Physiologically-based pharmacokinetic modeling for single and multiple dosing regimens of ceftriaxone in healthy and chronic kidney disease populations: a tool for model-informed precision dosing

Introduction: Ceftriaxone is one of commonly prescribed beta-lactam antibiotics with several label and off-label clinical indications. A high fraction of administered dose of ceftriaxone is excreted renally in an unchanged form, and it may accumulate significantly in patients with impaired renal functions, which may lead to toxicity.Methods: In this study, we employed a physiologically-based pharmacokinetic (PBPK) modeling, as a tool for precision dosing, to predict the biological exposure of ceftriaxone in a virtually-constructed healthy and chronic kidney disease patient populations, with subsequent dosing optimizations. We started developing the model by integrating the physicochemical properties of the drug with biological system information in a PBPK software platform. A PBPK model in an adult healthy population was developed and evaluated visually and numerically with respect to experimental pharmacokinetic data. The model performance was evaluated based on the fold error criteria of the predicted and reported values for different pharmacokinetic parameters. Then, the model was applied to predict drug exposure in CKD patient populations with various degrees of severity.Results: The developed PBPK model was able to precisely describe the pharmacokinetic behavior of ceftriaxone in adult healthy population and in mild, moderate, and severe CKD patient populations. Decreasing the dose by approximately 25% in mild and 50% in moderate to severe renal disease provided a comparable exposure to the healthy population. Based on the simulation of multiple dosing regimens in severe CKD population, it has been found that accumulation of 2 g every 24 h is lower than the accumulation of 1 g every 12 h dosing regimen.Discussion: In this study, the observed concentration time profiles and pharmacokinetic parameters for ceftriaxone were successfully reproduced by the developed PBPK model and it has been shown that PBPK modeling can be used as a tool for precision dosing to suggest treatment regimens in population with renal impairment

The Potential Contribution of Biopolymeric Particles in Lung Tissue Regeneration of COVID-19 Patients

The lung is a vital organ that houses the alveoli, which is where gas exchange takes place. The COVID-19 illness attacks lung cells directly, creating significant inflammation and resulting in their inability to function. To return to the nature of their job, it may be essential to rejuvenate the afflicted lung cells. This is difficult because lung cells need a long time to rebuild and resume their function. Biopolymeric particles are the most effective means to transfer developing treatments to airway epithelial cells and then regenerate infected lung cells, which is one of the most significant symptoms connected with COVID-19. Delivering biocompatible and degradable natural biological materials, chemotherapeutic drugs, vaccines, proteins, antibodies, nucleic acids, and diagnostic agents are all examples of these molecules‘ usage. Furthermore, they are created by using several structural components, which allows them to effectively connect with these cells. We highlight their most recent uses in lung tissue regeneration in this review. These particles are classified into three groups: biopolymeric nanoparticles, biopolymeric stem cell materials, and biopolymeric scaffolds. The techniques and processes for regenerating lung tissue will be thoroughly explored

Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN

In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients’ lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image’s background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%

Effects of COVID-19 on Synaptic and Neuronal Degeneration

Neurons are the basic building blocks of the human body’s neurological system. Atrophy is defined by the disintegration of the connections between cells that enable them to communicate. Peripheral neuropathy and demyelinating disorders, as well as cerebrovascular illnesses and central nervous system (CNS) inflammatory diseases, have all been linked to brain damage, including Parkinson’s disease (PD). It turns out that these diseases have a direct impact on brain atrophy. However, it may take some time after the onset of one of these diseases for this atrophy to be clearly diagnosed. With the emergence of the Coronavirus disease 2019 (COVID-19) pandemic, there were several clinical observations of COVID-19 patients. Among those observations is that the virus can cause any of the diseases that can lead to brain atrophy. Here we shed light on the research that tracked the relationship of these diseases to the COVID-19 virus. The importance of this review is that it is the first to link the relationship between the Coronavirus and diseases that cause brain atrophy. It also indicates the indirect role of the virus in dystrophy

The Effect of ZnO, MgO, TiO2, and Na2O Modifiers on the Physical, Optical, and Radiation Shielding Properties of a TeTaNb Glass System

Novel glass samples with the composition 75TeO2–5Ta2O5–15Nb2O5–5x (where x = ZnO, MgO, TiO2, or Na2O) in mole percent were prepared. The physical, optical, and gamma radiation shielding properties of the glass samples were studied over a wide energy spectrum ranging between 0.015 and 20 MeV. The glasses’ UV–vis spectra were utilized to evaluate the optical energy gap and refractive index. Glass samples had a refractive index ranging from 2.2005 to 2.0967. The results showed that the sample doped with zinc oxide (ZnO) recorded the highest density (ρglass), molar polarizability (αm), molar refraction (Rm), refractive index (n), and third-order nonlinear optical susceptibility (χ3) and the lowest optical energy gap (Eopt) among the samples under investigation. When comparing the current glass system with various standard glass shielding materials, the prepared glass system showed superior shielding performance at energies ranging between 40 and 85 keV. These findings indicate that the prepared glass systems can be used in diagnostic X-rays, especially in dental applications

Iron catalyst for decomposition of methane: Influence of Al/Si ratio support

20% iron catalysts supported on combined alumina and silica through different proportions (Al2O3:SiO2: 100:0.00, 90.0:10.0, 80.0:20.0 and 0.0:100.0) were tested for the catalytic decomposition of methane to produce H2 and carbon. The catalysts were prepared via impregnation technique. The physicochemical properties of the catalysts were characterized with TGA, XRD, H2-TPR, and TEM techniques. The results specified no reaction for all temperatures when the catalysts were supported with bare silica. Indeed, the 20%Fe/(Si-80)-800 catalyst with 20% silica support operated at 800 °C produced CH4 conversion profile that rapidly descended from 64% to 5% due to the high content of silica. Plain alumina supported catalyst demonstrated the best conversion and stability operated at 800 °C. The CH4 conversion started from 83.4% and lasts at 82.2% after 300 min on stream. Keywords: Alumina, Fe catalyst, Hydrogen production, Methane decomposition, Silic

Development and Evaluation of a Physiologically Based Pharmacokinetic Model for Predicting Haloperidol Exposure in Healthy and Disease Populations

The physiologically based pharmacokinetic (PBPK) approach can be used to develop mathematical models for predicting the absorption, distribution, metabolism, and elimination (ADME) of administered drugs in virtual human populations. Haloperidol is a typical antipsychotic drug with a narrow therapeutic index and is commonly used in the management of several medical conditions, including psychotic disorders. Due to the large interindividual variability among patients taking haloperidol, it is very likely for them to experience either toxic or subtherapeutic effects. We intend to develop a haloperidol PBPK model for identifying the potential sources of pharmacokinetic (PK) variability after intravenous and oral administration by using the population-based simulator, PK-Sim. The model was initially developed and evaluated to predict the PK of haloperidol and its reduced metabolite in adult healthy population after intravenous and oral administration. After evaluating the developed PBPK model in healthy adults, it was used to predict haloperidol–rifampicin drug–drug interaction and was extended to tuberculosis patients. The model evaluation was performed using visual assessments, prediction error, and mean fold error of the ratio of the observed-to-predicted values of the PK parameters. The predicted PK values were in good agreement with the corresponding reported values. The effects of the pathophysiological changes and enzyme induction associated with tuberculosis and its treatment, respectively, on haloperidol PK, have been predicted precisely. For all clinical scenarios that were evaluated, the predicted values were within the acceptable two-fold error range

Characterization and medicinal applications of Karakoram shilajit; angiogenesis activity, antibacterial properties and cytotoxicity

Shilajit is a natural substance found in the Himalayan region from Nepal to Pakistan. It is a decomposition product of Royle’s spurge , white clover , and different species of molds. The decomposition takes place over a time span of centuries by the action of microorganism. In the present study, shilajit samples from four different origins including siachen khaplu shilajit (SKS), kharmang pari saspolo shilajit (KPSS), kharmang ghandus shilajit (KGS), and kharmang shilajit center (KSC) of district Skardu, Pakistan were investigated. These samples were characterized using scanning electron microscopy (SEM), Energy-dispersive x-ray spectroscopy (EDS), x-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and UV-visible spectroscopy (UV/vis). SEM revealed a notable difference in the shape and size of collected samples. All samples were found to possess crystalline nature, which is confirmed from XRD. The presence of multi-components and complex silicates confirmed the presence of humic substances (HS) in shilajit. A slight disparity in physiological properties of four samples were revealed due to geographical variations and ecological conditions, which determine the natural synthesis of shilajit. All samples exhibited antibacterial effects against Gram negative bacteria; Escherichia coli (E.coli) . About 76%, 98%, and 100% of bacteria were killed by SKS, both KPSS and KGS, and KSC samples, respectively. The cell viability analysis revealed that the KPSS (66%) and KGS (53%) were cyto-compatible as compared to the SKS (23%) and KSC (25%) samples. The Chick Chorionic Allantoic Membrane (CAM) assay was used to observe the angiogenic potential for SKS, KSC, and KGS samples. Hence, shilajit sample could be a potential candidate for the medicinal applications and offer a new approach to biomedical applications

Efficient Disposal of Rhodamine 6G and Acid Orange 10 Dyes from Aqueous Media Using ZrO₂/CdMn₂O₄/CdO as Novel and Facilely Synthesized Nanocomposites

It is essential to remove rhodamine 6G and acid orange 10 dyes from contaminated water because they can induce cancer and irritate the lungs, skin, mucous, membranes, and eyes. Hence, in the current work, the Pechini sol–gel method was used for the facile synthesis of ZrO2/CdMn2O4/CdO as novel nanocomposites at 600 and 800 °C. The synthesized nanocomposites were used as novel adsorbents for the efficient removal of rhodamine 6G and acid orange 10 dyes from aqueous media. The nanocomposites, which were synthesized at 600 and 800 °C, were abbreviated as EK600 and EK800, respectively. The synthesized nanocomposites were characterized by EDS, XRD, N2 adsorption/desorption analyzer, and FE-SEM. The patterns of XRD showed that the average crystal size of the EK600 and EK800 nanocomposites is 68.25 and 85.32 nm, respectively. Additionally, the images of FE-SEM showed that the surface of the EK600 nanocomposite consists of spherical, polyhedral, and rod shapes with an average grain size of 99.36 nm. Additionally, the surface of the EK800 nanocomposite consists of polyhedral and spherical shapes with an average grain size of 143.23 nm. In addition, the BET surface area of the EK600 and EK800 nanocomposites is 46.33 and 38.49 m2/g, respectively. The optimal conditions to achieve the highest removal of rhodamine 6G and acid orange 10 dyes are pH = 8, contact time = 24 min, and temperature = 298 kelvin. The greatest removal capacity of the EK600 and EK800 adsorbents towards rhodamine 6G dye is 311.53 and 250.63 mg/g, respectively. Additionally, the greatest removal capacity of the EK600 and EK800 adsorbents towards acid orange 10 dye is 335.57 and 270.27 mg/g, respectively. The removal of rhodamine 6G and acid orange 10 dyes using the EK600 and EK800 adsorbents is spontaneous, exothermic, follows the Langmuir adsorption isotherm, and fits well with the pseudo-first-order kinetic model

Effects of Chronic Inhalation of Electronic Cigarette Vapor Containing Nicotine on Neurobehaviors and Pre/Postsynaptic Neuron Markers

Nicotine-exposed animal models exhibit neurobehavioral changes linked to impaired synaptic plasticity. Previous studies highlighted alterations in neurotransmitter levels following nicotine exposure. Vesicular glutamate transporter (VGLUT1) and vesicular gamma-aminobutyric acid (GABA) transporter (VGAT) are essential for the transport and release of glutamate and GABA, respectively, from presynaptic neurons into synapses. In our work, an e-cigarette device was used to deliver vapor containing nicotine to C57BL/6J mice for four weeks. Novel object recognition, locomotion, and Y-maze tests were performed to investigate the behavioral parameters. Protein studies were conducted to study the hippocampal expression of VGLUT1, VGAT, and postsynaptic density protein 95 (PSD95) as well as brain cytokine markers. Long-term memory and locomotion tests revealed that e-cigarette aerosols containing nicotine modulated recognition memory and motor behaviors. We found that vapor exposure increased VGLUT1 expression and decreased VGAT expression in the hippocampus. No alterations were found in PSD95 expression. We observed that vapor-containing nicotine exposure altered certain brain cytokines such as IFNβ-1 and MCP-5. Our work provides evidence of an association between neurobehavioral changes and altered hippocampal VGLUT1 and VGAT expression in mice exposed to e-cigarette vapors containing nicotine. Such exposure was also associated with altered neurobehaviors, which might affect neurodegenerative diseases