool, developed through the R programming language. The results revealed a significant increase in the number of publications on plant nativity and endemism research since 1991. Of the total of 349 documents that were analyzed, 38 are single authored documents, and the average number of documents per author is 0.278. Each document has received an average of 33.67 citations, with 3.18 citations per year per document. The relationship between the number of authors and the number of articles that they have published follows a power-law distribution. Based on Bradford’s law, only eight were found to be the core sources. Mexico, followed by the USA, has produced the highest number of documents on plant nativity and endemism. The present study suggests that it is necessary to categorize data on endemism by floristic provinces, not by political subdivisions. Synecological studies are also needed, since endemic plants exist within communities. Models that accurately predict levels of endemism on the basis of easily measurable environmental variables should be useful for the rapid identification of endemic-rich areas. Recognition of the importance of plant endemism and the implementation of conservation measures is crucial for preserving biodiversity hotspots. Both in situ and ex situ conservation efforts are essential for protecting endemic species and preventing their extinction. By integrating these approaches, we can contribute to the long-term conservation and management of endemic plant species.

The efficient operation of power-electronic-based systems heavily relies on the reliability and longevity of battery-powered systems. An accurate prediction of the remaining useful life (RUL) of batteries is essential for their effective maintenance, reliability, and safety. However, traditional RUL prediction methods and deep learning-based approaches face challenges in managing battery degradation processes, such as achieving robust prediction performance, to ensure scalability and computational efficiency. There is a need to develop adaptable models that can generalize across different battery types that operate in diverse operational environments. To solve these issues, this research work proposes a TransRUL model to enhance battery RUL prediction. The proposed model incorporates advanced approaches of a time series transformer using a dual encoder with integration positional encoding and multi-head attention. This research utilized data collected by the Centre for Advanced Life Cycle Engineering (CALCE) on CS_2-type lithium-ion batteries that spanned four groups that used a sliding window technique to generate features and labels. The experimental results demonstrate that TransRUL obtained superior performance as compared with other methods in terms of the following evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and R2 values. The efficient computational power of the TransRUL model will facilitate the real-time prediction of the RUL, which is vital for power-electronic-based appliances. This research highlights the potential of the TransRUL model, which significantly enhances the accuracy of battery RUL prediction and additionally improves the management and control of battery-based systems.

This paper presents a novel robust output feedback control that simultaneously performs both stabilization and trajectory tracking for a class of underactuated nonholonomic systems despite model uncertainties, external disturbance, and the absence of velocity measurement. To solve this challenging problem, a generalized normal form has been successfully created by employing an input–output feedback linearization approach and a change in coordinates (diffeomorphism). This research mainly focuses on the stabilization problem of nonholonomic systems that can be transformed to a normal form and pose several challenges, including (i) a nontriangular normal form, (ii) the internal dynamics of the system are non-affine in control, and (iii) the zero dynamics of the system are not in minimum phase. The proposed scheme utilizes combined backstepping and sliding mode control (SMC) techniques. Furthermore, the full-order high gain observer (HGO) has been developed to estimate the derivative of output functions and internal dynamics. Then, full-order HGO and the backstepping SMC have been integrated to synthesize a robust output feedback controller. A differential-drive type (2,0) the wheeled mobile robot has been considered as an example to support the theoretical results. The simulation results demonstrate that the backstepping SMC exhibits robustness against bounded uncertainties.

Due to the aromatase enzyme’s involvement in estrogen biosynthesis, aromatase inhibitors have emerged as the preferred treatment for postmenopausal women with ER+ breast cancer. Using computational chemistry tools, we investigate how the human placental aromatase cytochrome P450 interacts with various phorbols with distinct chains at C-12, C-13, and C-20, as well as the well-known aromatase inhibitors anastrozole, exemestane, and letrozole. To identify phorbol-aromatase interactions, we performed a protein–ligand docking using the structures of our ligands and proteins using the Flare software (version 2.0, Cresset Software, Litlington, UK). These preliminary findings show that the phorbols considered (P-12,13-diAcPh, P-12,13-diiBu, P-12AcPh-13iBu, P-12Ang-13iBu, P-20Ac-12AcPh-13iBu and P-20Ac-12Ang-13iBu) had the highest binding energies in comparison with the commercially available aromatase inhibitors (anastrozole, letrozole, exemestane) used in this study. A subset of the previously described binding residues of testosterone (TST), the endogenous ligand, were also found to be responsible for the phorbol diesters’ binding to the aromatase enzyme, as demonstrated by the findings. This further suggests that the phorbol diesters can bind efficiently to CYP19A1 and may be able to alter its activity because they had higher binding energies than the commercially available drugs.

Since the Palaeozoic era, fire as a potent driver of environmental changes, has dramatically shaped the terrestrial ecosystems. Fire affects soil structure and composition, which in turn affects the floral diversity of an area. This research work aims to examine the impact of fire on vegetation and the physicochemical nature of the soil in fire-affected and fire-free sites across the Mahaban and the surrounding forests, Swabi District, Khyber Pakhtunkhwa, Pakistan. Quadrat quantitative ecological techniques were used for vegetation sampling in fire-free and fire-affected sites. In total, 219 plant species belonging to 173 genera and 70 families were recorded. Among the 219 plant species, 173 species were recorded from fire-free sites and the remaining 122 species were from fire-affected sites. The incidence of fire results in elevated organic matter, nitrogen, phosphorus, and lower calcium carbonate concentrations in the soil. The greatest species richness and evenness were observed across the fire-free sites. Our study concludes that the influence of edaphic and topographic factors on species richness varies between fire-affected and fire-free sites. Fire has significantly altered the nutrient availability in the studied region, and this is confirmed by soil analysis and vegetation research. It is suggested that further research in the field of fire ecology can produce valuable insights.

Purpose: This paper conducts a review of the different research carried out recently on the behavior of non-structural elements (NSEs) and the life cycle assessment (LCA) during an earthquake. It focuses on the study conducted recently and identifies the gaps and way forward for future work. Methods: A systematic literature review was carried out among the different research works. The proposed literature review includes (i) identifying the recent research work using the keywords in available search engines, (ii) studying different research papers and selecting the relevant papers only, and (iii) vulnerability and LCA for NSEs and their research gaps. Results and discussions: A summary is given of the importance and type of NSEs under earthquakes, including life cycle cost assessment for NSE, environment life cycle assessment (ELCA) and social life cycle assessment (SLCA) for different facilities and the embodied energies. Conclusions and recommendations: This paper highlights the problems associated with NSEs. For new constructions, modifications to improve the performance of NSEs, particularly infill walls are under research, however for old buildings, their location is also vital. Numerical methods are performed using different tools available; however, implementation is a big challenge to economize the life cycle and its impact on the community.

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.

Thorax disease is a life-threatening disease caused by bacterial infections that occur in the lungs. It could be deadly if not treated at the right time, so early diagnosis of thoracic diseases is vital. The suggested study can assist radiologists in more swiftly diagnosing thorax disorders and in the rapid airport screening of patients with a thorax disease, such as pneumonia. This paper focuses on automatically detecting and localizing thorax disease using chest X-ray images. It provides accurate detection and localization using DenseNet-121 which is foundation of our proposed framework, called Z-Net. The proposed framework utilizes the weighted cross-entropy loss function (W-CEL) that manages class imbalance issue in the ChestX-ray14 dataset, which helped in achieving the highest performance as compared to the previous models. The 112,120 images contained in the ChestX-ray14 dataset (60,412 images are normal, and the rest contain thorax diseases) were preprocessed and then trained for classification and localization. This work uses computer-aided diagnosis (CAD) system that supports development of highly accurate and precise computer-aided systems. We aim to develop a CAD system using a deep learning approach. Our quantitative results show high AUC scores in comparison with the latest research works. The proposed approach achieved the highest mean AUC score of 85.8%. This is the highest accuracy documented in the literature for any related model.

Cancer is reported to be a major cause of death worldwide, accounting for 10 million in 2020 based on 19 [...]

Environmental contamination with a myriad of potentially toxic elements (PTEs) is triggered by various natural and anthropogenic activities. However, the industrial revolution has increased the intensity of these hazardous elements and their concentration in the environment, which, in turn, could provoke potential ecological risks. Additionally, most PTEs pose a considerable nuisance to human beings and affect soil, aquatic organisms, and even nematodes and microbes. This comprehensive review aims to: (i) introduce potentially toxic elements; (ii) overview the major sources of PTEs in the major environmental compartments; (iii) briefly highlight the major impacts of PTEs on humans, plants, aquatic life, and the health of soil; (iv) appraise the major methods for tackling PTE-caused pollution; (v) discuss the concept and applications of the major eco-technological/green approaches (comprising phytoextraction, rhizofiltration, phytostabilization, phytovolatilization, and phytorestoration); (vi) highlight the role of microbes in phytoremediation under PTE stress; and (vii) enlighten the major role of genetic engineering in advancing the phytoremediation of varied PTEs. Overall, appropriate strategies must be developed in order to stop gene flow into wild species, and biosafety issues must be properly addressed. Additionally, consistent efforts should be undertaken to tackle the major issues (e.g., risk estimation, understanding, acceptance and feasibility) in order to guarantee the successful implementation of phytoremediation programs, raise awareness of this green technology among laymen, and to strengthen networking among scientists, stakeholders, industrialists, governments and non-government organizations.

A novel combustion mode, namely tri-fuel (TF) combustion using a diesel pilot to ignite the premixed methane–hydrogen–air (CH4–H2–air) mixtures, was experimentally investigated under various H2 fractions (0%, 10%, 20%, 40%, 60%) and ultra-lean conditions (equivalence ratio of φ= 0.5). The overarching objective is to evaluate the effect of H2 fraction on flame characteristics and engine performance. To visualize the effect of H2 fraction on the combustion process and flame characteristics, a high-speed color camera (Photron SA-Z) was employed for natural flame luminosity (NFL) imaging to visualize the instantaneous TF combustion process. The engine performance, flame characteristics, and flame stability are characterized based on cylinder pressure and color natural flame images. Both pressure-based and optical imaging-based analyses indicate that adding H2 into the CH4–air mixture can dramatically improve engine performance, such as combustion efficiency, flame speed, and flame stability. The visualization results of NFL show that the addition of H2 promotes the high-temperature reaction, which exhibits a brighter bluish flame during the start of combustion and main combustion, however, a brighter orangish flame during the end of combustion. Since the combustion is ultra-lean, increasing the H2 concentration in the CH4–air mixture dramatically improves the flame propagation, which might reduce the CH4 slip. However, higher H2 concentration in the CH4–air mixture might lead to a high-temperature reaction that sequentially promotes soot emissions, which emit a bright yellowish flame.

Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.

Personalised drug delivery systems with the ability to offer real-time imaging and control release are an advancement in diagnostic and therapeutic applications. This allows for a tailored drug dosage specific to the patient with a release profile that offers the optimum therapeutic effect. Coupling this application with medical imaging capabilities, real-time contrast can be viewed to display the interaction with the host. Current approaches towards such novelty produce a drug burst release profile and contrasting agents associated with side effects as a result of poor encapsulation of these components. In this study, a 3D-printed drug delivery matrix with real-time imaging is engineered. Polycaprolactone (PCL) forms the bulk structure and encapsulates tetracycline hydrochloride (TH), an antibiotic drug and Iron Oxide Nanoparticles (IONP, Fe3O4), a superparamagnetic contrasting agent. Hot melt extrusion (HME) coupled with fused deposition modelling (FDM) is utilised to promote the encapsulation of TH and IONP. The effect of additives on the formation of micropores (10–20 µm) on the 3D-printed surface was investigated. The high-resolution process demonstrated successful encapsulation of both bioactive and nano components to present promising applications in drug delivery systems, medical imaging and targeted therapy.

The public–private partnership (PPP) is a potential procurement strategy for delivering complex construction projects. However, implementing PPPs has not been explored extensively in developing countries like Pakistan. A performance framework is developed in this study to evaluate the application of PPP projects based on 10 key performance indicators (KPIS) and 41 performance measures (PMS). This framework was reviewed by experts for coverage and relevance, then validated through two case studies involving road construction. A triangulation approach was adopted to collect the relevant data through multiparty focus group sessions, archives, and site observations, which enhances the reliability of the data. Results showed there is a difference in performance for six KPIS, but similar practices were reported for four KPIS. The developed performance evaluation framework (PEF) for PPP projects is suitable for developing countries transitioning toward adopting this procurement strategy.

Tibial dyschondroplasia debilities apoptotic and inflammasomal conditions that can further destroy chondrocytes. Inflammasomes are specialized protein complexes that process pro-inflammatory cytokines, e.g., interleukin-1β (IL-1β) and IL-18. Moreover, there is mounting evidence that many of the signaling molecules that govern programmed cell death also affect inflammasome activation in a cell-intrinsic way. During the last decade, apoptotic functions have been described for signaling molecules involving inflammatory responses and cell death pathways. Considering these exceptional developments in the knowledge of processes, this review gives a glimpse of the significance of these two pathways and their connected proteins in tibial dyschondroplasia. The current review deeply elaborates on the elevated level of signaling mediators of mitochondrial-mediated apoptosis and the inflammasome. Although investigating these pathways’ mechanisms has made significant progress, this review identifies areas where more study is especially required. It might lead to developing innovative therapeutics for tibial dyschondroplasia and other associated bone disorders, e.g., osteoporosis and osteoarthritis, where apoptosis and inflammasome are the significant pathways.

Mesoporous Silica Nanoparticles (MSNs) have received increasing attention in biomedical applications due to their tuneable pore size, surface area, size, surface chemistry, and thermal stability. The biocompatibility of MSNs, although generally believed to be satisfactory, is unclear. Physicochemical properties of MSNs, such as diameter size, morphology, and surface charge, control their biological interactions and toxicity. Experimental conditions also play an essential role in influencing toxicological results. Therefore, the present study includes studies from the last five years to statistically analyse the effect of various physicochemical features on MSN-induced in-vitro cytotoxicity profiles. Due to non-normally distributed data and the presence of outliers, a Kruskal–Wallis H test was conducted on different physicochemical characteristics, including diameter sizes, zeta-potential measurements, and functionalisation of MSNs, based on the viability results, and statistical differences were obtained. Subsequently, pairwise comparisons were performed using Dunn’s procedure with a Bonferroni correction for multiple comparisons. Other experimental parameters, such as type of cell line used, cell viability measurement assay, and incubation time, were also explored and analysed for statistically significant results.

Three dimensional printing (3DP), or additive manufacturing, is an exponentially growing process in the fabrication of various technologies with applications in sectors such as electronics, biomedical, pharmaceutical and tissue engineering. Micro and nano scale printing is encouraging the innovation of the aforementioned sectors, due to the ability to control design, material and chemical properties at a highly precise level, which is advantageous in creating a high surface area to volume ratio and altering the overall products’ mechanical and physical properties. In this review, micro/-nano printing technology, mainly related to lithography, inkjet and electrohydrodynamic (EHD) printing and their biomedical and electronic applications will be discussed. The current limitations to micro/-nano printing methods will be examined, covering the difficulty in achieving controlled structures at the miniscule micro and nano scale required for specific applications.

There are continued advances in the internet and communication fields regarding the deployment of 5G-based applications. It is expected that by 2030, 6G applications will emerge as a continued evolution of the mobile network. Blockchain technology is one of the leading supporting technologies predicted to provide a secure and unique network to 6G-enabled devices, transactions, and applications. It is anticipated that the 6G mobile networks will be virtualized, have cloud-based systems, and aim to be the foundation for the Internet of Everything. However, along with the development of communication technologies, threats from malicious parties have become more sophisticated, making security a significant concern for the 6G era in the future. Despite enormous efforts by researchers to improve security and authentication protocols, systems still face novel intrusion and attacks. Recently, multifactor authentication techniques (MFA) have been deployed as potential solutions to attacks in blockchains. The 6G applications and the cellular network have specific vulnerabilities that need to be addressed using blockchain-based MFA technologies. The current paper is a systematic review that discusses the three technologies under consideration; then, several studies are reviewed that discuss MFA techniques in general and use blockchains as potential solutions to future security and authentication issues that may arise for 6G applications.

Wireless power transfer (WPT) is a well-known problem, and has received wide attention in the next generation industrial applications and consumer electronics. On the other hand, frequency diverse array (FDA) is a new concept with the ability to generate a range-angle dependent beampattern. Therefore, some researchers are engaged in designing WPT systems based on the FDA framework (FDA-WPT) instead of phased arrays. Unlike phased arrays, the FDA beampattern is time-variant. Therefore, existing beam collection efficiency models based on the phased array are not suitable for the FDA-WPT system. More importantly, the time-variant property of FDAs is usually ignored in the literature, and the system configuration of the target area where the power-harvesting end is located does not conform to the actual WPT scenario. In this paper, we derive and present accurate models of the FDA-WPT system. The power transfer performance of the corrected FDA-WPT system is then compared with the phased array based WPT system. Simulation results demonstrate that time-variant consideration in the FDA-WPT model causes difficulty in controlling the main beam direction to focus the power. The accurate FDA-WPT is theoretically investigated, and numerical simulations are implemented to validate the theoretical analysis.

The spread of electric vehicles (EV) contributes substantial stress to the present overloaded utility grid which creates new chaos for the distribution network. To relieve the grid from congestion, this paper deeply focused on the control and operation of a charging station for a PV/Battery powered workplace charging facility. This control was tested by simulating the fast charging station when connected to specified EVs and under variant solar irradiance conditions, parity states and seasonal weather. The efficacy of the proposed algorithm and experimental results are validated through simulation in Simulink/Matlab. The results showed that the electric station operated smoothly and seamlessly, which confirms the feasibility of using this supervisory strategy. The optimum cost is calculated using heuristic algorithms in compliance with the meta-heuristic barebones Harris hawk algorithm. In order to long run of charging station the sizing components of the EV station is done by meta-heuristic barebones Harris hawk optimization with profit of USD 0.0083/kWh and it is also validated by swarm based memetic grasshopper optimization algorithm (GOA) and canonical particle swarm optimization (PSO).

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

The novel coronavirus severe acute respiratory syndrome-CoV-2 (SARS-CoV-2) is responsible for COVID-19 infection. The COVID-19 pandemic represents one of the worst global threats in the 21st century since World War II. This pandemic has led to a worldwide economic recession and crisis due to lockdown. Biomedical researchers, pharmaceutical companies, and premier institutes throughout the world are claiming that new clinical trials are in progress. During the severe phase of this disease, mechanical ventilators are used to assist in the management of outcomes; however, their use can lead to the development of pneumonia. In this context, mesenchymal stem cell (MSC)-derived exosomes can serve as an immunomodulation treatment for COVID-19 patients. Exosomes possess anti-inflammatory, pro-angiogenic, and immunomodulatory properties that can be explored in an effort to improve the outcomes of SARS-CoV-2-infected patients. Currently, only one ongoing clinical trial (NCT04276987) is specifically exploring the use of MSC-derived exosomes as a therapy to treat SARS-CoV-2-associated pneumonia. The purpose of this review is to provide insights of using exosomes derived from mesenchymal stem cells in management of the co-morbidities associated with SARS-CoV-2-infected persons in direction of improving their health outcome. There is limited knowledge of using exosomes in SARS-CoV-2; the clinicians and researchers should exploit exosomes as therapeutic regime.

Porous materials, especially microparticles (MP), are utilized in almost every field of engineering and science, ranging from healthcare materials (drug delivery to tissue engineering) to environmental engineering (biosensing to catalysis). Here, we utilize the single needle electrospraying technique (as opposed to complex systems currently in development) to prepare a variety of poly(ε-caprolactone) (PCL) MPs with diverse surface morphologies (variation in pore size from 220 nm to 1.35 µm) and architectural features (e.g., ellipsoidal, surface lamellar, Janus lotus seedpods and spherical). This is achieved by using an unconventional approach (exploiting physicochemical properties of a series of non-solvents as the collection media) via a single step. Sub-micron pores presented on MPs were visualized by electron microscopy (demonstrating a mean MP size range of 7–20 μm). The present approach enables modulation in morphology and size requirements for specific applications (e.g., pulmonary delivery, biological scaffolds, multi-stage drug delivery and biomaterial topography enhancement). Differences in static water contact angles were observed between smooth and porous MP-coated surfaces. This reflects the hydrophilic/hydrophobic properties of these materials.

Extensive burns result in a local wound response and distant-organ injury (DOI) caused by oxidative-stress and inflammation. Melatonin (MT) shows promise in alleviating oxidative-stress and inflammation, but its role in thermal injury is largely unexplored. The present systematic review and meta-analysis were designed to assess the effects of MT on oxidative-stress and inflammatory markers against severe burn-induced DOI. Mean difference (MD)/standard mean difference (SMD) with 95% confidence interval (CI) were estimated using fixed-effect/random-effects models. Eighteen experimental studies met the inclusion criteria. Compared with the control group, MT significantly decreased the levels of malondialdehyde (SMD, −1.03; 95% CI, −1.30, −0.76, p < 0.00001) and 4-hydroxynonenal (MD, −1.06; 95% CI, −1.57, −0.56, p < 0.0001). Additionally, MT increased the levels of glutathione (SMD, 1.94; 95% CI, 1.27, 2.61, p < 0.00001) and superoxide-dismutase (SMD, 0.76; 95% CI, 0.08, 1.45, p = 0.03). Finally, MT significantly decreased the levels of tumor necrosis factor-α (SMD, −1.34; 95% CI, −1.92 to −0.77; p < 0.00001) and C-reactive protein (MD, −12.67; 95% CI, −16.72 to −8.62; p < 0.00001). Meta-analysis indicates that severe burn followed by immediate MT (10 mg/kg) intervention shows significant beneficial effects after 24-h against DOI by regulating oxidative-stress and the inflammatory response.

SARS-CoV-2 has affected people from all age groups, races and ethnicities. Given that many infected individuals are asymptomatic, they transmit the disease to others unknowingly, which has resulted in the spread of infection at an alarming rate. This review aims to provide an overview of the pathophysiology, preventive measures to reduce the disease spread, therapies currently in use, an update on vaccine development and opportunities for vaccine delivery. The World Health Organization has advised several precautions including social distancing, hand washing and the use of PPE including gloves and face masks for minimizing the spread of SARS-CoV-2 infection. At present, several antiviral therapies previously approved for other infections are being repositioned to study their efficacy against SARS-CoV-2. In addition, some medicines (i.e., remdesivir, chloroquine, hydroxychloroquine) have received emergency use authorisation from the FDA. Plasma therapy has also been authorised for emergency use for the treatment of COVID-19 on a smaller scale. However, no vaccine has been approved so far against this virus. Nevertheless, several potential vaccine targets have been reported, and development of different types of vaccines including DNA, mRNA, viral vector, inactivated, subunit and vaccine-like particles is in process. It is concluded that a suitable candidate delivered through an advanced drug delivery approach would effectively boost the immune system against this coronavirus.

Ultrasensitive detection of biomarkers is highly significant for disease prognosis and public health treatment. Despite wide acceptance in routine laboratory tests, the conventional enzyme-linked immunosorbent assay (ELISA) has been of limited use for early biomarker detection due to insufficient sensitivity and multiple long incubation time. Several nanoprobes have been introduced to circumvent the limitation, however, rapid, simple, and chemical-free nanoprobe synthesis and sensitive detection methods, particularly for ELISA, are still lacking. In this study, we have synthesized a gold nanoprobe, conjugated with multiple 6X-histidine (6X-his) peptide and nickel-horseradish peroxidase (Ni2+-HRP), for enhancing the colorimetric signal in ELISA. The developed nanoprobe has been tested for the detection of immunologically significant C-reactive protein (CRP) in ELISA format. The performance of designed probe is validated by testing standard and serum samples, and the detection limit of 32.0 pg/mL with R2 = 0.98 is confirmed. Furthermore, a comparative analysis of the developed nanoprobe was performed with ELISA developed on conventional guidelines, the proposed immunoassay showed an increase of 12-fold sensitivity for detecting CRP due to the high loading of 6Xhis peptide and binding of multiple Ni2+-HRP on a gold nanoparticle. Additionally, the proposed assay provides a simple, fast, and cost-efficient (not requiring multiple antibodies) detection of CRP with easy nanoprobe synthesis. Moreover, the developed Histag-HRP functionalized nanoconjugate immunoassay is flexible and can be applied to other biomarkers efficiently by using disease specific antibody.

Child-appropriate dosage forms are critical in promoting adherence and effective pharmacotherapy in pediatric patients, especially those undergoing long-term treatment in low-resource settings. The present study aimed to develop orodispersible films (ODFs) for isoniazid administration to children exposed to tuberculosis. The ODFs were produced from the aqueous solutions of natural and semi-synthetic polymer blends using electrospinning. The spinning solutions and the resulting fibers were physicochemically characterized, and the disintegration time and isoniazid release from the ODFs were assessed in simulated salivary fluid. The ODFs comprised of nanofibers with adequate thermal stability and possible drug amorphization. Film disintegration occurred instantly upon contact with simulated salivary fluid within less than 15 s, and isoniazid release from the ODFs in the same medium followed after the disintegration profiles, achieving rapid and total drug release within less than 60 s. The ease of administration and favorable drug loading and release properties of the ODFs may provide a dosage form able to facilitate proper adherence to treatment within the pediatric patient population.

Drug administration via the transdermal route is an evolving field that provides an alternative to oral and parenteral routes of therapy. Several microneedle (MN) based approaches have been developed. Among these, coated MNs (typically where drug is deposited on MN tips) are a minimally invasive method to deliver drugs and vaccines through the skin. In this review, we describe several processes to coat MNs. These include dip coating, gas jet drying, spray coating, electrohydrodynamic atomisation (EHDA) based processes and piezoelectric inkjet printing. Examples of process mechanisms, conditions and tested formulations are provided. As these processes are independent techniques, modifications to facilitate MN coatings are elucidated. In summary, the outcomes and potential value for each technique provides opportunities to overcome formulation or dosage form limitations. While there are significant developments in solid degradable MNs, coated MNs (through the various techniques described) have potential to be utilized in personalized drug delivery via controlled deposition onto MN templates.

In the current study, the development of mucoadhesive tablets for buccal delivery of a non-steroidal anti-inflammatory drug was investigated. Binary complexes with piroxicam and cyclodextrins (β-cyclodextrin (β-CD), methylated-β-cyclodextrin (Me-β-CD), and hydroxypropyl-β-cyclodextrin (HP-β-CD)) were prepared by the co-evaporation method. All formulations were characterized by means of differential scanning calorimetry, infrared spectroscopy and powder X-ray diffractometry. Mucoadhesive tablets of binary systems were formulated by direct compression using chitosan as mucoadhesive polymer. The in vitro release profiles of tablets were conducted in simulated saliva and, the drug permeation studies, across porcine buccal mucosa. The results suggest that the rank order effect of cyclodextrins for the drug release was Me-β-CD > HP-β-CD > β-CD, whereas the ex vivo studies showed that the tablets containing chitosan significantly increased the transport of the drug compared to their free complexes. Finally, histological assessment revealed loss of the superficial cell layers, which might be attributed to the presence of cyclodextrins.

Topography and microstructure engineering are rapidly evolving areas of importance for biomedical and pharmaceutical remits. Here, PVA (Polyvinyl alcohol) microbubbles (diameter range ~126 to 414 μm) were used to fabricate beaded (beads-on) nanofibers using an electrohydrodynamic atomization (EHDA) technique. Mean fiber diameter, inter-bead distance, and aspect ratio (AR) were investigated by regulating EHDA process parameters. PVA fibers (diameter range ~233 to 737 nm) were obtained possessing bead ARs in the range of ~10 to 56%. AR was used to modulate hydrophilicity and active release.