Exploring the link between economic policy uncertainty, financial development, ecological innovation and environmental degradation; evidence from OECD countries.

Governments have been concerned with balancing economic growth and environmental sustainability. Nevertheless, it has been noted that sustainable development is interconnected with economic variables, the institutional framework, and the efficacy of ecological regulatory measures. This study experimentally examines the correlation of economic policy uncertainty (EPU), financial development (FD), ecological innovation (EI), corruption (IQ), foreign direct investment (FDI), trade openness (TR), natural resource rent (NRR), and CO2 emission. We utilized longitudinal data from the Organization for Economic Cooperation and Development (OECD) countries from 2003 to 2021 to address the existing research void. This study used sequential processes of the linear panel data model (SELPDM) and the SYS-GMM approaches in obtaining consistent and efficient results. The inverse U-shaped relationship between FD and environmental degradation (ED) is confirmed by the long-term elasticity estimates generated by the SELPDM method Elasticity estimates for the long-run show that rigorous ecological regulations, higher renewable energy utilization, higher FD and less corruption, an interaction between FD and rigorous ecological regulations all contribute to reduced ED. Its also being observed that both EPU, FDI and trade openness are positively affecting the ED. It confirms the idea of pollution refuge between the OECD countries. The causality test results show that corruption and FD had reciprocal links with ED, while FDI, trade openness and strict environmental policies were also found to have bidirectional linkage with ED. To achieve sustainable development and prevent environmental degradation in the long term, we propose implementing an institutional financial framework and FD in OECD nations. This may be accomplished by focusing on the effectiveness of environmental regulatory laws and creating a conducive institutional environment.

Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review.

Background and Aims: Alzheimer's disease (AD) is a degenerative neurological condition that worsens over time and leads to deterioration in cognitive abilities, reduced memory, and, eventually, a decrease in overall functioning. Timely and correct identification of Alzheimer's is essential for effective treatment. The systematic study specifically examines the application of deep learning (DL) algorithms in identifying AD using three-dimensional (3D) imaging methods. The main goal is to evaluate these methods' current state, efficiency, and potential enhancements, offering valuable insights into how DL could improve AD's rapid and precise diagnosis. Methods: We searched different online repositories, such as IEEE Xplore, Elsevier, MDPI, PubMed Central, Science Direct, ACM, Springer, and others, to thoroughly summarize current research on DL methods to diagnose AD by analyzing 3D imaging data published between 2020 and 2024. We use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure the organization and understandability of the information collection process. We thoroughly analyzed the literature to determine the primary techniques used in these investigations and their findings. Results and Conclusion: The ability of convolutional neural networks (CNNs) and their variations, including 3D CNNs and recurrent neural networks, to detect both temporal and spatial characteristics in volumetric data has led to their widespread use. Methods such as transfer learning, combining multimodal data, and using attention procedures have improved models' precision and reliability. We selected 87 articles for evaluation. Out of these, 31 papers included various concepts, explanations, and elucidations of models and theories, while the other 56 papers primarily concentrated on issues related to practical implementation. This article introduces popular imaging types, 3D imaging for Alzheimer's detection, discusses the benefits and restrictions of the DL-based approach to AD assessment, and gives a view toward future developments resulting from critical evaluation.

A review of intact glycopeptide enrichment and glycan separation through hydrophilic interaction liquid chromatography stationary phase materials.

Protein glycosylation, one of the most important biologically relevant post-translational modifications for biomarker discovery, faces analytical challenges due to heterogeneous glycosite, diverse glycans, and mass spectrometry limitations. Glycopeptide enrichment by removing abundant hydrophobic peptides helps overcome some of these obstacles. Hydrophilic interaction liquid chromatography (HILIC), known for its selectivity, glycan separations, intact glycopeptide enrichment, and compatibility with mass spectrometry, has seen recent advancements in stationary phases like Amide-80, glycoHILIC, amino acids or peptides for improved HILIC-based glycopeptide analysis. Utilization of these materials can improve glycopeptide enrichment through solid-phase extraction and separation via high-performance liquid chromatography. Additionally, using glycopeptides themselves to modify HILIC stationary phases holds promise for improving selectivity and sensitivity in glycosylation analysis. Additionally, HILIC has capability to assess the information about glycosites and structural information of glycans. This review summarizes recent breakthroughs in HILIC stationary materials, highlighting their impact on glycopeptide analysis. Ongoing research on advanced materials continues to refine HILIC's performance, solidifying its value as a tool for exploring protein glycosylation.

A homozygous missense variant in YTHDC2 induces azoospermia in two siblings.

Male infertility is a complex multifactorial reproductive disorder with highly heterogeneous phenotypic presentations. Azoospermia is a medically non-manageable cause of male infertility affecting ∼1% of men. Precise etiology of azoospermia is not known in approximately three-fourth of the cases. To explore the genetic basis of azoospermia, we performed whole exome sequencing in two non-obstructive azoospermia affected siblings from a consanguineous Pakistani family. Bioinformatic filtering and segregation analysis of whole exome sequencing data resulted in the identification of a rare homozygous missense variant (c.962G>C, p. Arg321Thr) in YTHDC2, segregating with disease in the family. Structural analysis of the missense variant identified in our study and two previously reported functionally characterized missense changes (p. Glu332Gln and p. His327Arg) in mice showed that all these three variants may affect Mg2+ binding ability and helicase activity of YTHDC2. Collectively, our genetic analyses and experimental observations revealed that missense variant of YTHDC2 can induce azoospermia in humans. These findings indicate the important role of YTHDC2 deficiency for azoospermia and will provide important guidance for genetic counseling of male infertility.

A gene selection algorithm for microarray cancer classification using an improved particle swarm optimization.

Gene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (deoxyribonucleic acid microarray] facilitates in computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This novel algorithm establishes a balance between the exploitation and exploration capabilities of the improved inertia weight adaptive particle swarm optimization. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) algorithm is employed for solution explorations. Each particle in the SIW-APSO increases its position and velocity iteratively through an evolutionary process. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been employed to identify several genes in the cancer dataset. The classification algorithm contains ELM, K-centroid nearest neighbor, and support vector machine to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method.

Depression detection with machine learning of structural and non-structural dual languages.

Depression is a serious mental state that negatively impacts thoughts, feelings, and actions. Social media use is rapidly growing, with people expressing themselves in their regional languages. In Pakistan and India, many people use Roman Urdu on social media. This makes Roman Urdu important for predicting depression in these regions. However, previous studies show no significant contribution in predicting depression through Roman Urdu or in combination with structured languages like English. The study aims to create a Roman Urdu dataset to predict depression risk in dual languages [Roman Urdu (non-structural language) + English (structural language)]. Two datasets were used: Roman Urdu data manually converted from English on Facebook, and English comments from Kaggle. These datasets were merged for the research experiments. Machine learning models, including Support Vector Machine (SVM), Support Vector Machine Radial Basis Function (SVM-RBF), Random Forest (RF), and Bidirectional Encoder Representations from Transformers (BERT), were tested. Depression risk was classified into not depressed, moderate, and severe. Experimental studies show that the SVM achieved the best result with anaccuracy of 0.84% compared to existing models. The presented study refines thearea of depression to predict the depression in Asian countries.

Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks.

Autism spectrum disorder (ASD) is a complex psychological syndrome characterized by persistent difficulties in social interaction, restricted behaviours, speech, and nonverbal communication. The impacts of this disorder and the severity of symptoms vary from person to person. In most cases, symptoms of ASD appear at the age of 2 to 5 and continue throughout adolescence and into adulthood. While this disorder cannot be cured completely, studies have shown that early detection of this syndrome can assist in maintaining the behavioural and psychological development of children. Experts are currently studying various machine learning methods, particularly convolutional neural networks, to expedite the screening process. Convolutional neural networks are considered promising frameworks for the diagnosis of ASD. This study employs different pre-trained convolutional neural networks such as ResNet34, ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 to diagnose ASD and compared their performance. Transfer learning was applied to every model included in the study to achieve higher results than the initial models. The proposed ResNet50 model achieved the highest accuracy, 92%, compared to other transfer learning models. The proposed method also outperformed the state-of-the-art models in terms of accuracy and computational cost.

Development and validation of a reversed-phase HPLC-UV method for simultaneous determination of levosulpiride and omeprazole in human plasma: Applicability of the method for evaluation of pharmacokinetic drug-drug interactions.

Levosulpiride and omeprazole are co-prescribed for gastrointestinal disorders associated with depression and anxiety. Objective of the study was to develop a sensitive, robust and simple method for simultaneous analysis of levosulpiride and omeprazole in human plasma and applicability of the method in determination of pharmacokinetics drug-drug interaction. In the presented study, a reversed-phase HPLC-UV method was developed for the simultaneous determination of levosulpiride and omeprazole using pantoprazole as the internal standard. Experimental conditions were optimized and the developed method was validated as per standard guidelines (USP and ICH). Furthermore, the developed method was applied for evaluation of pharmacokinetics drug-drug interaction between levosulpiride (50 mg) and omeprazole (40 mg) in healthy human volunteers. Sharpsil C8 column (4.6 × 250 mm, 5 µm), Ultisil C8 column (4.6 mm × 150 mm, 5 µm) and Agilent C18 column (4.6 × 250 mm, 5 µm) were evaluated as stationary phase. The best resolution was achieved with Agilent C18 (4.6 x 250 mm, 5 µm) column and was selected for further study. The mobile phase consisted of a mixture of acetonitrile and phosphate buffer (pH 7.2) in 60:40 by volume, and was pumped at a flow rate of 1 mL/min. Detector wavelength was set at 280 nm. Levosulpiride and omeprazole were extracted from human plasma with ethyl acetate and dichloromethane (4:1, v/v). The calibration curves for both levosulpiride (5-150 ng/mL) and omeprazole (10-1500 ng/mL) were linear. The lower limit of quantification and limit of detection for levosulpiride were 5 and 2 ng/mL, while for omeprazole these were 10 and 3 ng/mL, respectively. Pharmacokinetics analysis showed that co-administration of omeprazole increased the AUC and Cmax of levosulpiride, while the clearance was reduced. Both the changes were insignificant. Similarly, no significant change in the pharmacokinetic parameters of omeprazole was observed with co-administration of levosulpiride.

Reverse Vaccinology Approach to Identify Novel and Immunogenic Targets against Streptococcus gordonii.

Streptococcus gordonii is a gram-positive, mutualistic bacterium found in the human body. It is found in the oral cavity, upper respiratory tract, and intestines, and presents a serious clinical problem because it can lead to opportunistic infections in individuals with weakened immune systems. Streptococci are the most prevalent inhabitants of oral microbial communities, and are typical oral commensals found in the human oral cavity. These streptococci, along with many other oral microbes, produce multispecies biofilms that can attach to salivary pellicle components and other oral bacteria via adhesin proteins expressed on the cell surface. Antibiotics are effective against this bacterium, but resistance against antibodies is increasing. Therefore, a more effective treatment is needed. Vaccines offer a promising method for preventing this issue. This study generated a multi-epitope vaccine against Streptococcus gordonii by targeting the completely sequenced proteomes of five strains. The vaccine targets are identified using a pangenome and subtractive proteomic approach. In the present study, 13 complete strains out of 91 strains of S. gordonii are selected. The pangenomics results revealed that out of 2835 pan genes, 1225 are core genes. Out of these 1225 core genes, 643 identified as non-homologous proteins by subtractive proteomics. A total of 20 essential proteins are predicted from non-homologous proteins. Among these 20 essential proteins, only five are identified as surface proteins. The vaccine construct is designed based on selected B- and T-cell epitopes of the antigenic proteins with the help of linkers and adjuvants. The designed vaccine is docked against TLR2. The expression of the protein is determined using in silico gene cloning. Findings concluded that Vaccine I with adjuvant shows higher interactions with TLR2, suggesting that the vaccine has the ability to induce a humoral and cell-mediated response to treat and prevent infection; this makes it promising as a vaccine against infectious diseases caused by S. gordonii. Furthermore, validation of the vaccine construct is required by in vitro and in vivo trials to check its actual potency and safety for use to prevent infectious diseases caused by S. gordonii.

Bayesian-Edge system for classification and segmentation of skin lesions in Internet of Medical Things.

BACKGROUND: Skin diseases are severe diseases. Identification of these severe diseases depends upon the abstraction of atypical skin regions. The segmentation of these skin diseases is essential to rheumatologists in risk impost and for valuable and vital decision-making. Skin lesion segmentation from images is a crucial step toward achieving this goal-timely exposure of malignancy in psoriasis expressively intensifies the persistence ratio. Defies occur when people presume skin diseases they have without accurately and precisely incepted. However, analyzing malignancy at runtime is a big challenge due to the truncated distinction of the visual similarity between malignance and non-malignance lesions. However, images' different shapes, contrast, and vibrations make skin lesion segmentation challenging. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. MATERIALS AND METHODS: This paper introduces a skin lesions segmentation model that integrates two intelligent methodologies: Bayesian inference and edge intelligence. In the segmentation model, we deal with edge intelligence to utilize the texture features for the segmentation of skin lesions. In contrast, Bayesian inference enhances skin lesion segmentation's accuracy and efficiency. RESULTS: We analyze our work along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions from seminal works and a systematic viewpoint and examine how these dimensions have influenced current trends. CONCLUSION: We summarize our work with previously used techniques in a comprehensive table to facilitate comparisons. Our experimental results show that Bayesian-Edge networks can boost the diagnostic performance of skin lesions by up to 87.80% without incurring additional parameters of heavy computation.

Core-shell niobium(v) oxide@molecularly imprinted polythiophene nanoreceptors for transformative, real-time creatinine analysis.

Creatinine, a byproduct of muscle metabolism, is typically filtered by the kidneys. Deviations from normal concentrations of creatinine in human saliva serve as a crucial biomarker for renal diseases. Monitoring these levels becomes particularly essential for individuals undergoing dialysis and those with kidney conditions. This study introduces an innovative disposable point-of-care (PoC) sensor device designed for the prompt detection and continuous monitoring of trace amounts of creatinine. The sensor employs a unique design, featuring a creatinine-imprinted polythiophene matrix combined with niobium oxide nanoparticles. These components are coated onto a screen-printed working electrode. Thorough assessments of creatinine concentrations, spanning from 0 to 1000 nM in a redox solution at pH 7.4 and room temperature, are conducted using cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). The devised sensor exhibits a sensitivity of 4.614 µA cm-2 nM-1, an impressive trace level limit of detection at 34 pM, and remarkable selectivity for creatinine compared to other analytes found in human saliva, such as glucose, glutamine, urea, tyrosine, etc. Real saliva samples subjected to the sensor reveal a 100% recovery rate. This sensor, characterized by its high sensitivity, cost-effectiveness, selectivity, and reproducibility, holds significant promise for real-time applications in monitoring creatinine levels in individuals with kidney and muscle-related illnesses.

Designing a multi-epitope vaccine against Shigella dysenteriae using immuno-informatics approach.

Shigella dysenteriae has been recognized as the second most prevalent pathogen associated with diarrhea that contains blood, contributing to 12.9% of reported cases, and it is additionally responsible for approximately 200,000 deaths each year. Currently, there is no S. dysenteriae licensed vaccine. Multidrug resistance in all Shigella spp. is a growing concern. Current vaccines, such as O-polysaccharide (OPS) conjugates, are in clinical trials but are ineffective in children but protective in adults. Thus, innovative treatments and vaccines are needed to combat antibiotic resistance. In this study, we used immuno-informatics to design a new multiepitope vaccine and identified S. dysenteriae strain SD197's membrane protein targets using in-silico methods. The target protein was prioritized using membrane protein topology analysis to find membrane proteins. B and T-cell epitopes were predicted for vaccine formulation. The epitopes were shortlisted based on an IC50 value <50, antigenicity, allergenicity, and a toxicity analysis. In the final vaccine construct, a total of 8 B-cell epitopes, 12 MHC Class I epitopes, and 7 MHC Class II epitopes were identified for the Lipopolysaccharide export system permease protein LptF. Additionally, 17 MHC Class I epitopes and 14 MHC Class II epitopes were predicted for the Lipoprotein-releasing ABC transporter permease subunit LolE. These epitopes were selected and linked via KK, AAY, and GGGS linkers, respectively. To enhance the immunogenic response, RGD (arginine-glycine-aspartate) adjuvant was incorporated into the final vaccine construct. The refined vaccine structure exhibits a Ramachandran score of 91.5% and demonstrates stable interaction with TLR4. Normal Mode Analysis (NMA) reveals low eigenvalues (3.925996e-07), indicating steady and flexible molecular mobility of docked complexes. Codon optimization was carried out in an effective microbial expression system of the Escherichia coli K12 strain using the recombinant plasmid pET-28a (+). Finally, the entire in-silico analysis suggests that the suggested vaccine may induce a significant immune response against S. dysenteriae, making it a promising option for additional experimental trials.

Multilayer cyberattacks identification and classification using machine learning in internet of blockchain (IoBC)-based energy networks.

The world's need for energy is rising due to factors like population growth, economic expansion, and technological breakthroughs. However, there are major consequences when gas and coal are burnt to meet this surge in energy needs. Although these fossil fuels are still essential for meeting energy demands, their combustion releases a large amount of carbon dioxide and other pollutants into the atmosphere. This significantly jeopardizes community health in addition to exacerbating climate change, thus it is essential need to move swiftly to incorporate renewable energy sources by employing advanced information and communication technologies. However, this change brings up several security issues emphasizing the need for innovative cyber threats detection and prevention solutions. Consequently, this study presents bigdata sets obtained from the solar and wind powered distributed energy systems through the blockchain-based energy networks in the smart grid (SG). A hybrid machine learning (HML) model that combines both the Deep Learning (DL) and Long-Short-Term-Memory (LSTM) models characteristics is developed and applied to identify the unique patterns of Denial of Service (DoS) and Distributed Denial of Service (DDoS) cyberattacks in the power generation, transmission, and distribution processes. The presented big datasets are essential and significantly helps in identifying and classifying cyberattacks, leading to predicting the accurate energy systems behavior in the SG.

An integrated in-silico approach for drug target identification in human pathogen Shigella dysenteriae.

Shigella dysenteriae, is a Gram-negative bacterium that emerged as the second most significant cause of bacillary dysentery. Antibiotic treatment is vital in lowering Shigella infection rates, yet the growing global resistance to broad-spectrum antibiotics poses a significant challenge. The persistent multidrug resistance of S. dysenteriae complicates its management and control. Hence, there is an urgent requirement to discover novel therapeutic targets and potent medications to prevent and treat this disease. Therefore, the integration of bioinformatics methods such as subtractive and comparative analysis provides a pathway to compute the pan-genome of S. dysenteriae. In our study, we analysed a dataset comprising 27 whole genomes. The S. dysenteriae strain SD197 was used as the reference for determining the core genome. Initially, our focus was directed towards the identification of the proteome of the core genome. Moreover, several filters were applied to the core genome, including assessments for non-host homology, protein essentiality, and virulence, in order to prioritize potential drug targets. Among these targets were Integration host factor subunit alpha and Tyrosine recombinase XerC. Furthermore, four drug-like compounds showing potential inhibitory effects against both target proteins were identified. Subsequently, molecular docking analysis was conducted involving these targets and the compounds. This initial study provides the list of novel targets against S. dysenteriae. Conclusively, future in vitro investigations could validate our in-silico findings and uncover potential therapeutic drugs for combating bacillary dysentery infection.

Highly Stretchable Polyurethane Porous Membranes with Adjustable Morphology for Advanced Lithium Metal Batteries.

A highly flexible, tunable morphology membrane with excellent thermal stability and ionic conductivity can endow lithium metal batteries with high power density and reduced dendrite growth. Herein, a porous Polyurethane (PU) membrane with an adjustable morphology was prepared by a simple nonsolvent-induced phase separation technique. The precise control of the final morphology of PU membranes can be achieved through appropriate selection of a nonsolvent, resulting a range of pore structures that vary from finger-like voids to sponge-like pores. The implementation of combinatorial DFT and experimental analysis has revealed that spongy PU porous membranes, especially PU-EtOH, show superior electrolyte wettability (472%), high porosity (75%), good mechanical flexibility, robust thermal dimensional stability (above 170 °C), and elevated ionic conductivity (1.38 mS cm-1) in comparison to the polypropylene (PP) separator. The use of PU-EtOH in Li//Li symmetric cell results in a prolonged lifespan of 800 h, surpasing the longevity of PU or PP cells. Moreover, when subjected to a high rate of 5 C, the LiFePO4/Li half-cell with a PU-EtOH porous membrane displayed better cycling performance (115.4 mAh g-1) compared to the PP separator (104.4 mAh g-1). Finally, the prepared PU porous membrane exhibits significant potential for improving the efficiency and safety of LMBs.

Formation mechanisms and degradation methods of polycyclic aromatic hydrocarbons in biochar: A review.

Biochar has been widely used in soil amendment and environmental remediation. Polycyclic aromatic hydrocarbons (PAHs) could be produced in preparation of biochar, which may pose potential risks to the environment and human health. At present, most studies focus on the ecotoxicity potential of biochar, while there are few systematic reviews on the formation mechanisms and mitigation strategies of PAHs in biochar. Therefore, a systematical understanding of the distribution, formation mechanisms, risk assessment, and degradation approaches of PAHs in biochar is highly needed. In this paper, the distribution and content of the total and bioavailable PAHs in biochar are reviewed. Then the formation mechanisms, influencing factors, and potential risk assessment of PAHs in biochar are systematically explored. After that, the effective strategies to alleviate PAHs in biochar are summarized. Finally, suggestions and perspectives for future studies are proposed. This review provides a guide for reducing the formation of biochar-associated PAHs and their toxicity, which is beneficial for the development and large-scale safe use of environmentally friendly biochar.