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To enhance public safety, crowd detection and prevention systems have essentially become a natural means to manage diverse crowded areas, such as urban settings, transportation hubs, and event venues. Recent systems take advantage of the synergy between machine learning, data mining, and image processing to extract/analyze features from crowded zones and recognize patterns and anomalies from the crowd behavior. Additionally, image processing tools play a key role in real-time monitoring by analyzing video feeds to detect crowd density, flow direction, and identify potential risks like overcrowding or emergencies. However, most existing solutions focus on the detection phase and often overlook integrated error handling and robust decision-making frameworks to ensure accurate and actionable crowd prevention. Aiming to solve these issues, we take advantage of the prediction capabilities of machine learning models and the analysis and clustering strengths of Formal Concept Analysis (FCA) chosen for its strong mathematical foundation and superior clustering capabilities compared to traditional methods, as highlighted in recent works such as K-means or hierarchical clustering. We used the first technique to extract useful knowledge from areas’ produced images while mitigating potential error accumulation through modular error-checking mechanisms. A neural network is used to mark human bodies, determine the position of walking individuals, and predict crowd levels. Such information is, thereafter, inputted to the FCA-based decision system to ensure an explicit representation and modelling of crowd data, thanks to lattice structures. These latter’s hierarchical view helped us identify the crowded areas and manage them as clustered zones, based on their common crowd information. We also define bottom-up parsing algorithms to recommend the suitable crowd prevention plan w.r.t. the crowd level. Experiments have successfully proved the ability of FCA to exclude low-crowd zones, locate crowded areas, and provide actionable crowd management insights, which may complement crowd counting techniques.

Abstract Perovskite solar cells (PSCs) offer an efficient, inexpensive alternative to current photovoltaic technologies, with the potential for manufacture via high-throughput coating methods. However, challenges for commercial-scale solution-processing of metal-halide perovskites include the use of harmful solvents, the expense of maintaining controlled atmospheric conditions, and the inherent instabilities of PSCs under operation. Here, we address these challenges by introducing a high volatility, low toxicity, biorenewable solvent system to fabricate a range of 2D perovskites, which we use as highly effective precursor phases for subsequent transformation to α-formamidinium lead triiodide (α-FAPbI3), fully processed under ambient conditions. PSCs utilising our α-FAPbI3 reproducibly show remarkable stability under illumination and elevated temperature (ISOS-L-2) and “damp heat” (ISOS-D-3) stressing, surpassing other state-of-the-art perovskite compositions. We determine that this enhancement is a consequence of the 2D precursor phase crystallisation route, which simultaneously avoids retention of residual low-volatility solvents (such as DMF and DMSO) and reduces the rate of degradation of FA+ in the material. Our findings highlight both the critical role of the initial crystallisation process in determining the operational stability of perovskite materials, and that neat FA+-based perovskites can be competitively stable despite the inherent metastability of the α-phase.

During analyzing morbidity data, it is obvious that the ratio of infections has decreased significantly since the beginning of the 20th century, but the proportion of metabolic and inflammatory diseases has increased. This may be related to the degradation of soils. At the same time, mobilization of nutrient elements primarily depends on the activity of soil microbiota, which is subjected to negative anthropogenic impact. Although plant nutritional value has a direct impact on human health; however, modern agricultural practices that aimed at deep cultivation are causing disturbances in the soil microbiota composition. Subsequently, this resulted in maladaptation of the human immune system, as contacts with xenobiotics occur instead of evolutionarily calibrated interactions; potentially endangering the gut microbiota. This review aims to represent recent data on the relationships among human intestinal, soil, and plant microbiota. Nowadays, it is evident that there is a broad range of influences on human health not only from intestinal microbiota but also from its connection with the environmental microbiota (i.e., soil microorganisms in particular). Today, with respect to the background of active use of modern technologies, including genetical ones, we have the opportunity to examine such volumes of data that will allow us to fully analyze microbiological diversity of the different ecological niches in terms of their common features, differences, and mutual influences. Such studies will make it possible to identify potential factors determining the composition of microbiota in different loci, assess their potential impact on human health, and adjust methods to diagnose and restore the optimal composition of human, plant, and soil microbiota.

Abstract Controlling crystal growth alignment in low-dimensional perovskites (LDPs) for solar cells has been a persistent challenge, especially for low-n LDPs (n 1.7 eV) impeding charge flow. Here we overcome such transport limits by inducing vertical crystal growth through the addition of chlorine to the precursor solution. In contrast to 3D halide perovskites (APbX3), we find that Cl substitutes I in the equatorial position of the unit cell, inducing a vertical strain in the perovskite octahedra, and is critical for initiating vertical growth. Atomistic modelling demonstrates the thermodynamic stability and miscibility of Cl/I structures indicating the preferential arrangement for Cl-incorporation at I-sites. Vertical alignment persists at the solar cell level, giving rise to a record 9.4% power conversion efficiency with a 1.4 V open circuit voltage, the highest reported for a 2 eV wide band gap device. This study demonstrates an atomic-level understanding of crystal tunability in low-n LDPs and unlocks new device possibilities for smart solar facades and indoor energy generation.

Abstract Concentrated Growth Factor (CGF) is a highly effective biomaterial known for its ability to promote tissue regeneration. While it’s been studied extensively in intraoral procedures and bone grafting, its benefits in maxillary sinus lifting lack substantial evidence. This review aimed to evaluate CGF’s effectiveness in maxillary sinus augmentation, focusing on clinical, radiographic, and histological outcomes. A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, Google Scholar, and Cochrane Library databases using specific mesh terms and keywords and adhered to PRISMA guidelines. Studies up to March 2024 involving transcrestal or lateral maxillary sinus floor elevation with CGF, either alone or in combination with grafting materials, were included. Out of 783 publications, 13 studies met the eligibility criteria. The review assessed CGF’s role in minimizing postoperative complications, enhancing new bone formation, and improving implant survival. Despite data variability among studies, the majority reported that CGF positively influenced maxillary sinus augmentation outcomes. While more robust randomized clinical trials are required to draw definitive conclusions, initial results are promising, and the findings suggest that CGF utilization in maxillary sinus augmentation seems to enhance clinical outcomes by promoting vascularization and regeneration at the surgical sites and improving both the quality and quantity of newly formed sinus bone. Furthermore, it exhibits potential for improving postoperative sequelae and achieving high implant survival rates.

Switchgear is a vital component of modern power systems, responsible for regulating the flow of electrical power. Contact issues, irregular loads, and other similar problems can cause switchgear to overheat, leading to unexpected disturbances and potential damage to the power equipment. Thermal imaging shows significant potential and is increasingly employed to detect faults in power equipment. However, the unique characteristics of thermal images often pose challenges to accurate fault detection. This research aims to study the effectiveness of transfer learning architectures for switchgear fault detection. This paper applies eleven transfer learning architectures, including SqueezeNet, GoogLeNet, InceptionV3, DenseNet201, ResNet50, Xception, InceptionResNetV2, ShuffleNet, EfficientNetB0, AlexNet, and VGG19. The results of the testing phase demonstrated that the application of transfer learning by fine-tuning pre-trained convolutional neural network architectures was highly effective in the classification of thermal images captured from switchgear units. The models achieved accuracy rates between 83.87% and 98.38%, and values of F1-Scores between 83.11% and 98.34% in the pre-trained architectures.

Polymeric materials have long been at the forefront of materials science due to their exceptional physical and chemical properties. Designing new polymeric materials traditionally relies on a combination of physical and chemical principles along with empirical trial and error methods. A big issue in this approach is that the traditional trial and error experiments/simulations are often time consuming and sometimes inefficient in order to achieve targeted properties. Machine learning (ML) offers a promising solution to this issue. By leveraging ML algorithms, researchers can accelerate the design process, predict material properties more rapidly, and optimize formulations. ML approaches can analyze vast amounts of data, uncover hidden patterns, and generate predictive models that significantly reduce the time needed to develop materials with desired properties. This paradigm shift allows for more efficient exploration of the material space and faster innovation in polymeric material design. The current review article focuses on the application of ML methods to help design polymeric materials, more especifically, thermosets, thermoplastics and elastomers. Additionally, the review explores future possibilities and opportunities arising from the state-of-the-art advancements in ML, offering perspectives on how these technologies might evolve and influence the polymer science domain.

High efficiency and stability are two crucial factors in piezocatalysis reactions. In this research, the efficiency of Fe2O3 piezo catalysts significantly increased by coupling it with Na2WO4. Results indicate that Fe2O3 and Na2WO4 have a synergetic effect on the degradation of Amoxicillin (AMX), with Fe2O3/Na2WO4 degrading 88 % of AMX in the same conditions that pure Fe2O3 and Na2WO4 degrade only 42 % and 20.6 %, respectively. The degradation efficiency of Fe2O3 was improved by 114 % when it was coupled with Na2WO4. Fe2O3/Na2WO4 piezo catalyst was successfully used in 8 cycles while it keeps almost 85 % of its efficiency which indicates the stability of prepared piezo catalysts. Studying the mechanism of degradation by Fe2O3/Na2WO4 revealed that superoxide free radicals have a critical function in the decomposition of AMX. The kinetic study also indicates that at lower temperatures, the reaction follows a first-order reaction.

Abstract Background The translucency of different zirconia generations at each time point after thermocycling aging is still lacking. Methods Four zirconia materials were used with a total of 60 samples produced from monolithic third generation (5Y) 5 mol% yttria-stabilized zirconia polycrystalline ceramic and fourth generation zirconia (4Y) 4 mol% yttria-stabilized zirconia polycrystalline ceramic, represented by [group1:[CM-5Y] Ceramill Zolid fx (3rd generation zirconia) (Amann Girrbach, Koblach, Austria), group 2:[CM-4Y] Ceramill Zolid HT + (4th generation zirconia) (Amann Girrbach, Koblach, Austria), group 3:[CC-5Y] Cercon XT/ML (Dentsply Sirona, Germany) (3rd generation), and group 4:[CC-4Y] Cercon HT/ML (Dentsply Sirona, Germany) (4th generation)]. The L*a*b* figures were measured by using a spectrophotometer at baseline and after 10,000, 30,000, and 50,000 cycles of thermocycling. At each interval, the translucency of the samples was estimated by using the translucency formula CIEDE2000. The Scheffe post-hoc compared differences among each of the four materials. The Repeated measures ANOVA tested the differences between the materials at each of the different thermocycling intervals (p

Background/Objectives: Prostate cancer (PCa) is one of the most prevalent cancers in men. While PSA testing aids in early detection, it often identifies clinically insignificant PCa (ciPCa), which may not necessitate treatment. Prostate-specific membrane antigen (PSMA) PET scans have emerged as a promising tool to evaluate of localised PCa. This review aims to assess the current evidence of using PSMA PET scans for localised PCa. Methods: Peer-reviewed publications on PSMA PET scans in localised PCa, from inception to May 2024, were retrieved from PubMed. The outcomes evaluated included diagnostic performance in identifying intraprostatic lesions, detecting csPCa (ISUP GG ≥ 2), and role peri-treatment. Results: The addition of PSMA PET/CT to MRI improved the sensitivity (from 83% to 97%) and NPV (72% to 91%) of detecting csPCa. PSMA PET helped improve risk stratification in active surveillance by identifying MRI-occult lesions in up to 29% of patients, of which up to 10% may harbour underlying unfavourable pathology. In local staging, PSMA PET/MRI outperforms MRI in identifying extra-prostatic extension (77% vs. 73%) and seminal vesicle invasion (90% vs. 87%). PSMA PET scans are also superior to MRI in nodal staging and bone scans in identifying bony metastasis. PSMA PET scans appear useful in guiding treatment of localised PCa and aiding follow-up. Conclusions: PSMA PET scans are valuable for evaluating localised PCa by improving the detection of csPCa and enhancing local staging. However, most available studies are retrospective, and long-term oncological outcomes remain underreported due to the relative novelty of PSMA PET scans.

Crash severity prediction is a challenging research area, where the objective is to accurately assess the extent of severity of an injury resulting from road traffic accidents. The main aim of existing studies is to precisely assess the potential severity of crashes under diverse circumstances, such as weather conditions, vehicle attributes, road characteristics and layout, and traffic control factors. This effort aids authorities in establishing effective emergency response systems. The novelty and objective of our work involve contributing to this research area by employing a graph architecture to capture relationships among various crash records to uncover any hidden patterns that traditional ML models might overlook. The current study extends existing knowledge by leveraging Graph Neural Networks (GNN) and comparing their performance to popular ensemble-based models, which include Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Artificial Neural Networks (ANNs). Real data from the United Kingdom (UK) was employed to achieve our goal. The data was obtained from the Department for Transport open data portal. All models underwent training using the training dataset, followed by performance evaluation using diverse metrics such as the accuracy, precision, recall, f1-score, Matthews Correlation Coefficient (MCC), confusion matrix, and computational cost on the test dataset. Overall, our proposed GNN-based model demonstrated better performance when compared to other models. Specifically, the GNN model outperformed all other models across all metrics. For instance, the accuracy of the GNN model was 85.55% as compared to 83.36%, 83.18%, and 83.27% for the XGBoost, RF, and ANN models, respectively. The GNN model assisted in identifying hidden patterns by considering non-linear relationships among crash records. Thus, the model had the potential to improve its ability to predict severe accidents, which could in turn significantly improve emergency response efforts and reduce the likelihood of severe accidents resulting in fatalities.

Cystic fibrosis (CF) is one of the most common genetic disorders; resulting in a wide variety of complications, including respiratory infections. Such infections are often ineffectively treated within the framework of a classical paradigm of the infectious process. However, little attention is paid to the unique microecological conditions that are formed in CF respiratory tract. This study aimed to exploring the microecological conditions and to finding out how they may influence the pathogenesis of CF infections. These conditions emerge under the influence of local disruptions in the respiratory functions; inflammatory processes, and complicated relations of the individual microorganisms between each other and between the human bodies as their ecological substrates. As a result, microorganisms that are usually safe for the healthy people become extremely dangerous for CF patients; having adapted to a new ecological niche and having got definite resource advantage, which is attributed to the respiratory tract tissue destruction. Additionally, it is still unknown; how do the anaerobic microbes contribute to CF infections, and whether they collaborate with the traditional CF pathogens or compete with them. In this article, we are analyzing CF respiratory infections from the ecological perspective and proposing in our opinion a more comprehensive picture of their pathogenesis.

Chronic infections; emerging as complications of some primary diseases, can hardly be considered as classical infectious processes. Such infections may be caused by the non- traditional pathogenic bacteria; as in the cases of cystic fibrosis; chronic obstructive pulmonary disease (COPD), inflammatory bowel diseases, etc. Emergence of these complications is caused by several disorders in the ecosystem; constituting the human body and its microbiota. It is reasonable to extrapolate some ecological principles of the bacterial community's assembly in the humans; as there is a wide variety of factors affecting this, which can be considered as ecological ones. The human microbiota is formed during changes of the different bacterial succession types; under the influence of stochastic and deterministic ecological processes, in addition to different types of the selection pressure. During certain diseases, such as COPD, the normal course of the bacterial community's formation is disrupted. It is associated with anatomical and physiological defects, which cause changes in the properties of the ecological niche; occupied by the communities (i.e., airways in the case of COPD). Altered niches can be occupied by the virulent microorganisms, which get resource advantage and form new stable community. The severity of such chronic infections is correlated with biological diversity in the communities, which tends to decrease in the more severe cases and in the exacerbations. The aim of this study was to create an ecological model of the chronic infections; through synthesizing the ecological and medical knowledge. This model, such as the COPD; may establish new approach to the chronic infections etiopathogenesis, which enables to get better understanding of the clinical course in these diseases and to propose more effective therapies in the future.

Abstract Background and aims The World Health Organization has recently declared the frequent outbreaks of diphtheria in Nigeria as a public health concern. Although vaccination efforts have been successful in Nigeria, unfortunately, the recent 2023 outbreak in Nigeria has been nothing short of distressing. Of course, cases of diphtheria incidence are under‐reported in Nigeria. This present article aims to proffer a possible multifaceted approach to tackle outbreaks of diphtheria in Nigeria and improve immunization rates against the disease among the Nigerian population Methods In writing this study, literature search was done about diphtheria in Nigeria using the following keywords: “diphtheria, prevalence, vaccination, efforts, challenges, and Nigeria” on PubMed, Web of Science, Google Scholar, and ResearchGate within 10 years. Result This study found that an estimated seven million people remain unvaccinated and are at risk for infection in the country, especially people living in the Northern part of the country. Between the June 30 and August 31, 2023, Nigeria recorded an unusual increase in the number of confirmed cases of diphtheria, where a total of 5898 suspected cases were reported from 59 local government areas in 11 states across Nigeria. The majority (99.4%) of suspected cases of the disease were reported from six states: Kano (1816), Katsina (234), Yobe (158), Bauchi (79), Kaduna (45), and Borno (33). Conclusion If Nigeria is to emerge beyond these frequent epidemics of diphtheria, the Nigerian government must work on tackling this issue on multiple fronts simultaneously, that is, at the national and international levels, as we believe that these levels would give a holistic way to unmask diphtheria in Nigeria.

Abstract Background In this phase Ib/II open-label study, tumor immune suppression was targeted in patients with advanced refractory solid tumors and patients with recurrent/refractory non-small cell lung cancer (NSCLC) using galunisertib with nivolumab. Methods Eligible patients were ≥ 18 years old, had an Eastern Cooperative Oncology Group performance status ≤ 1, and were treatment-naive for anti-programmed cell death-1, its ligand, or transforming growth factor β receptor 1 kinase inhibitors. Phase Ib was an open-label, dose-escalation assessment of the safety and tolerability of galunisertib with nivolumab in patients with advanced refractory solid tumors. Phase II evaluated the safety of galunisertib with nivolumab in NSCLC patients who had received prior platinum-based treatment but were immuno-oncology agent-naive. Results This trial was conducted between October 2015 and August 2020. No dose-limiting toxicities were observed in phase I. In the phase II NSCLC cohort (n = 25), patients received 150 mg twice daily galunisertib (14 days on/14 days off dosing schedule for all phases) plus nivolumab at 3 mg/kg (intravenously every 2 weeks). In this phase, the most frequent treatment-related adverse events (AEs) were pruritus (n = 9, 36%), fatigue (n = 8, 32%), and decreased appetite (n = 7, 28%). No grade 4 or 5 treatment-related AEs were observed. Six (24%) patients had confirmed partial response (PR) and 4 (16%) had stable disease; 1 additional patient had confirmed PR after initial pseudo-progression. The median duration of response was 7.43 months (95% confidence interval [CI]: 3.75, NR). Among the 7 responders, including the delayed responder, 1 had high PD-L1 expression (≥ 50%). The median progression-free survival was 5.26 months (95% CI: 1.77, 9.20) and the median overall survival was 11.99 months (95% CI: 8.15, NR). Interferon gamma response genes were induced post-treatment and cell adhesion genes were repressed, although the association of these observations with tumor response and clinical outcomes was not statistically powered due to limited samples available. Conclusions The study met its primary endpoint as galunisertib combined with nivolumab was well tolerated. Preliminary efficacy was observed in a subset of patients in the Phase 2 NSCLC cohort. Trial registration Trial registered with ClinicalTrials.gov (NCT02423343; 22.04.2015).

The molecular and crystal structure of the title compound, (C8H11N4O2)2[PtCl6], synthesized from hexachloroplatinic acid and caffeine in methanol, was studied by single-crystal X-ray diffraction. The caffeinium cations form a double layer via hydrogen bonds and π-stacking interactions. The Hirshfeld surface analysis showed that the largest contribution to the crystal packing is made by H...H (31.2%), H...Cl/Cl...H (22.6%), O...H/H...O (21.9%) contacts for the cation and H...Cl/Cl...H (79.3%) contacts for the anion.

In this paper we review the possible mechanisms for the production of primordial black holes (PBHs) during a slow-reheating period in which the energy transfer of the inflaton field to standard model particles becomes effective at slow temperatures, offering a comprehensive examination of the theoretical foundations and conditions required for each of formation channel. In particular, we focus on post-inflationary scenarios where there are no self-resonances and the reheating epoch can be described by the inflaton evolving in a quadratic-like potential. In the hydrodynamical interpretation of this field during the slow-reheating epoch, the gravitational collapse of primordial fluctuations is subject to conditions on their sphericity, limits on their spin, as well as a maximum velocity dispersion. We show how to account for all conditions and show that PBHs form with different masses depending on the collapse mechanism. Finally we show, through an example, how PBH production serves to probe both the physics after primordial inflation, as well as the primordial powerspectrum at the smallest scales.

Asphalt or oil spills containing asphaltene can contaminate soil, water bodies, and ecosystems. Natural balances are disrupted by this contamination. Asphaltene contaminates water sources such as rivers, lakes, and aquifers, making them unfit for human consumption. Aquatic habitats are damaged, and aquatic organisms cannot reproduce, grow, or maintain health. Air pollutants, including sulfur dioxide, nitrogen oxides, volatile organic compounds, and particulate matter, are released when asphaltene-containing materials are burned. Here, we have developed a new method for the degradation of asphaltene that is fast, clean, and cost-effective. The asphaltene was degraded using NixMnyO piezo catalyst in this study. By using NixMnyO under mechanical force, the result showed that asphaltene was degraded to the extent of 94.7 %. Piezo based on NixMnyO has shown promising reusability when compared to conventional catalysts. Despite being used for 11 runs, it maintained 94% of its activity for 11 consecutive cycles. As well as analyzing the kinetics and thermodynamics of piezo asphaltene degradation, a mechanism pathway was developed for piezo degradation of asphaltene. Radical scavenger experiments showed that superoxide radicals, holes, and hydroxyl radicals are involved in the degradation of asphaltene by NixMnyO piezo catalysts. However, hydroxyl radicals and holes are responsible for the majority of asphaltene degradation.

ABSTRACT: Objective: The present study aimed to estimate the location of the incisive canal (IC) concerning the upper central incisor roots (U1) in order to explore the amount of incisor retraction as per the envelope of discrepancy amongst Chinese patients with different vertical facial growth patterns. Methods: This is a cross-sectional study that used a total of 207 pretreatment cone-beam computed tomography (CBCT) scans of adults with a skeletal class I relationship. Sixty-nine cases were included in each of the normodivergent, hypodivergent, and hyperdivergent facial groups. The IC volume was measured using Mimics 21 software. The IC width and IC-U1 proximity were measured using in vivo 6 software. Linear measurements were conducted at 3 vertical levels. Results: The IC has shown a larger volume in the hyperdivergent group and male patients. Overall, the IC has recorded linear width greater than the inter-root distance of U1 in 59.1%, 66%, and 68.8% amongst the normodivergent, hypodivergent, and hyperdivergent facial groups, respectively, and in 58.3% of males and 70.8% of females. The overall sagittal distances between the U1 and IC were 4.00 ± 0.82 mm, 4.60 ± 0.83 mm, and 3.60 ± 0.80 mm amongst the normodivergent, hypodivergent, and hyperdivergent facial groups, respectively. Conclusions: The maximum sagittal distances between U1 and IC were 4.8 mm, 5.4 mm, and 4.4 mm amongst the normodivergent, hypodivergent, and hyperdivergent facial groups, respectively. Thereby, our findings have revised the retraction aspect of the envelope of discrepancy as per the different vertical facial growth patterns, which could serve a reference for the clinical practice involved considerable incisors movement, especially among Chinese patients.

Objective: To investigate the performance of a commercially available artificial intelligence (AI) algorithm for the detection of pulmonary embolism (PE) on contrast-enhanced computed tomography (CT) scans in patients hospitalized for coronavirus disease 2019 (COVID-19). Patients and Methods: Retrospective analysis was performed of all contrast-enhanced chest CT scans of patients admitted for COVID-19 between March 1, 2020 and December 31, 2021. Based on the original radiology reports, all PE-positive examinations were included (n=527). Using a reversed-flow single-gate diagnostic accuracy case-control model, a randomly selected cohort of PE-negative examinations (n=977) was included. Pulmonary parenchymal disease severity was assessed for all the included studies using a semiquantitative system, the total severity score. All included CT scans were sent for interpretation by the commercially available AI algorithm, Aidoc. Discrepancies between AI and original radiology reports were resolved by 3 blinded radiologists, who rendered a final determination of indeterminate, positive, or negative. Results: A total of 78 studies were found to be discrepant, of which 13 (16.6%) were deemed indeterminate by readers and were excluded. The sensitivity and specificity of AI were 93.2% (95% CI, 90.6%-95.2%) and 99.6% (95% CI, 98.9%-99.9%), respectively. The accuracy of AI for all total severity score groups (mild, moderate, and severe) was high (98.4%, 96.7%, and 97.2%, respectively). Artificial intelligence was more accurate in PE detection on CT pulmonary angiography scans than on contrast-enhanced CT scans (P

Abstract Background Although the role of percutaneous coronary intervention (PCI) as an initial strategy in the management of chronic coronary syndrome (CCS) remains debatable, the current evidence recommends intensive medical treatment as first-line therapy, with PCI reserved for those who remain symptomatic. Purpose This study evaluated the midterm (up to 6 months) outcomes of PCI versus optimal medical therapy (OMT) alone in patients with CCS. Materials and methods Two hundred ten patients with CCS and >70% lesion involving one or more coronary arteries were equally randomized (1:1) to one of two groups: 105 patients underwent PCI with OMT and 105 patients were assigned to OMT alone. All patients were followed up for 6 months for the occurrence of major adverse cardiac events and the improvement in symptoms and exercise capacity. Results There was no statistically significant difference between PCI and medical-therapy regarding the primary end point after 6 months of follow-up. Both groups showed improvement in anginal symptoms, although greater improvement was observed in the PCI group (P < .001). The increment of distance in a 6-minute walk test in the two groups was significant, which is higher in the PCI group (P < .001). The improvement in Seattle Angina Questionnaire score and the improvement in quality of life (assessed by the EQ-5D-5L score) in the two groups were statistically significant, which are higher in the PCI group (P < .001 for each). Conclusion PCI had no added prognostic value over OMT in patients with CCS. However, the ability of PCI to improve symptoms was more rapid and more effective than OMT.

Abstract The objective of this paper is to prove some new dynamic inequalities of Hardy type on time scales which generalize and improve some recent results given in the literature. Further, we derive some new weighted Hardy dynamic inequalities involving many functions on time scales. As special cases, we get continuous and discrete inequalities.

The distribution of any phenomenon on the surface of the earth is the essence of geography because geography is concerned with the spatial distribution of phenomena on the surface of the earth as all phenomena are linked to each other and health services are an important and necessary human geographic phenomenon in development plans, and health services are represented by health institutions that are concerned with public health and health The individual being part of the community that the optimal signature of health services contributes to finding ease of access and increasing the efficiency of using them for the majority of the population of the judiciary, a goal that the research seeks to achieve according to geographical technologies.

Gasoline is one of the main petroleum products used in transportation sectors in the Kurdistan region. The amount of gasoline provided from official refineries present in the region, which cover only a small portion of the total gasoline demand. The second source, which is the main one, is provided from outside of the region by private companies and includes three grades of gasoline (regular, premium, and super). The third source is blended gasoline, prepared locally in the region. Accordingly, to evaluate the available gasoline in the region, more than 100 samples of the three grades of gasoline were collected from different oil stations in Erbil City. Physical and chemical specifications were carried out, including sulfur content, octane number (RON and MON), density, vapour pressure, distillation curve, aromatic content, and olefin content using ASTM methods. The obtained results were compared with regional standards, Iraqi standards and worldwide standards. The results revealed that the majority of the gasoline samples had greater sulfur content, greater vapour pressure, low Initial Boiling Point (IBP), low distillation temperature at 10% and regular 50% and final boiling points (FBP). In addition, the majority of samples failed to meet all specifications in tests conducted following international, Kurdistan and Iraqi standards. The low quality of gasoline leads to an increase the environmental pollution, increase fire hazards of cars, and destroyed engine cars.

Purpose The aim of this study was to assess the safety and effectiveness of percutaneous vulvoperineal varices sclerotherapy using 2% polidocanol foam under fluoroscopic guidance. Subjects and Methods This is a retrospective review of prospectively collected data of 70 patients treated between June 2020 and November 2021, with a mean age of 31 years (range: 24–42 years). All patients were not pregnant with residual vulvoperineal varices after embolization of their refluxing tributaries of the ovarian vein and/or internal iliac veins presenting with vulvoperineal heaviness/pain with or without lower limb varicosities. Technical and clinical successes together with procedural complications were assessed. Results The technical success rate was 100%. No minor or major complications have been reported. Follow-up was at 1 week, 2 weeks, 6 months, and 12 months post sclerotherapy. Clinical success was observed in all patients with improvement in their symptoms. Conclusion Direct fluoroscopic-guided sclerotherapy for vulvoperineal varices offers a safe and effective treatment option with the ability of visualization and embolization of the pelvic leak points to avoid recurrence.