Engineering Insights into Tailored Metal-Organic Frameworks for CO2 Capture in Industrial Processes.

Despite the known impacts on climate change of carbon dioxide emissions, the continued use of fossil fuels for energy generation leading to the emission of carbon dioxide (CO2) into the atmosphere is evident. Therefore, innovation to address and reduce CO2 emissions from industrial operations remains an urgent and crucial priority. A viable strategy in the area is postcombustion capture mainly through absorption by aqueous alkanolamines, which focuses on the separation of CO2 from flue gas, despite its limitations. Within this context, porous materials, particularly metal-organic frameworks (MOFs), have arisen as favorable alternatives owing to their significant adsorption capacity, selectivity, and reduced regeneration energy demands. This research evaluates the engineering insights into tailored MOFs for enhanced CO2 capture, focusing on three series of MOFs (ZIF, UiO-66, and BTC) to investigate the effects of organic ligands, functional groups, and metal ions. The evaluation encompassed a range of aspects including adsorption isotherms of pure gases [CO2 and nitrogen (N2)] and mixed gas mixture (CO2 and N2 with 15:85% ratio), along with utilization of the ideal adsorbed solution theory (IAST) to simulate multicomponent gas adsorption isotherms. Moreover, the reliability of IAST for mixed gas adsorption prediction has been investigated in detail. The research offers valuable insights into the correlation between the characteristics of MOFs and their effectiveness in gas separation and how these characteristics contribute to the differences between IAST predictions and experimental results. The findings enhance the understanding of how to enhance MOF characteristics in order to reduce CO2 emissions and also highlight the need for advanced models that consider thermodynamic nonidealities to accurately predict the behavior of mixed gas adsorption in MOFs. As a result, the incorporation of MOFs with enhanced predictability and reliability into CO2 capture industrial processes is facilitated.

Population pharmacokinetic/target engagement modelling of tozorakimab in healthy volunteers and patients with chronic obstructive pulmonary disease.

AIMS: This study describes the pharmacokinetic (PK)/target engagement (TE) relationship of tozorakimab, an anti-interleukin (IL)-33 antibody, by building a mechanistic population PK/TE model using phase 1 biomarker data. METHODS: The analysis included tozorakimab PK and TE in serum assessed in 60 tozorakimab-treated participants, including healthy adults and patients with mild chronic obstructive pulmonary disease. Scenarios evaluated three dose frequencies (once every 2, 4 or 6 weeks) administered subcutaneously at seven doses of tozorakimab (30, 60, 90, 120, 150, 300 or 600 mg). For each dose, simulations were performed with 5000 virtual individuals to predict systemic TE. Inhibition of IL-33/soluble ST2 (sST2) complex levels at trough PK at steady state was assessed in each dosing scenario. The PK/TE modelling analyses were performed using a nonlinear mixed-effect modelling approach. RESULTS: The final two-compartment PK model with tozorakimab binding IL-33 in the central compartment adequately described the systemic PK and TE of tozorakimab at population and individual levels. The mean PK parameter estimates of absorption rate, central volume of distribution and clearance were 0.48 (90% confidence interval [CI]: 0.40-0.59, 1/day), 12.64 (90% CI: 8.60-18.62, L) and 0.87 (90% CI: 0.65-1.16, L/day), respectively. Consistent with the observed value, tozorakimab bioavailability was 45%. For all three dose frequencies, predicted inhibition of systemic IL-33/sST2 levels was more than 95% at doses greater than 90 mg. CONCLUSIONS: The PK/TE model reliably quantified the relationship between PK and systemic TE of tozorakimab, with potential utility for predicting clinical dose-response relationships and supporting clinical dose selection.

Classification of Pneumonia via a Hybrid ZFNet-Quantum Neural Network Using a Chest X-ray Dataset.

INTRODUCTION: Deep neural networks (DNNs) have made significant contributions to diagnosing pneumonia from chest X-ray imaging. However, certain aspects of diagnosis and planning can be further enhanced through the implementation of a quantum deep neural network (QDNN). Therefore, we introduced a technique that integrates neural networks with quantum algorithms named the ZFNet-quantum neural network for detecting pneumonia using 5863 X-ray scans with binary cases. METHODS: The hybrid model efficiently pre-processes complex and high-dimensional data by extracting significant features from the ZFNet model. These significant features are given to the quantum circuit algorithm and further embedded into a quantum device. The parameterized quantum circuit algorithm using qubits, superposition theorem, and entanglement phenomena generates 4 features from 4098 features extracted from images via a deep transfer learning model. Moreover, to validate the outcome measures of the proposed technique, we used various PennyLane quantum devices to detect pneumonia and normal control images. By using the Adam optimizer, which exploits an adaptive learning rate that is fixed to 10-6 and six layers of a quantum circuit composed of quantum gates, the proposed model achieves an accuracy of 96.5%, corresponding to 25 epochs. RESULTS: The integrated ZFNet-quantum learning network outperforms the deep transfer learning network in terms of testing accuracy, as the accuracy gained by the convolutional neural network (CNN) is 94%. Therefore, we use a hybrid classical-quantum model to detect pneumonia in which a variational quantum algorithm enhances the outcomes of a ZFNet transfer learning method. CONCLUSION: This approach is an efficient and automated method for detecting pneumonia and could significantly enhance outcome measures related to the speed and accuracy of the network in the clinical and healthcare sectors.

Role of quality assessment of the recycled packaging material in determining its safety profile as food contact material.

Food packaging waste significantly impacts global environmental changes, prompting the adoption of a green circular economy approach. Recycling packaging waste is a critical element of this strategy. However, it faces challenges related to the quality of recycled materials and concerns about their safety. Thus, this review aimed to highlight different analytical methods alone or in combination to evaluate the quality of the recycled material. Furthermore, the safety and health aspects related to the migration of contaminants and their relevant regulations have also been discussed. An important parameter while selecting an appropriate recycling method is the composition and nature of the recyclate, for instance, HDPE (High-Density Polyethylene), PET (Polyethylene Terephthalate), and PP (Polypropylene) materials can be recycled using mechanical and chemical recycling, however, PVC (Polyvinyl Chloride) and PS (Polystyrene) present challenges during mechanical recycling due to lower molecular weight and complex compositions, thus are often downcycled into lower-grade products. Still, recycled papers can be more problematic than recycled plastics due to the nature of the materials and the impact of recycling. The literature review suggested that three quality properties i.e., presence of low molecular weight compounds, degree of degradation, and composition should be analyzed by using different spectroscopic, thermo-mechanical, and chromatographic techniques to obtain a detailed understanding of recycled material quality. Furthermore, recycling should be done in such a way that the migration of contaminants should be lower than the migratory limits set by the relevant authorities to avoid any toxicological effects.

The Role of 24-Hour Holter Electrocardiogram in the Early Detection of Atrial Fibrillation in Newly Diagnosed Acute Ischemic Stroke Patients.

Introduction Stroke is a leading cause of death and disability globally, with atrial fibrillation (AF) recognized as a significant risk factor due to its association with increased stroke recurrence and mortality. Timely detection of AF is crucial to prevent recurrent strokes and improve outcomes. This study primarily aimed to evaluate the utility of 24-hour Holter monitoring for AF detection in acute ischemic stroke patients. Methods This retrospective observational study examined data from 207 patients admitted with acute ischemic stroke to a tertiary-care hospital over a two-year period. Patients with pre-existing AF, transient ischemic attacks, unconfirmed diagnoses, and missing Holter reports were excluded. A total of 140 patients were included in the analysis. The study investigated AF detection rates, the relationship between AF and stroke risk factors, other Holter findings, and the time delay in attaching Holter monitors. Results Of the 140 patients evaluated, AF was detected in nine (6.4%), exclusively in those aged ≥65 years. The most prevalent risk factors among the study participants were hypertension (74.3%) and diabetes (61.4%). No significant correlations were observed between AF and the analyzed stroke risk factors. The median delay for Holter device attachment was 3,503 minutes (approximately two days and 10 hours), with longer delays noted in males (4,084 mins (approximately two days and 20 hours) vs. 2,565 mins (approximately one day and 18 hours), p=0.005). Premature atrial complexes (PACs) were notably associated with the absence of AF, suggesting their potential role as markers for undiagnosed AF. Conclusion The study highlights the limited utility of 24-hour Holter monitoring in detecting AF in acute ischemic stroke patients, advocating for extended monitoring durations, especially in older patients. To improve AF detection, potential strategies include using longer monitoring periods and optimizing hospital workflows to reduce delays in attaching Holter devices. These approaches can minimize the risk of underdiagnosing paroxysmal AF, thereby preventing recurrent strokes and improving patient outcomes. Further investigation into PACs as potential predictive markers for AF is warranted.

How does external debt and governance quality impact renewable energy consumption: novel policy insights from BRICS countries.

Amidst global environmental reforms, the role of energy systems is under scrutiny to promote ecological welfare through low-carbon alternatives. Amongst the solutions, the role of renewable energy as a clean source has become popular to mitigate climate change. However, the impact of debt on renewable energy consumption remains limited in the economic literature. The debt initiatives provide funding for environmental initiatives primarily, while it is also credited as a barrier to limiting the growth of clean energy programs. Within such discussion, the current study extended the dialogue by examining how external debt impacts energy transition in Brazil, Russia, India, China, and South Africa (BRICS) economies in the presence of institutional quality, education expenditures, and banking development. Using the novel CS-ARDL, AMG, and CCEMG tests, the study results showed that external debt decreases renewable energy consumption, while institutional quality, educational expenditures, banking developments, and economic growth are essential elements of green energy developments. Based on these conclusions, this study provides novel policy guidelines to align BRICS energy and economic agendas.

Fast Fractional Fourier Transform-Aided Novel Graphical Approach for EEG Alcoholism Detection.

Given its detrimental effect on the brain, alcoholism is a severe disorder that can produce a variety of cognitive, emotional, and behavioral issues. Alcoholism is typically diagnosed using the CAGE assessment approach, which has drawbacks such as being lengthy, prone to mistakes, and biased. To overcome these issues, this paper introduces a novel paradigm for identifying alcoholism by employing electroencephalogram (EEG) signals. The proposed framework is divided into various steps. To begin, interference and artifacts in the EEG data are removed using a multiscale principal component analysis procedure. This cleaning procedure contributes to information quality improvement. Second, an innovative graphical technique based on fast fractional Fourier transform coefficients is devised to visualize the chaotic character and complexities of the EEG signals. This elucidates the properties of regular and alcoholic EEG signals. Third, thirty-four graphical features are extracted to interpret the EEG signals' haphazard behavior and differentiate between regular and alcoholic trends. Fourth, we propose an ensembled feature selection method for obtaining an effective and reliable feature group. Following that, we study many neural network classifiers to choose the optimal classifier for building an efficient framework. The experimental findings show that the suggested method obtains the best classification performance by employing a recurrent neural network (RNN), with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the sixteen selected features. The proposed framework can aid physicians, businesses, and product designers to develop a real-time system.

Geochemical insights of arsenic mobilization into the aquifers of Punjab, Pakistan.

It is well known that groundwater arsenic (As) contamination affects million(s) of people throughout the Indus flood plain, Pakistan. In this study, groundwater (n = 96) and drilled borehole samples (n = 87 sediments of 12 boreholes) were collected to investigate geochemical proxy-indicators for As release into groundwater across floodplains of the Indus Basin. The mean dissolved (µg/L) and sedimentary As concentrations (mg/kg) showed significant association in all studied areas viz.; lower reaches of Indus flood plain area (71 and 12.7), upper flood plain areas (33.7 and 7.2), and Thal desert areas (5.3 and 4.7) and are indicative of Basin-scale geogenic As contamination. As contamination in aquifer sediments is dependent on various geochemical factors including particle size (3-4-fold higher As levels in fine clay particles than in fine-coarse sand), sediment types (3-fold higher As in Holocene sediments of floodplain areas vs Pleistocene/Quaternary sediments in the Thal desert) with varying proportion of Al-Fe-Mn oxides/hydroxides. The total organic carbon (TOC) of cored aquifer sediments yielded low TOC content (mean = 0.13 %), which indicates that organic carbon is not a major driver (with a few exceptions) of As mobilization in the Indus Basin. Alkaline pH, high dissolved sulfate and other water quality parameters indicate pH-induced As leaching and the dominance of oxidizing conditions in the aquifers of upper flood plain areas of Punjab, Pakistan while at the lower reaches of the Indus flood plain and alluvial pockets along the rivers with elevated flood-driven dissolved organic carbon (exhibiting high dissolved Mn and Fe and a wide range of redox conditions). Furthermore, we also identified that paired dissolved AsMn values (instead of AsFe) may serve as a geochemical marker of a range of redox conditions throughout Indus flood plains.

An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network.

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.

A Phase 1, Randomized, Double-Blind, Placebo-Controlled, Single Ascending Dose Study to Evaluate the Pharmacokinetics, Immunogenicity, Safety, and Tolerability After Subcutaneous Administration of Tozorakimab in Healthy Chinese Participants.

Tozorakimab is a high-affinity human immunoglobulin G1 monoclonal antibody that neutralizes interleukin (IL)-33, an IL-1 family cytokine. This phase 1, single-center, randomized, double-blind, placebo-controlled, single ascending dose study (NCT05070312) evaluated tozorakimab in a healthy Chinese population. Outcomes included the characterization of the pharmacokinetic (PK) profile and immunogenicity of tozorakimab. Safety outcomes included treatment-emergent adverse events (TEAEs) and clinical laboratory, electrocardiogram, and vital sign parameters. Healthy, non-smoking, male, and female Chinese participants aged 18-45 years with a body mass index 19-24 kg/m2 were enrolled. In total, 36 participants across 2 cohorts of 18 participants were randomized 2:1 to receive a single subcutaneous dose of tozorakimab (300 mg [2 mL] or 600 mg [4 mL]) or matching placebo (2 or 4 mL). Tozorakimab showed dose-dependent serum PK concentrations with an approximate monophasic distribution in serum over time and a maximum observed peak concentration of 20.1 and 33.7 µg/mL in the 300- and 600-mg cohorts, respectively. No treatment-emergent anti-drug antibodies for tozorakimab were observed in any of the participants. There were no clinically relevant trends in the occurrence of TEAEs across the treatment groups. There were no clinically relevant trends over time in clinical laboratory (hematology, clinical chemistry, and urinalysis), electrocardiogram, or vital sign parameters in any treatment group. Overall, tozorakimab demonstrated dose-dependent systemic exposure in healthy Chinese participants and was well tolerated, with no safety concerns identified in this study.

The effect of fibroblast growth factor-2 on the outcomes of tooth replantation: A systematic review of animal studies.

Background/Aim: The ideal treatment of tooth avulsion is replantation. However, replanting teeth may lead to root resorption. Fibroblast growth factor-2 (FGF-2) is a cytokine that plays an important role in wound repair and tissue regeneration. Recently, FGF-2 has been studied a potential regenerative agent to prevent root resorption and ankylosis. The aim of this review is to analyze and summarize the currently available literature focusing on using FGF-2 based regenerative modalities to improve the outcomes of tooth replantation. Materials and Methods: An electronic search was conducted via PubMed/Medline, Google Scholar and ISI Web of Knowledge, using the Medical Subject Headings (MeSH) terms "Basic fibroblast growth factor," "Fibroblast growth factor-2," "tooth replantation," and "replantation" for studies published between January 2001 and June 2021. Data was extracted and quality assessment was carried using the ARRIVE guidelines. Results: Nine animal studies were included in this review. In six studies, FGF-2 had a favorable effect on the tissue regeneration around roots of replanted teeth when compared to other treatment groups. However, quality assessment of the studies revealed many sources of bias and deficiencies in the studies. Conclusions: Within the limitations of this study, it may be concluded that FGF-2 may improve the outcomes of delayed replantation of avulsed teeth. However, more long-term animal studies, with improved experimental designs, and clinical trials are required to determine the clinical potential of the growth factor in improving the outcomes of delayed tooth replantation.

Recent advances in modified starch based biodegradable food packaging: A review.

This study reviews the importance of resistant starch (RS) as the polymer of choice for biodegradable food packaging and highlights the RS types and modification methods for developing RS from native starch (NS). NS is used in packaging because of its vast availability, low cost and film forming capacity. However, application of starch is restricted due to its high moisture sensitivity and hydrophilic nature. The modification of NS into RS improves the film forming characteristics and extends the applications of starch into the formulation of packaging. The starch is blended with other bio-based polymers such as guar, konjac glucomannan, carrageenan, chitosan, xanthan gum and gelatin as well as active ingredients such as nanoparticles (NPs), plant extracts and essential oils to develop hybrid biodegradable packaging with reduced water vapor permeability (WVP), low gas transmission, enhanced antimicrobial activity and mechanical properties. Hybrid RS based active packaging is well known for its better film forming properties, crystalline structures, enhanced tensile strength, water resistance and thermal properties. This review concludes that RS, due to its better film forming ability and stability, can be utilized as polymer of choice in the formulation of biodegradable packaging.

A Randomized Phase I Study of the Anti-Interleukin-33 Antibody Tozorakimab in Healthy Adults and Patients With Chronic Obstructive Pulmonary Disease.

Tozorakimab is a human monoclonal antibody that neutralizes interleukin (IL)-33. IL-33 is a broad-acting epithelial "alarmin" cytokine upregulated in lung tissue of patients with chronic obstructive pulmonary disease (COPD). This first-in-human, phase I, randomized, double-blind, placebo-controlled study (NCT03096795) evaluated the safety, tolerability, pharmacokinetics (PKs), immunogenicity, target engagement, and pharmacodynamics (PDs) of tozorakimab. This was a 3-part study. In part 1, 56 healthy participants with a history of mild atopy received single escalating doses of either intravenous or subcutaneous tozorakimab or placebo. In part 2, 24 patients with mild COPD received multiple ascending doses of subcutaneous tozorakimab or placebo. In part 3, 8 healthy Japanese participants received a single intravenous dose of tozorakimab or placebo. The safety data collected included treatment-emergent adverse events (TEAEs), vital signs, and clinical laboratory parameters. Biological samples for PKs, immunogenicity, target engagement, and PD biomarker analyses were collected. No meaningful differences in the frequencies of TEAEs were observed between the tozorakimab and placebo arms. Three tozorakimab-treated participants with COPD experienced treatment-emergent serious adverse events. Subcutaneous or intravenous tozorakimab demonstrated linear, time-independent PKs with a mean half-life of 11.7-17.3 days. Treatment-emergent anti-drug antibody frequency was low. Engagement of tozorakimab with endogenous IL-33 in serum and nasal airways was demonstrated. Tozorakimab significantly reduced serum IL-5 and IL-13 levels in patients with COPD compared with placebo. Overall, tozorakimab was well tolerated, with a linear, time-independent serum PK profile. Additionally, biomarker studies demonstrated proof of mechanism. Overall, these data support the further clinical development of tozorakimab in COPD and other inflammatory diseases.

Pharmacologically active herbal remedies against atherosclerosis, characterization and DoE based marker quantification by densitometry, and cell based assays on THP-1 cell lines.

Atherosclerosis is a complex condition that develops at varying rates in multiple configurations and blood vessels. The primary cause of morbidity and mortality worldwide, particularly in the industrialized nations, continues to be atherosclerosis. Ayurveda, Siddha, and Unani systems of medicine, among other traditional medical systems, utilize polyherbal compositions. The treatment of atherosclerosis has been improved with a novel multibotanical combination. In this study, we sought to formulate, characterize, and standardize a polyherbal formulation based on design of experiments (DoE), densitometric studies and to predict for antioxidant activity using molecular docking analysis based on LC- MS identified phytomarkers. In addition we have assessed its cell viability by MTT assay along with Ao/EtBr staining technique and intracellular ROS assay using THP-1 cell lines. Reported findings showed that the HPTLC based quantified components of selected multiherbals has the ability to treat for atherosclerosis. This document could be used to quickly authenticate the formulation as the method optimized was based on CCD design which shows desirability of 0.962 and 0.839. Cell based assays scientifically proves that the formulation was not toxic based on MTT assay along with AO/EtBr staining technique and has excellent antioxidant activities based on intracellular ROS assay using THP-1 cell lines. The observed findings would be crucial for future clinical aspects since the bioactive molecules contained in the extracts may have anticipated effects with other compounds and show a superior therapeutic potential. As a result, this study offers standardized and potentially therapeutic information about effective polyherbal formulation for atherosclerosis.