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Abstract Background Rabbit hemorrhagic disease (RHD) is an acute infectious disease that damages the rabbit industry by producing significant mortality rates in young and adult rabbits. RHD is better controlled by vaccination. Objective The current study's goal was to prepare and evaluate the immuno-enhancing effect of montanide ISA70 and aluminum hydroxide (Al(OH)3) gel incorporated within the inactivated RHDV2 vaccine and assess the vaccine's protective efficacy against the homologous and heterologous local RHDV2 strains in rabbits. Methods Inactivated RHDV vaccines were prepared using Montanide ISA70 oil or Al(OH)3 gel adjuvants and submitted to sterility, safety, and potency tests. 200 rabbits were equally divided into 4 groups: G1 (control), G2 (vaccinated with gel-incorporated vaccine), G3 (vaccinated with montanide-incorporated vaccine), and G4 (vaccinated with gel- and montanide-incorporated vaccines). Individual blood samples were collected from one week to six months from all groups. The vaccine's potency was measured by the HI test and protection percentage post challenge. Results Data revealed slightly increasing HI titer means reaching the 1st peak at 4 weeks post-vaccination (7.33, 7.67, and 7.33 log2 in the 2nd, 3rd, and 4th groups, respectively), then slightly decreasing and peaked again, giving 9.33 log2 for the2nd group at 3 months post-vaccination (MPV), 10.67 log2 for 3rd the group, and 10.33 log2 for the 4th group at 5 months post-vaccination. Titer gradually decreased but remained protective. The protection rate ranged from 80–100% and 80–90% for homologous and heterologous local RHDV2 vaccines, respectively, within 3 weeks and 6 months post-challenge. The montanide oil RHDV2 vaccine induced better protection than the aluminum gel RHDV2 vaccine. Conclusion The results demonstrated evidence of cross-protection between RHDV2 strains. The oil emulsion vaccine induced higher and longer-lasting antibody titers than those obtained with the RHDV2 aluminum gel vaccine.

Bilateral edematous optic disc swelling from papilledema is caused by elevated intracranial pressure (ICP). Idiopathic intracranial hypertension (IIH), a clinical syndrome with elevated ICP of unclear etiology, is a frequent cause of this condition. IIH typically affects obese middle-aged females. Papilledema usually has a fairly symmetrical bilateral pattern. Unilateral papilledema is a rare disorder that must be detected early to avoid optic nerve damage. However, the etiology of unilateral papilledema remains unclear. Based on bilateral optic nerve sheath diameter measurements, we aimed to find an explanation for the unilaterality in this rare case.

Abstract Newly synthesized vaccines prepared from formalin-killed bacteria Streptococcus pyogenes were investigated in the current study to evaluate the effectiveness of the newly synthesized vaccine as well as their safety by injected intraperitoneal. The study involved several steps 1st step is the preparation of the vaccine followed by the 2nd step: Evaluate the effectiveness and vaccine safety against pathogenic S. pyogenes through 4 different groups including control (Group I). Group II (Bacterial, infected group), Group III (Vaccine), and the Last group was the challenged group after the vaccination (Vacc + Bac). Different Immunological and biochemical parameters were measured in addition to hematological and histopathological examinations. For example, oxidative/antioxidants, inflammatory biomarkers, fragmentation and cell damage, and finally the histopathological study. The current study showed an increase in all oxidative, inflammatory, and cell damage (DNA fragmentation assays), additionally markedly elevation in histopathological cell damage in the infected group (Group II) compared with the control group. The vaccine and challenged after vaccination group (vaccine + Bacteria), showed great improvement in oxidative biomarkers (LPO) and an increase in antioxidants biomarkers (GSH, SOD, GST, DPPH, ABTS, GR and GPx), Also the inflammation and histopathological examination. The newly synthesized vaccine improved the resistance of Oreochromis niloticus and can be used as a preventive therapy agent for pathogenic bacteria S. pyogenes.

Background and purpose: Researches show that limited models have been designed to measure the maturity of digital transformation in libraries, however, no valid and reliable tool has been provided to measure the maturity of digital transformation in academic libraries so far. Identifying the factors affecting the maturity of digital transformation and managing its correct use in the libraries of medical sciences universities is of particular importance in improving the efficiency and effectiveness and as a result, the success and growth of the libraries. Therefore, the purpose of the current research is to compile and validate a suitable tool to determine the factors affecting the maturity of digital transformation in the libraries of medical sciences universities. Materials and methods: The type of qualitative (exploratory) study was based on the plan of developing the tool, which was carried out in two phases: in the first phase, to collect information related to the identification of factors affecting the maturity of digital transformation, the meta-composite method was used under “ Sandelowski and Barroso's model. Then, in the second phase of the Delphi method, the factors were localized and adapted to the topic. The intended community in the met composite section was all related sources from reliable databases, with 82 sources. After extracting, screening, and considering the input and output criteria of the researchers, 41 Persian and Latin sources were selected. The statistical population in the Delphi phase included 15 professors and experts in the field of digital maturity and digital technologies in universities of medical sciences who were selected in a non-probable and purposeful way according to the criteria for entering the research (relevance of work experience and organizational position and field of study of the experts to the subject research, conflict with the discussed issue, having at least 5 years of work experience) as samples. The measurement tool was a structured questionnaire of 69 questions created by the researcher, which was compiled by reviewing the previous findings and then sent to the experts in two stages with the help of the fuzzy Delphi technique, and finally 61 final questions were obtained. Results: In this research, based on the findings of the meta-combination section, 69 primary categories for the maturity of digital transformation were identified first by reviewing the literature and backgrounds. Then, in the next step, a relationship was established between these categories, and they were categorized into 13 items. Then, the identified main categories were divided into 2 general categories (internal and external factors). Then, for quality control, the validity and internal reliability of check methods were used. The size of the Kappa coefficient (0.78) in this research indicated the good internal reliability of the obtained results. Finally, for validation, the developed tool was provided to the members of the Delphi panel, which had 15 members. After two stages of fuzzy Delphi and after making corrections and removing irrelevant items (8 items) and the agreement score of experts in the rest of the factors (61 items), it was observed that the disagreement of experts in the first and second steps did not have a significant difference and the agreement score of experts in all commons was more than 70%; therefore, the survey was stopped in the second stage, and all factors were confirmed. The results of the fuzzy Delphi analysis led to the confirmation of 61 items of the digital transformation maturity questionnaire in the libraries of medical sciences universities in 2 main components (inter-organizational factors and extra-organizational factors) and 13 subcomponents. Conclusion: The developed tool provides researchers with criteria for determining the state of maturity for digital transformation in the libraries of medical sciences universities. The discovered factors include 10 internal organizational factors (digital culture development, digital infrastructure development factor, new technology infrastructure development factor, human resources development factor, management resource development factor, digital strategy development factor, digital support factor, organizational rules and regulations factor, financial resources factor and experience development factor (digital users) and 3 components of extra-organizational factors (collaboration and support of upstream and academic institutions, the factor of development of national culture in the field of digital transformation and the factor of national laws and regulations) which can be used as a suitable tool to measure the maturity of digital transformation in libraries.

Abstract Using recent information and communication technologies for monitoring and management initiates a revolution in the smart grid. These technologies generate massive data that can only be processed using big data tools. This paper emphasizes the role of big data in resolving load forecasting, renewable energy sources integration, and demand response as significant aspects of smart grids. Meters data from the Low Carbon London Project is investigated as a case study. Because of the immense stream of meters' readings and exogenous data added to load forecasting models, addressing the problem is in the context of big data. Descriptive analytics are developed using Spark SQL to get insights regarding household energy consumption. Spark MLlib is utilized for predictive analytics by building scalable machine learning models accommodating meters' data streams. Multivariate polynomial regression and decision tree models are preferred here based on the big data point of view and the literature that ensures they are accurate and interpretable. The results confirmed the descriptive analytics and data visualization capabilities to provide valuable insights, guide the feature selection process, and enhance load forecasting models' accuracy. Accordingly, proper evaluation of demand response programs and integration of renewable energy resources is accomplished using achieved load forecasting results.

Objective: Staging workup and detection of distant metastases is important in newly diagnosed breast cancer in order to make treatment decisions and establish the prognosis. There is wide variation in current recommendations for staging investigations in breast cancer. Routine staging is performed for all patients in Bahrain because of lack of consistent guidelines. Optimization of the criteria for staging is important for identification of metastases, while minimizing harm and costs. The aim of this study was to evaluate factors associated with distant metastases in newly diagnosed patients with breast cancer, in order to establish local guidelines for proper selection of patients for systemic staging. Materials and Methods: Patients with newly diagnosed breast cancer at Salmaniya Medical Complex in Bahrain who underwent staging investigations between January 2016 and December 2022 were identified from a pathology database. Patients with previous history of cancer, synchronous tumors, bilateral breast cancer and ductal carcinoma in situ were excluded. Clinical, radiological and pathological data were retrospectively analyzed. Results: A total of 593 patients underwent staging computed tomography and bone scans or a PET scan. Distant metastases were identified in 20.7% of cases. M1 disease was significantly associated with multifocality/multicentricity, high grade tumors, hormone receptor-negative cancers, high Ki67 index, advanced tumor stage, node-positive disease, triple-negative breast cancer, use of PET scans and those who underwent neoadjuvant chemotherapy. Age was not associated with identification of distant metastases. Conclusion: The prevalence of distant metastases in this population of newly diagnosed patients with breast cancer was higher than previously reported. Routine staging of all patients at presentation was not indicated, especially for asymptomatic patients with early breast cancer. This study identified certain groups of patients with a higher risk of distant metastasis, in whom metastatic workup should be performed. These findings may allow for the development of a local guideline that addresses the question of which breast cancer patients need staging investigations for distant metastases.

Nefazodone, a derivative of triazolones, belongs to a group of heterocyclic aromatic compounds. It is used as an antidepressant for treating depression, including major depressive disorder. Unlike other antidepressant groups such as selective serotonin reuptake inhibitors, tricyclic antidepressants, or monoamine oxidase inhibitors, Nefazodone does not share chemical similarities. Recent research has focused on studying the reactivity and chemical structure influenced by Nefazodone's medicinal features in the drug's adsorption process on single-wall Carbon Nanotube (CNT) as an adsorbent in the gas phase using density functional theory (DFT), Becke, 3-parameter, Lee–Yang–Parr (B3LYP) 6-311+G(d,p) basis set (DFT/B3LYP/6-311+G(d,p)). The effect of electronegative atoms and phenyl groups on the adsorption of Nefazodone on CNT has been studied by calculating the adsorption energy for all active sites. On the other hand, thermodynamic values, such as Gibbs free energy (−4873.09 kJ), Enthalpy (−4872.83 kJ), and Entropy (903.09 J/mol.kelvin), as well as thermodynamic capacity (497.45 J/mol.kelvin), were calculated to show the reactivity of Nefazodone. The stability and reactivity were examined through the energies of the highest occupied molecular orbital (HOMO) (−5.53 eV) and lowest unoccupied molecular orbital (LUMO) (−0.58 eV) of Nefazodone, highlighting ten regions with chemical activity, all of which are thermodynamically stable. Some Electronic parameters such as chemical potential (µ), electronegativity (χ), softness (σ), hardness (η), and electrophilicity index (ω) were calculated. The comparison of chemical potential values between Nefazodone(−3.05 eV) and the more stable complex (−3.81 eV) illustrates the more reactivity for the complex. This suggests that Nefazodone can be transferred to biological systems through such an adsorption mechanism.

Abstract Emerging technologies are continually redefining the paradigms of smart farming and opening up avenues for more precise and informed farming practices. A tiny machine learning (TinyML)‐based framework is proposed for unmanned aerial vehicle (UAV)‐assisted smart farming applications. The practical deployment of such a framework on the UAV and bespoke internet of things (IoT) sensors which measure soil moisture and ambient environmental conditions is demonstrated. The key objective of this framework is to harness TinyML for implementing transfer learning (TL) using deep neural networks (DNNs) and long short‐term memory (LSTM) ML models. As a case study, this framework is employed to predict soil moisture content for smart agriculture applications, guiding optimal water utilisation for crops through time‐series forecasting models. To the best of authors’ knowledge, a framework which leverages UAV‐assisted TL for the edge internet of things using TinyML has not been investigated previously. The TL‐based framework employs a pre‐trained data model on different but similar applications and data domains. Not only do the authors demonstrate the practical deployment of the proposed framework but they also quantify its performance through real‐world deployment. This is accomplished by designing a custom sensor board for soil and environmental sensing which uses an ESP32 microcontroller unit. The inference metrics (i.e. inference time and accuracy) are measured for different ML model architectures on edge devices as well as other performance metrics (i.e. mean square error and coefficient of determination [R2]), while emphasising the need for balancing accuracy and processing complexity. In summary, the results show the practical feasibility of using drones to deliver TL for DNN and LSTM models to ultra‐low performance edge IoT devices for soil humidity prediction. But in general, this work also lays the foundation for further research into other applications of TinyML usage in many different aspects of smart farming.

Abstract Background The house fly, Musca domestica, is a significant carrier of diseases that can impact public health. Repeated use of pyrethroid insecticides may act as a selection pressure for mutations and amino acid substitutions in the house fly voltage-sensitive sodium channel (VSSC), which ultimately confers resistance. The objectives of this study were to determine the presence of knockdown resistance (kdr) mutations using molecular tools and to set up a CDC bottle bioassay specific for house flies in the United Arab Emirates (UAE) to screen for deltamethrin resistance. Methods Adult flies were collected from 19 locations in Abu Dhabi, UAE, and DNA was extracted, followed by PCR amplification of specific alleles (PASA) and conventional PCR using several primers to amplify regions of the VSSC gene. Sanger sequencing was performed on PCR products. We also designed primers that detect four kdr mutations using complementary DNA (cDNA) in reverse transcriptase (RT)-PCR followed by Sanger sequencing. Additionally, a CDC bottle bioassay was set up for detecting deltamethrin resistance in adult house flies. Results In PASA, the primers successfully amplified the target bands (480, 280 and 200 bp). The kdr allele was found in flies collected from 18 of the 19 locations, at the highest and lowest prevalence of 46.9% and 9.4%, respectively. Resistant homozygous (RR) insects constituted 5.0% of the tested populations, and heterozygous (RS) insects accounted for 36.5%. The RR genotype was prevalent in house flies collected at 10 of 19 sampling locations. House fly populations were mostly in Hardy–Weinberg equilibrium, except in three locations. In addition to verifying the presence of the previously identified kdr mutation L1014F, in this study we detected two kdr mutations, L1014H and T929I, that have not previously been reported in the UAE. Also, for the first time in the UAE, a CDC bottle bioassay for deltamethrin resistance was used, which found that 60 min and 4.5 µg/ml were the diagnostic time and dose, respectively. Using this assay, we detected deltamethrin resistance in house flies from two of 16 locations, with a resistance level of 12.5%. Conclusions Using DNA sequencing, we confirmed the presence of a known kdr mutation and uncovered two new kdr mutations in house flies from Abu Dhabi. Additionally, we detected deltamethrin resistance in these flies using a CDC bottle bioassay. Further research is recommended to comprehensively identify more kdr mutations in UAE house fly populations and assess their impacts on control strategies. Graphical Abstract

Efficient path planning optimization strategies are required to maximize flying time while consuming the least energy. This research offers a novel approach for energy-efficient path planning for Unmanned Aerial Vehicles (UAVs) that combines a hybrid evolutionary algorithm and Q-learning while accounting for the UAV’s velocity and distance from obstacles. To overcome the constraints of traditional optimization approaches, the hybrid methodology combines genetic algorithms and Q-learning. The suggested approach optimizes path-planning decisions based on real-time information by considering the UAV’s velocity and distance from obstacles. Genetic Algorithm (GA) creates a wide collection of candidate pathways. In contrast, Q-learning uses reinforcement learning to make educated selections based on the UAV’s present velocity and proximity to static obstacles. This integration allows the UAV to modify its path dynamically based on its energy requirements and environmental constraints. The main goal is to develop a UAV path planning scheme capable of dealing with obstacle-filled environments to improve energy efficiency and collision avoidance during flight missions. Our experimental results show that the hybrid technique outperforms the classical GA method in terms of energy efficiency by significantly reducing energy consumption while maintaining a suitable collision rate and the best path cost to the desired locations. The analysis results improve the performance of the hybrid GA/QL algorithm by more than 57.14% compared to classical GA.

Montessori considers as an effective method that is commonly used in nurseries to improve the mental performance and develop the cognitive skills toward children. Tangible user interfaces (TUI) is an effective tool that allows interaction with physical objects in a way that makes this interaction augmented through embedded computation. This paper proposed a new concept of Montessori, which is Interactive Technological Montessori (ITM) using TUI. It aims to measure the impact of using TUI on enhancing the effectiveness of Montessori and make a new futuristic design vision for Montessori activities to motivate children positively. The findings of this paper revealed that Merging TUI with ITM has a great potential to increase the efficiency of Montessori. In addition to considering the appropriate design principles and Multi aging group work help children to be motivated positively to interact with the Montessori activities.

Abstract This study proposes a highly sensitive portable device that utilizes electromagnetic waves and data analytics for instantaneous Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS‐CoV‐2) detection. The device consists of a Radio Frequency (RF) circuit that interprets reflected and transmitted electromagnetic waves to identify virus signatures in physiologically significant matrices, including human saliva and diluted nasopharyngeal swabs. The sensor's accuracy is validated in both pre‐clinical and clinical settings, where clinical measurements demonstrate an instantaneous detection accuracy of 94%, sensitivity of 95%, and specificity of 97.5% between the sensor's physical parameters and SARS‐CoV‐2 detection. The sensor's accurate real‐time response is due to its unique design and precise modeling techniques. In addition, the same sensing system is tested across different viruses and its ability to differentiate between influenza A, respiratory syncytial, and SARS‐CoV‐2 viruses is proven. Hence this work presents a holistic system that can predict the viral concentration of SARS‐CoV‐2, as well as differentiate between different viruses instantaneously and without adding any amplifying agent.

Vaccines against the virulent Newcastle disease virus (NDV) are broadly existing and can provide protection; nevertheless, better immunization practices are required to avoid clinical disease and limit virus circulation. This study evaluating the immunogenicity and protective efficiency of locally prepared clone 30 live-attenuated vaccine against the challenge impact of virulent NDV genotype IV and genotype VIId prevalent in Egypt in comparison with the commercially prepared live Lasota vaccine as a positive control group. The efficacy of the vaccine was evaluated based on the antibody titer, protection rate, oropharyngeal, and cloacal shedding. Therefore, 150 one day old specific pathogen free chicks (SPF) were divided in to three groups 50 bird per group (G).G1and G2 received 100 µl containing 6 log 10 EID50 via the oculo-nasal route of clone 30 vaccine and lasota vaccine in order, while G3 (un vaccinated) received sterile saline at the same route and dose. On day 21 post vaccination (pv) 40 bird from each group were challenged with a dose of 6.5 log10/ml EID50 intramuscular per bird for both genotype IV and genotype VIId (20 bird /genotype virus),the other 10 birds left from each group were kept separate for antibody level monitoring for the 6th week pv. Results revealed that, during vaccine preparation, the clone 30 virus showed a high virus titer when propagated in SPF embryonating chicken eggs (SPF-ECEs), which reached 1012/EID50/ml. The protection rate due to the clone 30 vaccine and the lasota vaccine was alike and showed 75% and 70% against challenge with genotype IV and genotype VIId, respectively, there were no significant differences (p > 0.05) between the antibody titer produced by the clone 30 vaccinated group and lasota group. Both the clone 30 and lasota vaccines showed nearly similar levels of oropharyngeal and cloacal shedding. The results clarify that, although there were no detected differences between the immune response and the protective efficacy of clone 30 vaccine and lasota vaccine but, the use of clone 30 vaccine is still advantageous for its superior immunogenicity and low post-vaccinal reaction, which will make the clone 30 vaccine suggestive for primary immunization, especially in immunologically naive birds. In conclusion, the prepared Clone 30 vaccine in the current study is safe for chicks and can be used as an effective vaccine against the circulating NDV.

Background Mothers’ care seeking behavior for childhood illness is a key factor of utilizing healthcare for children. We examined predictors of mothers’ care seeking for common childhood illnesses. Methods This was a cross-sectional study, using data from the Afghanistan Health Survey (AHS) 2015. Data were used from women who sought healthcare for their unwell children. The women were asked whether their children were sick with fever, cough, faster breathing, or diarrhea in the past 2 weeks. The outcome variable was defined as whether the mother sought healthcare for her unwell child from a public clinic, a private clinic, or from a pharmacy store. The Andersen’s healthcare seeking behavior model was used and multinomial regression analysis applied. Results There were 4979 women, aged 15-49 years, whose under-5 children were sick in the past 2 weeks. Thirty-nine percent of women sought healthcare for their children from a health provider. Mother’s age, child’s age, child’s sex, socioeconomic status, mothers’ perceived severity of childhood illness, and number of under-5 children were predictors of mothers’ care seeking behavior. The likelihood of healthcare seeking was lower for older children (Adjusted odds ratio [OR] [95% CI]: 0.51 [0.37-0.70] from public clinics; 0.33 [0.23-0.47] from private clinics; 0.36 [0.22-0.61] from pharmacy stores), and for girls (Adjusted OR [95% CI]: 0.74 [0.59-0.93] from private clinics). The likelihood of healthcare seeking was greater for children whose mothers knew symptoms of childhood illness (Adjusted OR [95% CI]: 2.97 [1.44-6.16] from public clinics; 7.20 [3.04-17.04] from pharmacy stores). The likelihood of healthcare seeking for children was greater in older mothers (Adjusted OR [95% CI]: 1.54 [1.11-2.12]). It was less likely for the mothers who have more children to seek healthcare for their children (Adjusted OR [95% CI]: 0.53 [0.43-0.65] from public clinics; 0.61 [0.48- 0.79] from private clinics; 0.51 [0.37-0.69] from pharmacy stores). Conclusion Health policy-makers may opt to use our findings, particularly mothers’ knowledge (perceived severity) of symptoms of childhood illness to develop interventions to enhance timely assessment and effective treatment of common preventable childhood illnesses.

People face difficulties in obtaining various care services, which leads to a severe delay in health care treatment, especially in light of the events that accompanied the spread of the Covid-19 epidemic, and therefore obtaining them may cause serious illness or even death. This paper is about MEDICAL+ and MEDICAL PLUS+ applications that were developed to enables the patients to gain healthcare treatment in the easiest, cheapest and timeless way. The applications were developed using web developing languages and tools to enables the user to book appointments, performing online chatting sessions with the healthcare providers and receiving the medical prescriptions directly after the online session in the same application.

Abstract Changing morbidity and mortality due to COVID-19 across the pandemic has been linked with factors such as the emergence of SARS-CoV-2 variants and vaccination. Mutations in the Spike glycoprotein enhanced viral transmission and virulence. We investigated whether SARS-CoV-2 mutation rates and entropy were associated COVID-19 in Pakistan, before and after the introduction of vaccinations. We analyzed 1,705 SARS-CoV-2 genomes using the Augur phylogenetic pipeline. Substitution rates and entropy across the genome, and in the Spike glycoprotein were compared between 2020, 2021 and 2022 (as periods A, B and C). Mortality was greatest in B whilst cases were highest during C. In period A, G clades were predominant, and substitution rate was 5.25 × 10–4 per site per year. In B, Delta variants dominated, and substitution rates increased to 9.74 × 10–4. In C, Omicron variants led to substitution rates of 5.02 × 10–4. Genome-wide entropy was the highest during B particularly, at Spike E484K and K417N. During C, genome-wide mutations increased whilst entropy was reduced. Enhanced SARS-CoV-2 genome substitution rates were associated with a period when more virulent SARS-CoV-2 variants were prevalent. Reduced substitution rates and stabilization of genome entropy was subsequently evident when vaccinations were introduced. Whole genome entropy analysis can help predict virus evolution to guide public health interventions.

Abstract Vickers and Knoop testers are the most common tools used to measure the hardness of materials. However, a very small tilt in the sample surface even in the order of a fraction of a degree produces a noticeable asymmetry, which affects the accuracy of the measured hardness. In this investigation, a mathematical approach has been used to correct asymmetry in the Vickers and Knoop indentations in both metallic and ceramic materials. Measurements were taken for metals such as aluminium 6061(Al-6061), 304 stainless steel as well as various zirconia toughened ceramic materials including tetragonal zirconia doped with: 2.5 mol% Y2O3 (TZ2.5Y), 3 mol% Y2O3 (TZ3Y) and the composite containing 20 wt% alumina (TZ3Y20A), all prepared from commercial powders. A hardness tester equipped with Vickers and Knoop indenters was used for hardness and elastic modulus determination. Optical and scanning electron microscopes have been used to get the indentation micrographs. The method enabled accurate determination of both hardness and elastic modulus from corrected Vickers and Knoop indentations. The hardness and elastic modulus values obtained in this study are in good agreement with reported data for similar materials. The results obtained in this study have been successfully validated using the Zwick reference block. The developed method is readily applicable for the most widely used Vickers hardness machines for the correction of asymmetric indentations if existing, consequently leading to accurate determination of the hardness.

Abstract Background Neutrophil to lymphocyte ratio (NLR), as a recent inflammatory index, has been reported to be a prognostic tool in different diseases. However, implication of this ratio in heart failure (HF) is less investigated. In this systematic review and meta-analysis, we aimed to assess the potential impact of NLR on HF clinical outcomes. Methods Relevant English published records in PubMed, Scopus, Embase, and Web of Science were screened up to July 2023. Articles reporting clinical outcomes (follow-up or in-hospital mortality, readmission, HF prediction, extended hospital stay length, pulmonary vascular resistance, atrial fibrillation, renal disease and functional capacity) in HF sufferers were collected for further analysis with addition of NLR difference stratified by death/survived and HF status. Results Thirty-six articles (n = 18231) were finally selected which reported NLR in HF sufferers (mean: 4.38, 95% confidence interval (CI): 4.02–4.73). We found 25 articles reported NLR and total mortality (either follow-up death (N = 19): 4.52 (95% CI: 4.03–5.01) or in-hospital death (N = 10): 5.33 (95% CI: 4.08–6.57)) with mean NLR of 4.74 (95% CI: 4.28–5.20). NLR was higher among deceased patients compared to survived ones (standard mean difference: 0.67 (95% CI: 0.48–0.87), P

Abstract In this study, a Co-MOF was synthesized via a co-precipitation procedure and then used as support for stabilizing Ag ions and producing Co-MOF@Ag2O nanocomposite by microwave irradiation. The characterization of synthesized Co-MOF@Ag2O nanocomposite was performed by using different techniques such as field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray (EDX) analyses, X-ray diffraction (XRD), Thermogravimetric analysis (TGA), Brunauer–Emmett–Teller (BET) and Fourier-transform infrared (FT-IR). The prepared Co-MOF@Ag2O nanocomposite was applied as a heterogeneous nano-catalyst in the synthesis of pyrazolopyranopyrimidines in water at 50 °C via the one-pot multicomponent reaction of ethyl acetoacetate, hydrazine hydrate, aromatic aldehydes and barbituric acid derivatives. Through this straightforward and effective protocol, different tricyclic fused pyrazolopyranopyrimidines were synthesized at high yields, and short reaction times, through an uncomplicated work-up process with no by-product. The Co-MOF@Ag2O nanocomposite has been effectively recycled for four consecutive cycles without appreciable loss in its activity. Cost-effectiveness, no need for column chromatography, mild conditions, catalyst recyclability, and eco-friendly nature make it a promising candidate compared to other methods.

Background and objectives: Automated drug dispensing systems (ADDs) have been introduced to improve the efficiency of dispensing and patient safety. The available questionnaires measure patient satisfaction with particular aspects of ADDs. Also, the level of patient satisfaction with ADDs is not widely established. This study aimed to develop and validate a novel questionnaire to assess patient satisfaction with ADDs. Methods: Content and construct validity procedures were used to validate the 20-item questionnaire with four domains, including pharmacy administration, dispensing practice, patient education, and the dispensing system. Two hundred consenting participants took part in this study, from those who visited the outpatient pharmacy in a government hospital. Results: The internal consistency of all four scale items shows acceptable reliability (>0.7). In the exploratory factor analysis, three items were removed due to poor factor loading and cross-loading. In the confirmatory factor analysis, the model has acceptable fit indices, including the comparative fit index (0.937), Tucker–Lewis’s index (0.924), standardized root mean square residual (0.051), root mean square error of approximation (0.057), and χ2/df (1.67). The convergent and discriminant validity were established, since the average variance extracted (AVE) was ≥0.5 and the squared correlation (SC) values of one construct with other constructs were less than the AVE of the specific construct. Conclusion: This study offered a reliable and valid 17-item questionnaire incorporating a multi-dimensional four-factor model to evaluate patient satisfaction with ADDs. The validated questionnaire can be utilized to explore patients’ perspectives on ADDs.

Medication adherence is an essential aspect of healthcare for patients and is important for achieving medical objectives. However, the lack of standard techniques for measuring adherence is a global concern, making it challenging to accurately monitor and measure patient medication regimens. The use of sensor technology for medication adherence monitoring has received much attention lately since it makes it possible to continuously observe patients’ medication adherence behavior. Sensor devices or smart wearables utilize state-of-the-art machine learning (ML) methods to analyze intricate data patterns and provide predictions accurately. The key aim of this work is to develop a sensor-based hand gesture recognition model to predict medication activities. In this research, a smart sensor device-based hand gesture prediction model is developed to recognize medication intake activities. The device includes a tri-axial gyroscope, geometric, and accelerometer sensors to sense and gather data from hand gestures. A smartphone application gathers hand gesture data from the sensor device, which is then stored in the cloud database in a .csv format. These data are collected, processed, and classified to recognize the medication intake activity using the proposed novel neural network model called Sea Horse Optimization–Deep Neural Network (SHO-DNN). The SHO technique is implemented to update the biases and weights and the number of hidden layers in the DNN model. By updating these parameters, the DNN model is improved in classifying the samples of hand gestures to identify the medication activities. The research model demonstrates impressive performance, with an accuracy of 98.59%, sensitivity of 97.82%, precision of 98.69%, and an F1 score of 98.48%. Hence, the proposed model outperformed the most available models in all the aforementioned aspects. The results indicate that this model is a promising approach for medication adherence monitoring in healthcare applications, instilling confidence in its effectiveness.