In plants and animals, endogenous biological molecules, termed damage-associated molecular patterns (DAMPs) or alarmins, are released by damaged, stressed or dying cells following abiotic stress such as radiation and drought stress. In turn, a cascade of downstream signaling events is initiated leading to the up-regulation of defense-related genes. In the present study, in an effort to investigate the conservation status of the molecular mechanisms implicated in the danger signaling, thorough in silico phylogenetic and structural analyses of the effector biomolecules were performed in taxonomically diverse plant species. On the basis of our results, the defense mechanisms appear to be largely conserved within the plant kingdom. Of note is our finding that the sequence and/or function of several components of these mechanisms were found to be conserved in animals, as well.

Metabolic enzymes are often targeted for drug development programs of metabolic diseases such as diabetes and its complications. Many secondary metabolites isolated from natural products have shown therapeutic action against these enzymes. However, some commercially available synthetic drugs have shown unfriendly impacts with various side effects. Thus, this research has focused on a comprehensive study of secondary metabolites showing better inhibitory activities towards metabolic enzymes such as α-amylase, α-glucosidase, aldose reductase, and lipase. Further receptor-based virtual screening was performed against the various secondary metabolites database designed in-silico. Using Gold combined with subsequent post-docking analyses, the score was obtained as methyl xestospongic ester (Gold score 65.83), 2,″4″-O-diacetylquercitrin (Gold score 65.15), kaempferol-3-O-neohesperidoside (Gold score 53.37) and isosalvianolic acid C methyl ester (Gold score 53.44) for lipase, aldol reductase, α-amylase, and α-glucosidase, respectively. Besides, vitexin and isovitexin for α-amylase; N-trans-Caffeoyl-tyramin for α-glucosidase; purpurolide F and schaftoside for lipase; acteoside and orientin for aldose reductase could be potential drugs for respective enzymes based on in-silico analyses, supported by experimental IC₅₀ values reported. They could bind to the competitive sites of the various targets of metabolic enzymes, and finally, toxicity analysis using ProTox-II was also performed.

The numerous biological activities of phenothiazine and sulphonamide derivatives, especially their antibacterial qualities, have attracted so much interest. In this study, the synthesis and characterization of phenothiazine-3-sulphonamide derivatives and evaluation their antimicrobial activity against the following pathogenic microorganisms is reported. Two gram-positive bacteria; (Staphylococcus aureus, and Streptococcus pyogenes), two gram-negative bacteria; (Escherichia coli, and Salmonella typhi) as well as one fungus (Aspergillus fumigatus) were used while ciprofloxacin, gentamycin and ketoconazole served as standard drugs. The synthesis of the derivatives was achieved through a base catalyzed the reaction of 4-chloroaniline with 1-naphthylamine to form 1-(naphthalen-1-yl) benzene-1,4-diamine. This intermediate was then treated with sulphur and iodine to yield the phenothiazine derivative while subsequent treatment of the phenothiazine compound with sulphonyl chlorides gave the final products. The synthesized compounds were characterized via proton nuclear magnetic resonance (1HNMR), carbon-13 nuclear magnetic resonance (13C NMR) and Fourier-transform infrared (FTIR) spectroscopic techniques. The minimum inhibitory concentration (MIC) of each compound was then determined using the agar well diffusion method. To predict the binding energies and patterns of the synthesized compounds with target proteins of the above mentioned microorganisms, molecular docking simulations were run using Autodock Vina software (version 4.2). The spectra data of the compounds for FTIR, 1H NMR and 13C NMR (δ) spectral data were consistent with the assigned structures of the synthesized compounds. The binding energies (kcal/mol) for in silico antimicrobial studies were in the range -5.1 to -7.6 kcal/mol. The MIC values were in the range -3.5 to 1.0 mg/L. The results of the in vitro test revealed that the synthesized compounds exhibits promising antimicrobial activity, and showed excellent bactericidal and fungicidal activities. The results obtained showed that the synthesized compounds possess drug-like properties and are good starting materials for drug production.

Different dopamine receptor (DR) subtypes are involved in pathophysiological conditions such as Parkinson´s Disease (PD), schizophrenia and depression. While many DR-targeting drugs have been approved by the FDA, only a very small number is truly selective for one of the DR subtypes. Additionally, most of them show promiscuous activity at related G-protein coupled receptors, thus suffering from diverse side-effect profiles. Multiple studies have shown that combined in silico / in vitro approaches are a valuable contribution to drug discovery processes. They can also be applied to divulge the mechanisms behind ligand selectivity. In this study, novel DR ligands were investigated in vitro to assess binding affinities at different DR subtypes. Thus, nine D2R/D3R-selective ligands (micro- to nanomolar binding affinities, D3R-selective profile) were successfully identified. The most promising ligand exerted nanomolar D3R activity (KI = 2.3 nM) with 263.7-fold D2R/D3R selectivity. Subsequently, ligand selectivity was rationalized in silico based on ligand interaction with a secondary binding pocket, supporting selectivity data determined in vitro. The developed workflow and identified ligands could aid in the further understanding of structural motifs responsible for DR subtype selectivity, thus, benefitting drug development in D2R/D3R-associated pathologies like PD.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

Leishmaniasis is a vector-borne parasitic infection caused by the bite of female Phlebotomine sandflies. World Health Organization (WHO) estimates 100,000 cases to be reported annually on a global scale, moreover, 13 million people were infected between 2010 and 2015 worldwide. Treatment of leishmaniasis by conventional synthetic compounds is met by challenges pertaining to adverse effects which call for the discovery of newer anti-leishmanial molecules. This study was performed to evaluate the effect and modes of action of a sesquiterpene alcoholic molecule Farnesol on Leishmania major, the causative agent of Zoonotic Cutaneous leishmaniasis. The cytotoxic effect of Farnesol against L. major promastigotes, amastigotes and macrophages was assessed by MTT test and counting. The IC50 on promastigotes by Farnesol on L. major was evaluated by flow cytometry. In the findings, Promastigotes were reduced at 167 µM/mL & the mean numbers of L. major amastigotes in macrophages were significantly decreased in exposure to Farnesol at 172 µM/ml. In addition, Farnesol demonstrated no cytotoxicity on macrophages as the IC50 value was 945 µM/ml; it induced significant apoptosis dose-dependent on L. major promastigotes. In silico protein-ligand binding analyses indicated the effect of Farnesol in perturbation of the ergosterol synthesis pathway of Leishmania with attributes suggesting inhibition of Lanosterol-α-demethylase, the terminal enzyme of ergosterol synthesis machinery. Findings from flow cytometry reveal the role of farnesol in apoptosis-induced killing in promastigotes. Farnesol was effective at very lower concentrations when compared to Paromomycin. Further studies are crucial to evaluate the therapeutic potentials of Farnesol alone and in combination with other conventional drugs using clinical settings.

Acute lymphoblastic leukemia (ALL) is the most common cancer among children worldwide, characterized by an overproduction of undifferentiated lymphoblasts in the bone marrow. The treatment of choice for this disease is the enzyme L-asparaginase (ASNase) from bacterial sources. ASNase hydrolyzes circulating L-asparagine in plasma, leading to starvation of leukemic cells. The ASNase formulations of E. coli and E. chrysanthemi present notorious adverse effects, especially the immunogenicity they generate, which undermines both their effectiveness as drugs and patient safety. In this study, we developed a humanized chimeric enzyme from E. coli L-asparaginase, which would reduce the immunological problems associated with current L-asparaginase therapy. For these, the immunogenic epitopes of E. coli L-asparaginase (PDB: 3ECA) were determined and replaced with those of the less immunogenic Homo sapiens asparaginase (PDB:4O0H). The structures were modeled using the Pymol software and the chimeric enzyme was modeled using the SWISS MODEL service. A humanized chimeric enzyme with four subunits similar to the template structure was obtained, and the presence of asparaginase enzymatic activity was predicted by protein-ligand docking.

The vastness of chemical-space constrains traditional drug-discovery methods to the organic laws that are guiding the chemistry involved in filtering through candidates. Leveraging computing with machine-learning to intelligently generate compounds that meet a wide range of objectives can bring significant gains in time and effort needed to filter through a broad range of candidates. This paper details how the use of Generative-Adversarial-Networks, novel machine learning techniques to format the training dataset and the use of quantum computing offer new ways to expedite drug-discovery.

An abdominal aortic aneurysm (AAA) is usually asymptomatic until rupture, which is associated with extremely high mortality. Consequently, early detection of AAAs is of paramount importance in reducing mortality; however, most AAAs are detected by medical imaging incidentally. The aim of this study was to investigate the feasibility of machine learning-based pulse wave (PW) analysis for the early detection of AAAs using a database of in silico PWs. PWs in the large systemic arteries were simulated using one-dimensional blood flow modelling. A database of in silico PWs representative of subjects (aged 55, 65 and 75 years) with different AAA sizes was created by varying the AAA-related parameters with major impacts on PWs – identified by parameter sensitivity analysis – in an existing database of in silico PWs representative of subjects without AAA. Then, a machine learning architecture for early detection of AAAs was proposed, which was trained and tested using the new in silico PW database. The parameter sensitivity analysis revealed that the AAA maximum diameter and stiffness of the large systemic arteries were the dominant AAA-related biophysical properties that significantly influence the PW. The simulated PW indexes extracted from the database showed that the PW was not only influenced by the presence of an AAA but was also significantly affected by multiple cardiovascular parameters that compromised the detection of AAAs by using individual PW indexes. Alternatively, the trained machine learning model performed well in classifying normal and AAA conditions using digital photoplethysmogram PWs from the database. These findings suggest that machine learning-based PW analysis is a promising approach for AAA screening using PW signals acquired by wearable devices.

Chemical exposures from diverse sources merge on a limited number of molecular pathways described as toxicity pathways. Changes in the same set of molecular pathways in different cell and tissue types may generate seemingly unrelated health conditions. Today, no approaches are available to predict in an unbiased way sensitivities of different disease states and their combinations to multichemical exposures across the exposome. We propose an inductive in-silico workflow where sensitivities of genes to chemical exposures are identified based on the overlap of existing genomic datasets, and data on sensitivities of individual genes is further used to sequentially derive predictions on sensitivities of molecular pathways, disease states, and groups of disease states (syndromes). Our analysis predicts that conditions representing the most significant public health problems are among the most sensitive to cumulative chemical exposures. These conditions include six leading types of cancer in the world (prostatic, breast, stomach, lung, colorectal neoplasms, and hepatocellular carcinoma), obesity, type 2 diabetes, non-alcoholic fatty liver disease, autistic disorder, Alzheimer's disease, hypertension, heart failure, brain and myocardial ischemia, and myocardial infarction. Overall, our predictions suggest that environmental risk factors may be underestimated for the most significant public health problems.

Sugarcane mosaic virus (SCMV) is a (genus, Potyvirus; family, Potyviridae) is a widespread, deleterious, and the most damaging pathogen of sugarcane ((Saccharum officinarum L. and Saccharum spp.) which causes a substantial barrier to producing high sugarcane earning. Sugarcane mosaic disease (SCMD) is caused by, single or compound infection of SCMV, disseminated by the several aphid vectors in a non-persistent manner. SCMV has flexuous filamentous particle of 700-750 nm long, which encapsidated a positive-sense, single-stranded RNA molecule of 9575 nucleotides. RNA interference (RNAi)-mediated antiviral innate immunity is an evolutionary conserved, key biological process in eukaryotes and has evolved as an antiviral defence system to interfere with viral genomes for controlling infections in plants. The current study aims to analyze sugarcane (Saccharum officinarum L. and Saccharum spp.) locus-derived microRNAs (sof-miRNAs/ssp-miRNAs) with predicted potential for targeting the SCMV +ssRNA-encoded mRNAs, using a predictive approach that involves five algorithms. The ultimate goal of this research is to mobilize the in silico- predicted endogenous sof-miRNAs/ssp-miRNAs to experimentally trigger the catalytic RNAi pathway and generate sugarcane cultivars to evaluate the potential antiviral resistance surveillance ability and capacity for SCMV. Experimentally validated mature sugarcane (S. officinarum, 2n = 8X = 80) and (S. spp., 2n = 100-120) sof-miRNA/ssp-miRNA sequences (n = 28) were downloaded from the miRBase database and aligned with the SCMV genome (KY548506). Among the 28 targeting mature locus-derived sof-miRNAs/ssp-miRNAs evaluated, one sugarcane miRNA homolog, sof-miR159c, was identified to have a predicted miRNA binding site, at nucleotide position 3847 of the SCMV genome targeting CI ORF. To verify the accuracy of the target prediction accuracy and to determine, whether the sugarcane sof-miRNA/ssp-miRNA could bind the predicted SCMV mRNA target(s), we constructed an integrated Circos plot. A genome-wide in-silico-predicted miRNA-mediated target gene regulatory network has implicated to validate interactions necessary to warrant in vivo analysis. The current work provides valuable computational evidence for the generation of SCMV-resistant sugarcane cultivars.

The type of implant at the fracture site has effects not only from biological perspective but also from mechanical perspective in fracture healing. Biodegradable implants such as magnesium (Mg) based alloys have shown faster secondary bone healing properties as compared to bioinert implants such as titanium (Ti). The general reasoning behind this is the benefit of Mg from biocompatibility perspectives. We study the effect of Ti and Mg as base materials for implants with their different mechanical properties. The focus of our work is on the displacements at the fracture site of the tibia and their influence on the stimulus for bone healing. We have found that in comparison to Ti, Mg implants have minimal stress shielding problem, only which led to better mechanical stimulus at the fracture site.

Natural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature’s treasures, however, could be used more effectively. Yet, reliable pipelines for large scale target prediction of natural products are still rare. We have developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs) – a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17β- hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target prediction and guidance on using the respective tools.

The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) was isolated in 2012 and is well known to cause the respiratory syndrome. The orf1ab gene is known to mediate MERS-CoV replication. In this study, we have discussed the in silico prediction of potential siRNAs targeting MERS-CoV-orf1ab gene for antiviral therapeutics. To identify the potential siRNAs, various factors were considered. We have excluded the siRNAs with off-target effects and potential binding with human mRNAs. By using available softwares, total twenty-one functional, off-target reduced potential siRNA were selected from four hundred and sixty-two siRNAs based on greater potency and specificity. We have tested only seven siRNAs initially to evaluate their performance by reverse transfection approach by lipofectamine mediated delivery in Vero cells. The evaluation results showed no cytotoxicity at various concentrations of siRNAs used. The results obtained in this study provided preliminary information about the cytotoxicity which will help us to further evaluate siRNAs in other cell cultures to find out the replication inhibition efficiency of MERS-CoV. Finally, it is concluded that the in silico prediction and designing resulted in filtration and selection of potential siRNAs with high accuracy, efficiency, and strength which can be further utilized for the development of oligonucleotide-based therapeutics.

Sugarcane mosaic virus (SCMV) is a deleterious pathogen which causes widespread Sugarcane mosaic disease (SCMD) and is classified in the genus Potyvirus (Potyviridae), disseminated by the aphid vector. RNA interference (RNAi)-mediated antiviral innate immunity is a key biological process and antiviral defence system to interfere with viral genomes for controlling plant pathogens. The current study aims to analyze sugarcane (Saccharum officinarum L. and Saccharum spp.) locus-derived microRNAs (sof-miRNAs/ssp-miRNAs) with predicted potential for targeting the SCMV +ssRNA-encoded mRNAs, using ‘five algorithms’ approach. The ultimate goal in this research is to mobilize the in silico endogenous predicted sof-miRNAs/ssp-miRNAs to trigger RNAi catalytic pathway experimentally and generate sugarcane cultivars for evaluating potential antiviral resistance monitoring capability and capacity for SCMV. Experimentally validated mature sugarcane (S. officinarum, 2n = 8X = 80) and (S. spp., 2n = 100-120) sof-miRNAs/ssp-miRNAs (n = 28) were acquired for alignment with the SCMV genome. Of the 28 targeting mature locus-derived sof-miRNAs/ssp-miRNAs investigated, one sugarcane miRNA homolog, sof-miR159c, was concluded to localize potential binding site at genomic nucleotide site 3847 targeting CI ORF of SCMV. In order to validate target prediction accuracy, whether the sugarcane sof-miRNA/ssp-miRNA might bind predicted SCMV mRNA target(s), we created an integrated Circos plot. Genome-wide in-silico-predicted miRNA-mediated target gene regulatory network validated interactions that warrant in vivo analysis. The current work provides valuable evidence and biological material for generating SCMV-resistant sugarcane varieties.

Apple chlorotic leaf spot virus (ACLSV) is a widespread, deleterious and the most damaging pathogen of fruit tree plants including domesticated apple (Malus domestica) ―a great threat to apple production worldwide. The positive-sense single-stranded RNA genome of ACLSV (7.5 Kbp) encodes three proteins: RNA polymerase (Rep), movement protein (MP) and coat protein (CP). RNA interference (RNAi)-mediated antiviral innate immunity is a key sequence-specific biological phenomenon in eukaryotes to control plant viruses. The aim of this study was to analyze apple (M.domestica) locus-derived microRNAs (mdm-miRNAs) with predicted potential for targeting the ACLSV +ssRNA-encoded mRNAs, using ‘four algorithms’ approach. The ultimate goal in this research is to mobilize the in silico endogenous predicted mdm-miRNAs to trigger RNAi catalytic pathway experimentally and generate apple tree varieties for evaluating potential antiviral resistance monitoring capability and capacity for ACLSV. Experimentally validated mature apple (M.domestica, 2n = 2X = 34) mdm-miRNAs (n = 322) were acquired from miRBase database and tested for alignment with the ACLSV genome. Of the 322 targeting mature locus-derived mdm-miRNAs investigated, nine apple mdm-miRNA homologs (mdm-miR395k, mdm-miR5225c and mdm-miR7121 (a, b, c, d, e, f, g, h) were predicted by all ‘four algorithms’. Only fifty eight mdm-miRNAs were predicted consensus binding sites by union of consensus between two algorithms. The miRanda, RNA22, TAPIR algorithms predicted binding of mdm-miR395k at unique nucleotide position 4691, as the most effectively interacting mdm-miRNAs in targeting the ORF1 sequence. In order to validate target prediction accuracy, whether the apple mdm-miRNAs might bind predicted ACLSV mRNA target(s), we created an integrated Circos plot. Genome-wide in-silico-predicted miRNA-mediated target gene regulatory network validated interactions that warrant in vivo analysis. The current work provides valuable evidence and biological material for generating ACLSV-resistant apple varieties.

To explore a new set of anticancer agents, a novel series of pyrazolo[4,3-e]pyrido[1,2-a]pyrimidine derivatives 7a-l have been designed and synthesized via cyclocondensation reactions of pyrazolo-enaminone 5 with a series of arylidene malononitriles; compound 5 was obtained from 5-amino-4-cyanopyrazole (3). The structures of the target compounds 7a-l were investigated by spectral techniques and elemental analysis (IR, UV-Vis, 1H NMR, 13C NMR and ESI-MS). All compounds were evaluated for their in vitro cytotoxicity employing a panel of different human tumor cell lines, A375, HT29, MCF7, A2780, FaDu as well as non-malignant NIH 3T3 and HEK293 cells. It has been found that the conjugate 7e was the most active towards many cell lines with EC50 values ranging between 9.1 and 13.5 µM, respectively. Moreover, in silico docking studies of 7e with six anticancer drug targets, i.e. DHFR, VEGFR2, HER-2/neu, hCA-IX, CDK6 and LOX also was performed, in order to gain some insights into their putative mode of binding interaction and to estimate the free binding energy of this bioactive molecule.

In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable yet never achieved before. This study aims to computationally predict siRNA nanoparticles in vivo efficacy, which saves time and resources. A data set containing 120 entries was prepared by combining molecular descriptors of the ionizable lipids together with two nanoparticles formulation characteristics. Input descriptor combinations were selected by an evolutionary algorithm. Artificial neural networks, support vector machines and partial least squares regression were used for QSAR modeling. Depending on how the data set is split, two training sets and two external validation sets were prepared. Training and validation sets contained 90 and 30 entries respectively. The results showed the successful predictions of validation set log(dose) with R2val = 0.86 – 0.89 and 0.75 – 80 for validation sets one and two respectively. Artificial neural networks resulted in the best R2val for both validation sets. For predictions that have high bias, improvement of R2val from 0.47 to 0.96 was achieved by selecting the training set lipids lying within the applicability domain. In conclusion, in vivo performance of siRNA nanoparticles was successfully predicted by combining cheminformatics with machine learning techniques.

The tumour-associated isoenzymes, hCA IX and hCA XII catalyze the hydration of carbon dioxide to bicarbonate and protons. These isoforms are highly overexpressed in many types of cancer, where they contribute for the acidification of the tumor environment promoting the tumor cell invasion and metastasis. In this work, in order to identify novel dual hCA IX and XII inhibitors, virtual screening techniques and biological assays were combined. A structure based virtual screening towards hCA IX and XII was performed using a database of approximately 26000 natural compounds. The best shared hits were submitted to a thermodynamic analysis and 3 promising best hits were identified and evaluated in terms of their hCA IX and XII inhibitor activity. In vitro biological assays are in line with the theoretical studies and revealed that Syringin, Lithospermic acid, and (-)- Dehydrodiconiferyl alcohol behave as good hCA IX and hCA XII dual inhibitors.

Aims: In late December 2019, early reports predicted the onset of a potential Coronavirus outbreak in china, given the estimate of a reproduction number for the 2019 Novel Coronavirus (COVID-19). Because of high ability of transmission and widespread prevalence, the mortality of COVID-19 infection is growing fast worldwide. Absent of an anti-COVID-19 has put scientists on the urge to repurpose already approved therapeutics or to find new active compounds against coronavirus. Here in this study, a set of computational approaches were performed in order to repurpose antivirals for dual inhibition of the frontier proteases involved in virus replication, papain-like protease (PLpro; corresponding to nsp3) and a main protease (Mpro), 3C‑like protease (3CLpro; corresponding to nsp5). Materials and Methods: In this regard, a rational virtual screening procedure including exhaustive docking techniques was performed for a database of 160 antiviral agents over 3CLpro and PLpro active sites of SARS-CoV-2. The compounds binding energies and interaction modes over 3CLpro and PLpro active sites were analyzed and ranked with the aid of free Gibbs binding energy. The most potent compounds, based on our filtering process, are then proposed as dual inhibitors of SARSC-CoV-2 proteases. Key findings: Accordingly, seven antiviral agents including two FDA approved (Nelfinavir, Valaganciclovir) and five investigational compounds (Merimepodib, Inarigivir, Remdesivir, Taribavirine and TAS106-106) are proposed as potential dual inhibitors of the enzymes necessary for RNA replication in which Remdesivir as well as Inagrivir have the highest binding affinity for both of the active sites. Significance: The mentioned drug proposed to inhibit both PLpro and 3CLpro enzymes with the aim of finding dual inhibitors of SARSC-CoV-2 proteases.

The progress in markerless technologies is bringing clinicians tools to rapidly evaluate tests such as Timed Up and Go (TUG) without compromising the time of the assessment, yet it raises questions about the potential trade-off in accuracy compared to traditional marker-based systems, suggesting that the precision of these evaluations is not yet conclusively established. The OpenCap system is an easy tool to process 3D kinematics with two iPhone cameras placed in front of the subject. Previous research has primarily focused on walking and movement towards the cameras with the subject facing the cameras (WF) and thus has been unable to disentangle the differences in the movement directions. We have compared the OpenCap system with a standard marker-based system for both walking directions. In OpenCap, we found significantly worse results capturing walking facing away from the cameras (RW). Compared to the marker-based system, our findings indicate troubles measuring kinematic parameters but provide good accuracy in measuring spatial-temporal parameters. These insights pave the way for refining markerless assessment technologies, potentially enhancing their utility in clinical settings.

The main inhibitory neurotransmitter in the mammalian brain is 4-aminobutyric acid (GABA). Phthalimide acts as a sodium channel antagonist, one of the finest targets for treating epilepsy is the sodium channel and JNK kinase activity is inhibited to treat epilepsy. a novel compound with multiple targets of action for the treatment of epilepsy also anti-oxidant activity has been designed. The compounds are designed in two series aldehydes and aniline. Docking of all the designed compounds has been done with the suitable protein GABA-AT, sodium channel, JNK protein, and anti-oxidant from protein bank. QSAR was done for constructing many linear regression models. In combination with the developed Docking and QSAR outcomes, ADMET has been performed for the active compounds. The result shows that 11 of the compounds have excellent binding energy in docking with all the receptors and also have good QSAR results and follow the ADMET. The structural understanding of human GABA-AT inhibition was obtained from molecular dynamics simulation studies for compound no. 11 at 5 nanoseconds. Synthesis and pharmacological activity also suggest that the designed compound no.11 be considered the best Novel Compound.

In CNS drug discovery, estimation of brain exposure of lead compounds is critical for their optimization. Compounds need to cross the blood-brain barrier (BBB) to reach the pharmacological targets in the CNS. The BBB is a complex system involving passive and active mechanisms of transport and efflux transporters such as P-glycoproteins (P-gp) and breast cancer resistance protein (BCRP), which play an essential role in CNS penetration of small molecules. Several in vivo, in vitro and in silico methods are available to estimate human brain penetration. Preclinical species are used as in vivo models to understand the unbound brain exposure by deriving the Kp,uu parameter, the brain/plasma ratio of exposure corrected with the plasma and brain free fraction. MDCK-mdr1 (Madin Darby canine kidney cells transfected with the MDR1 gene encoding for the human P-gp) assay is the most commonly used in vitro assay to estimate compound permeability and human efflux. The in silico methods to predict brain exposure, such as CNS MPO, CNS BBB scores and various Machine Learning models, help save costs and speed up compound discovery and optimization at all stages. These methods enable the screening of virtual compounds and building of a CNS penetrable compounds library as well as optimization of lead molecules for CNS penetration. Therefore, it is crucial to understand these methods' reliability and ability to predict CNS penetration. We review the in silico, in vitro and in vivo data and their correlation with each other and assess published in silico approaches to predict the BBB penetrability of compounds.

Bioactive fractions were isolated from the leaves extract of Kalanchoe pinnata and subjected to scrutiny for their prospective anti-obesity properties. An array of in vitro phytochemical, antioxidant, and enzyme inhibition assays were executed, which dis-cerned fractions F1 and F2 as the most effective fractions. These fractions were sub-sequently studied through in vivo experiments, affirming that F2 as the most potent fraction. Further characterization of F2 was conducted via HPTLC MS-MSn tech-niques. The outcomes demonstrated that F2 produced a notable anti-obesity effect in obese mice, reducing their body weight and lipid metrics, and leading to advanta-geous changes in their organs. An analytical examination of F2 revealed the existence of four principal compounds, which were subsequently subjected to in silco molecular docking and dynamic analysis, confirming their aptitude for binding to selected pro-teins. These findings imply that the utilization of Kalanchoe pinnata leaves could pro-vide a promising therapeutic strategy for the treatment of obesity.

Current article presents the study of psychological wellbeing of adolescents (n=168, age 15-17) gifted in math, humanities and sports and educated in advanced programs for gifted children. Theoretical framework of this study is eudemonic concept of psychological wellbeing by C. Ryff. Psychological wellbeing is measured with Ryff wellbeing scales in Russian adaptation by L.V. Zhukovskaya and E.G. Troshikhina. The study is aimed at understanding differences in psychological wellbeing of gifted teenagers connected to gender and type of giftedness. The results suggest that general wellbeing score did not differ for adolescents with different types of giftedness or of different genders. Separate components of psychological wellbeing, such as purpose in life and self-acceptance, are influenced by activity connected to the talent. Gender differences are subjected to age-specific trends of personal development in adolescence. Type of giftedness might reinforce these trends.

Cervical cancer is a significant global health concern, with human papillomavirus (HPV) infection being a crucial risk factor. Photodynamic therapy (PDT) is an attractive, minimally invasive treatment for HPV-related cervical lesions, which uses photosensitizers and light to selectively de-stroy abnormal cells. We aim to provide an overview of the various sorts of dyes that are utilized in PDT for decreasing the incidence and mortality of cervical cancer. Besides, the article discusses ongoing clinical trials for PDT in low-grade squamous intraepithelial neoplasia (LSIL) and high-grade squamous intraepithelial lesions (HSIL), as well as preclinical approaches for PDT in cervical cancer using various dyes. Moreover, it highlights potential dyes for PDT, examine them in in-silico condition, their pros and cons and also the solutions for enhancing their anticancer com-patibility. We also display that PDT is a promising therapeutic strategy for the diagnosis and treatment of HPV-associated cervical lesions. Moreover, it shows that using different classes of dyes improves the anticancer effects of PDT. Finally, among all dyes which are used in PDT, Fullerene demonstrated high tendency to over expressed receptors in cervical cancer cells and seems to be a proper candidate for be used in PDT more than before, but further research is necessary to assess its long-term efficacy and safety.

Zinc, being a part of many proteins and enzymes, plays an important role in the vital processes of the body. Racemic α-lipoic acid (LA) has a wide range of biological activities: antioxidant, antidiabetic, anticancer, and it is also used in diseases such as Parkinson's and Alzheimer's. Nowadays, the synthesis and structural studies of new zinc complexes with biologically active compounds are of great importance for drug discovery. This work shows the studies carried out on the synthesis, structure, identification, also biological activity using in silico methods of a complex compound based on LA and zinc. A complex compound of zinc with LA (ZnLA) was synthesized in the presence of LA and zinc acetate in ethanol. The identification of ZnLA by (FT-IR, XRD and TGA-DTA), also DFT calculations at the B3LYP/def2-TZVPP level of theory was performed. The molecular docking investigation involved docking ZnLA with two different protein receptors such us Glycogen phosphorylase (2GPA) and Kv potassium channel beta subunit (1ZSX) using CB-Dock 2 server and Auto-Dock Vina program to determine the mechanism of action and biological activity. This study reveals potential ligand-protein interactions at the atomic level and suggests that the treatment of diabetes (non-insulin dependent) with ZnLA.

Single nucleotide polymorphisms (SNP) are associated with diseases and drug response variabilities in humans. Elucidating the damaging and disease-associated SNPs using wet-laboratory approaches can be challenging and resource-demanding due to the large number of SNPs in the human genome. Due to the growth in the field of computational biology and bioinformatics, algorithms have been developed to help screen and filter out the most deleterious SNPs that are worth considering for wet-laboratory studies. Here we review the existing in-silico based methods used to predict and characterize the effects of SNPs on protein structure and function. This cutting-edge approach will facilitate the search for novel therapeutics, help understand the etiology of diseases and fast-track the personalized medicine agenda.

Background: Currently, limited number of therapeutic options are available to treat Diabetes mellitus, and to find a potential candidate, laboratory work takes time and also needs animal studies. So, the aim of this study was to determine the efficacy of some natural phytocompounds with the aid of molecular docking, bioinformatics and in silico drug design approaches. Method: Two proteins (Human CYP3A4 linked to metformin and Human dipeptidyl peptidase-IV) were selected and molecular docking studies were conducted using Pymol, AutoDock Vina, PyRx, and Discovery Studio. Different important pharmacokinetic parameters like ADME and toxicity data were obtained from online databases SwissADME and pkCSM program. Results: It was found that human dipeptidyl peptidase-IV (PDB ID: 4A5S) has exhibited a maximal affinity of -9.7 Kcal/mol for bryonolic acid and hesperidin, but only -6.7 kcal/mol for metformin hydrochloride. Similarly, the highest affinity of hesperidin for human CYP3A4 bound to metformin (PDB ID: 5G5J) is -10.7 Kcal/mol compared to metformin hydrochlor (-6.3 Kcal/mol). Besides, all the compounds have been documented outstanding ADMET profile, and accepted by drug-likeness or Lipinski rule. Conclusions: The present study suggested that these compounds can be further investigated in vitro and in vivo to establish them as lead compounds against Diabetes mellitus..

The current globally dominant SARS-CoV-2 variants are showing immune escape and reduced susceptibility to antiviral drugs. Therefore, agencies responsible for drug evaluation and regula-tion such as the FDA and EMA are revising their emergency authorization use of several COVID-19 neutralizing antibodies. These NAbs proved to be unlikely effective against new variants espe-cially Omicron descendants and several pharmaceutical companies are pursuing the development of more potent neutralizing antibodies. To address the issue of using in silico prediction for the rapid assessment of anti-SARS-CoV-2 MAbs neutralization power, we used a computational method we developed previously, to evaluate 10 SARS-CoV-2 antibodies propensity to neutralize the new Omicron’s subvariants Eris (EG.5) and Pirola (BA.2.86) based on comparative binding affinity and previous experimental and clinical observations. Nine of these MAbs were once granted emergency use authorization, and one is currently under clinical investigation. The rapid, cost-effective in silico evaluation provided reliable predictions consistent with published clinical and experimental data. Furthermore, our data showed new potential therapeutic MAbs combi-nation that could be effective for the treatment countermeasure of the new Omicron’s subvariants Eris (EG.5) and Pirola (BA.2.86).

L-17 is a thiadiazine derivative with putative anti-inflammatory, neuroprotective, and antidepressant-like properties. In this study, we applied combined in silico and in vivo electrophysiology techniques to reveal the potential mechanism of action of L-17. PASS 10.4 Professional Extended software suggested that L-17 might have pro-cognitive, antidepressant, and antipsychotic effects. Docking energy assessment with AutoDockVina predicted that the binding affinities of L-17 to the serotonin transporter (SERT) and serotonin receptors 3 and 1A (5-HT3 and 5-HT1A) are compatible to the selective serotonin reuptake inhibitor (SSRI) fluoxetine and selective antagonists of 5-HT3 and 5-HT1A receptors, granisetron and WAY100135, respectively. However, while the binding mechanisms of L-17 to the SERT and 5-HT1A receptor were similar to fluoxetine and WAY100135, its interacting with 5-HT3 receptor might be substantially different from this of granisetron. Acute administration of L-17 led to dose-dependent inhibition of firing activity of 5-HT neurons of the dorsal raphe nucleus. This inhibition was partially reversed by subsequent administration of WAY100135. Based on both in silico and in vivo electrophysiology assessments, we suggest that L-17 is a potent 5-HT reuptake inhibitor and a putative partial agonist of 5-HT1A receptors. As such, L-17 in particular and thiadiazine derivatives, in general, might be a representative of a new class of antidepressant drugs. Since L-17 also possesses neuro- and cardioprotective properties, it can be useful in affective illness developing due to the general medical condition, such as post-stroke and post-myocardial infarction (MI) depression.

Conventional chemotherapy for colorectal cancer (CRC) gives only a small increase in patient survival, since it is often diagnosed in late stages, when tumor has disseminated to other organs. Besides, it is common to observe that malignant cells acquire tumor escape mechanisms which leads to therapy resistance. Considering these facts, the discovery of new molecules with therapeutic potential has become an invaluable tool in chemoprevention. In this context, we previously evaluated two hybrids (SAC-CAFA-MET and SAC-CAFA-PENT) which exhibited selective cytotoxicity against SW480, with better results than the conventional chemotherapeutic agent (5-fluorouracil; 5-FU). Here, we investigated a little deeper in the possible mechanism of these molecules to identify potential therapeutic alternatives for the treatment of CRC. Both compounds induced cell damage and reduced ROS formation. Further evaluations showed that SAC-CAFA-MET induces cell death independent from caspases and p53, but probably mediated by the negative regulation of the proapoptotic Bcl-2. In addition, the lack of activation of caspase 8 and the positive regulation of caspase 3 induced by SAC-CAFA-PENT suggest this compound acts through an apoptotic mechanism, probably initiated by intrinsic pathway. Besides, the down regulation of IL-6 by SAC-CAFA-PENT suggests it also induces a significant anti-inflammatory process. In addition, docking studies would suggest caspase-3 modulation as the primary mechanism by which hybrids elicits apoptosis in human colorectal adenocarcinoma SW480. Meanwhile, DFT calculations suggest that hybrids would produce effects in modulation of ROS in SW480 cells via hydrogen atom transfer pathway (HAT). Finally, both, SAC-CAFA-MET and SAC-CAFA-PENT displayed a favorable pharmacokinetic profile. The current work highlights the potential of the lead compounds SAC-CAFA-MET and SAC-CAFA-PENT as potential agents for colorectal cancer chemoprevention.

Purpose: When it comes to improving citizen service delivery standard and processes, or broadly, management in government (MIG), many reform programs and improvement efforts are ongoing across different ministries, departments, and agencies. This paper is part of a larger empirical research where we are interested to know what those programs and projects are, and how they are doing, and what the gaps are. Design/methodology/approach: Analyzing the publicly available data on the government portal and donors’ website, this article provides an overall idea of current reforms in various places of government. In parallel, field research was conducted through visiting agency offices, observing office environment and mechanisms, and interviewing public managers who are working at district and sub-district level offices of directorates or agencies.Findings: As a trend, state-led development process and the traditional mode of administration have moved to New Public Management based quality and performance approach. All donors have their own strategy framework document like ‘Country Assistance strategy’, ‘Country Operations and Business Plan’, Logical Framework Document, Focus or Priority Streams, etc. Many of the projects are sectoral and agency or ministry specific and have an inherent limitations, due to ‘cadre’ based organizational structural arrangement. So, it is common ‘not to own the projects’, rather ‘using the fund as much as possible anyway within the given project duration’. When the fund ends, implementing units, either government or NGOs, abandon the projects. That is why many projects are actually half done without significant and sustained direction.Originality/value: We can conclude that the typical ‘top-down’ or ‘trickle down’ conception is still dominant in design and implementation of reforms programs and service quality efforts. So, how to reach systemically at the bottom where public managers provide services to citizens, needs even more deliberation.

COVID-19 pandemic has spurred intense research efforts to identify effective treatments for SARS-CoV-2. In silico studies have emerged as a powerful tool in the drug discovery process, particularly in the search for drug candidates that interact with various SARS-CoV-2 receptors. These studies involve the use of computer simulations and computational algorithms to predict the potential interaction of drug candidates with target receptors. The primary receptors targeted by drug candidates include the RNA polymerase, main protease, spike protein, ACE2 receptor, TMPRSS2, and AP2-associated protein kinase 1. In silico studies have identified several promising drug candidates, including Remdesivir, Favipiravir, Ribavirin, Ivermectin, Lopinavir/Ritonavir, and Camostat mesylate, among others. The use of in silico studies offers several advantages, including the ability to screen a large number of drug candidates in a relatively short amount of time, thereby reducing the time and cost involved in traditional drug discovery methods. Additionally, in silico studies allow for the prediction of the binding affinity of drug candidates to target receptors, providing insight into their potential efficacy. However, it is crucial to consider both the advantages and limitations of these studies and to complement them with experimental validation to ensure the efficacy and safety of identified drug candidates.

This study empirically analyzes the effectiveness of resource optimization utilizing artificial intelligence (AI) within cloud computing environments. It compares systems that have been transitioned from a Monolithic structure to Microservices Architecture (MSA) with those adopting a simple Rehost strategy, to validate the operational efficiency of two systems whose resource efficiency has been enhanced compared to traditional legacy systems. The research is based on systems that determined resource efficiency post-cloud migration using APM tools. It points out the limitations of traditional heuristic operation methods in resource optimization, which lead to issues such as application performance degradation, resource over-allocation, and increased operational costs. This study aims to overcome the conventional resource optimization standards that depended on the average peak usage of CPU and memory infrastructure, by introducing AI-based optimization tools, and applying on-premise standards to cloud and container environments to achieve resource optimization. The paper emphasizes the importance of resource optimization through AI-based automation tools, exploring the possibilities of resolving real-world application performance issues in cloud environments, reducing unnecessary resource usage costs, and cutting down on labor risks and operational expenses. Through this, it presents strategic directions on how AI technology can offer financial and operational advantages in managing cloud resources.

While obesity is recognized as a key global public health issue, there has been no research to date on the perceptions of care for people with this condition held by individuals in positions of organizational power. The aim of this study was therefore to describe the perceptions and experiences of clinical leaders and managers of providing care to inpatients with obesity at a metropolitan public health service. This study applied an interpretative phenomenological analysis (IPA) approach to qualitative research, conducting interviews with 17 participants. Their perceptions of care for inpatients with obesity encompassed both their personal understanding as an individual, and their observations about the organizational, patient and carer perspectives. Three overall themes were identified: 1) The problem of inpatients with obesity, 2) Inpatients with obesity as sources of risk and 3) Personal and professional perceptions of inpatients with obesity. While clinical leaders and managers were aware of potential impact of stigma and weight bias on the care given to this cohort, elements of implicit bias, stereotyping, ‘othering’ and ambivalence were frequently present in the data. Ongoing efforts to improve care for patients with obesity must therefore include efforts to address perceptions and attitudes at all organisational levels of the workforce.

Electrical remodelling as a result of the homeodomain transcription factor 2 (Pitx2)‐dependent gene regulation was linked to atrial fibrillation (AF) and AF patients with single nucleotide polymorphisms at chromosome 4q25 responded favorably to Class I antiarrhythmic drugs (AADs). The possible reasons behind this remain elusive. The purpose of this study was to assess the efficacy of AADs disopyramide, quinidine, and propafenone on human atrial arrhythmias mediated by Pitx2-induced remodelling, from a single cell to the tissue level, using drug binding models with multi-channel pharmacology. Experimentally calibrated populations of human atrial action potential (AP) models in both sinus rhythm (SR) and Pitx2-induced AF conditions were constructed by using two distinct models to represent morphological subtypes of AP. Multi-channel pharmacological effects of disopyramide, quinidine, and propafenone on ionic currents were considered. Simulated results showed that Pitx2-induced remodelling increased maximum upstroke velocity (dVdtmax) and conduction velocity (CV), and decreased AP duration (APD) and wavelength (WL). At the concentrations tested in this study, these AADs decreased dVdtmax and CV and prolonged APD in the setting of Pitx2-induced AF. Our findings of alterations in WL indicated that quinidine and disopyramide may be more effective against Pitx2-induced AF than propafenone by prolonging WL.

The liver is a key node of whole-body nutrient and fuel metabolism and is also the principal site for detoxification of xenobiotic compounds. As such, hepatic metabolite concentrations and/or turnover rates inform the status of both hepatic and systemic metabolic diseases as well as the disposition of medications. As a tool to better understand liver metabolism in these settings, in vivo magnetic resonance spectroscopy (MRS) offers a non-invasive means of monitoring hepatic metabolic activity in real time both by direct observation of concentrations and dynamics of specific metabolites as well as by observation of their enrichment by stable isotope tracers. This review summarizes the applications and advances in human liver metabolic studies by in vivo MRS over the past 35 years and discusses future directions and opportunities that will be opened by the development of ultra-high field MR systems and by hyperpolarized stable isotope tracers.

Membrane Inlet Mass Spectrometry (MIMS) was used to analyze monochloramine solutions (NH₂Cl) and ammonia solutions in a compact FTICR. Chemical ionization enables identification and quantification of the products present in the permeate. The responses of protonated monochloramine and ammonium increase linearly with the solution concentration. The enrichments were respectively 1.2 and 5.5. Pervaporation is dependent on pH and only the basic form of ammonia NH₃ pervaporates through the membrane. Unexpectedly, the small ammonia molecule permeated very slowly. It could be due to interactions with water molecules inside the membrane that create clusters. Moreover, NH₂Cl solutions, in addition to the NH₃Cl⁺ signal, presented a strong NH₄⁺ signal at m/z 18.034 . Ammonia presence in the low-pressure zone before ionization is probable as NH₄⁺ was detected with all the precursors used, particularly CF₃⁺ and trimethylbenzene that presents a proton affinity higher than monochloramine. Ammonia may be formed inside the membrane due to the fact that NH₂Cl is unstable and may react with the water present in the membrane. Those results highlight the need for caution when dealing with chloramines in MIMS and more generally with unstable molecules.

In this study, we have first studied the trend in meteorological data from the Harmaleh, Vanai and Farsesh stations in the 50-year period in the Dez catchment area. The meteorological data will be then forecasted using SWAT and Mann-Kendall. Forecasting the results in the Mann-Kendall and SWAT model has been done using the code written in MATLAB software and RCP (4.5, 8.5) scenarios, respectively. Studying the results of the trend in the data of meteorological stations in this catchment area indicated that these two parametric and non-parametric methods have been used to determine trends in meteorological data. The results of the parametric method are positive in all meteorological parameters. Non-parametric method over a period of 50 years shows the presence of trends in the data. The comparison on the forecasting results at maximum temperature suggested that during summer, we will see an increase in temperature compared to the ground state in all three forecasts. The results of the minimum temperature forecast show a decrease in the minimum increase during the winter and the precipitation forecast indicates that at the end of autumn (Nov) precipitation decreased by 20 mm in the Mann-Kendall and 4.5 RCP while RCP8.5 suggests the increase in precipitation compared to the ground state. Studying the runoff forecast results using SWOT show that at the end of winter (Feb) and almost all spring (Mar, Apr) a decrease of about 40%, 15% and 14% will be seen, respectively

The goal of this paper is to review the relevant literature on autism questionnaires, models, analytic tools, and subgrouping, focusing on the opportunities and limitations. We examined how the size and nature of the database and the number and type of parameters measured determine the use of analytic tools and the expected type of results. To support our position about the examined aspects, we rely on various parts of the reviewed articles. We emphasize that the individual results in each article can only be interpreted in the light of the methods that brought about the conclusions. The reviewed literature suggests a heterogeneous palette in autism subtypes instead of distinct, well-characterized subgroups, or a single-dimensional continuous spectrum.

Encouraged by the administration the gentrification process has been useful in economic terms for the reactivation of socio-cultural fabrics of historic urban spaces in decline. What was initially considered an advantage has led to the touristification of historic centres, and in turn to the alter-ation of their original use. In these settings, the demographic void caused by increasingly shunning local identity has combined with pressure from excessive tourism and the obsolescence of heritage protection bodies in charge of conservation. Given the crisis affecting the definitions of the current system, this study aims to review the envi-ronmental agents of heritage value in relation to the processes of touristification and gentrification. Data obtained from different methodologies are analysed using a multidisciplinary database, a model which enables the analysis of the relevant information from the different interacting fields. The case study focuses on the historic town centre of Seville specifically between 2015 and 2020. Elements are defined as indicators for these processes and the analysis of the case study will com-prise the main results of this research.

The estrogen hormone receptor (ER) mediated gene expression mainly regulate the development and physiology of primary and secondary reproductive system alongside bone-forming, metabolism and behaviour. Over-expressed ER is associated with several pathological conditions and play a key role in breast cancer occurrence, progression and metastasis. Hibiscus sabdariffa L. or roselle is a rich source of naturally occurring polyphenolic compounds including anthocyanins and reportedly have strong estrogenic activity. To validate these findings, we have investigated the estrogen receptor binding affinity and safety of some previously recorded polyphenols using a suite of computational methods. Our investigation showed the estrogen-receptor binding potential of Pelargonidin, Delphinidin, Cyanidin, and Hibiscetin are more efficient than popular breast cancer drugs, Tamoxifen and Raloxifene, with favourable toxicological parameters and low half maximal inhibitory concentration. This is the first report to investigate the phytochemical basis of estrogenic activity of Hibiscus sabdariffa L.

This paper considers the solution of the equations for ruin probabilities in infinite continuous time. Using the Fourier Transform and certain results from the theory of complex functions, these solutions are obtained as com- plex integrals in a form which may be evaluated numerically by means of the inverse Fourier Transform. In addition the relationship between the re- sults obtained for the continuous time cases, and those in the literature, are compared. Closed form ruin probabilities for the heavy tailed distributions: mixed exponential; Gamma (including Erlang); Lognormal; Weillbull; and Pareto, are derived as a result (or computed to any degree of accuracy, and without the use of simulations).

The complex progression, specialized care, and comorbidities of chronic diseases place a financial and health burden on society. In some instances, the high hospital readmission rate is also a result of chronic diseases. The readmission rates can be decreased if the quality of life for patients is increased and healthcare costs are reduced through effective management and preventive measures. As a result, healthcare professionals, service providers, and policy-makers are looking for innovative approaches to reduce healthcare costs while enhancing the quality of care. There is an emerging trend in deploying technological advancements like artificial intelligence (AI), the Internet of Things (IoT), sensors, wearables, social media, mobile apps, and genomics to decrease hospital readmissions. In some instances, predictive analytics, early warning systems, personal-ized care management, remote monitoring and Telehealth, decision support systems, patient education and engagement, and other areas of artificial intelligence and machine learning have outperformed traditional approaches in lowering hospital readmissions. Although AI effectively reduces readmission, there are potential risks if it is not combined with efficient interventions. Therefore, real-time monitoring and intervention are required for AI systems to increase patient safety and decrease hospital readmissions. To achieve autonomy, prevent harm, achieve fairness, and achieve explicability, the ethical principles relating to AI systems should be taken into ac-count. This study examines the significant impact on health and the economy of hospital read-missions for chronic diseases and the role of AI in reducing readmissions.

As the human-body controls depend on the brain neuro current, here assay is related to thinking nerve detection. For this purpose, the brain current was analyzed by electrochemical three-electrode systems using 0.1-mm micro wire in vivo probe, which probe was made by using carbon nanotube paste coated needle type. Here working electrode was inserted in the rat brain’s muscle core with a 0.3-mm-diameter needle type Ag/AgCl Cl-coated Ag wire reference, and counter electrode of 0.1-mm-diameter Pt micro needle was inserted 5 mm deep into the in vivo muscle skin, under anesthesia with 2.0 ~ -2.0 V potential windows, 50 mv/s cyclic scan and 1.0x10-5A chrono amperometric sensitivity on the body systems. Under the optimum conditions, diagnostic application was performed to such as smell signal, muscular strength, five sense assay and thinking neuro current.

Cocaine addiction is a global health problem that causes substantial damage to the health of addicted individuals around the world. Dopamine synthesizing neurons in the brain play a vital role in the addiction to cocaine. But the underlying molecular mechanisms that help cocaine exert its addictive effect have not been very well understood. Bioinformatics can be a useful tool in the attempt to broaden our understanding in this area. In the present study, Gene Set Enrichment Analysis (GSEA) was carried out on the upregulated genes from a dataset of Dopamine synthesizing neurons of post-mortem human brain of cocaine addicts. As a result of this analysis, 3 miRNAs have been identified as having significant influence on transcription of the upregulated genes. These 3 miRNAs hold therapeutic potential for the treatment of cocaine addiction.

Background. Increasing evidence demonstrate that concentration of protein in infant formula >1.9g/100Kcal with high levels of insulinogenic aminoacids is associated with accelerated weight gain, increased fat mass accumulation and risk of adiposity. Purpose of this study was to conduct a systematic review to determine the metabolic effects in infants feed with infant formula optimized in protein. Methods. Systematic review was conducted according PRISMA Statement. RCTs with one intervention group (infant formula with 1.6-1.9gr of protein/100Kcal) and at least one comparative control group (infant formula with >1.9gr of protein/100Kcal) were included. Standardized mean differences (SMD), through random model were calculated. Results. 15 RCT were included. Optimized protein in infant formula was associated with less gain of BMI at 24 months of follow-up (SMD -0.25, IC95% -0.36 to -0.13, p 0.01) and less fat mass accumulation (SMD -0.68, IC95% -0.98 to -0.37, p 0.01). Optimized protein was also associated to less gain of weight, weight/age Z-score, weight/length Z-score, BUN (mmol/dL) and IGF1 (ng/ml). No effect on length/age Z-score was observed. Conclusions. Robust evidence showed optimized protein (1.6gr/100Kcal to 1.9gr/100Kcal) in infant formula is associated with metabolic benefits in infants with less weight gain, BMI and fat mass accumulation.

Finding new, safe strategies to prevent and control rheumatoid arthritis is an urgent task. Of particular interest in this regard are bioactive peptides and peptide-rich protein hydrolyzates, which represent a new trend in the development of functional foods and nutraceuticals. The resulting tissue hydrolyzate of the chicken embryo (CETH) has been evaluated for acute toxicity and tested against chronic arthritis induced by Freund's full adjuvant in rats. The anti-arthritic effect of CETH was studied on the 28th day of the experiment after two weeks of oral administration of CETH at doses of 60 and 120 mg/kg body weight. Arthritis was evaluated on the last day of the experiment on the injected animal paw using X-ray computerized microtomography and histopathology analysis methods. The CETH effect was compared with the non-steroidal anti-inflammatory drug diclofenac sodium (5 mg/kg). Oral administration of CETH was accompanied by effective dose-dependent correction of morphological changes caused by the adjuvant injection. CETH had relatively high recovery effects in terms of parameters for reducing inflammatory edema, inhibition of osteolysis, prevention of osteophitosis, reduction of the inflammatory reaction of periarticular tissues, and cartilage degeneration. This study presents a potential theoretical strategy for the safe correction of this pathological process and, for the first time, shows that CETH may be a powerful potential nutraceutical agent or bioactive component of functional products in the treatment of rheumatoid arthritis.

The spring-in phenomenon of the composite parts can affect the assembly process. This study aims to predict the spring-in phenomenon of carbon fiber reinforced plastic (CFRP) part. Here, we predict the spring-in of the CFRP part using a coupled analysis of the forming and cooling processes during the stamping process. First, a simulation of the entire forming process, such as the transfer of the composite laminate, gravity analysis, and forming was performed to obtain the temperature distribution of the CFRP part. Subsequently, a finite-element (FE) simulation of the cooling process was conducted to predict the spring-in phenomenon of the shaped CFRP part using the temperature data obtained in the forming simulation. Finally, a CFRP part was manufactured and compared with the results of the FE simulation.

Soups show diverse health functions, which should be related with their nutrient profiles. NMR spectroscopy is a robust, sensitive and rapid method to unveil or identify the chemical composition of food or food-derived metabolites. In the current study, 1H-NMR spectroscopy approach was applied to identify the metabolic profiling of two kinds of home-cooked freshwater fish soups (crucian carp and snakehead fish) before and after in vitro gastrointestinal digestion. The nutritional profiles of these two kinds of soups were identified at first. With the help of OPLS-DA method (Orthogonal Partial Least Squares Discriminant Analysis), these two different kinds freshwater fish under different states were completely discriminated. The metabolites changes in digested fish soups could reveal the information of chemical compounds which play important roles in the body. Furthermore, the metabolic patterns of different kinds of fish soups could reflect the various nutrition profiling characteristics for dietary therapy.

Experimental optimization of analytical methods based on solid-phase microextraction (SPME) is a complex and labor-intensive process associated with uncertainties. Using the theoretical basics of SPME and finite element analysis software for the optimization proved to be an efficient alternative. In this study, an improved finite element analysis-based model for SPME of volatile organic compounds (VOCs) by porous coatings was developed mainly focussing on the mass transport in coatings. Benzene and the Carboxen/polydimethylsiloxane (Car/PDMS) coating were used as the model VOC and a porous SPME coating, respectively. It has been established that in the coating, volumetric fractions of Carboxen, PDMS, and air are 33, 42 and 24%, respectively. Knudsen diffusion in micropores can slow down a mass transport of analytes in the coating. When PDMS was considered as the solid part of the coating, lower root-mean-square deviation of the modeling results from experimental data was observed. It has been shown that the developed model can be used to model the extraction of VOCs from air and water samples encountered in a typical SPME development method procedure. It was possible to determine system equilibration times and use them to optimize sample volume and Henry’s law constant. The developed model is relatively simple, fast, and can be recommended for optimization of extraction parameters for other analytes and SPME coatings. The diffusivity of analytes in a coating is an important property needed for improved characterization of existing and new SPME polymers and analytical method optimization.

The COVID-19 infections in Pakistan are spreading at an exponential rate and a point may soon be reached where rigorous prevention measures would need to be adopted. Mathematical models can help define the scale of an epidemic and the rate at which an infection can spread in a community. I used ARIMA Model, Diffusion Model, SIRD Model and Prophet Model to forecast the magnitude of the COVID-19 pandemic in Pakistan and compared the numbers with the reported cases on the national database. Results depicts that Pakistan could hit peak number of infectious cases between June 2020 and July, 2020.

Embryoid bodies (EBs) are multicellular three-dimensional (3D) aggregates generated from induced pluripotent stem cells (iPSCs) in suspension and serve as useful biological sources for many downstream applications. Imaging of live EBs has been hampered mainly due to the inherent limitations of the imaging techniques applied to date. This study aimed to image human iPSC (hiPSC) derived EBs to obtain their 3D volume, determining size, morphology, and cell viability from day 7 to 14 using Light Sheet Fluorescence Microscopy (LSFM). Furthermore, chromosomal stability was assessed using Multicolor fluorescence in situ hybridization (M-FISH) from day 8 to 14. EB volume increased from day 7 to 13 which, decreased at day 14. From day 7 to 11, the EBs mainly appeared spherical and morphed into an ellipsoidal shape by day 13. All EBs showed varied external morphologies and larger cavities at day 14. The EB karyotype was diploid 46XY at day 8 and exhibited a low level of aneuploidy from day 10 to 14. This study shows that an increase in cell death affects the morphology and chromosomal stability in EBs derived from hiPSC. We demonstrate that the combination of LSFM and M-FISH helps characterize EBs that will assist future stem cell therapies.

The concept of collecting data of people using a domestic space is not novel. However, the methods and processes used to decipher this raw data and transform it into useful and appropriate information (i.e., sequence, duration, and timing derived from domestic activities) have been challenging and the focus of many research groups. But how are the results of the decoded transposition received, interpreted and used by the various professionals (e.g., occupational therapists and architects) who consume the information? This paper describes the inclusive evaluation process undertaken, which involved architects, engineers and end-users (not the occupant, but the care team managing the occupant). Finally, our study evidence the importance of making accessible to a multidisciplinary audience spatial, temporal and sequential aspects derived from people's domestic routines. Shedding light on how a systematic approach to collecting, processing and mapping low-level sensor data into higher forms and representations can be a valuable source of knowledge for improving the domestic living experience.

In the recent years, Cannabis and hemp-based products have become increasingly popular for various applications ranging from recreational use, edibles, beverages to health care products and medicines. The rapid detection and differentiation of phytocannabinoids is, therefore, essential to assess the potency, therapeutic and nutritional values of cannabis cultivars. Here, we implemented the SpiderMass technology for the in vivo detection of cannabidiol acid (CBDA) and tetrahydrocannabinol acid (THCA) and other endogenous organic plant compounds to access distribution gradients within the plants and differentiate cultivars. The SpiderMass system is composed of an IR- laser handheld microsampling probe connected to the mass spectrometer through a transfer tube. The analysis was performed in situ on different plant organs from freshly cultivated Cannabis plants in only a few seconds. SpiderMass analysis easily discriminated the two acid phytocannabinoid isomers by MS/MS and the built statistical models differentiated between four Cannabis cultivars. Different abundancies of acid phytocannabinoids were also found along the plant as well as between different cultivars. All together, these results introduce the direct analysis by SpiderMass as a compelling analytical alternative for forensic and hemp industrial analysis.

This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.

Background & Aim: Drug sustainability (DS) is a surrogate marker for treatment efficacy. We aimed to compare the DS of two main biologics used to treat moderate-to-severe ulcerative colitis (UC), infliximab (IFX) and vedolizumab (VDZ), in a real-world setting. Methods: We conducted a retrospective cohort study at a tertiary medical center in Israel. We included patients treated between Dec 1st, 2017, and May 1st, 2021, who were followed for up to 300 weeks. DS was defined as corticosteroid, surgical, and hospitalization-free treatment. Results: 217 patients with UC were included. VDZ had a significantly longer median DS of 265.6 weeks compared to IFX's 106.5 weeks (p=0.001) in treatment-naïve patients, even when adjusting for disease severity (HR 0.55 95 CI 0.3-0.98, p=0.042). In treatment-experienced patients, DS was comparable between IFX and VDZ (p=0.593). Conclusion: VDZ showed significantly longer DS in treatment-naïve patients with UC compared to IFX, also when adjusted for disease severity. There was no difference in DS between VDZ and IFX in treatment-experienced patients and patients switching from one drug to another. VDZ may be a suitable first-line treatment for biologic-naïve patients with moderate-to-severe UC.

Background: Cancer has been one of the most critical health problems during the past decades which require serious consideration, especially in developing countries. Methods: 80 parents of the children with different types of cancers were studied via both questionnaire and person-to-person interview after announcing their informed consent. Results: The highest delay in patients' initial referral was due to the following factors: lack of attention or ignoring the first symptoms, delay in referring to the physician, low economic status and even lack of family support for the patient. In addition, visiting several doctors after the initial diagnosis, uncertainty about the first proposed method, and high cost of treatment can be mentioned as the main causes of delays in the start of treatment. Conclusion: Education plays an important role in identifying the signs of cancer. In addition, proper relationship and cooperation between the health system and physicians as well as provision of adequate information to patients could lead to the long-term cooperation of these patients in continuing their treatment.

MALDI-TOF (Matrix-Assisted Laser Desorption/Ionization Time-of Flight) is a type of mass spectrometry (MS) that has been widely implemented for the rapid identification of microorganisms over the last decade. The accuracy and flexibility of this method has encouraged researchers to implement the analysis of protein spectra obtained by MALDI-TOF for the discrimination of close-related species and bacterial typing. In this study, a standardized methodology based on the detection of species-specific protein peaks from the spectra obtained with MALDI-TOF is described. The methodology was applied to a collection of Cryptococcus spp. (n=70) previously characterized by Amplified Fragment Length Polymorphism (AFLP) and sequencing of the ITS1-5.8S-ITS2 region. An expanded ad-hoc database was also built for their discrimination with MALDI-TOF. This approach did not allow the discrimination of the interspecies hybrids. However, the performance of peak analysis with the application of the PLS-DA and SVM algorithms in a two-step analysis allowed 96.95% and 96.55% correct discrimination of C. neoformans from the interspecies hybrids, respectively. Besides, PCA analysis prior to SVM provided 98.45% correct discrimination of the 3 analyzed species in a one-step analysis. The method is cost-efficient, rapid and user-friendly. The procedure can also be automatized for an optimized implementation in the laboratory routine.

This work communicates a review on Balmer series hydrogen beta line measurements and applications for analysis of white dwarf stars. Laser-induced plasma investigations explore electron density and temperature ranges comparable to white dwarf star signatures such as Sirius B, the companion to the brightest star observable from the earth. Spectral line shape characteristics of the hydrogen beta line include width, peak separation, and central dip-shift, thereby providing three indicators for electron density measurements. The hydrogen alpha line shows two primary line-profile parameters for electron density determination, namely, width and shift. Both Boltzmann plot and line-to-continuum ratios yield temperature. The line-shifts recorded with temporally- and spatially- resolved optical emission spectroscopy of hydrogen plasma in laboratory settings can be larger than gravitational redshifts that occur in absorption spectra from radiating white dwarfs. Published astrophysical spectra display significantly diminished Stark or pressure broadening contributions to red-shifted atomic lines. Gravitational redshifts allow one to assess the ratio of mass and radius of these stars, and subsequently, the mass from cooling models.

This study aimed to evaluate the diagnostic roles of various immunohistochemical markers in urothelial carcinoma in situ (uCIS) through a meta-analysis and review of diagnostic test accuracy. Immunohistochemical markers CK20, CD44, AMACR, and p53 were evaluated in the present study. We analyzed the expression rates of immunohistochemical markers and compared their diagnostic accuracies. The estimated expression rates were 0.803 (95% confidence interval [CI]: 0.726–0.862), 0.142 (95% CI: 0.033–0.449), 0.824 (95% CI: 0.720–0.895), and 0.600 (95% CI: 0.510–0.683) for CK20, CD44, AMACR, and p53, respectively. In the comparison between uCIS and reactive/normal urothelium, the expression of CK20, AMACR, and p53 in uCIS was significantly higher than in reactive/normal urothelium. CD44 showed significantly lower expression in uCIS than in the reactive/normal urothelium. Among the markers, AMACR had the highest sensitivity, specificity, and diagnostic odds ratio. The AUC on SROC was the highest for CK20. In conclusion, immunohistochemical markers, such as CK20, CD44, AMACR, and p53, can be useful in differentiating uCIS from reactive/normal urothelium.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article's main findings; (4) Conclu-sions: indicate the main conclusions or interpretations. The abstract should be an objective rep-resentation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

The deep neural network - BERT model (Bidirectional Encoder Representations from Transformers) and the stocks cumulative abnormal return is used in this article to analyze the sentiment of financial texts. The proposed approach, unlike those used so far, does not require the creation of dictionaries, takes into account the broad context of words and their meaning in financial texts, eliminates the problem of ambiguity of words in various contexts, does not require manual labelling of data and is free from the subjective assessment of the researcher. The sentiment of financial texts in the meaning presented in this paper is directly related to the market reaction to the information contained in these texts. For texts belonging to one of the two classes (positive or negative) with the highest probability the BERT model gives the results of predictions with a precision level of 62.38% for the positive class and 55% for the negative class. The results at this level can be used in event study, market efficiency research, investment strategy development or support of investment analysts using fundamental analysis.

The relaxation process of lithium batteries caused by load variation is considered. It is shown that such processes have strong dependence on internal physical and chemical processes and battery technical conditions. Theoretical expressions of the relaxation process caused by a step-like load variation have been obtained for 1^st and 2^nd order equivalent electrical circuits. The experimental investigations show that the obtained models fit the real relaxation processes and the behavior of the identified parameters could be explained by specific features of physical and chemical processes within the lithium battery. It should be noted that the obtained results can be generalized for a different type of electrochemical power source. The proposed approach makes it possible to provide means for electrochemical power source characterization and diagnostic, the main advantages of which are good time localization of measurement procedures and inexpensive apparatus implementation.

With the increasing of copper consumption, the in-use stock of copper tends to increase. This paper used the “average use life method” to quantify the amount of copper in-use stock, and calculated the average age of in-use stock. It was indicated that the total in-use stock had an overall smooth trends, and reached its peak in 2007 was about 68.9 Mt (million tons), in addition, in-use stock per capita reached its peak ,234 kg/capita in 2001. The results demonstrated that during the period 1992-2002, the average age of copper in-use stock was continually decreased, but gradually increased since the year 2003. The fixed assets depreciation method used in this paper is applied to analyze depreciated copper in-use stock, and to analyze the relationship with economic indicator (GDP). It is demonstrated that it was inconsistent with the theory of Environmental Kuznets Curve (EKC) before 2000, this might be the demand for copper in the service sector is greater than the reduced strength of alternatives. Finally, scenario analysis of future copper in-use stock and depreciated copper in-use stock per capita in the U.S. were presented. The corresponding average age of the in-use stock will have a slight rise in the next decade.

The economical load dispatch problem is a critical concern in thermal power systems to ensure cost-effective operation. This paper conducts a comparative analysis between the Social Spider Algorithm (SSA) and Particle Swarm Optimization (PSO) for economical load dispatch in a 6-unit thermal power system with the integration of 11,000 plug-in electric vehicles (PEVs). Both SSA and PSO are population-based optimization techniques designed to minimize the overall fuel cost while satisfying the load demand by determining the optimal power output of each unit. SSA emulates the social behavior of spiders, while PSO simulates the collective intelligence of a swarm of particles. Performance evaluation of the algorithms considers power system data, such as unit fuel costs, minimum and maximum loads, and total load demand. The objective is to minimize fuel cost while meeting the load demand. Comparative analysis of SSA and PSO includes convergence speed, solution quality, and computational efficiency. Experimental results indicate that SSA outperforms PSO in achieving a more optimal and economical load dispatch solution for the 6-unit thermal power system. SSA demonstrates faster convergence and provides superior-quality solutions compared to PSO. This paper contributes to the field by highlighting SSA's effectiveness in achieving cost-efficient operation of thermal power systems. The findings suggest that SSA holds promise as an optimization technique for similar power system optimization problems, enhancing overall operational efficiency and reducing costs for thermal power plants.

This paper explores the dynamic intersection of Artificial Intelligence (AI) and the Internet of Things (IoT), focusing on emerging trends in intelligent data analysis and privacy protection. Employing a systematic literature review, we analyze recent advancements and challenges in AI-IoT integration. The paper highlights key developments in sensor data anomaly detection, AI-driven big-data analytics, and IoT-based communication techniques. It delves into the complexities of privacy protection in IoT, examining innovative solutions like federated learning and blockchain technologies. The study also addresses the challenges in AI for IoT, prioritizing the most pressing issues such as data security, ethical implementation, and compliance with regulations like GDPR. The research underscores the need for adaptable solutions that balance technological advancements with privacy and security considerations, setting a path for future exploration in AI-empowered IoT applications.

The intrinsic dynamic and static nature of the non-covalent Br-*-Br interactions in the neutral polybromine clusters is elucidated for Br4–Br12, applying QTAIM dual functional analysis (QTAIM-DFA). The asterisk (*) emphasizes the existence of the bond critical point (BCP) on the interaction in question. Data from the fully optimized structures correspond to the static nature of interactions. The intrinsic dynamic nature is originated from those of the perturbed structures generated using the coordinates derived from the compliance constants for the interactions and the fully optimized structures. The non-covalent Br-*-Br interactions in the L-shaped clusters of the Cs symmetry are predicted to have the typical hydrogen bond nature without covalency, although the first ones in the sequences have the vdW nature. The L-shaped clusters are stabilized by the n(Br)->σ*(Br–Br) interactions. The compliance constants for the corresponding non-covalent interactions are strongly correlated to the E(2) values based on NBO. Indeed, the MO energies seem not contribute to stabilize Br4 (C2h) and Br4 (D2d), but the core potentials stabilize them, relative to the case of 2Br2, maybe due to the reduced nuclear-electron distances in the average for the dimmers.

With Internet of Things (IoT) gaining presence throughout different industries a lot of new technologies have been introduced to support this undertaking. Implications on one such technology, wireless systems allowed for the use of different communication methods to achieve the goal of transferring data reliably, with more cost efficiency and over longer distances. Anywhere from a single house with only a few IoT devices such as a smart light bulb or a smart thermostat connected to the network, all the way to a complex system that can control power grids throughout countries, IoT has been becoming a necessity in everyday lives. This paper presents an overview of the devices, systems and wireless technologies used in different IoT architectures (Healthcare, Vehicular Networks, Mining, Learning, Energy, Smart Cities, Behaviors and Decision Making), their upbringings and challenges to this date and some foreseen in the future.

Background: Redo mitral valve surgery is the standard of care for failed mitral bioprostheses or recurrence of mitral regurgitation after repair. Nonetheless, catheterbased valve-in-valve (ViV) or valve-in-ring (ViR)-procedures increasingly become viable alternatives in high risk subpopula-tions. Despite reported good initial results, only little is known about longer-term outcomes. The present series report long-term outcomes of transcatheter mitral ViV- and ViR-procedures. Methods: All consecutive patients (n=54) undergoing transcatheter mitral ViV- or ViR-procedures for failed bioprostheses or recurring regurgitation after mitral repair were retrospectively en-rolled in the time period between 2011 and 2021. The mean age was 76.5 ± 6.5 years, and 30 (55.6%) of the patients were male. The procedures were done using a commercially available balloon-expandable transcatheter heart valve. Clinical and echocardiographic follow-up data were obtained out of the hospital’s database and analyzed. Follow-up reached up to 9.9 years with a total of 164.3 patient-years. Results: A total 25 patients received a ViV- and 29 patients a ViR-procedure. Both groups were at high surgical risk with an STS-PROM of 5.9 ± 3.7 % in ViV- and 8.7 ± 9.0% in ViR-patients, respectively (p<0.01). The procedures itself were mainly unevent-ful with no intraoperative deaths and a low conversion rate (n=2/54; 3.7%). VARC-2 procedural success was low (ViV 20.0% and ViR 10.3%; p=0.45), which was either driven by high rates of transvalvular pressure gradients “>5mmHg” (ViV 92.0% and ViR 27.6%; p<0.01) or residual regurgitation “>trace” (ViV 28.0% and ViR 82.7%; p<0.01). ICU-stay was prolonged in both groups (ViV 3.8 ± 6.8 days and ViR 4.3 ± 6.3 days; p=0.96) with acceptable hospital stay (ViV 9.9 ± 5.9 days and ViR 13.5 ± 8.0 days; p=0.13). Despite 30-days mortality was acceptable (ViV 4.0% and ViR 6.9%; p=1.00), the mean posthospital survival time was disappointingly low (ViV 3.9 ± 2.6 years and ViR 2.3 ± 2.7 years; p<0.01). Overall survival I the entire group was 33.3%.Cardiac reasons for death were frequent in both groups (ViV 38.5% and ViR 52.2%). Cox-regression analysis identified ViR-procedures as a predictor of mortality (HR 2.36, CI 1.19 - 4.67, p=0.01). Conclusions: Despite acceptable immediate outcomes in this high-risk subpopulation, long-terms results are discouraging. Transvalvular pressure gradients as well as residual regurgitations remained drawbacks in this real-world population. The indication for catheterbased mitral ViV- or ViR-procedures thoughtfully must be made in consideration of conventional redo-surgery or conservative treatment.

In an effort to discover an effective and selective antitumour agent, synthesis and anti-cancer potential of 4-(pyridin-4-yl)-6-(thiophen-2-yl)pyrimidin-2(1H)-one (SK-25), which has been reported earlier by us with significant cytotoxicity towards MiaPaCa-2 malignant cells, with an IC₅₀ value of 1.95 μM and was found to instigate apoptosis. In the present study, the antitumour efficacy of SK-25 was investigated on Ehrlich ascites tumour (solid), Sarcoma 180 (solid) tumour and Ehrlich ascites carcinoma. The compound was found to inhibit tumour development by 94.71% in Ehrlich ascites carcinoma (EAC), 59.06% in Ehrlich tumour (ET, solid) and 45.68% in Sarcoma-180 (solid) at 30 mg/kg dose. Additionally, SK-25 was established to be non-toxic at a maximum tolerated dose of 1000 mg/kg in acute oral toxicity in Swiss-albino mice. Computer-based predictions also show that the compounds could have an interesting DMPK profile. The current study provides insight for further investigation of the antitumour potential of the molecule.

Based on the multiple coordination framework of “promising government-effective market-ordered society”, this paper takes 68 cases of adding elevators to old buildings as samples, and selects six conditional variables: approval optimization, financial subsidies, financing innovation, technological innovation, capable people leading and neighborhood committee guidance. Using Crisp-Set Qualitative Comparative Analysis (csQCA), this paper discusses the combination of conditions that affect the success or failure. It is found that: approval optimization and neighborhood committee guidance are sufficient conditions for the success; successful cases can be divided into three types: “political and social dual-drive type”, “market and social dual-drive type” and “ternary co-drive type”; lack of approval optimization, lack of financing innovation, and the existence of capable people leading are necessary conditions for the failure. The failure cases can be divided into “government and market double failure type” and “market failure type”.

We analyzed the peak spring tidal current speeds, annual mean tidal power densities (TPD) and annual energy production (AEP) obtained from experiment 06.1, referred as the "HYCOM model" throughout, of the three dimensional (3D), global model HYCOM in an area covering the Baja California Pacific and the Gulf of California. The HYCOM model is forced with astronomical tides and surface winds alone, and therefore is particularly suitable to assess the tidal current and wind-driven current contribution to in-stream energy resources. We find two areas within the Gulf of California, one in the Great Island Region and one in the Upper Gulf of California, where peak spring tidal flows reach speeds of 1.1 meters per second. Second to fifth-generation tidal stream devices would be suitable for deployment in these two areas, which are very similar in terms of tidal in-stream energy resources. However, they are also very different in terms of sediment type and range in water depth, posing different challenges for in-stream technologies. The highest mean TPD value when excluding TPDs equal or less than 50 W/m² (corresponding to the minimum velocity threshold for energy production) is of 172.8 W/m², and is found near the town of San Felipe, at (lat lon) = (31.006 -114.64); here energy would be produced during 39.00% of the time. Finally, wind-driven currents contribute very little to the mean TPD and the total AEP. Therefore, the device, the grid, and any energy storage plans need to take into account the periodic tidal current fluctuations, for optimal exploitation of the resources.

In this paper, we discuss major issues of mathematics teaching and learning in Nepal. The issues coming from theories such as social and radical constructivism suggest that teachers are not trained to use such approach in teaching mathematics, and there is a lack of teaching aids and materials and technological tools. The issues related to social aspects are gender issues, language issues, social justice issues, and issues related to the achievement gap. The cultural issues are related to the diversity of language and ethnicity. The issues related to political aspects are equity and access, economic status, pedagogical choice, and professional organizations and unions. The issues related to technology include the technological skills, use of technology, and affordance. Finally, we suggest that all the stakeholders should pay attention to resolving these issues by improving the curriculum, training teachers, resourcing the classroom with locally made and new technological tools.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma and a heterogeneous disease characterized by mature B-cell neoplasms. The standard immunochemotherapy for DLBCL is R-CHOP, which includes rituximab, cyclophosphamide, doxoru-bicin, vincristine, and prednisone. Up to 30-40% of patients with DLBCL either exhibit primary refractory disease to standard treatment or experience relapse after the initial response. A 72-year-old man diagnosed with DLBCL involving chromosomal translocations t(1;22)(q21;q11.2) and t(6;18)(p25;q21) showed primary refractory disease after the fourth cycle of R-CHOP. The patient ultimately experienced cardiac involvement by the lymphoma and received salvage chemotherapy. He passed away about 15 months away after the diagnosis of DLBCL. We conducted fluorescence in situ hybridization (FISH) for further analysis of the chromosomal translocations. The breakpoint of chromosome 1q21 was located at a distance of around 151 Mb from the telomeric end of chromosome 1p. The breakpoint in chromosome 22q11 contains the immunoglobulin lambda locus. Furthermore, the breakpoint of chromosome 6p was in the telomeric region of chromosome 6p21. The breakpoint of chromosome 18q21 contains BCL2. This case report presents the first documented co-occurrence of chromosomal translocations t(1;22)(q21;q11.2) and t(6;18)(p25;q21) in a patient with DLBCL. These chromosomal translocations may indicate a worse clinical outcome.

In the dynamic field of mental health care, the nuanced application of Artificial Intelligence (AI) through Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) is a pivotal innovation in patient empowerment and service optimization. This study introduces a distinctive online therapy platform that capitalizes on the synergy of NLP and ASR to offer unprecedented levels of interactive and personalized therapeutic interventions. The architecture of our system is meticulously detailed, featuring an ASR component with an impressive Word Error Rate (WER) of 14% when trained on the diverse French subsets of the Mozilla Common Voice dataset, complemented by a high-precision NLP framework skilled in processing and responding to user inputs. The evaluation of our system highlights its efficacy in enhancing therapy sessions and user satisfaction, with an emphasis on the qualitative aspects of user feedback. The paper addresses challenges such as dataset representativeness and language model refinement and articulates the strategic solutions employed to overcome them. The paper concludes with forward-looking perspectives on AI's role in mental health services, advocating for the creation of sophisticated, language-specific datasets and models to satisfy the increasing demands of online therapy, reflecting a growing commitment of patients in the management of their therapy. This research underscores the transformative impact of AI in advancing mental health care into the digital age, representing a significant evolution over existing methodologies.

Breast cancer treatment mostly revolved around radiation therapy and surgical interventions, these treatments doesn’t provide satisfactory relief to the patients and carry unmanageable side-effects. Nanomaterials show promising results in treating cancer cells and have many advantages such as high biocompatibility, bioavailability and effective therapeutic capabilities. Interestingly, fluorescent magnetic nanoparticles have been used in many biological and diagnostic applications, but there is no report of use of fluorescent magnetic submicronic polymer nanoparticles (FMSP-nanoparticles) in the treatment of human breast cancer cells. In the present study, we have tested the effect FMSP-nanoparticles on human breast cancer cells (MCF-7). We have tested different concentrations (1.25µg/1mL, 12.5µg/mL and 50µg/1mL) of FMSP-nanoparticles in MCF-7 cells and evaluated the nanoparticles response morphometrically. Our results revealed that FMSP-nanoparticles produced a concentration dependent effect on the cancer cells, dose of 1.25µg/mL produced no significant effect on the cancer cell morphology and cell death, whereas dosages of 12.5µg/mL and 50µg/mL respectively showed significant nuclear augmentation, disintegration, chromatic condensation followed by dose dependent cell death. Our results demonstrate FMSP-nanoparticles have ability to induce cell death in MCF-7 cells and may be considered as a potential anti-cancer agent for breast cancer treatments.

Physics behind collimated highly directional nature of lasers, and factors that keep the seven coloured waves that form white light together during their journey from Sun to Earth, in the face of the natural disruptive forces, is not fully understood. Energy levels were measured, in terms of alterations in induced current and voltage, in beams from a red laser, white LED light and the Sunlight before and during their disruption by diffusers (frosted glass for the lasers) and diffractors (diffraction grating for the white light) using a photovoltaic solar cell panel attached to a digital multimeter. Results show that disruption of the beams results in release of extra energy named as ‘Latent Light Binding’ Energy’. It is hypothesized that the ‘binding’ energy keeps laser waves firmly bound together both end-on and side-on enabling laser beams to travel long distances in collimated manner. Likewise, the 7 coloured waves that constitute white light are kept together, probably side-on, in their journey from the Sun to the Earth. The observation that diffraction of sunbeam is associated with increased power generation provides a new lead to improve harnessing of solar energy, where, currently, the focus is mainly on improving efficiency of photovoltaic cell.

This study introduces a specialized Automatic Speech Recognition (ASR) system, leveraging the Whisper Large-v2 model, specifically adapted for radiological applications in French language. Our methodology focused on adapting the model to accurately transcribe medical terminology and diverse accents within the French language context, achieving a notable Word Error Rate (WER) of 17.121%. The research involved extensive data collection and preprocessing, utilizing a wide range of French medical audio content. The results demonstrate the system's effectiveness in transcribing complex radiological data, underscoring its potential to enhance medical documentation efficiency in French-speaking clinical settings. The discussion extends to the broader implications of this technology in healthcare, including its potential integration with Electronic Health Records (EHRs) and its utility in medical education. The study also explores future research directions, such as tailoring ASR systems to specific medical specialties and languages. Overall, this research contributes significantly to the field of medical ASR systems, presenting a robust tool for radiological transcription in the French language and paving the way for advanced technology-enhanced healthcare solutions.

Piceatannol is a naturally occurring polyphenolic compound that is widely found in grapes, blueberries, mushrooms, passion fruit and other edible fruits and vegetables. It has a variety of biological activities and pharmacological effects, including anti-inflammatory, antioxidation, antiaging, antiviral, antitumor, anticancer, antiatherosclerosis, antiparasitic, immunoregulatory, and cardiovascular protection effects. The aim of this study was to investigate the antiherpesvirus effects of piceatannol. Pseudorabies virus (PRV) belongs to the family Herpesviridae. PRV has a wide host range and can cause cytopathic effects (CPEs) in PK-15 cells. Therefore, PRV was used as a model to study the antiherpesvirus effect of piceatannol. In this study, we evaluated the antiviral activity of piceatannol against PRV in vitro and in vivo. The results showed that piceatannol could reatrain PRV multiplication in PK-15 cells in a dose-dependent manner. The 50% inhibitory concentration (IC50) was 0.0307 mg/mL, the IC50 was 0.0307 mg/mL, and the selectivity index (SI, CC50/IC50) was 3.68. The addition of piceatannol at different stages of PRV infection inhibited the viral adsorption and intracellular replication phases of PRV. Piceatannol significantly reduced the expression levels of the IE180, EP0, UL29, UL44, US6 and UL27 genes of PRV within 48 h, significantly reduced the expression levels of the PRV gB and gD proteins, and reduced PRV-induced apoptosis. Molecular docking showed that piceatannol had good binding activity with the PRV gB and gD proteins. The results of animal experiments in vitro indicated that piceatannol could delay the onset of disease, improve the survival rate of the mice (14.3%), reduce the viral load in the brain and kidney of the mice, alleviate the pathological changes in the mouse tissues and organs, and increase the levels of TNF-α, IEN-γ and IL-4 in the serum of the mice. These data indicate that piceatannol has good anti-PRV activity in vitro and in vivo, indicating that it could be a novel antiherpesvirus infection agent in the future.

Diabetes mellitus is a global health issue that necessitates the development of novel therapeutic approaches. Today numerous experimental methods have been used and set up to evaluate novel drugs with antidiabetic effects. In addition, several research studies have been done to induce diabetes under in vitro and in vivo conditions, but only a small number of these efforts are currently in a usable stage. This review aims to provide useful guidance for researchers who seek to evaluate the antidiabetic potential of drugs, agents, or new medications using the standard methods published by authorities and academics worldwide organizations and research centers. In this review, we collected 132 relevant articles from prominent databases such as PubMed, ScienceDirect, Scopus and others to summarize in vitro and in vivo experimental validation of antidiabetic treatment from 1943 to present.

The Church of the Company of Jesus in Quito (1605-1765) is one of the most remarkable examples of colonial religious architecture on the World Heritage List. This church has multiple constructive phases and several interventions with no clear record of the entire architectural site, including the historical mortars. A total of 14 samples of coating mortars inside the central nave were taken, with the protocols suggested by the research team and a comparative sample of the architectural group that does not have intervention. The analysis presented in this paper focuses on mineralogical characterization, semi-quantitative analysis by X-ray diffraction and scanning electron microscopy with microanalysis of the samples. The results showed the presence of volcanic aggregate lime and gypsum, used in lining mortars and joint mortars. Mineralogical and textural composition data have allowed the mortar samples to be relatively dated.

Background: Breast cancer remains a significant health concern among women globally. Despite advancements in awareness and diagnostic techniques, it persists as a leading cause of death, with profound impacts on affected individuals' quality of life. Primary and secondary prevention, including regular screenings and practices like Breast Self-Examination (BSE), are pivotal in ensuring early diagnosis. The NHS in Italy offers screenings for women aged 50-69 every two years, managed by the Local Health Authority. However, the participation rates, especially among the Chinese female population residing in Italy, are not well-understood. Methods: Using a snowball method, we electronically disseminated a survey to investigate how Chinese women living in Italy engage with available NHS screening programs. The survey also explores their practice of BSE, the use and impact of technological tools on prevention, and aims to understand their depth of knowledge and misconceptions about breast cancer. Results: The data revealed a significant gap in breast cancer screening adherence and knowledge among Chinese women in Italy, with a notable discrepancy between the general population and those who have previously encountered cancer. Conclusions: The results highlight the urgent need for interventions that are culturally sensitive, stressing that these actions are not only desirable but essential.

Temporary pavilions play an important role as experimental fields for architects, designers and engineers, apart from providing exhibition spaces. Novel structural and formal solutions applied in pavilions also can give them unusual appearance that attracts eyesight of spectators. In this article authors explore the possibility of combination of structural novelty, visual attractiveness and low-cost by a design and construction of a temporary pavilion. For that purpose, an innovative structural system and design approach was applied, i.e. membrane structure designed in Rhino and Grasshopper environments with the use of Kiwi!3D IsoGeometric analysis tool. The designed pavilion, named Obverse/Reverse, was built in Opole, Poland, for the occasion of World Architecture Day in July 2019. Design and construction was performed by the authors in cooperation with students’ organisation Humanisation of Urban Environment from the Faculty of Architecture Wroclaw University of Science and Technology. The Rresultant pavilion proved the possibility of obtaining a low-budgets but visually attractive architectural solution with the adaption of parametrical design tools and some scientific background with innovative structural systems.

This article explores the relationship between convex functions defined on integers ($\mathbb{Z}$) and their extension to real numbers ($\mathbb{R}$). We introduce key definitions and investigate the hypothesis that there exists a unique convex curve within this family of functions, leading to a proof by contradiction. Our findings highlight the preservation of convexity as functions transition from integers to real numbers.

Among the alternatives pointed out as able to make livestock a more efficient activity and reduce its environmental impact, the use of feeds with high digestibility and the use of non-conventional fodder species can be highlighted. Tithonia diversifolia (Hemsl.) A. Gray (TD) has already gained attention as a feed for ruminants. Nonetheless, there has been little discussion about the impacts of this plant on the rumen dynamics that lead to better animal performance. The aim of this study was to assess how increasing levels of TD could affect ruminal microbial biomass synthesis, degradability, and in vitro gas production (IVGP) of diets. Four diets were evaluated: a control diet (TD0: 400 g kg-1 soybean meal and corn grain, and 600 g kg-1 Tifton 85 hay (Cynodon spp)) and three increasing levels of TD (TD9: 90, TD27: 270, and TD45: 450 g kg-1 – dry matter basis) as a replacement for dietary roughage. A 96-hour IVGP assay was carried out and five incubation times (2, 4, 10, 24, and 96 hours) were used for degradability determination. Gas produced, methane (CH4), degradability of organic matter (IVDOM), short-chained fatty acid (SCFA) production, partitioning factor (PF), and microbial biomass (MB) were evaluated among treatments. There was no significant effect (P&gt;0.05) between TD inclusion and IVGP at most incubation times; only at 6 hours of incubation did gas production increase linearly with TD inclusion (R2=0.19; P&lt;0.05). TD inclusion had no effects on CH4. IVDOM increased linearly with TD inclusion at 6 and 10 hours, with TD45 being more degradable than the control diet at 6 hours only. There was a positive linear relationship (R2=0.20; P&lt;0.05) between TD inclusion and PF. TD45 had significantly higher PF values than the control. MB also increased linearly (R2=0.30; P&lt;0.05) with TD inclusion, and all diets with TD had significantly more MB than the control diet. The inclusion of TD at the levels used in this work was revealed to have a positive impact on microbial protein synthesis, which could be related to the higher microbial efficiency of increased substrate quality.

Oxalate, a compound produced by many edible plants and as a terminal metabolite in the liver of mammals, is a toxin that has a detrimental role to human health. Humans and other mam-mals do produce the enzymatic machinery to degrade oxalate. However, numerous oxa-late-degrading bacteria reside in the mammalian gut and thus provide an important function for hosts. The current review focuses on the environmental factors that influence the efficacy of pro-biotic oxalate-degrading bacteria, relative to oxalate metabolism. We describe the mechanism of oxalate catabolism and its consumption by obligate and facultative anaerobic oxalate-degrading bacteria, in both in vitro and in vivo environments. We also explore the environmental variables that impact oxalate degradation. Studies on single species degrade oxalate have not shown a strong impact on oxalate metabolism especially in high oxalate conditions such as consumption of foods high in oxalate (such as coffee and chocolate for humans or halogeton in animal feed). Considering effective variables which enhance oxalate degradation could be used in application of effective probiotic as a therapeutic tool in individuals with hyperoxaluria. This study indicates probiotics can be considered a good source of naturally occurring oxalate degrading agent in human colon

The World Anti-Doping Agency has prohibited gene doping in the context of progress in gene therapy. There is a risk that the artificial regulation of genes using plasmids could be applied for gene doping. However, no gold standard method to detect this has been established. Here, we aimed to develop a method to detect multiple transgene fragments as proof of gene doping. First, gene delivery model mice as a mimic of gene doping were created by injecting firefly luciferase plasmid with polyethylenimine (PEI) into the abdominal cavity. The results confirmed successful establishment of the model, with sufficient luminescence upon in vivo imaging. Next, multiple transgene fragments in the model were detected in plasma cell-free (cf)DNA, blood-cell-fraction DNA, and stool DNA using the TaqMan-qPCR assay, with the highest levels in plasma cfDNA. Using just a single drop of whole blood from the model, we also attempted long-term detection. The results showed that multiple transgene fragments were detected until 11 days. These findings indicate that the combination of plasma cfDNA or just one drop of whole blood with TaqMan-qPCR assay is feasible to detect plasmid-PEI-based gene doping. Our findings could accelerate the development of methods for detecting gene doping in humans.

Land surface temperature (LST) is a crucial parameter in climate and ecology, exerting significant influence on meteorological conditions, ecosystems, and human life. LST data sources are diverse, with remote sensing being the prevailing means of acquisition. FY-4A/AGRI offers high-quality LST products for East Asia. In this study, we evaluated the performance of the product in Hunan Province and conducted refined analysis of surface heat resources based on the 4km/1h resolution product over a two-year period. The results demonstrate that the FY-4A LST product effectively captures surface temperature (R=0.893), albeit with a relatively high error level (Bias=-6.295℃; RMSE=8.58℃), particularly in capturing high LST values. The performance of this product is superior in the eastern flat terrain area of Hunan Province compared to its performance in the western mountainous region due to environmental conditions causing systematic errors that contribute to instability in detection deviation for this product. Surface heat resources are more abundant in eastern Hunan Province than in mountainous areas located west and southwardly, and the detailed distribution of them at finer scales is mainly influenced by the terrain and climate conditions. There is no obvious seasonal difference in the distribution of heat resources except in winter, and rapid urbanization within Chang-Zhu-Tan urban agglomeration during two years has significantly altered the spatial distribution pattern of surface heat resources across Hunan Province. These findings provide a quantitative baseline for assessing FY-4A satellite's detection capability while serving as a reference for further application of its LST products and establishing foundations for divisional classification and utilization strategies pertaining to surface heat resources within Hunan Province.

Analysing the human body by direct observation can lead to misinterpretation of underlying trends, whereas in vitro organ models facilitate their understanding. Likewise, in vitro organ models serve as valuable tools for assessing the toxicity of compounds. However, none of the existing models had the features we considered critical for toxicity assessment, so we developed an in vitro liver model for toxicity assessment. We used four different types of primary liver cells: Hepatic sinusoidal endothelial cells, hepatic stellate cells, Kupffer cells and hepatocytes. We cultured them in different combinations of composition and volume of cell medium, hepatocyte proportion of total cells, and addition of extracellular matrix. We added rifampicin, ibuprofen and 5-fluorouracil to this model and observed the microanatomy and physiology changes for a week with preclinical and clinical instruments. Among the different model configurations, we selected the feature combination of the in vitro model that had similar biomarker values to those measured by the clinical instruments used in clinical diagnostics. When we exposed the selected model configuration to rifampicin, ibuprofen and 5-fluorouracil, the viability of Kupffer cells and liver sinusoidal endothelial cells was significantly lower than in the untreated samples. In contrast, we observed negligible viability of hepatocytes compared to the untreated samples. We have built an in vitro liver model that resembles the liver microenvironment and we have analysed it with clinical instrumentation to facilitate the data translation. In this sense, we have established that Kupffer and LSEC cells are suitable candidates for the search for clinical diagnostic markers of liver function.

Knowledge about ecological conditions and processes in centers of endemism (CoEs) is still limited with respect to various systematic groups of organisms, ecosystem types, ecological conditions and ecosystem functions. Endemics and CoEs play a prominent role in nature conservation monitoring and management, and in the organization of zoos, aquaria and botanical gardens. We review the meaning of different groups of organisms and indicators for the characterization of endemic-rich regions, the importance of heterogeneity in space, continuity in time, isolation and ex-situ management for the survival of endemic and threatened species. Currently, conversion of land cover and land use change are the most important causes of biodiversity decline and extinction risk of endemic and endangered species concentrated in CoEs. These are followed by climate change, including severe weather, and then natural processes such as volcanism, landslides or tsunamis. For conservation purposes the management of zoos, aquaria, botanical gardens, of regional land use, and social aspects of the diversity of endemics and CoEs have to be taken into account as well. We need better answers to questions about the relationship between ecoregions, CoEs and regional awareness of biodiversity, which is linked with human culture including the management of zoos, aquaria and botanical gardens, aesthetics, wellbeing, health and trade.

Introduction: The combination of Virtual Screening (VS) techniques with in vivo screening in the zebrafish model is currently being used in tandem for drug development in a faster and more efficient way. Areas covered: We review the different virtual screening techniques, the use of zebrafish as a vertebrate model for drug discovery and the synergy that exists between them. Expert opinion: We highlight the advantages of combining virtual and zebrafish larvae screening for drug discovery. On the one hand, VS is a faster and cheaper tool for searching active compounds and possible candidates for therapy than in vivo screening when processing large compound libraries. On the other hand, zebrafish larvae form a vertebrate model which allows in vivo screening of large amounts of the compounds. Importantly physiology and chemical response are mostly conserved between zebrafish and mammals. The availability of the transgenic and mutant zebrafish lines allows an analysis of a specific phenotype upon treatment along with toxicity, off-target effect, side effects, and dosage. Advantages of VS, in vivo whole animal approach screening, and the screening combinations are also reviewed.

Human eye is a specialized organ with complex anatomy and physiology, because it is characterized by different cell types with specific physiological functions. Given the complexity of the eye, ocular tissues are finely organized and orchestrated. In the last few years many in vitro models have been developed, in order to meet the 3Rs principle (Replacement, Reduction and Refinement) for eye toxicity testing. This procedure is highly necessary to ensure that the risks associated with ophthalmic products meet appropriate safety criteria. In vitro preclinical testing is now a well-established practice of significant importance for evaluating the efficacy and safety of cosmetic, pharmaceutical, and nutraceutical products. Along with in vitro testing, also computational procedures, herein described, for evaluating the pharmacological profile of potential ocular drug candidates including their toxicity, are in rapid expansion. In this review the ocular cell types and functionality are described providing an overview about the scientific challenge for the development of three-dimensional in vitro models.

Detecting suicidal ideation in adults with major depression is crucial for timely intervention and prevention of self-harm. As suicide is influenced by various biological, socio-cultural and psychological factors, traditional screening methods have accuracy and efficiency limitations. In certain cultures, societal stigma and marginalization can compel individuals with depression to conceal their suffering. Such individuals often turn to online social media platforms and share their experiences with peers under the protection of anonymity. Our research explored the potential of machine learning detection of suicidal ideation among Romanian adults with major depression that contributed to a web-based depression support forum. A trained algorithm (C4.5 decision tree) analyzed 125 posts fed to on a free access online support forum over 5 years (2014 – 2018) and classified them based on suicidal ideation content. 32 texts (25%) were identified as having a high probability of suicidal ideation content. 65% of the authors were male, with a mean age of 36.7±10.3 years and an average duration of illness of 3.4±1.4 years. Texts indicating positive suicidal ideation were generally shorter and elicited more general responses but fewer professional responses compared to those without suicidal ideation content. The study's main limitations include the relatively small number of classified texts, the absence of prospective information and the lack of qualitative evaluation of the excerpts' content. As socio-demographic and linguistic actuarial results were comparable to data reported by real life studies, we may consider basic text mining techniques as a screening tool that is able to detect suicidal ideation in texts written in unstructured Romanian language.

This comprehensive study delves into the implementation and effectiveness of the flipped classroom model in engineering EMI (English as a Medium of Instruction) courses in Taiwan. Situated within the strategic framework of Taiwan's Bilingual Nation 2023 policy, this research aims to enhance the global competitiveness of Taiwan's semiconducting manufacturing engineering sector. The study’s primary focus is on understanding the perceptions and learning outcomes of EFL (English as a Foreign Language) students in engineering courses. Through a methodical approach using descriptive statistics and chi-square tests, the study analyzes data from 33 junior engineering students specializing in mechanical and mechatronic fields. The findings highlight a significant positive influence of the flipped classroom model on various aspects of student learning, including increased engagement, heightened creativity, and enhanced problem-solving skills. Additionally, the study emphasizes the critical role of timely L1 support in facilitating effective learning in EMI settings. These outcomes not only demonstrate the flipped classroom's viability as a sustainable educational approach but also provide crucial insights for educators and policymakers. By aligning cutting-edge teaching methods with the evolving needs of engineering education, this study contributes to the discourse on educational innovation. It ultimately guides the development of future engineering programs that cater to diverse student needs and fosters the cultivation of skilled engineering professionals.

Paving blocks are concrete pieces placed in exposed places to the weather, which are sub-jected to loads and wear. Hence, quality control in the manufacture of paving blocks is es-sential to guarantee the properties and durability of the product in construction projects. In Ecuador, the requirements are described in the Ecuadorian technical standard "NTE INEN 3040", and tensile splitting strength is a fundamental requirement to guarantee product quality. It is analyzed using quality control measurements such as dimensions, the weight of the fresh paving block in the vibro-compacted process, and the percentage of water absorption in order to know how the variables influence and manage to predict the tensile splitting strength to avoid product non-conformity in advance, having a timely and better control of the manufacturing process. The data was obtained from a company that can produce 30 000 units per day of rectangular paving blocks with 6 cm thickness. Mul-tivariate models such as multiple linear regression, regression trees, random forests and neural networks are performed to predict the tensile splitting strength variable through two groups of predictors; the first group is the thickness mm, width mm, length mm, mass of fresh paving block g and percentage of water absorption %. The second group of pre-dictor variables is the density of the fresh paving block kg/m3 and the percentage of water absorption %. It is concluded that the multiple linear regression method performs better in predicting the first group of predictor variables with a mean square error (MSE) of 0.110086, followed by the neural network without hidden layers resulting in an MSE of 0.112198. The best method for the second set of predictors was the neural network with-out hidden layers with a mean square error ( MSE ) of 0.112402, closely followed by the multiple linear regression model with an MSE of 0.115044.

Soil-transmitted helminthiasis (STH), a neglected tropical disease (NTD) remains a major health problem all over the world including Ghana, which has STH prevalence of 25.4%. To control the disease, the government of Ghana currently concentrates on implementing mass drug administration (MDA) efforts focusing only among school-aged children. However, various studies have suggested that focusing on only a specific group for MDA may not be cost-effective. Moreover, some adults such as teachers and school-workers spend large fraction of their time with children, who shed more parasite in environment due to unhygienic behavior, and thus have a higher risk of getting infected as compared to other adults. In this study we use a mathematical model to evaluate age-structured and risk-based policies for implementing MDA while capturing transmission dynamics of STH in Ghana. A cost model was developed that included various costs related to MDA to study cost-effectiveness of current policies of MDA in Ghana against novel policies to control STH in Ghana. We carry out analysis for five different scenarios— I: no MDA (baseline), II: current MDA policy (focusing children) in Ghana, III: MDA for different age groups (adults and children groups) for unlimited budget, IV: MDA for different age groups with limitations of number of individuals treated, and, V: MDA for different groups based on their risk of getting infected (adults school workers (high-risk group), adults non-school workers and children groups). Our results suggest that it might be more cost-effective to allocate treatment through MDA to at least some proportion of adults along with children. In case of unlimited budget, the best strategy in Scenario IV would be to treat approximately 22% of adults and approximately 45% of children. The most cost-effective among the 5 scenarios is suggested through scenario V, where high-risk adults group and children are provided MDA at higher level than low-risk adults. In conclusion, age-structured and risk-based allocation of treatment and resources is crucial to reducing STH load in developing countries.

This study was to designed to develop sorafenib-loaded polymeric nanoparticles, surface-modified with pluronic F-127 for the effective treatment of hepatocellular (HCC) and renal cell carcinomas (RCC). Poly lactic coglycolic acid (PLGA) was used to encapsulate different concentrations of sorafenib by a simple modified solvent evaporation method applying pluronic F-127 as a surfactant and coating agent. The preformulation and characterization studies were conducted to evaluate the incompatibilities among sorafenib, PLGA and pluronic F-127, and physicochemical properties of the formulations, respectively. The sizes of the coated and plain PLGA nanoparticles were ~140 ± 14.7 nm and ~ 120 ± 10.1 nm, respectively. It has been confirmed from the in vitro release studies that PLGA sustained the release of sorafenib, avoiding the initial burst release, while the in vivo studies demonstared that surface-modified nanoparticles have greater affinity in the target aorgans than plain PLGA nanoparticles and free drug keeping the dose constant (20 mg/kg body weight). The delivery of sorafenib-loaded polymeric PLGA nanoparticles into the target tissues and their pharmacokinetic parameters have been improved significantly compared to the reported formulations.

The pinning force density FpJc,B=Jc×B (where Jc is the critical current density and B is the applied magnetic field) is one of the main parameters that characterize the resilience of a superconductor to carry a dissipative-free transport current in an applied magnetic field. Kramer (1973 J. Appl. Phys. 44 1360), and Dew-Hughes (1974 Phil. Mag. 30 293) proposed a widely used scaling law for the pinning force density amplitude: FpB=Fp,max×p+qp+qppqq×BBc2p×1-BBc2q, where Fp,max, Bc2, p, and q are free-fitting parameters. Since the late 1970-s till now, several research groups have reported experimental data on the dependence of Fp,max on the average grain size, d, in Nb3Sn-based conductors. Godeke (2006 Supercond. Sci. Techn. 19 R68) proposed that the dependence obeys the law Fp,maxd=A×ln1d+B. However, this scaling law has several problems, for instance, the logarithm is taken from a non-dimensionless variable, and Fp,maxd&lt;0 for large grain sizes, and Fp,maxd→∞ for d→0. Here, we reanalysed the full inventory of publicly available Fp,maxd data for Nb3Sn conductors and found that the dependence can be described by Fp,maxd=Fp,max0×exp-dδ law, where the characteristic length, δ, varies within a remarkably narrow range, that is, δ=175±13 nm, for samples fabricated by different technologies. The interpretation of the result is based on the idea that the in-field supercurrent flows within a thin surface layer (thickness of δ) near the grain boundary surfaces (similar to London’s law, where the self-field supercurrent flows within a thin surface layer with a thickness of the London penetration depth, λ, and the surface is a superconductor-vacuum surface). An alternative interpretation is that δ represents the characteristic length of the exponential decay flux pinning potential from the dominant defects in Nb3Sn superconductors, which are grain boundaries.