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objectives: Congenital uterine anomalies (CUAs) occur because of malformation of the female genital tract at any stage of the Müllerian duct development process. This retrospective study investigated the prevalence of CUAs and tubal blockage in infertile Omani women who underwent a hysterosalpingogram procedure as part of the evaluation of infertility. Methods: The radiographic reports of patients aged 19–48 years who underwent hysterosalpingogram as part of infertility evaluation during the 2013–2018 period, were collected and analyzed for the presence and the type of CUAs. Results: The records of N = 912 patients were evaluated, 44.3% of whom had been investigated for primary infertility and 55.7% for secondary infertility. Patients with primary infertility were significantly younger than those with secondary infertility. Of the 27 (3.0%) patients who were found to have CUAs, 19 had arcuate uterus. No correlation was found between the type of infertility and the CUAs. Conclusions: CUAs were prevalent in 3.0% of the cohort, most of whom were diagnosed with arcuate uterus.

Huma Ali,1 Savita Dixit,1 Daoud Ali,2 Abdullah A Alkahtane,2 Saud Alarifi,2 Bahy A Ali,2,3 Saad Alkahtani2 1Department of Chemistry, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India; 2Department of Zoology, College of Science, King Saud University, Riyadh, Saudi Arabia; 3Department of Nucleic Acids Research, Genetic Engineering and Biotechnology Research Institute, City Science Research and Technology Application, Alexandria, Egypt Abstract: Quercetol is a polyphenolic molecule present in vegetables and fruits, and is beneficial to human and animal health. The current work aimed to test cytotoxic and apoptotic effects of quercetol on HepG2 cells. Quercetol was isolated from Ocimum sanctum and characterized by gas chromatography–tandom mass spectrometry (GC-MS/MS), nuclear magnetic resonance spectroscopy, and Fourier transform infrared spectroscopy. Quercetol (50–600 µg/mL) was examined for cytotoxic activity by tetrazolium salt and neutral red uptake tests and comet assay for genotoxicity, using HepG2 cells, over 24 hours. Data from 3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide and neutral red uptake tests demonstrated quercetol-induced cytotoxicity in HepG2 cells in a concentration-dependent manner. With 4',6-diamidino-2-phenylindole staining, a significant induction of chromosomal condensation was observed at 300 µg/mL of quercetol. DNA fragmentation analysis showed that quercetol produced cell death in HepG2 cells in a concentration-dependent manner. Thus, our study suggests that an environmentally relevant concentration of quercetol, which was a chemically standardized extract from O. sanctum, induced cell death and DNA damage in HepG2 cells. Keywords: NMR, FTIR, quercetol, HepG2 cells, MTT assay, apoptosis, comet assay

Saud Alarifi,1 Daoud Ali,1 Amin A Al-Doaiss,1,2 Bahy A Ali,1,3 Mukhtar Ahmed,1 Abdulaziz A Al-Khedhairy11Department of Zoology, College of Science, King Saud University, Riyadh, Saudi Arabia; 2Anatomy and Histology Department, Faculty of Medicine, Sana'a University, Sana'a, Republic of Yemen; 3Genetic Engineering and Biotechnology Research Institute, City for Scientific Research and Technology Applications, Alexandria, EgyptAbstract: Titanium dioxide (TiO2) nanoparticles are among the top five nanoparticles used in consumer products, paints, and pharmaceutical preparations. Given that exposure to such nanoparticles is mainly via the skin and inhalation, the present study was conducted in male Wistar albino rats (Rattus norvegicus). Our aim was to investigate the effect of TiO2 nanoparticles on hepatic tissue in an attempt to understand their toxicity and the potential effect of their therapeutic and diagnostic use. To investigate the effects of TiO2 nanoparticles on liver tissue, 30 healthy male Wistar albino rats were exposed to TiO2 nanoparticles at doses of 63 mg, 126 mg, and 252 mg per animal for 24 and 48 hours. Serum glutamate oxaloacetate transaminase and alkaline phosphatase activity was altered. Changes in hepatocytes can be summarized as hydropic degeneration, cloudy swelling, fatty degeneration, portal and lobular infiltration by chronic inflammatory cells, and congested dilated central veins. The histologic alterations observed might be an indication of hepatocyte injury due to the toxicity of TiO2 nanoparticles, resulting in an inability to deal with accumulated residues from the metabolic and structural disturbances caused by these nanoparticles. The appearance of cytoplasmic degeneration and destruction of nuclei in hepatocytes suggests that TiO2 nanoparticles interact with proteins and enzymes in hepatic tissue, interfering with antioxidant defense mechanisms and leading to generation of reactive oxygen species which, in turn, may induce stress in hepatocytes, promoting atrophy, apoptosis, and necrosis. More immunohistochemical and ultrastructural investigations are needed in relation to TiO2 nanoparticles and their potential effects when used as therapeutic and diagnostic tools.Keywords: TiO2 nanoparticles, rats, liver, histology, TUNEL assay, alkaline phosphatase, apoptosis

Abstract A nanocomposite composed of rGO and γ-Fe2O3 was prepared using ultrasonication for the adsorption of malachite green (MG) dye. The preferential plane diffractions at 2θ values of 35.54° and 26.45° about γ-Fe2O3 (311) and rGO (002) with 19.85 and 20.92 nm crystallite sizes, respectively confirmed the successful formation of nanocomposite nature of the adsorbent. Moreover, XPS and FTIR results also confirmed the composite formation due to the existence of peaks relevant to the composite’s components. The adsorbent’s surface charge (pHPZC = ~ 7.1) was also estimated using the salt addition technique. To minimize experiments and optimize adsorption parameters for the removal of MG by the nanocomposite at 25 °C, central composite design (CCD) using response surface methodology (RSM) was used. The optimal adsorption parameters obtained from the within-range numerical optimization based on 0.923 Derringer’s desirability function were 200 mg/100 mL dose, 80 ppm dye solution, 7.99 pH and 112.68 min. contact time for ~ 90% MG dye removal and 40.64 mg/g adsorption capacity (qm) by rGO/γ-Fe2O3. However, ~ 98% MG dye removal with 64.26 mg/g (qm) was achieved upon extended-range estimation of adsorption parameters. The adsorption data exhibited the best co-relationship with Freundlich isotherm and pseudo-2nd order kinetic model. According to a thermodynamic analysis, the MG adsorption process on the rGO/γ-Fe2O3 surface is exothermic, spontaneous, and less random. Moreover, the thermal stability, desorption, regeneration and reusability of rGO/γ-Fe2O3 nanocomposite were also explored. Finally, this study shows that RSM can be an excellent technique to optimize the dye adsorption process at industrial scale.

Abstract The purpose of this research is to design a model to evaluate the level of digitization of the innovation process centered on artificial intelligence in knowledge-based companies, so that the digital maturity of the innovation process in an organization can be measured. The results of 188 indicators were distributed in the form of a 5-point Likert questionnaire and by Delphi method in two times among 18 experts in this field. The result of the work was 5 components as input to the model, which was sent in the form of a questionnaire to 230 knowledge-based companies of Pardis Technology Park. 198 companies completed it and sent it back. From this number of samples, 150 data were separated for training data and 48 data as model test based on a random function. In the last stage, i.e. modeling, the adaptive neural-fuzzy inference method was used for the model. The method of grid separation or lookup table (PG) in MATLAB 2023 software was used to evaluate the performance of the model using root mean square, error (RMSE) and relative error (E). This research was able to provide an intelligent model with a very low error. As a result, it was able to achieve effective indicators in the degree of digitization of the innovation process. Extended Abstract Introduction An innovation process, whether in its general form from the stage of idea formation to the stage of entering the market and commercialization, or in each of the parts separately, must be in such a way that it has the most productivity (efficiency and effectiveness combined during the process). Moving in this direction reduces financial, time and human costs. The relevance of innovation to ensure the competitiveness of companies has been confirmed among researchers and professionals (Schiuma, 2012). Also, innovation is a risky process that requires resources, competence, culture and attitudes that cannot even be promoted and managed easily (D'Este et al., 2012). Due to the mismatch in digital skills and awareness, employees are not able to understand the reasons and potential of implementing new technology and displacement. Therefore, the next challenge in the digital ecosystem is to promote and define conditions, roadmaps and management models for implementing digital innovation strategies, for managing digital knowledge and fostering continuous innovation (Nonaka & Takeuch, 2019). Innovation processes rely on external institutions, i.e. innovation intermediaries, research and development laboratories, or innovation centers (Corre & Mischke, 2005). Global and virtual competition, as well as the rapid development of digital technologies and solutions, raise efficiency standards, increase the speed of market dynamics, and reduce product life cycles (Schiuma, 2012). Each actor involved in innovation must be aware of the organization's vision, goals, and strategies in order to effectively contribute and generate value (Lianto et al., 2018; Nonaka & Takeuchi, 2019). To understand and effectively manage technology, codification and exploitation of generated knowledge, specialized skills and new governance models are required (Joshi et al., 2010). Theoretical framework Digital innovation process Completely rebuilding business around new opportunities and new demands is possible through digital technology. Eyring et al., (2022) in an article, mentioned these processes under the title of digitization. Digitization is a useful and effective necessity. It provides a vision of digital assets that offer opportunities to business or even to industry. Digitization sometimes reconceptualizes their products by serving a business model through their artifacts. Digital transformation describes a sometimes broad process of change that may have multiple goals, while innovation focuses on the moment of invention and the implementation of that invention. Innovation may cause fundamental changes and vice versa, but both are not synonymous. Advancement of digitization and digital changes may be started as an innovation action. This may be catalyzed by new business opportunities, but ultimately must reach beyond the innovation function to reshape the entire organization (Globe, 2018). Businesses need a dynamic tool to support their digital innovation management efforts. Artificial intelligence Artificial intelligence is sometimes called machine intelligence, refers to the intelligence shown by machines in various situations, which is in contrast to the natural intelligence in humans; in other words, artificial intelligence refers to systems that can react similar to human intelligent behavior, including understanding complex situations, simulating human thinking processes and reasoning methods and successfully responding to them, learning and having the ability to acquire knowledge and reason to solve problems (Teece et al., 1997). The scientific study of algorithms and statistical models are used by computer systems that use patterns and inference to perform tasks rather than using clear instructions. Machine learning is the science of making computers learn about a specific subject without the need for an explicit program. As a subset of artificial intelligence, machine learning algorithms create a mathematical model based on sample data or "training data" in order to predict or make decisions without overt planning (Du et al., 2019). Research methodology The sampling method was carried out in the form of theoretical saturation at the stage that no new material was obtained from the articles as a new index, and other indices were common in meaning and concept. In this study, Pardis Technology Park companies and its branches, including Azadi Factory and Hi-Wi centers were considered. Because these companies are active within the innovation ecosystem, they are familiar with the literature in this field, which facilitated the completion of the questionnaires. Questionnaires were sent through the Press Line program and within social network groups, and for some through phone and email. All these companies had the approval of knowledge-based company level 1 to 3 and the completers had educational qualifications of at least bachelor's degree to doctorate. After examining the opinions of the experts, it was determined that a consensus was reached by having an average above 4 for all indicators. In adaptive neural fuzzy inference, the network separation method or lookup table (PG) was used in MATLAB 2023 software. In this method, the number of membership functions is 5 functions representing very low, low, medium, high and very high. Research findings A number of 290 articles were selected among the whole, which had the citation higher than 1. The texts of these 290 articles were studied, and finally 149 articles related to the selected topic and the literature and background of this research were used. In fact, the general goal and main question of this research was to model the innovation process centered on artificial intelligence, was carried out successfully with a very low model error during the test. As a result of reviewing these 149 articles and specialized texts, 189 indicators related to the issue of digital innovation were extracted. As the next goal, 42 main and effective indicators were obtained from this literature Conclusion In addition to the main goal and special goals of the research that were achieved through the presentation of the model, by examining the relationships obtained according to the surface diagrams, the sensitivity and impact of each component can be determined. "Variables" in the output, i.e. the degree of digitalization of the innovation process were analyzed. According to the findings and comparing them with the background and findings of previous researches, it can be stated that the sensitivity rate and impact of input 1, that is, the benefit of digital technologies based on artificial intelligence, is more than the other 4 inputs on the digital level of innovation process, in the sense that having these technologies is the main axis. It is less effective to benefit from the output; and if it is not present in the laboratory, from other components. The lowest sensitivity and impact on the output in the model belonging to the third and fourth components, i.e. network and smart learning, were identified next to the component of benefiting from technologies, and this could mean that in the conditions of benefiting from digital technologies, according to the findings of the research, it is suggested to design the intelligent model of innovation in different industries and for each industry separately for future research. In the literature, researchers encountered a wide range of these digital innovation models such as banking, schools and institutions of higher education, health, etc. The second suggestion is that future researches can design a separate model for each of the input components so that the input component of this research is placed in the output position and the indicators determined in this research are used as their input. It is possible to design 5 other models according to the five components of this research, and connecting these models to develop a final and macro model can bring new achievements.

Behçet's Disease (BD) is a multisystem inflammatory disorder that can lead to severe vascular complications, including Budd-Chiari Syndrome (BCS), a rare but life-threatening condition characterized by hepatic vein obstruction. The co-occurrence of BD and inherited thrombophilia, such as Factor V Leiden mutation, significantly increases the risk of thrombosis, complicating the clinical management of affected individuals. In this case, a 16-year-old female initially presented with nonspecific symptoms of generalized fatigue and bone pain, which later progressed to abdominal distension and significant hepatosplenomegaly. Imaging and further diagnostic evaluation confirmed BCS as the initial manifestation of BD, a rare but severe complication. Genetic testing revealed a heterozygous mutation for Factor V Leiden and the presence of the HLA-B51 allele, highlighting a thrombogenic synergy between BD and inherited thrombophilia. Aggressive anticoagulation therapy was initiated, resulting in partial recanalization of the hepatic veins and stabilization of the patient's condition. This case emphasizes the need for early consideration of BCS in BD patients, especially in those with concurrent prothrombotic disorders, as timely intervention is crucial for improving clinical outcomes. The interplay of autoimmune and genetic factors in this case provides valuable insights into the complex pathophysiology and management of BCS associated with BD.

The National Aeronautics and Space Administration (NASA) and other space organizations are preparing for new space exploration and habitation initiatives. These include human return to the Moon through the Artemis mission, human exploration of Mars and deeper space habitats, and the establishment of permanent human bases on the Moon, Mars, and beyond. Such installations demand a highly reliable, resilient, safe, and autonomous power system to support the habitats’ different functions, systems, and electrical loads. This paper presents a review of the different power sources, energy storage options, microgrid architectures, in addition to challenges and needs for future extraterrestrial habitat microgrids. In addition, this paper reviews the economic challenges and life cycle costs of future extraterrestrial microgrids. Of special focus in this paper are the degradation causes and mechanisms of space microgrid components due to harsh environments in which such microgrids will operate. With the information presented in this paper, which accurately compares different technologies’ performance for the same function, researchers in the space community will be able to make better decisions regarding the requirements and technology development of an extraterrestrial power system.

Abstract The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids. The goal is to minimize dynamic viscosity and maximize thermal conductivity by varying the volume fraction, temperature, and nanomaterial mixing ratio. The proposed strategy integrates machine learning, multi-objective optimization, and multi-criteria decision-making. Three machine learning techniques—GMDH-type neural network, gene expression programming, and combinatorial algorithm—are applied to model dynamic viscosity and thermal conductivity as functions of the input variables. Then, the high-performing models provide the foundation for optimization using the well-established multi-objective particle swarm optimization algorithm. Finally, the decision-making technique TOPSIS is employed to identify the most desirable points from the Pareto front, based on various design scenarios. To validate the proposed strategy, a ternary hybrid nanofluid composed of graphene oxide (GO), iron oxide (Fe₃O₄), and titanium dioxide (TiO₂) was employed as a case study. The results demonstrated that the combinatorial approach excelled in accurately modeling (R = 0.99964–0.99993). The optimization process revealed that optimal VFs span a broad range across all mixing ratios, while optimal temperatures were consistently near the maximum value (65 °C). The decision-making outcomes indicated that the mixing ratio was consistent across all design scenarios, with the volume fraction serving as the key differentiating factor.

OBJECTIVES: Traditional scoring systems have been widely used to predict acute pancreatitis (AP) severity but have limitations in predictive accuracy. This study investigates the use of machine learning (ML) algorithms to improve predictive accuracy in AP. METHODS: A retrospective study was conducted using data from 101 AP patients in a tertiary hospital in Türkiye. Data were preprocessed, and synthetic data were generated with Gaussian noise addition and balanced with the ADASYN algorithm, resulting in 250 cases. Supervised ML models, including random forest (RF) and XGBoost (XGB), were trained, tested, and validated against traditional clinical scores (Ranson’s, modified Glasgow, and BISAP) using area under the curve (AUC), F1 score, and recall. RESULTS: RF outperformed XGB with an AUC of 0.89, F1 score of 0.82, and recall of 0.82. BISAP showed balanced performance (AUC = 0.70, F1 = 0.44, and recall = 0.85), whereas the Glasgow criteria had the highest recall but lower precision (AUC = 0.70, F1 = 0.38, and recall = 0.95). Ranson’s admission criteria were the least effective (AUC = 0.53, F1 = 0.42, and recall = 0.39), probable because it lacked the 48th h features. CONCLUSION: ML models, especially RF, significantly outperform traditional clinical scores in predicting adverse outcomes in AP, suggesting that integrating ML into clinical practice could improve prognostic assessments.

Purpose To investigate the ability to salvage an acutely thrombosed dialysis access using rheolytic pharmacomechanical thrombectomy (PMT), the short- and medium-term patency rates and safety at a tertiary care hospital.

Abstract Background Many variables may affect approaches of psychiatrists to methamphetamine-associated psychotic disorder (MAP) treatment. This study was aimed to reach adult psychiatrists actively practicing in Turkey through an internet-based survey and to determine their practices and attitudes to MAP treatment. Methods In this internet-based study, participants were divided into three groups based on their answers: Those who do not follow-up any MAP patient were group 1 (n = 78), partially involved in the treatment process of at least one patient diagnosed with MAP were group 2 (n = 128), completely involved in the treatment process of at least one patient diagnosed with MAP were group 3 (n = 202). Results Psychotropic preferences in insomnia (p

Abstract To illustrate the anti-diabetic properties of Berberis orthobotrys seeds was the aim of the current study. After a series of experiments, two doses of aqueous methanolic extract of the seeds were selected i.e., 151 mg/kg and 301 mg/kg to assess the anti-diabetic potential in normoglycemic, alloxan-induced and glucose-loaded diabetic rats. The effect of the seeds extract on total cholesterol and triglycerides was also assessed. Histological examination of the pancreas and the GC-MS analysis of blood and urine samples of rats were also carried out. At 151 mg/kg and 301 mg/kg doses, the aqueous methanolic extracts of Berberis orthobotrys seeds showed promising results for their anti-diabetic potential comparable to Glibenclamide. Studies on the phytochemistry of AMEBO also exposed the existence of alkaloids, steroids, glycosides, flavonoids and resins. Serum lipid measurements showed a substantial reduction in triglycerides and total cholesterol on days 7 and 15. The pancreas’s islets were protected by the extract, as shown by a histological analysis. The primary metabolite Berberine, which was qualitatively detected by GC-MS in blood and urine samples of rats, may be the alkaloid in command of insulin release or recovery of beta cells after alloxan destruction. More study is necessary to comprehend the underlying signaling process.

Abstract This study presents an in-depth analysis and evaluation of the performance of a standard 200 W solar cell, focusing on the energy and exergy aspects. A significant research gap exists in the comprehensive integration of numerical models with advanced machine-learning approaches, specifically emotional artificial neural networks (EANN), to simulate and optimize the electrical characteristics and efficiency of solar panels. To address this gap, a numerical model alongside a novel EANN was employed to simulate the system’s electrical characteristics, including open-circuit voltage, short-circuit current, system resistances, maximum power point characteristics, and characteristic curves. Mathematical equations for calculating efficiency levels under varying operational conditions were developed. The system’s operational and electrical parameters, alongside environmental conditions such as solar radiation, wind speed, and ambient temperature, were empirically observed and documented over a day. A comparative analysis was conducted to validate the model by comparing its results with manufacturer data and experimental observations. During the trial from 7:00 to 17:00, energy efficiency varied from 10.34 to 14.00%, averaging 13.6%, while exergy efficiency ranged from 13.57 to 16.41%, with an average of 15.70%. The results from the EANN model indicate that the proposed method for forecasting energy, exergy, and power is feasible, offering a significant reduction in computational expense compared to traditional numerical models. The integration of numerical modeling with EANN enhances simulation accuracy and the developed equations enable real-time efficiency calculations. Empirical validation under varying environmental conditions improves predictive capabilities for solar panel performance. Additionally, operational efficiency assessments aid in better design and deployment of solar energy systems, and computational costs for large-scale solar energy simulations are reduced.

Abstract In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in a radial distribution network considering uncertainties of renewable generation and network demand. A novel multi-objective improved gradient-based optimizer (MOIGBO) enhanced with Rosenbrock’s direct rotational technique to overcome premature convergence is proposed to determine the problem optimal decision variables. The deterministic optimization framework without uncertainty minimizes active energy loss, unmet customer energy, and renewable generation costs. The study also examines the impact of dispersed and hybrid renewable resources on solving the problem. In the robust optimization framework considering the deterministic obtained results, the focus is on determining the maximum uncertainty radius (MUR) of renewable resource generation and network demand based on the uncertainty risk. The MURs and system robustness are optimally determined using information gap decision theory (IGDT) and the MOIGBO, considering various uncertainty budgets under worst-case scenarios. The deterministic results indicate that the MOIGBO effectively balances the objectives and identifies the final solution within the Pareto front, according to fuzzy decision-making. The results also reveal that the dispersed case yields better objective values than the hybrid case. Furthermore, the MOIGBO outperforms MOGBO and multi-objective particle swarm optimization (MOPSO) in improving distribution network operations. The robust results show that maximum system robustness is achieved at 30% uncertainty risk due to forecasting errors, with MUR values of 0.54% for resource production and 12.56% for load demand.

Abstract Energy hubs, with their diverse regeneration and storage sources, can engage concurrently in energy transfer and storage. It is anticipated that managing the energy of these hubs within energy networks could enhance economic, environmental, and technical metrics. This article explains how electrical and thermal network hubs manage their energy consumption in the context of the multi-criteria objectives of efficiency, sustainability, reliability of the network operator, and operation. The hubs have solar power, a bio-waste unit, and wind turbines among other sustainable energy sources. They have compressed air, heat, and hydrogen storage units installed. Thermal energy is produced by means of a heat pump from electrical energy. Combining heat and power technology is used by both the bio-waste unit and the hydrogen storage unit. Subject to the operating model and reliability restrictions of these networks, the suggested strategy seeks to reduce the overall estimated costs of energy procurement, dependability, and emissions within the designated networks. Additional constraints of the problem encompass the operational model of sources and storages, conceptualized as an energy hub. This plan takes into account uncertainties about demand, energy costs, renewable energy sources, and the availability of network equipment. Reliability is accurately predicted by scenario-based techniques to stochastic optimization. The simultaneous modeling of economic, operational, reliability, and environmental indicators as well as the evaluation of the capabilities of heat pumps, biowaste units, compressed air and hydrogen storage units, and heat pumps in the hub performance are seen to be the new aspects of this approach. In summary, numerical results validate the usefulness of the proposed approach in enhancing the technical and financial aspects of thermal and electrical networks via efficient hub energy management. The incorporation of renewable hubs, equipped with storage units and heat pumps, has led to improvements in the economic, operational, reliability, and environmental conditions by approximately 44.1%, 28–90%, 85.6%, and 72.1% respectively, in comparison to load distribution studies.

The COVID-19 pandemic underscored the challenges of performing mandatory Quantitative Fit Tests (QNFT) for healthcare professionals and the limitations of self-administered fit checks. To address this, it is crucial to develop faster and more efficient methods for detecting, locating, and quantifying Filtering Facepiece Respirators (FFRs) leakage, providing wearers with immediate feedback on their safety. Infrared (IR) technology, which relies on temperature variation analysis around the face seal, has proven effective for locating leakage but has not yet achieved automated quantification. This paper introduces a validated protocol for creating a comprehensive database to advance automatic leakage detection. The database includes synchronized and calibrated IR and RGB video data, along with QNFT results, collected from 62 participants wearing four different N95 FFR models in four distinct positions. High-performance IR and RGB cameras were used to precisely capture temperature variations, while a PortaCount® instrument served as the reference for fit quantification. Preliminary results using the MediaPipe approach with synchronized and calibrated RGB and IR videos demonstrate that precise tracking of the human face is achievable even with an FFR. The normalized cross-correlation methods further highlight the capability of IR imaging to accurately monitor and detect leakage. This breakthrough paves the way for real-time, automated detection of N95 FFR leakage, potentially deployable at operator workstations. This large, high-quality, open-access database is available to the scientific community to drive innovation in respiratory protection research and beyond.

Abstract Background The genus Astragalus is the largest and one of the most diverse genera of flowering plants, particularly in the Northern Hemisphere, with a significant concentration of species in the Irano-Turanian region. Within this genus, section Hymenostegis is notable for its complexity and high levels of endemism, especially in northwestern Iran. During recent field explorations in West Azarbaijan province, a distinct population of Astragalus was identified, differing from known species within section Hymenostegis. This study aimes to describe and analyze this new species and clarify its relationship with closely related taxa using an integrative approach. Results Astragalus kuzehrashensis sp. nov. is described following detailed morphological comparisons with its closest relatives, A. chehreganii and A. hakkianus. The new species is distinguished by unique stipule characteristics, leaflet shape, inflorescence structure, and seed micromorphology. Molecular phylogenetic analysis using ITS and ycf1 sequences reveals that A. kuzehrashensis forms a clade with A. chehreganii and A. hakkianus, although it exhibits genetic differences. Genome size estimation confirms that A. kuzehrashensis is hexaploid (2n = 6x = 48), aligning it more closely with A. hakkianus than with the tetraploid A. chehreganii. The distinct morphological characters and genomic data support recognizing A. kuzehrashensis as a new species within section Hymenostegis. Conclusions The identification of A. kuzehrashensis underscores the importance of an integrative taxonomic approach, combining morphological, molecular, and cytogenetic data to resolve species boundaries within complex groups like Astragalus section Hymenostegis. This study highlights the rich biodiversity of the Irano-Turanian floristic region, emphasizing the need for ongoing exploration and conservation efforts, particularly in areas of high endemism. Our findings contribute to a deeper understanding of the taxonomy and evolutionary relationships within the genus Astragalus.

Abstract Background The prevalence and chronic nature of Inflammatory Bowel Diseases (IBD) is a significant global concern. As the essential part of treatments approach, patient adherence to treatment protocols and self-management practices are crucial to = IBD management. Healthcare initiatives focused on chronic conditions are strongly needed to consider various aspects of gamification and how it can positively affect self-management. Aim The current cognitive study aims to develop a mobile application to integrate the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) instructional design model and elaborate on a gamification design based on the reputable Sukr Wheel framework. Methods The current study uses the ADDIE approach to integrate behavior change strategies derived from the self-management theory using the Sukr Wheel gamification (My IBD Buddy) framework on the Android platform. Results The final evaluation was conducted over 14 days. User satisfaction comprised 22 participants aged 20 to 64, all diagnosed with inflammatory bowel diseases. System usability was measured on a scale ranging from 50 to 100. The average usability score for the entire user group was 80.68, indicating a “good” level of satisfaction among the program users based on the ranking scale. Conclusion “My IBD Buddy” mobile application, equipped with gamification for IBD patients, enhances self-efficacy and self-management.

Mixed connective tissue disease (MCTD) is a rare autoimmune syndrome characterized by overlapping features of systemic sclerosis, systemic lupus erythematosus (SLE), and polymyositis, first described by Sharp et al. in 1972. This case report discusses a 38-year-old female who presented with symptoms indicative of these overlapping conditions, including sclerodactyly, Raynaud's phenomenon, and muscle weakness, confirmed by laboratory findings and imaging. Despite aggressive treatment with immunosuppressive agents such as methotrexate, corticosteroids, and infliximab, the patient's condition progressively deteriorated, leading to severe complications, including interstitial lung disease and gastrointestinal involvement. Mixed connective tissue disease remains a complex and poorly understood condition with a range of clinical presentations and no distinct risk factors. The prognosis varies significantly, with some patients experiencing life-threatening complications while others may achieve complete remission. Diagnostic criteria, such as the Alarcon-Segovia criteria, and serological markers like anti-U1RNP antibodies, are critical in identifying and managing this rare and challenging condition.

Abstract This study introduces a high-performance 4-channel Metal-Insulator-Metal (MIM) diplexer, employing silver and Teflon, optimized for advanced photonic applications. The proposed diplexer, configured with two novel band-pass filters (BPFs), operates across four distinct wavelength bands (843 nm, 1090 nm, 1452 nm, 1675 nm) by precisely manipulating the passband dimensions. Utilizing Finite-Difference Time-Domain (FDTD) simulations, the designed diplexer achieves exceptional sensitivity values of 3500 nm/RIU, 4250 nm/RIU, 3375 nm/RIU, and 4003 nm/RIU, along with high figures of merit (FOM) ranging from 113.4 to 124.7 1/RIU. Also, the compact design (400 nm × 830 nm) underscores its suitability for integrated photonic circuits and advanced sensing applications. Furthermore, to further enhance accuracy in detecting refractive index (RI) changes, a multilayer perceptron (MLP) neural network was employed, ensuring the highest sensor accuracy. The accuracy of the MIM diplexer’s RI measurements was statistically validated through a one-sample t-test, confirming the sensor’s reliability. Comparative analysis with existing sensors highlights the diplexer’s superior sensitivity and efficiency, setting a new benchmark in optical communication and photonic sensing technologies. This work paves the way for future advancements in miniaturized, high-sensitivity optical devices, offering robust solutions for next-generation communication and sensing systems.

Abstract Background This study aimed to assess the correlation between the impostor phenomenon and the level of academic self-efficacy. The secondary aim was to determine the sociodemographic variables affecting these two conditions. Based on the acquired findings, it is possible to take supportive and preventive measures. Methods This descriptive study was carried out at the xxx University Faculty of Medicine. A total of 1133 residents were invited to participate in the study. Participants completed a range of scales, including the Medical Achievement Self-Efficacy Scale (MASS) and the Clance Impostor Phenomenon Scale (CIPS). The data were collected by using Google Forms. The response rate was 23.04% (n = 261). Results The age and gender did not significantly correlate with the MASS or the CIPS scores. The participants were divided into five groups at 12-month intervals according to the time completed in their departments. The median CIPS score of the 0–12 month group was 62 (Q1:53,Q3:70), and the median MASS score was 55 (Q1:44,Q3:67). A significant correlation was found between the time spent in the department and the CIPS score and the MASS score (p

Questions: The mountains of Fereydunshahr County are one of the centers of plant endemism within the Zagros, however, its flora and vegetation remain relatively understudied. In this study we undertook research on the plant species diversity of the subalpine and alpine zones of this area, their life forms, chorology, and vegetation types. Study area: Mountains of Fereydunshahr County, Central Zagros, West Iran. Methods: Plant specimens were collected during the growing seasons of 2018 to 2020. A complete species list was prepared including their life forms, chorotypes, elevation range, and major vegetation types. Results: A total of 308 vascular plant species have been identified belonging to 185 genera and 47 families. The largest plant families recorded during the study are Asteraceae with 44 species, Fabaceae 32, Brassicaceae 29, and Lamiaceae 27. At genus level Astragalus with 23 species is the richest. Hemicryptophyte with 162 (53%) species is the major life form. Most of the species are Irano-Turanian elements (52%). A total of 57 species (19%) are endemic to Iran and 23 species (7%) are endemic to Zagros. Most species belong to the montane-subalpine zone (33%), followed by subalpine (20%), montane (15%), lowland-montane (10%), alpine (9%), and lowland-subalpine (5%). In the alpine zone a high proportion of the species are endemic, while the montane zone has a very low proportion of endemics. From the identified species, 24% belong to subalpine and alpine thorn-cushion grasslands, 19% to montane steppe shrublands, 5% to subalpine tall-umbelliferous vegetation types, 5% to wetlands, and 5% to chasmophyte vegetation. Conclusions: The area has a rich flora, but at the same time is under high pressure from anthropogenic activities, especially a very high level of overgrazing. The region is not a protected area, therefore, establishment of a protected area and efficient conservation planning for the region is highly recommended. Taxonomic reference: Flora of Iran (Assadi et al. 1989–2021) and, for families not yet covered in the previous source, Flora Iranica (Rechinger 1963–2015). Abbreviations: ES = Euro-Siberian; IT = Irano-Turanian; M = Mediterranean; SS = Saharo-Sindian.

Abstract The Crimean Congo virus has been reported to be a part of the spherical RNA-enveloped viruses from the Bunyaviridae family. Crimean Congo fever (CCHF) is a fatal disease with having fatality rate of up to 40%. It is declared endemic by the World Health Organization. Many outbreaks of CCHF have been reported over the years. Former studies on CCHF have reported that the nucleoprotein of CCHF, being a pivotal protein in the replication process of the virus, is a potential target for antiviral drugs. However, there is no specific drug that can be used to treat this fatal disease and laboratory testing is prohibited due to its pathogen level 4. This study aims to find a possible potential inhibitor of the nucleoprotein of CCHFV using modern techniques leading ultimately to the development of effective and natural drugs. In this study, a virtual screening procedure involving a docking process followed by the Molecular Dynamics method is used to find out the potential inhibitors of the nucleoprotein of CCHFV. Phytochemicals having pharmacological properties and approved by the Food and Drug Administration are docked over the nucleoprotein of CCHFV. The study signifies the use of Withanolide E as a drug for the treatment of CCHFV as the study depicts the potential of Withanolide E to inhibit the nucleoprotein of CCHFV using reliable and modern techniques.

Background: Recently, the green synthesis process has been utilized to manufacture a large quantity of metal nanocrystallites due to its low cost and the availability of numerous natural resources and the find the activity of bacteria and viruses that in the body of humans. Aims and Objectives: In this study, nanocrystallites of selenium oxide were produced utilizing Hibiscus sabdariffa. Researchers have analyzed the antibacterial properties and nanostructure characteristics of selenium oxide nanocrystallites using various techniques methods, such as imaging microscopy, scanning electron microscopy, ultraviolet‒visible spectroscopy (UV‒VIS), transmission electron microscopy, atomic force microscopy, and X-ray diffraction (XRD) spectroscopy. Results: According to the results, the films are discovered to have a nanocrystalline structure in a cubic spinel configuration. The crystallites are semispherical in shape and are both uniform and easily distributed. The XRD data were recorded on card number 22-1314, and the 2 θ (hkl) value was 38.351 (311). The UV‒VIS spectrum of the material exhibited a plasmon resonance peak at 272 nm, confirming the presence here of selenium oxide. This study also investigated the response of four distinct strains of pathogenic bacteria to biosynthesized selenium oxide nanoparticles (NPs). The data indicate that the biosynthesized selenium oxide NPs were highly effective against Klebsiella spp. and had the lowest effectiveness against Staphylococcus aureus, Staphylococcus epidermidis, and Escherichia coli. Conclusions: The utilization of selenium oxide nanocrystals as antibacterial agents has yielded diverse outcomes, demonstrating their remarkable efficacy in combatting Klebsiella spp.

ABSTRACT This paper presents a detailed analysis of embodied carbon (EC) in various case studies using life cycle assessment (LCA) methodology. Through comprehensive assessments, including modules A, B and C, the study evaluates EC across different stages of building life cycles. This study also considers the EC savings achievable through current end‐of‐life strategies in the UK context. As Module A accounts for the highest EC in the case studies, the majority of reduction strategies should focus on this stage. The most impactful strategy for reducing EC emissions involves incorporating Ground Granulated Blast Furnace Slag (GGBS) as a replacement for cement. This approach has the potential to achieve a substantial reduction in the EC of concrete within the buildings under investigation, ranging from 60% to 70%. The study reveals that specification strategy can lead to significant Whole Life Embodied Carbon (WLEC) reductions, with the residential building achieving a 30.59% reduction, the college building a 46.86% reduction, and the hotel building a reduction of 23.69%. Effective mitigation strategies, such as utilizing recycled and reclaimed materials, demonstrate promising results, showcasing significant reduction in WLEC emissions in the buildings.

In recent years, research on enhancing the efficiency of clean and renewable energy systems has increased. This study examines how a micro-scale solar Brayton cycle application performs about the conical cavity thermal receiver shape. Additionally, it establishes the ideal receiver configuration under consideration. The new work explicitly addresses the optimization of a microscale conical model, building on earlier studies by the research team that stressed the significance of reducing total heat losses. The receiver model was created using Design Modeler and treated using CFD analysis in ANSYS 2021R2 Workbench software to limit the convective mode of heat loss. Surface optimization techniques were then used, and the results were examined. To confirm the achieved results, the direct optimization method was also utilized, and it gave the same results. The internal height and the two edges on the bottom width of the receiver were found to have the greatest influence on the value of the heat transfer coefficient. Thermally, the dimensions of the optimized conical shape were found to be 384, 198, 114, 48 and 57 mm for the internal height, total width, top width, left edge and right edge respectively. The results of this investigation showed that by reducing the heat transfer coefficient by up to 90%, the tested shape's thermal performance was significantly improved. It consequently led to an increase in overall system efficiency of around 1.3%–1.95%.

Abstract Determining situation of groundwater vulnerability plays a crucial role in studying the groundwater resource management. Generally, the preparation of reliable groundwater vulnerability maps provides targeted and practical scientific measures for the protection and management of groundwater resources. In this study, in order to evaluate the groundwater vulnerability of Kerman–Baghin plain aquifer, two developed indicators including composite DRASTIC index (CD) and nitrate vulnerability index (NVI) based on DRASTIC index were considered. Soft computing methods, including Gene Expression Programming (GEP), Evolutionary Polynomial Regression (EPR), Multivariate Adaptive Regression Spline (MARS), and M5 Model Tree (MTM5) have been used to provide formulations for prediction of NVI. Soft computing techniques were fed nine input parameters: depth to water level, net recharge, aquifer environment, soil environment, topography, effect of unsaturated area, hydraulic conductivity, land use, and potential risk related to land use. After calculating the vulnerability by soft computing methods, the results showed that the EPR model with Correlation Coefficient (R) of 0.9999 and Root Mean Square Error (RMSE) = 0.2105 has the best performance in the testing stage in comparison with MARS (R = 0.9966 and RMSE = 2.408), M5MT (R = 0.9956 and RMSE = 2.988), and GEP (R = 0.9920 and RMSE = 3.491). Although the EPR and GEP models have more complex mathematical computations than other soft computing models, the MARS and MT model that have quadratic polynomial and multivariable linear structures respectively, can be considered as the best alternative. According to the MARS model, the vulnerability of the region is divided into two categories: very low vulnerability (73.06%) and low vulnerability (26.94%). Overall, the statistical results of soft computing techniques were indicative of effective formulations for evaluating the DRASTIC index.

Appendiceal carcinoma, a rare malignancy comprising less than 1% of gastrointestinal cancers, often presents diagnostic challenges due to its atypical symptoms. This report details a 68-year-old female with a history of severe anemia, gastrointestinal bleeding, and persistent abdominal symptoms, ultimately diagnosed with chronic small bowel obstruction caused by an appendiceal adenocarcinoma. Despite initial treatment for Helicobacter pylori-induced gastritis, her symptoms persisted, prompting further imaging that revealed a narrowed small bowel segment. Exploratory laparotomy uncovered a stricturing tumor 2 meters from the duodenojejunal junction, leading to en bloc resection of the tumor, appendix, and part of the lateral abdominal wall. Histopathology confirmed moderately differentiated adenocarcinoma. A multidisciplinary team recommended a complete right hemicolectomy, successfully performed despite extensive adhesions and a mesenteric abscess. Postoperatively, the patient recovered well, with ongoing management for anemia and close surveillance. This case highlights the importance of considering rare malignancies in differential diagnoses for chronic small bowel obstruction.

Aberrant right gastric veins (ARGV) represent rare anatomical variations that can result in hepatic pseudolesions, mimicking malignancies due to their atypical drainage directly into the liver parenchyma. This case highlights a 44-year-old woman initially presenting with a breast mass incidentally found to have an ARGV-related pseudolesion in hepatic segment IVa. ARGV is clinically significant as it can alter hepatic blood flow dynamics, leading to hyperdense or hypodense regions on imaging. Recognizing these pseudolesions is essential to avoid misdiagnosis, unnecessary procedures, and to distinguish them from true hepatic lesions. This case emphasizes the importance of advanced imaging modalities in diagnosing such anomalies, ensuring accurate patient management.

Post-tin mining land holds promise for cultivating forage crops, with the potential to address Pb metal contamination through plant-based phytoremediation. However, the presence of heavy metals and depleted soil fertility resulting from tin mining activities may pose challenges to plant productivity and contribute to residual heavy metal accumulation. This study aims to assess the productivity and phytoremediation capacity of Pb by various grass species on reclaimed mining land. Using a randomised block design with three replications, the study was conducted over a four-year period in a post-mining area in Central Bangka Regency, Indonesia. Three grass species: Megathyrsus maximus (‘Riversdale’ and ‘Purple guinea’), Pennisetum purpureum (‘Taiwan’ and ‘Mott’), and Chrysopogon zizanioides were evaluated for forage production, quality, digestibility, and heavy metal content. It was shown that ‘Riversdale’ and ‘Purple guinea’ cultivars had relatively stable production for over four years, with the crude protein content of all grass types remaining relatively low (

Background: The space caused by an impacted maxillary canine or missing lateral incisor, can lead to malocclusion and dental crowding. This study aimed to examine the skeletal pattern of patients with impaction or missing of maxillary canine or lateral teeth.Methods and Materials: In this cross sectional study, the panoramic and lateral cephalometric radiographs of 10-25-year-old patients presenting to the Orthodontic Department of Mashhad Dental School over a five-year period were retrieved and analyzed.A total of 103 patients with maxillary canine impaction or missing of the lateral incisor were selected as the case group, and 103 patients without these anomalies were included as controls. After cephalometric tracing, skeletal classes were determined based on SNA, SNB, ANB angles. Facial vertical height was assessed using Jaraback Index. Chi-Square tests and Independent samples T-tests were used for statistical analysis. P-value0.05). Skeletal Class II was the most common pattern in both groups; however, Skeletal Class III was more frequent in males with a missing lateral incisor. Although long facial height was more prevalent in the case group and normal facial height was more frequent in the control group, no significant differences in Jaraback index were observed between the groups (P=0.441).Conclusion: In this study, skeletal Class II was the most common pattern. Skeletal Class III was more common in males with a missing upper lateral incisor. Tendency to maxillary deficiency was seen in patients with a missing upper lateral incisor.

In the current global energy conditions, with a growing concern for carbon emissions, the adoption of renewable energy sources is on the rise. Solar panels have emerged as a highly promising method for electrical-thermal energy generation and are widely employed in both industrial and residential settings. This study focuses on evaluating the impact of cooling on PV panel systems and its effect on electrical and thermal efficiency. A hybrid method utilizing both air and water on the PV panels is examined, and the results are compared to those of a reference panel. The experiments were conducted in Kashan, Iran, located at coordinates 34°06' N 51°23' E, in July 2023. By implementing the proposed cooling method, significant improvements in the maximum daily electrical, thermal, and total efficiencies can be achieved, surpassing 20%, 30%, and 50%, respectively. The findings indicate that cooling with water proves more advantageous in terms of thermal energy generation, although it slightly decreases the coefficient of energy due to the additional energy required for water pumping compared to air blowing. Furthermore, the study reveals that bifacial cooling, employing jets to cool both sides of the PV panel, significantly enhances thermal and electrical efficiency, particularly in hot and dry weather conditions.

Natural antimicrobial products are attracting interest in the treatment of periodontal diseases due to their minimal or nonexistent side effects. Propolis, a complex resin content produced by bees, has drawn particular interest for its antibacterial effects, including inhibitory effects on periodontopathogenic bacterial species. This study compared the effects of propolis, a natural product, and chlorhexidine, the gold standard antimicrobial agent, on MMP-1 and MMP-9 levels in gingival crevicular fluid (GCF). Subgingival irrigations of chlorhexidine and propolis solutions were performed in selected deep periodontal pockets of periodontitis patients along with scaling and root planing. The treatment protocol was administered three times at one-month intervals starting from the initial day of the study. GCF MMP-1 and MMP-9 levels, along with clinical periodontal parameters, were evaluated before and after treatment. The study results showed that a statistically significant decrease in clinical periodontal parameters in both groups (p

Abstract Optimization of thermophysical properties (TPPs) of MXene-based nanofluids is essential to increase the performance of hybrid solar photovoltaic and thermal (PV/T) systems. This study proposes a hybrid approach to optimize the TPPs of MXene-based Ionanofluids. The input variables are the MXene mass fraction (MF) and temperature. The optimization objectives include three TPPs: specific heat capacity (SHC), dynamic viscosity (DV), and thermal conductivity (TC). In the proposed hybrid approach, the powerful group method of data handling (GMDH)-type ANN technique is used to model TPPs in terms of input variables. The obtained models are integrated into the multi-objective particle swarm optimization (MOPSO) and multi-objective thermal exchange optimization (MOTEO) algorithms, forming a three-objective optimization problem. In the final step, the TOPSIS technique, one of the well-known multi-criteria decision-making (MCDM) approaches, is employed to identify the desirable Pareto points. Modeling results showed that the developed models for TC, DV, and SHC demonstrate a strong performance by R-values of 0.9984, 0.9985, and 0.9987, respectively. The outputs of MOPSO revealed that the Pareto points dispersed a broad range of MXene MFs (0-0.4%). However, the temperature of these optimal points was found to be constrained within a narrow range near the maximum value (75 °C). In scenarios where TC precedes other objectives, the TOPSIS method recommended utilizing an MF of over 0.2%. Alternatively, when DV holds greater importance, decision-makers can opt for an MF ranging from 0.15 to 0.17%. Also, when SHC becomes the primary concern, TOPSIS advised utilizing the base fluid without any MXene additive.

Abstract In this study, stochastic multi-objective allocation of wind turbines (WTs) in radial distribution networks is performed using a new multi-objective improved horse herd optimizer (MOIHHO) and an unscented transformation (UT) method for modeling the uncertainties of WTs power and network load. The objective function aims to minimize power loss, improve reliability, and reduce the costs associated with wind turbines (WTs), presenting these goals as a three-dimensional function. The Multi-Objective Improved Horse Herd Optimizer (MOIHHO) is derived from an enhanced version of the traditional horse herd optimizer. This enhancement utilizes mirror imaging based on convex lens principles to address issues of premature convergence. Additionally, the decision-making process is designed to identify the final fuzzy solution among the non-dominant solutions within the Pareto front set. The simulation results are presented with and without considering uncertainty in two scenarios of deterministic and stochastic WT allocation on 33- and 69-bus distribution networks and different objectives are compared. Also, the effect of incorporating uncertainties are evaluated on power loss and reliability using the MOIHHO. Moreover, the superiority of the MOIHHO is investigated in achieving better objective function value compared with conventional MOHHO, multi-objective particle swarm optimization (MOSPO), multi-objective gray wolf optimizer (MOGWO), and multi-objective gazelle optimization algorithm (MOGOA). The obtained results demonstrated that considering the UT-based stochastic scenario, the power losses cost is increased, and the reliability is weakened for 33- and 69-bus networks in comparison with the deterministic scenario.

Abstract Introduction Rectal prolapse is prevalent in children and the elderly, impacting quality of life significantly. Traditional surgical interventions carry risks, especially in pediatric patients. Ethanol sclerotherapy offers a less invasive alternative, inducing fibrosis and thickening of the rectal wall. Despite its potential benefits, procedural complications are possible, emphasizing the need for careful patient selection and procedural expertise. This study aims to evaluate the safety and efficacy of sclerotherapy in treating rectal prolapse in a tertiary referral center in southern Iran. Methods Patient records from Nemazee Hospital covering January 2014 to December 2023 were retrospectively analyzed. Pediatric patients undergoing ethanol sclerotherapy for rectal prolapse were included. Data on demographics, presentation, procedures, and outcomes were collected. Ethical approval was obtained, and specific inclusion/exclusion criteria were applied. Statistical analyses were conducted using SPSS version 26. Results One hundred thirty patients were evaluated, with a mean age of 10.74 ± 5.320 years. Most patients experienced constipation (56.9%). 74.2% responded well to sclerotherapy, with 12.9% needing a second injection. Complications were minimal, with bleeding being the most common (4.6%). Recurrence occurred in 18.6% of cases. Male patients showed a higher total complication rate (P = 0.010). Diarrhea-dominant patients had no recurrences post-sclerotherapy. Age significantly influenced treatment response and recurrence (P = 0.017, P = 0.035). Conclusion Male predominance contradicted global pediatric rectal prolapse ratios, possibly influenced by cultural factors. Sclerotherapy remains effective, though response rates vary. Older age correlated with lower response rates and higher recurrence. Constipation-dominant prolapse was associated with increased recurrence risk. Male patients had a higher complication rate, highlighting the need for tailored management strategies.

Abstract Background Alcohol septal ablation (ASA) is recommended for moderate to severe symptomatic patients with hypertrophic obstructive cardiomyopathy. The current guidelines don not recommend particular alcohol doses and the amount of alcohol injected into the septal artery is based more on the interventionalist’s decision rather than on systematic evidence. Our objective is to execute a comprehensive assessment of the efficacy and safety of low alcohol doses (1–2 ml)) in comparison to large ones (2–4 ml). Methods Multiple databases, encompassing PubMed, WOS, Scopus, Embase, and Cochrane library were systemically assessed from inception to October 2023. Randomized controlled trials (RCTs) and observational studies comparing low-alcohol and high-alcohol dose in patients with HOCM was included. The main outcome was creatine kinase-MB (CK-MB). The secondary ones are septal thickness, left ventricular outflow pressure gradient, and atrioventricular (AV) block. Results Eight studies, with a total of 1446 individuals, were finally involved in our investigation. low alcohol doses showed lower CK-MB levels relative to high ones (MD=-0.93, 95% CI [-1.15 to -0.72], P = 0.00001). Furthermore, in terms of septal thickness, left ventricular outflow pressure gradient, and AV block, neither of the two doses was preferred. Conclusion We propose that the low alcohol dose is equally effective as the high dose. moreover, the low alcohol dose was associated with a lower CK-MB levels in comparison to the high one. PROSPERO registration CRD42024511537

Abstract Background Drug-coated balloons (DCB) have promising results in the management of in-stent restenosis (ISR), still their role remains a major challenge, and not well established in contemporary clinical practice. Aims To provide a comprehensive appraisal of the efficacy and safety of DCBs in patients with in-stent restenosis (ISR). Methods We searched PubMed, Scopus, web of Science, Ovid, and Cochrane Central from inception until 30 March, 2023. We included randomized controlled trials (RCTs) that compared DCB versus DES in ISR patients. Our primary endpoints were major adverse cardiac events (MACE) and late lumen loss (LLL). Secondary clinical endpoints were all-cause death, cardiac death, MI, TLR, TVR, and stent thrombosis, and angiographic outcomes were MLD, and in-stent binary restenosis. Results Ten RCTs comprising 1977 patients were included in this meta-analysis. The incidence of MACE was 15.57% in the DCB group compared to 14.13% in the DES group, with no significant difference in the risk of MACE following DCB (odds ratio [OR] 1.04, 95% confidence interval [CI]: 0.87 to 1.44). Compared with the DES intervention, the risk of LLL was comparable to the DCB intervention (mean difference [MD] -0.08, 95% CI: -0.18 to 0.02), while the incidence of TLR was increased in the DCB intervention (OR: 1.54, 95% CI: 1.2 to 1.99). Conclusion DCB was comparable to DES implantation is ISR patients regarding clinical outcomes, however it showed an increase in TLR events. Moreover, a RCT with large sample size and longer follow-up duration is warrened to validate these results.

A unique research was developed to achieve an effective and efficient method for tracing and identifying Beluga sturgeon species, Huso huso using eDNA in concrete fish ponds. Sampling was performed from the water of the Beluga pond and fixed with precipitation premix solution. DNA was extracted from fixed water, and fin tissue of Beluga. Three short fragments were selected from a nuclear and two mitochondrial genes region of Beluga DNA. The specificity of primers was checked by conventional PCR for Beluga with fin tissue DNA samples. In this study, two conventional methods of molecular identification were performed on residual DNA samples of fish pond water and fin tissue, exhibiting that they were not suitable for measuring the residual DNA of Beluga in fish ponds due to the low rate of DNA. PCR amplification of sturgeon’s DNA of mitochondrial COΙ gene in third PCR primers was successful in low level of DNA extracted from water column and sediment by conventional and Quantitative PCR. The linear relationship between the threshold cycle (ct) value and Beluga DNA concentration was measured by the Mini-Barcoding quantitative Real-Time PCR. DNA samples of fish ponds generated a uniform curve in water column and sediment. In identifying residual DNA in the Picogram level of Beluga pond, SYBER Green Real-Time PCR method can be an acceptable method for barcoding with high efficiency compared to other methods such as conventional PCR method and non-invasive which is advantageous for the conservation of critically endangered sturgeons such as Beluga. This research results can also be used as a model for tracing fish species in rivers and seas.

This paper reports on the correlation between broken contact fingers and the shape of the current–voltage (I–V) curve of a photovoltaic (PV) module. It was found that the broken contact fingers of a solar cell in the PV module cause a noticeable change in the I–V curve of the PV module when the solar cell was partially shaded. The broken contact fingers were inspected by microscopic imaging and electroluminescence (EL) imaging, and a further investigation was carried out using a single solar cell. The results show that the fill factor of the cell decreased from 0.75 of full contact to 0.47 after 16 contact fingers were broken, confirming the correlation between the I–V curve shape and broken contact fingers. This result reveals that the shape of the I–V curve of a PV module under individual-cell partial shading may be used as an indicator of broken contact fingers, which offers an alternative approach to EL imaging for detecting broken contact fingers in PV modules in daylight.

Abstract Insights into the impacts of freeze-thaw processes on soil microorganisms and their related functions in permafrost regions are crucial for assessing ecological consequences imposed by the shifts in freeze-thaw patterns. Through in-situ investigations on seasonal freeze-thaw processes in the active layer of permafrost in the Qinghai-Tibet Plateau, we found that microbial richness was higher and positively correlated with soil multifunctionality during the freeze-thaw stage (freezing and thawing periods) compared to the non-freeze-thaw stage (completely frozen and thawed periods). This relationship resulted from the higher microbial stability, which was highly consistent with the lower complexity, more keystone taxa, and greater robustness of networks. Although freeze-thaw strength exacerbated the greenhouse effect on climate, it was alleviated by the enhancement of diversity-soil multifunctionality relationship. These findings have substantial implications for exploring the responses of microbial-mediated soil multifunctionality and greenhouse effect in alpine permafrost to more drastic variations of freeze-thaw processes under future warming.