A key parametric study of ultrasonic exfoliation of 2D TiB2 using DI water as a unique medium.

Liquid Phase Exfoliation (LPE) is a very effective technique for the synthesis of few layered two dimensional (2D) nanosheets. There is a surge to find environment friendly solvents for efficient exfoliation of layered materials to produce 2D nanosheets. TiB2 is an important layered material with very little reported work on its 2D nanosheets. The present work is about successful LPE of TiB2 using deionized (DI) water as a clean, green and low cost dispersion medium to make TiB2 nanosheets. The impact of ultrasonication conditions i.e. input power and treatment duration for efficient synthesis of few layered 2D nanosheets in DI water is studied by Atomic Force Microscopy (AFM), X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM). It is found that by increasing input power, the layer thickness is reduced from bulk to 34 nm with lateral dimensions as huge as up to 5 µm. The increased treatment duration has further reduced the layer thickness to 21 nm associated with a decrease in lateral dimensions to about 1 µm. The mechanism of variation in the aspect ratio of the 2D nanosheets with ultrasonication power and treatment duration is explained. The optimum conditions for the fabrication of high aspect ratio 2D nanosheets of TiB2 owe to a greater acoustic cavitation intensity, an optimum treatment duration and a homogenous distribution of the cavitation events while using an appropriate size of the sonotrode in the sonicated volume during ultrasonication.

Biochar enhances the growth and physiological characteristics of Medicago sativa, Amaranthus caudatus and Zea mays in saline soils.

Biochar is a promising solution to alleviate the negative impacts of salinity stress on agricultural production. Biochar derived from food waste effect was investigated on three plant species, Medicago sativa, Amaranthus caudatus, and Zea mays, under saline environments. The results showed that biochar improved significantly the height by 30%, fresh weight of shoot by 35% and root by 45% of all three species compared to control (saline soil without biochar adding), as well as enhanced their photosynthetic pigments and enzyme activities in soil. This positive effect varied significantly between the 3 plants highlighting the importance of the plant-biochar interactions. Thus, the application of biochar is a promising solution to enhance the growth, root morphology, and physiological characteristics of plants under salt-induced stress.

In vitro and split-faced placebo-controlled in vivo study on the skin rejuvenating effects of cream loaded with bioactive extract of Indigofera argentea Burm.f.

The bioactive extracts of traditional medicinal plants are rich in polyphenols and help to rejuvenate skin. The study was designed to assess the skin rejuvenating effects of a stable cream enriched with 4% I. argentea (IaMe) extract. The quantity of polyphenols by spectrophotometric methods was TPC, 101.55 ± 0.03 mg GAE/g and total flavonoid content; 77.14 ± 0.13 mg QE/g, while HPLC-PDA revealed gallic acid; 4.91, chlorogenic acid 48.12, p-coumaric acid 0.43, and rutin 14.23 µg/g. The significant results of biological activities were observed as DPPH; 81.81% ± 0.05%, tyrosinase; 72% ± 0.23% compared to ascorbic acid (92.43% ± 0.03%), and kojic acid (78.80% ± 0.19%) respectively. Moreover, the promising sun protection effects Sun protection factor of extract (20.53) and formulation (10.59) were observed. The active cream formulation (w/o emulsion) was developed with liquid paraffin, beeswax, IaMe extract, and ABIL EM 90, which was stable for 90 days as shown by various stability parameters. The rheological results demonstrated the active formulation's non-Newtonian and pseudo-plastic characteristics and nearly spherical globules by SEM. The IaMe loaded cream was further investigated on human trial subjects for skin rejuvenating effects and visualized in 3D skin images. Herein, the results were significant compared to placebo. IaMe formulation causes a substantial drop in skin melanin from -1.70% (2 weeks) to -10.8% (12 weeks). Furthermore, it showed a significant increase in skin moisture and elasticity index from 7.7% to 39.15% and 2%-30%, respectively. According to the findings, Indigofera argentea extract has promising bioactivities and skin rejuvenating properties, rationalizing the traditional use and encouraging its exploitation for effective and economical cosmeceuticals.

A flexible nanofiber membrane containing dendritic oxygen probe for visual monitoring pressure distribution.

Pressure-sensitive paints (PSP) enable non-intrusive visualization of surface pressure distribution on model surface which is important for aerodynamic studies. However, conventional PSP materials suffer from photobleaching and inadequate sensitivity. In this work, we rationally designed and synthesized novel dendritic oxygen probes (PT1 and PT2) by covalently grafting fluorinated dendrons onto platinum tetrakis(pentafluorophenyl)porphyrin (PT0) (a common oxygen probe). Subsequently, PT2 loaded nanofibers membranes from polycaprolactone (PCL) were fabricated by electrospinning. Fabricated membranes showed high oxygen sensitivity (I0/I100 = 35.3) with excellent flexibility, good reversibility, and outstanding photostability (merely 2.0% intensity loss after prolonged irradiation). The pressure sensitivity was found around 0.73 % per kilopascal. Furthermore, significant variation in emission intensity with respect to the variation in air pressure (1.3-101.32 kPa), facilitates the naked eye visualization of the pressure distribution on the membrane surface. Such excellent oxygen and pressure sensitivity and photostability might be due to high fluorine contents of complex dendritic structure of PT2. This flexible fluorine-functionalized dendritic oxygen probe puts forward a facile and effective strategy to develop advanced PSP materials enabling accurate pressure mapping for aerodynamic studies.

An Enhanced Agile V-Model: Conformance to regulatory bodies and experiences from model's adoption to medical device development.

Context: Medical devices fall under the broad topic encompass everything from basic hardware to integrated software systems. The integration of software into hardware devices is not simple due to requirements of regional regulatory bodies. Therefore, medical businesses need to oversee not only the creation of devices but also the observance of guidelines and standards established by regulatory bodies. While plan-driven methodologies prevented software from evolving or changing, agile methodologies have inherent characteristics of insufficient planning and documentation. Objectives: The objective of our research is to propose a suitable process model for medical device development, keeping in mind the regulatory requirements. Methods: First, based on the detailed analysis of literature and McHughs proposed model, we suggested the Enhanced Agile V-Model (EAV), which combines plan-driven and agile approaches. Second, we mapped the proposed model to the MDEVSPICE framework to confirm that it adhered to the rules outlined in the standard IEC62304. Finally, the proposed model is evaluated through implication to case study of wave therapeutic medical device. Results: The support of both agile and waterfall approach in EAV model helps in accommodating new requirements in the medical devices and the proposed systems engineering approach helps in hardware and software integration. The mapping of the EAV model to the MDEVSPICE shows complete compliance. Moreover, the implication of the proposed model has been clearly shown statistically and successfully implemented in our case study. Further, device usability and efficiency metrics showed confidence of P < 0.05 and for device safety and efficiency, we conducted an experiment which shows significant improvement in selected parameters. Conclusion: The proposed model shows conformance to regulatory standards, and successfully implemented in development of wave therapeutic device. However, its applicability to more compact and straightforward medical products is unknown and can be determined by using this model to analyze the performance of other medical products.

Potential role of Citrus bergamia flower essential oil against oral pathogens.

BACKGROUND: Oral bacterial infections are difficult to treat due to emergence of resistance against antibiotic therapy. Essential oils are considered emerging alternate therapy against bacterial infections and biofilms. We investigated Citrus bergemia flower essential oil against oral pathogens. METHODS: The essential oil was analsyed using Gas Chromatography(GC-MS), in silico investigations, antioxidant, antimicrobial, antibiofilm and antiquorum sensing assays. RESULTS: Gas Chromatography analysis confirmed presence of 17 compounds including 1,6-Octadien-3-ol,3,7-dimethyl, 48.17%), l-limonene (22.03%) and p-menth-1-ol, 8-ol (7.31%) as major components. In silico analysis showed compliance of all tested major components with Lipinski's rule, Bioavailability and antimicrobial activity using PASS (prediction of activity spectrum of substances). Molecular docking with transcriptional regulators 3QP5, 5OE3, 4B2O and 3Q3D revealed strong interaction of all tested compounds except 1,6-Octadien-3-ol,3,7-dimethyl. All tested compounds presented significant inhibition of DPPH (2,2-diphenyl-1-picrylhydrazyl) (IC50 0.65 mg/mL), H2O2 (hydrogen peroxide) (63.5%) and high FRAP (ferrous reducing antioxidant power) value (239.01 µg). In antimicrobial screening a significant activity (MIC 0.125 mg/mL) against Bacillus paramycoides and Bacillus chungangensis was observed. Likewise a strong antibiofilm (52.1 - 69.5%) and anti-QS (quorum sensing) (4-16 mm) activity was recorded in a dose dependent manner. CONCLUSION: It was therefore concluded that C. bergemia essential oil posess strong antioxidant, antimicrobial and antibiofilm activities against tested oral pathogens.

Preparation, In Vitro Characterization, and Evaluation of Polymeric pH-Responsive Hydrogels for Controlled Drug Release.

The purpose of the current research is to formulate a smart drug delivery system for solubility enhancement and sustained release of hydrophobic drugs. Drug solubility-related challenges constitute a significant concern for formulation scientists. To address this issue, a recent study focused on developing PEG-g-poly(MAA) copolymeric nanogels to enhance the solubility of olmesartan, a poorly soluble drug. The researchers employed a free radical polymerization technique to formulate these nanogels. Nine formulations were formulated. The newly formulated nanogels underwent comprehensive tests, including physicochemical assessments, dissolution studies, solubility evaluations, toxicity investigations, and stability examinations. Fourier transform infrared (FTIR) investigations confirmed the successful encapsulation of olmesartan within the nanogels, while thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) studies verified their thermal stability. Scanning electron microscopy (SEM) images revealed the presence of pores on the surface of the nanogels, facilitating water penetration and promoting rapid drug release. Moreover, powder X-ray diffraction (PXRD) studies indicated that the prepared nanogels exhibited an amorphous structure. The nanogel carrier system led to a significant enhancement in olmesartan's solubility, achieving a remarkable 12.3-fold increase at pH 1.2 and 13.29-fold rise in phosphate buffer of pH 6.8 (NGP3). Significant swelling was observed at pH 6.8 compared to pH 1.2. Moreover, the formulated nexus is nontoxic and biocompatible and depicts considerable potential for delivery of drugs and protein as well as heat-sensitive active moieties.

Association of Oral Submucous Fibrosis Risk with GSTM1 and GSTT1 Gene Polymorphisms.

OBJECTIVE: To determine the association of GSTM1 and GSTT1 polymorphisms with oral submucous fibrosis (OSF). STUDY DESIGN: A case-control study. Place and Duration of the Study: Department of Human Genetics and Molecular Biology, University of Health Sciences, Lahore and Oral and Maxillofacial Surgery Department, de Montmorency, College of Dentistry/ Punjab Dental Hospital, Lahore, Pakistan, from 1st April 2019 to 31st April 2020. METHODOLOGY: OSF patients were diagnosed with different clinical staging of mouth opening by Vernier caliper with the help of a professional dentist in the Department of Oral and Maxillofacial, de Montmorency, College of Dentistry, Lahore. One hundred and eight blood samples of OSF patients and 108 samples of normal controls were collected. Genomic DNA was obtained from whole-blood extraction. Multiplex PCR amplification using GSTM1, GSTT1, and ß -Globin gene primers was performed. RESULTS: GSTM1 and GSTT1 null genotypes frequencies were found in 43.5% (47/108) and 13.9% (15/108) of controls, whereas 54.6% (59/108) and 25.9% (28/108) of OSF patients, respectively. OSF patients had a greater frequency rate of GSTM1 and GSTT1 null genotypes than controls [OR 1.56, 95% CI 0.91-2.67 (p=0.13)] and [OR 2.17, 95% CI 1.08-4.34 (p=0.04)], respectively. The GSTT1 genotype was found statistically significant with OSF (p=0.05), and risk was also determined. The cumulative effect of null genotypes of GSTM1/GSTT1 did not show any association with the controls and in OSF patients. Proportions of active and null alleles of the patient group were; 86.1%/13.9%; and in control, it was 92.6%/7.4% (OR = 2.01; CI: 0.82-4.97; p=0.18), respectively. CONCLUSION: The study determined a statistically significant association of GSTT1 gene polymorphism with OSF. KEY WORDS: Oral submucous fibrosis, GSTM1, GSTT1, Gene polymorphisms, Genetic risk.

Assessment of the Implementation of Pakistan's National Action Plan on Antimicrobial Resistance in the Agriculture and Food Sectors.

The agriculture and food (agrifood) sectors play key roles in the emergence, spread, and containment of antimicrobial resistance (AMR). Pakistan's first National Action Plan (NAP) on AMR was developed to guide One Health interventions to combat AMR through 2017-2022. To improve subsequent iterations, we assessed the implementation of Pakistan's NAP in the agrifood sectors (NAPag) in October 2022, using the Progressive Management Pathway on AMR tool developed by the Food and Agriculture Organization of the United Nations (FAO). The assessment tool addressed four crucial focus areas of the NAPag: governance, awareness, evidence, and practices. Each focus area contains multiple topics, which involve four sequential stages of activities to progressively achieve systematic management of AMR risk in the agrifood sectors. High-level representatives of the NAPag stakeholders provided information for the assessment through pre-event documentary review and workshop discussions. The assessment results showed that Pakistan's NAPag had an overall moderate coverage (59%) of the anticipated activities. Gaps were particularly notable in strengthening governance, good practices, and interventions in non-livestock sectors. Furthermore, only 12% of the evaluated activities were fully executed and documented, consistently remaining at the planning and piloting stages in the livestock sector across all the examined topics. Insufficient attention to non-livestock sectors, inadequate regulation and enforcement capacity, and resource constraints have hindered scalable and sustainable interventions under the current plan. This assessment provides valuable insights to strengthen the inclusiveness and contribution of the agrifood sectors in the next NAP iteration. In the short-to-medium term, strategic prioritization is necessary to optimize the use of limited resources and target the most critical gaps, such as improving awareness among key stakeholders and fortifying regulations for prudent antimicrobial use. In the long term, integration of AMR into the country's broader health, development, and agricultural transformation agendas will be needed to generate sustainable benefits.

Effects of Lamiaceae family plants and their bioactive ingredients on coronavirus-induced lung inflammation.

Coronaviruses (CoVs) are a family of viruses that cause infection in respiratory and intestinal systems. Different types of CoVs, those responsible for the SARS-CoV and the new global pandemic of coronavirus disease 2019 in people, have been found. Some plants were used as food additives: spices and dietary and/or medicinal purposes in folk medicine. We aimed to provide evidence about possible effects of two Lamiaceae family plants on control or treatment of CoVs-induced inflammation. The keywords including coronaviruses, Thymus vulgaris, Zataria multiflora, thymol, carvacrol, antivirus, and anti-inflammatory and antioxidant effects were searched in various databases such as PubMed, Web of Sciences (ISI), and Google Scholar until September 2022. The medicinal herbs and their main ingredients, thymol and carvacrol, showed antiviral properties and reduced inflammatory mediators, including IL-1ß; IL-6, and TNF-α, at both gene and protein levels but increased the levels of IFN-γ in the serum as anti-inflammatory cytokine. These medicinal herbs and their constituents also reduce oxidative stress and enhance antioxidant capacity. The results of molecular docking analyses also indicated that polyphenol components such as thymol, carvone, and carvacrol could inhibit the entry of the viruses into the host cells in molecular docking analyses. The antiviral, anti-inflammatory, and antioxidant effects of these plants may be due to actions of their phenolic compounds that modulate immune response and may be useful in the control and treatment of CoV-induced lung disorder.

Therapeutic drug monitoring of vancomycin in surgical patients through a validated HPLC method.

BACKGROUND: Vancomycin is being used for the treatment of a variety of infections caused by methicillin resistant Staphylococcus aureus and methicillin susceptible Staphylococcus aureus. Therapeutic drug monitoring (TDM) is highly recommended for ensuring the safe and effective therapy with vancomycin. A reliable and cost-effective bioanalytical method is required for TDM as well as pharmacokinetic studies of vancomycin. MATERIALS AND METHODS: A selective, sensitive, and cost effective HPLC method was developed and validated for quantification of vancomycin concentrations in human plasma. The mobile phase was a mixture of buffer (50 mM ammonium dihydrogen phosphate, pH 2.4) and acetonitrile 88 : 12 v/v. The separation was carried on C18 column (125 × 4.6 mm, particle size 5 µm) with isocratic flow rate of 0.370 mL/min at room temperature with UV detection at 215 nm. The method was validated for sensitivity, accuracy, and precision as well as stability of vancomycin in human plasma by following European Medicine Agency (EMA) guideline. Therapeutic drug monitoring of vancomycin was performed by quantifying the trough concentrations of vancomycin in 65 human plasma samples after administration of therapeutically relevant dose. RESULTS: The developed method was sensitive enough to quantify vancomycin concentrations as low as 0.25 mg/L in human plasma. Moreover, the method was proved accurate and precise in terms of quantifying the unknown concentration of vancomycin. The evaluation of short-term, long-term, and freeze-thaw stability proved the stability of vancomycin in human plasma. The TDM of vancomycin by using this method showed that 39 (60%) samples were within the target trough concentration range (TTCR), i.e. 10 - 20 mg/L, while 23 samples (35.4%) were below the TTCR, and 3 samples (4.6%) were above this range. CONCLUSION: The developed method is sensitive and cost effective for quantification of vancomycin in human plasma. The results of sample analysis shows that the developed method can be used reliably for TDM of vancomycin.

Herbal Treatments for Obesity: A Review.

Obesity is the most pervasive metabolic disorder, further linked with many other diseases, including diabetes, hypertension, cardiovascular disorders, and sleep apnea. To control the increasing weight of obese individuals, experts usually recommend exercise and lifestyle alterations, but medication and surgeries are also advised in severe cases. FDA-approved obesity-controlling drugs are effective but possess certain adverse effects, including dry mouth, drug abuse, dysregulation in monoamine neurotransmitters, insomnia, and many more. Medication processes are expensive; researchers have focused on safer and more effective alternative approaches than pharmaceutical drugs. Since ancient times, a diverse array of herbal plants have been used due to their therapeutic effect, as in-vitro and in-vivo experimentations have proved the effectiveness of herbal plants with no mortality. In this review, we have presented various herbs with their metabolically active secondary metabolites, including Berberis vulgaris L, Rhizoma Coptidis, Radix Lithospermi, Aloe vera, Clerodendrum multiflorum Burm f., Astragalus membranaceus (Fisch), Boerhaavia diffusa, Achyranthes aspera L., etc. All of these herbs are responsible for anti-obesity, anti-diabetic, and anti-inflammatory effects. Most of the previously published clinical trials and animal studies that confirmed the significant potential of these herbal plants and their active ingredients to reduce weight by decreasing the accumulated fats in the body have also been discussed in this review. Thus, it is concluded that scientists must consider and utilize these natural treasures for safe, effective, and cost-effective treatment. It will open new and novel ways for treatment regimes.

UAV sensor failures dataset: Biomisa arducopter sensory critique (BASiC).

Unmanned aerial vehicles (UAV) rely on a variety of sensors to perceive and navigate their airborne environment with precision. The autopilot software interprets this sensory data, acting as the control mechanism for autonomous flights. As UAVs are exposed to physical environment, they are vulnerable to potential impairments in their sensory mechanism. Their real-time interactions with the actual atmosphere make them susceptible to cyber exploitations as well, where sensory data alterations through counterfeit wireless signals pose a significant threat. In this context, sensor failures can result into unsafe flight conditions, as the fault handling logic may fail to anticipate the context of the issue, allowing autopilot to execute operations without necessary adjustments. Untimely control of sensor failures can result in mid-air collisions or crashes. To address these challenges, we created Biomisa Arducopter Sensory Critique (BASiC) dataset, a state-of-the-art resource for UAV sensor failure analysis. The BASiC dataset comprises 70 autonomous flight data, spanning over 7 hours. It encompasses 3+ hours of (each) pre-failure and post-failure data, along with 1+ hour of no-failure data. We selected the ArduPilot platform as our demonstration aerial vehicle to conduct the experiments. By engineering Software in the Loop (SITL) parameters, we effectively executed sensor failure test simulations. Our dataset incorporates six representative sensors failures which are critical to UAV operations: global positioning system (GPS) for precise aerial positioning, remote control for communication with the ground control station (GCS), accelerometer for measuring linear acceleration, gyroscope for rotational acceleration measurement, compass providing heading information, and barometer for maintaining flight height based on atmospheric pressure data. The availability of the BASiC dataset will benefit the research community, empowering researchers to explore and experiment with state-of-the-art deep learning models by tailoring them for time series signal analysis. It may also contribute in enhancing the safety and reliability of mission-critical autonomous UAV flights.

Surface-enhanced Raman spectroscopy for the characterization of bacterial pellets of Staphylococcus aureus infected by bacteriophage.

Drug-resistant pathogenic bacteria are a major cause of infectious diseases in the world and they have become a major threat through the reduced efficacy of developed antibiotics. This issue can be addressed by using bacteriophages, which can kill lethal bacteria and prevent them from causing infections. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for studying the degradation of infectious bacteria by the interaction of bacteriophages to break the vicious cycle of drug-resistant bacteria and help to develop chemotherapy-independent remedial strategies. The phage (viruses)-sensitive Staphylococcus aureus (S. aureus) bacteria are exposed to bacteriophages (Siphoviridae family) in the time frame from 0 min (control) to 50 minutes with intervals of 5 minutes and characterized by SERS using silver nanoparticles as SERS substrate. This allows us to explore the effects of the bacteriophages against lethal bacteria (S. aureus) at different time intervals. The differentiating SERS bands are observed at 575 (C-C skeletal mode), 620 (phenylalanine), 649 (tyrosine, guanine (ring breathing)), 657 (guanine (COO deformation)), 728-735 (adenine, glycosidic ring mode), 796 (tyrosine (C-N stretching)), 957 (C-N stretching (amide lipopolysaccharides)), 1096 (PO2 (nucleic acid)), 1113 (phenylalanine), 1249 (CH2 of amide III, N-H bending and C-O stretching (amide III)), 1273 (CH2, N-H, C-N, amide III), 1331 (C-N stretching mode of adenine), 1373 (in nucleic acids (ring breathing modes of the DNA/RNA bases)) and 1454 cm-1 (CH2 deformation of saturated lipids), indicating the degradation of bacteria and replication of bacteriophages. Multivariate data analysis was performed by employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to study the biochemical differences in the S. aureus bacteria infected by the bacteriophage. The SERS spectral data sets were successfully differentiated by PLS-DA with 94.47% sensitivity, 98.61% specificity, 94.44% precision, 98.88% accuracy and 81.06% area under the curve (AUC), which shows that at 50 min interval S. aureus bacteria is degraded by the replicating bacteriophages.

ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV.

A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications.

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users' context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).

Internet of Vehicles (IoV)-Based Task Scheduling Approach Using Fuzzy Logic Technique in Fog Computing Enables Vehicular Ad Hoc Network (VANET).

The intelligent transportation system (ITS) relies heavily on the vehicular ad hoc network (VANET) and the internet of vehicles (IoVs), which combine cloud and fog to improve task processing capabilities. As a cloud extension, the fog processes' infrastructure is close to VANET, fostering an environment favorable to smart cars with IT equipment and effective task management oversight. Vehicle processing power, bandwidth, time, and high-speed mobility are all limited in VANET. It is critical to satisfy the vehicles' requirements for minimal latency and fast reaction times while offloading duties to the fog layer. We proposed a fuzzy logic-based task scheduling system in VANET to minimize latency and improve the enhanced response time when offloading tasks in the IoV. The proposed method effectively transfers workloads to the fog computing layer while considering the constrained resources of car nodes. After choosing a suitable processing unit, the algorithm sends the job and its associated resources to the fog layer. The dataset is related to crisp values for fog computing for system utilization, latency, and task deadline time for over 5000 values. The task execution, latency, deadline of task, storage, CPU, and bandwidth utilizations are used for fuzzy set values. We proved the effectiveness of our proposed task scheduling framework via simulation tests, outperforming current algorithms in terms of task ratio by 13%, decreasing average turnaround time by 9%, minimizing makespan time by 15%, and effectively overcoming average latency time within the network parameters. The proposed technique shows better results and responses than previous techniques by scheduling the tasks toward fog layers with less response time and minimizing the overall time from task submission to completion.

Tacrolimus Ointment in Periorbital Atopic Dermatitis.

Periorbital atopic dermatitis (AD) is a common sign in ophthalmological practice and usually has a persistent and relapsing course. Treatment with topical corticosteroids has various side effects associated with their usage. Tacrolimus topical ointment has unique immunomodulatory properties that decrease skin inflammation and pruritus in AD. In this case series, we present a prospective case series of five patients (three males and two females) who received topical application of tacrolimus ointment 0.1-0.03% in the periorbital area twice daily for one to four weeks. The pre- and post-treatment images of all patients were recorded to compare the effects of the treatment. The cases were selected from patients attending the outpatient clinics of East Jeddah Hospital, Saudi Arabia. All patients were suffering from AD. Patients underwent a clinical assessment by tactile inspection (location, size, color, and surface condition) in the first week, secondweek, third month, and first year. We may conclude from this study that tacrolimus showed promising outcomes and is safe and effective for the treatment of flares or resistant periorbital AD in both adults and children.

Dual contrastive learning based image-to-image translation of unstained skin tissue into virtually stained H&E images.

Staining is a crucial step in histopathology that prepares tissue sections for microscopic examination. Hematoxylin and eosin (H&E) staining, also known as basic or routine staining, is used in 80% of histopathology slides worldwide. To enhance the histopathology workflow, recent research has focused on integrating generative artificial intelligence and deep learning models. These models have the potential to improve staining accuracy, reduce staining time, and minimize the use of hazardous chemicals, making histopathology a safer and more efficient field. In this study, we introduce a novel three-stage, dual contrastive learning-based, image-to-image generative (DCLGAN) model for virtually applying an "H&E stain" to unstained skin tissue images. The proposed model utilizes a unique learning setting comprising two pairs of generators and discriminators. By employing contrastive learning, our model maximizes the mutual information between traditional H&E-stained and virtually stained H&E patches. Our dataset consists of pairs of unstained and H&E-stained images, scanned with a brightfield microscope at 20 × magnification, providing a comprehensive set of training and testing images for evaluating the efficacy of our proposed model. Two metrics, Fréchet Inception Distance (FID) and Kernel Inception Distance (KID), were used to quantitatively evaluate virtual stained slides. Our analysis revealed that the average FID score between virtually stained and H&E-stained images (80.47) was considerably lower than that between unstained and virtually stained slides (342.01), and unstained and H&E stained (320.4) indicating a similarity virtual and H&E stains. Similarly, the mean KID score between H&E stained and virtually stained images (0.022) was significantly lower than the mean KID score between unstained and H&E stained (0.28) or unstained and virtually stained (0.31) images. In addition, a group of experienced dermatopathologists evaluated traditional and virtually stained images and demonstrated an average agreement of 78.8% and 90.2% for paired and single virtual stained image evaluations, respectively. Our study demonstrates that the proposed three-stage dual contrastive learning-based image-to-image generative model is effective in generating virtual stained images, as indicated by quantified parameters and grader evaluations. In addition, our findings suggest that GAN models have the potential to replace traditional H&E staining, which can reduce both time and environmental impact. This study highlights the promise of virtual staining as a viable alternative to traditional staining techniques in histopathology.