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Understanding healthcare professionals’ perceptions towards a computerised decision support system (CDSS) may provide a platform for the determinants of the successful adoption and implementation of CDSS. This cross-sectional study examined healthcare professionals’ perceptions, barriers, and facilitators to adopting a CDSS for antibiotic prescribing in Jordanian hospitals. This study was conducted among healthcare professionals in Jordan’s two tertiary and teaching hospitals over four weeks (June–July 2021). Data were collected in a paper-based format from senior and junior prescribers and non-prescribers (n = 254) who agreed to complete a questionnaire. The majority (n = 184, 72.4%) were aware that electronic prescribing and electronic health record systems could be used specifically to facilitate antibiotic use and prescribing. The essential facilitator made CDSS available in a portable format (n = 224, 88.2%). While insufficient training to use CDSS was the most significant barrier (n = 175, 68.9%). The female providers showed significantly lower awareness (p = 0.006), and the nurses showed significantly higher awareness (p = 0.041) about using electronic prescribing and electronic health record systems. This study examined healthcare professionals’ perceptions of adopting CDSS in antimicrobial stewardship (AMS) and shed light on the perceived barriers and facilitators to adopting CDSS in AMS, reducing antibiotic resistance, and improving patient safety. Furthermore, results would provide a framework for other hospital settings concerned with implementing CDSS in AMS and inform policy decision-makers to react by implementing the CDSS system in Jordan and globally. Future studies should concentrate on establishing policies and guidelines and a framework to examine the adoption of the CDSS for AMS.

Hundreds of human mpox cases are spreading outside of Western and Central Africa, which could be considered a significant world health problem. In this study, we sought to assess public knowledge and perceptions of human mpox. The study was a cross-sectional survey conducted in Jordan in September–October 2022. All participants were approached through social media portals. A total of 1195 participants agreed to participate in this study. The participants showed a low median knowledge score about human mpox (4.0 out of 15.0, IQR = 5.0), with only 27.9% of the participants (n = 334) correctly identifying that human mpox is not a bacterial disease, and around half of them (n = 589, 49.3%) knowing that this disease affects both genders. Higher monthly income (≥400 JD/month) was significantly associated with better knowledge about the disease. Social media was the most comprehensive source of information about mpox (n = 1018, 85.2%). Finally, 57.3% of the participants (n = 685) believed that mpox would affect social and economic life, like the COVID-19 pandemic. These findings highlight the urgent need for systematic strategies that should be promoted to increase public knowledge of mpox, which will be critical in improving the capacity to respond to the disease.

Background: Corticosteroids play a significant role in managing the vast majority of inflammatory and immunologic conditions. To date, population-based studies on knowledge and attitudes concerning corticosteroids are scarce. This study aims to comprehensively assess knowledge, perception, experience and phobia toward corticosteroid use among the general population in the era of COVID-19. Methods: A cross-sectional self-administrated questionnaire was used to collect the data from 6 countries. Knowledge and corticophobia scores, descriptive statistics and logistic regression were computed. Results: A total of 2354 participants were enrolled in this study; the majority were females (61.6%) with an average age of 30. Around 61.9% had been infected previously with COVID-19, and about one-third of the participants had experience with corticosteroid use. The mean knowledge score was relatively satisfactory (8.7 ± 4.5 out of 14), and Corticophobia ranked a high score in all countries. Age, female gender, and history of COVID-19 were positively correlated with developing corticophobia. Conclusion: Our study highlights that the general knowledge about steroids was satisfactory. However, the phobia toward its use upon indication is high. Therefore, enhancing awareness and providing essential counseling regarding the rational use of corticosteroids may reduce corticophobia.

During recent decades, the autoimmune disease neuromyelitis optica spectrum disorder (NMOSD), once broadly classified under the umbrella of multiple sclerosis (MS), has been extended to include autoimmune inflammatory conditions of the central nervous system (CNS), which are now diagnosable with serum serological tests. These antibody-mediated inflammatory diseases of the CNS share a clinical presentation to MS. A number of practical learning points emerge in this review, which is geared toward the pattern recognition of optic neuritis, transverse myelitis, brainstem/cerebellar and hemispheric tumefactive demyelinating lesion (TDL)-associated MS, aquaporin-4-antibody and myelin oligodendrocyte glycoprotein (MOG)-antibody NMOSD, overlap syndrome, and some yet-to-be-defined/classified demyelinating disease, all unspecifically labeled under MS syndrome. The goal of this review is to increase clinicians’ awareness of the clinical nuances of the autoimmune conditions for MS and NMSOD, and to highlight highly suggestive patterns of clinical, paraclinical or imaging presentations in order to improve differentiation. With overlay in clinical manifestations between MS and NMOSD, magnetic resonance imaging (MRI) of the brain, orbits and spinal cord, serology, and most importantly, high index of suspicion based on pattern recognition, will help lead to the final diagnosis.

Early diagnosis of acute promyelocytic leukemia (APL) is essential because of its associated life threatening coagulopathy and unique response to all trans-retinoic acid (ATRA) therapy. The characteristic cell morphology supplemented by cytochemistry offers the most rapid means for diagnosis. Here we describe a rare case of acute promyelocytic leukemia-hypogranular variant that poses particular diagnostic challenge.

Umbilical metastasis is one of the main characteristic signs of extensive neoplastic disease and is universally referred to as Sister Mary Joseph’s nodule. It indicates neoplasm of inner organs mostly located in the gastrointestinal tract or the pelvis. However, in approximately 15-30% the primary tumor remains occult. In most cases, Sister Mary Joseph’s nodule appears as an early and sometimes the only symptom of the malignancy. Here we report a rare fine needle aspiration (FNA) cytologic diagnosis of umbilical metastasis of an ovarian carcinoma.

Cytochrome P450 (CYP450) is a group of enzymes that play an essential role in Phase I metabolism, with 57 functional genes classified into 18 families in the human genome, of which the CYP1, CYP2, and CYP3 families are prominent. Beyond drug metabolism, CYP enzymes metabolize endogenous compounds such as lipids, proteins, and hormones to maintain physiological homeostasis. Thus, dysregulation of CYP450 enzymes can lead to different endocrine disorders. Moreover, CYP450 enzymes significantly contribute to fatty acid metabolism, cholesterol synthesis, and bile acid biosynthesis, impacting cellular physiology and disease pathogenesis. Their diverse functions emphasize their therapeutic potential in managing hypercholesterolemia and neurodegenerative diseases. Additionally, CYP450 enzymes are implicated in the onset and development of illnesses such as cancer, influencing chemotherapy outcomes. Assessment of CYP450 enzyme expression and activity aids in evaluating liver health state and differentiating between liver diseases, guiding therapeutic decisions, and optimizing drug efficacy. Understanding the roles of CYP450 enzymes and the clinical effect of their genetic polymorphisms is crucial for developing personalized therapeutic strategies and enhancing drug responses in diverse patient populations.

Construction site layout planning (CSLP) is the strategic arrangement and planning of construction site spaces, which has an enormous impact on the success of any construction project. Over the past two decades, multiple planning models have been developed to generate layouts that maintain safety and productivity within the construction environment. Yet these models vary significantly with disparate assumptions, many of which remain unstated. This study harnesses social network analysis (SNA) as a means to convert data into knowledge. It applies SNA to shed light on CSLP, providing a comprehensive overview of the existing models, and illuminating the critical parameters that should be considered in layout planning. This analysis delves deep into past methodologies and sets the potential for forthcoming research investigations. This study aims to be a reference for readers and researchers venturing into the realm of CSLP. Numerous related records and studies from diverse databases and sources were reviewed and analyzed. Out of these, 70 articles were singled out, from which 14 pivotal parameters were distilled as the foundation for any CSLP framework. Through the application of SNA, gaps within the existing research domain and literature were pinpointed. The study findings demonstrate the growing interest in shifting to cutting-edge approaches in CSLP. However, the results show that the majority of these models in the literature fall short of sufficiently addressing realistic facility representation, noise effects, or the construction impact on the surrounding environment. Accordingly, this research illuminates these knowledge gaps. The findings of this review guide future research by sketching a broad outline for future optimization models and planning studies.

Cephalexin is a first-generation β-lactam antibiotic used in adults and pediatrics to treat various streptococcal and staphylococcal infections. This review aims to summarize and evaluate all the pharmacokinetic (PK) data on cephalexin by screening out all pertinent studies in human beings following the per oral (PO) route. By employing different online search engines such as Google Scholar, PubMed, Cochrane Central, and Science Direct, 23 studies were retrieved, among which nine were in healthy subjects, five in diseased ones, and the remaining were drug–drug, drug–food, and bioequivalence-related. These studies were included only based on the presence of plasma concentration-time profiles or PK parameters, i.e., maximum plasma concentration (Cmax), half-life (t1/2) area under the curve from time 0-infinity (AUC0–∞), and clearance (CL/F). A dose-proportional increase in AUC0–∞ and Cmax can be portrayed in different studies conducted in the healthy population. In comparison to cefaclor, Cmax was recorded to be 0.5 folds higher for cephalexin in the case of renal impairment. An increase in AUC0–∞ was seen in cephalexin on administration with probenecid, i.e., 117 µg.h/mL vs. 68.1 µg.h/mL. Moreover, drug–drug interactions with omeprazole, ranitidine, zinc sulfate, and drug–food interactions for cephalexin and other cephalosporins have also been depicted in different studies with significant changes in all PK parameters. This current review has reported all accessible studies containing PK variables in healthy and diseased populations (renal, dental, and osteoarticular infections, continuous ambulatory peritoneal dialysis) that may be favorable for health practitioners in optimizing doses among the latter.

Among the most studied natural polymers, alginate, a natural polysaccharide extracted from algae is well known due to its applications related to remarkable properties such as biocompatibility, biodegradability and low toxicity. In order to optimize the physicochemical properties of alginate and starch-based biofilms reinforced by glycerol, we propose of different biofilms by Raman spectroscopy according to the mass proportion of alginate in water: 1%, 2%, 3%, 4% and 5%. The first vibrational study of alginate-based biofilms demonstrated a link between the mass proportion of alginate in water with the contribution of hydrogen bonds through vibrational modes at 1570, 1500 and 1414 cm−1 and with the vibrational contributions of gauche and trans conformational C-C stretching at 1098 and 1068 cm−1, respectively. This link seems to persist in the case of biofilms based on alginate reinforced by glycerol with a lower intensity. For the cases of alginate and starch-based biofilms and glycerol-reinforced alginate–starch biofilms, the impact of the mass proportion of alginate in water on the hydrogen bonds is evaluated by determining an area ratio τ=Area1414/Area1340 between the mode 1414 cm−1 assigned to the hydrogen bonds and 1340 cm−1 assigned to d(O-H) in plane. The vibrational results show interaction between the proportion of alginate and the hydrogen bonds.

We compared four methods to screen emergency department (ED) patients for an early electrocardiogram (ECG) to diagnose ST-elevation myocardial infarction (STEMI) in a 5-year retrospective cohort through observed practice, objective application of screening protocol criteria, a predictive model, and a model augmenting human practice. We measured screening performance by sensitivity, missed acute coronary syndrome (ACS) and STEMI, and the number of ECGs required. Our cohort of 279,132 ED visits included 1397 patients who had a diagnosis of ACS. We found that screening by observed practice augmented with the model delivered the highest sensitivity for detecting ACS (92.9%, 95%CI: 91.4–94.2%) and showed little variation across sex, race, ethnicity, language, and age, demonstrating equity. Although it missed a few cases of ACS (7.6%) and STEMI (4.4%), it did require ECGs on an additional 11.1% of patients compared to current practice. Screening by protocol performed the worst, underdiagnosing young, Black, Native American, Alaskan or Hawaiian/Pacific Islander, and Hispanic patients. Thus, adding a predictive model to augment human practice improved the detection of ACS and STEMI and did so most equitably across the groups. Hence, combining human and model screening––rather than relying on either alone––may maximize ACS screening performance and equity.

(1) Background: Tooth agenesis is one of the most common developmental dental anomalies often affecting the maxillary incisors area and premolar regions. (2) Purpose: The aim of this study was to assess the prevalence and distribution of permanent tooth agenesis and the associated dental abnormalities among orthodontically treated children. (3) Materials and Methods: This study was carried out utilizing 3000 pretreatment records of children who underwent orthodontic treatment, 1780 (59%) females and 1220 (41%) males, aged 10–25 years (mean age 15 years). Tooth agenesis and other dental anomalies were surveyed using their panoramic radiographs, according to gender, pattern, and location. The level of statistical significance was set at p < 0.05 using t-test or Chi-Square tests. (4) Results: The total number of missing teeth, excluding third molars, was 518 (17%) found in 326 (11%) children. The majority were the maxillary lateral incisors, which was 176 teeth (34%) (p < 0.05). Of them, 111 (63%) were in females, and 65 (37%) were in males. The second most common missing tooth was mandibular second premolars, which was 137 teeth (26%), followed by missing 73 (14%) maxillary second premolars. Impacted teeth had the highest associated dental anomaly prevalence (14.3%), while transposition showed the lowest anomaly prevalence (0.5%). (5) Conclusions: A prevalence of 11% for tooth agenesis was detected in this study. More teeth were missing in the maxilla compare with the mandible. A significant association was found between missing maxillary lateral incisors and missing premolars (p < 0.05). Associated dental anomalies included an increased number of peg-shaped maxillary lateral incisors, palatally displaced and impacted maxillary canines, ectopic teeth, and infra-occluded (submerged) primary second molars.

Buildings represent the largest potential for carbon reduction worldwide. This highlights the need for a simulation and optimization method for energy management. The early design stage of buildings represents an important phase in which choices can be made to optimize design parameters. These parameters can focus on multiple areas, including energy and thermal comfort. This paper introduces the optimization of early-stage sustainable building design considering end-user energy consumption. It proposes an optimization model that integrates multiple layers, which consist of a parametric energy simulation, artificial neural network, and genetic algorithm. The proposed optimization model considers a single objective function to obtain the optimal design. The targeted goal is to obtain minimal energy consumption for residential buildings during the early design stages. Key design parameters of the building were identified for optimization and feasible ranges for them were obtained using genetic algorithms. Finally, the results of this paper include the identification of the optimal building design for the thermal comfort analysis and optimal energy performance. The model was applied to a case study in Egypt and the results showed that using the developed optimization model can lead to a 25% reduction in energy consumption.

The aim of this study was to determine the prevalence of dental anomalies and whethera significant association exists between dental anomalies and consanguinity marriage among Arabic orthodontic patients in Israel. Pretreatment panoramic radiographs of 513 patients (38% males and 62% females) were examined to detect dental anomalies related to number, size, shape, and eruption. A chi-square test was carried out to determine the associations between dental anomalies, gender, and consanguinity marriage. The results indicated that 29% of the subjects manifested at least one dental anomaly. Impacted tooth was found to be the most frequent dental anomaly, with a prevalence of 13.5%, whereas fusion and transmigration were found to be the least common, with a prevalence of 0.2% each. The prevalence of dental anomalies was significantly greater in males (p < 0.001) than in females. In our sample a significant association was found between dental anomalies and consanguinity marriage, and it was found to be greater in subjects with a family history of consanguinity marriage. This association can be attributed to the negative effects of consanguinity marriage, which was also reported in many studies in other fields. This study suggests that consanguinity marriage can be one of the factors involved in the development of dental anomalies in certain at-risk populations.

Technological advances have enabled the monitoring and control of construction operations and assets remotely. Digital twins, based on computational modeling, have enabled the creation of a digital map for physical structures. Research on digital twins (DTs) for constructed facilities projects has gained widespread traction in the industry. While these applications have increased over the years, there has been sparse review of them. This paper systematically reviews the applications of digital twins in construction using content analysis. We identified and analyzed 53 academic journal and conference papers, which revealed several DT applications that could be categorized into nine areas: lifecycle analysis, facility management, energy, education, disaster, structural health monitoring, DT for cities, infrastructure management, and miscellaneous. This enables the visualization of the current state of DT, comparison with the desired state, and possible integrations with other technologies. Among the observed benefits of DTs are the ability to increase engagement and collaboration, reduce construction and operating costs, reduce human error, automate energy demand, manage assets throughout their lifecycle, and apply structural health monitoring. It also enables the collection of real-time data on an asset’s status, history, maintenance needs, and provides an interactive platform for managing an asset. Future directions include addressing how to standardize data acquisition as well as the semantic interoperability and heterogeneity of data. Additionally, modeling human cognitive processes as well as spatiotemporal information would be beneficial to a smart city and other infrastructure systems, especially in disaster situations.

Public–private Partnerships have become a common delivery method for diverse types of projects ranging from transportation and energy infrastructure to social infrastructure. Previous research has mainly focused on PPPs for infrastructure and other non-social projects. Although PPP projects for higher education institutions share some common attributes with their traditional counterparts, they also have unique aspects such as institutional culture as well as structure of ownership and management. Hence, the objectives of this research were to (1) conduct a systematic collection and analysis of PPP projects in higher education institutions in the United States; and (2) conduct a gap analysis to provide recommendations for future projects and lessons learned from past ones. A gap analysis of the published data on higher education PPPs was undertaken, identifying 45 educational PPP projects in the United States. The main areas of study were type of project (e.g., housing, commercial, mixed use, etc.), size, and investment made. Additionally, a questionnaire survey was disseminated to experts in the field to collect data on these projects and report on them. The results showed an increasing trend in project size between 1994 and 2018 with the majority being for housing developments while a smaller percentage was for commercial and utility projects. A geographical representation shows a large number of projects clustered in the Southern and Northeastern regions of the United States. Additionally, a questionnaire survey was used to identify samples of these projects and present them as a case study. The number of PPPs is expected to rise due to funding cuts and state appropriation cuts. Finally, the proposed recommendations can also be extrapolated for other social or infrastructure projects.

The global energy demand has been increasing and posing multiple challenges across the globe, including global warming, environmental pollution, and energy-sustainability issues. Thus, multiple countries have been adopting renewable-energy (RE) sources to provide clean, reliable, affordable, and sustainable energy. Previously, a number of renewable energy projects has been delivered in the form of a public–private partnership (PPP) to take advantage of the private sector’s investment, technology advancements, and expertise. In general, renewable-energy projects are considered large-scale universal projects that involve expertise from different countries and require a clear understanding of the barriers and key success factors (KSFs) across the globe. Thus, this paper focuses on providing a comprehensive understanding of the main barriers and success factors of renewable-energy projects across the globe. For that aim, a comprehensive literature review was first carried out to identify and report on the barriers and KSFs of renewable-energy projects. This was followed by a questionnaire survey wherein the opinions of 60 experts with wide experience in RE PPPs in multiple countries were collected and analyzed. The analysis shows that political and regulatory barriers are the main risks globally. Additionally, well-prepared contract documentations and skilled and efficient parties are the KSFs. However, these factors change from one continent to another. Additionally, this paper sheds light on the difference between the public and private sectors’ perceptions on the severity of the risks and the importance of the KSFs to each sector.

Autoantibodies against nodal and paranodal proteins, specifically anti-neurofascin antibodies (ANFAs), have been recently described in central and peripheral nervous system demyelinating disorders. We retrospectively reviewed the charts of six individuals evaluated at our Multiple Sclerosis Program who tested positive for serum ANFAs on Western blot. We describe these patients’ clinical and diagnostic findings and attempt to identify features that might guide clinicians in checking for ANFAs. In our series, the women-to-men ratio was 2:1. At presentation, the median age was 60 years (range 30–70). The clinical presentation was pleiotropic and included incomplete transverse myelitis (n = 3), progressive myelopathy (n = 1), recurrent symmetric polyneuropathy (n = 1), and nonspecific neurological symptoms (n = 1). Atypical features prompting further workup included coexisting upper and lower motor neuron features, older age at presentation with active disease, atypical spinal cord MRI features, and unusual cerebrospinal fluid findings. The serum ANFAs panel was positive for the NF-155 isoform in five patients (IgM n = 2; IgG n = 2; both n = 1) and the NF-140 isoform in two (IgG n = 2). Larger studies are needed to assess the relevance of ANFAs in demyelinating nervous system diseases, their impact on long-term clinical outcomes, and associated therapeutic implications.

The depletion of natural resources and greenhouse gas emissions related to the manufacture and use of ordinary Portland cement (OPC) pose serious concerns to the environment and human life. The present research focuses on using alternative binders to replace OPC. Geopolymer might be the best option because it requires waste materials enriched in aluminosilicate for its production. The research on geopolymer concrete (GPC) is growing rapidly. However, substantial effort and expenses are required to cast specimens, cures, and tests. Applying novel techniques for the said purpose is the key requirement for rapid and cost-effective research. In this research, supervised machine learning (SML) techniques, including two individual (decision tree (DT) and gene expression programming (GEP)) and two ensembled (bagging regressor (BR) and random forest (RF)) algorithms were employed to estimate the compressive strength (CS) of GPC. The validity and comparison of all the models were made using the coefficient of determination (R2), k-fold, and statistical assessments. It was noticed that the ensembled SML techniques performed better than the individual SML techniques in forecasting the CS of GPC. However, individual SML model results were also in the reasonable range. The R2 value for BR, RF, GEP, and DT models was 0.96, 0.95, 0.93, and 0.88, respectively. The models’ lower error values such as mean absolute error (MAE) and root mean square errors (RMSE) also verified the higher precision of ensemble SML methods. The RF (MAE = 2.585 MPa, RMSE = 3.702 MPa) and BR (MAE = 2.044 MPa, RMSE = 3.180) results are better than the DT (MAE = 4.136 MPa, RMSE = 6.256 MPa) and GEP (MAE = 3.102 MPa, RMSE = 4.049 MPa). The application of SML techniques will benefit the construction sector with fast and cost-effective methods for estimating the properties of materials.

Compressive and flexural strength are the crucial properties of a material. The strength of recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate concrete. Several factors, including the recycled aggregate replacement ratio, parent concrete strength, water–cement ratio, water absorption, density of the recycled aggregate, etc., affect the RAC’s strength. Several studies have been performed to study the impact of these factors individually. However, it is challenging to examine their combined impact on the strength of RAC through experimental investigations. Experimental studies involve casting, curing, and testing samples, for which substantial effort, price, and time are needed. For rapid and cost-effective research, it is critical to apply new methods to the stated purpose. In this research, the compressive and flexural strengths of RAC were predicted using ensemble machine learning methods, including gradient boosting and random forest. Twelve input factors were used in the dataset, and their influence on the strength of RAC was analyzed. The models were validated and compared using correlation coefficients (R2), variance between predicted and experimental results, statistical tests, and k-fold analysis. The random forest approach outperformed gradient boosting in anticipating the strength of RAC, with an R2 of 0.91 and 0.86 for compressive and flexural strength, respectively. The models’ decreased error values, such as mean absolute error (MAE) and root-mean-square error (RMSE), confirmed the higher precision of the random forest models. The MAE values for the random forest models were 4.19 MPa and 0.56 MPa, whereas the MAE values for the gradient boosting models were 4.78 MPa and 0.64 MPa, for compressive and flexural strengths, respectively. Machine learning technologies will benefit the construction sector by facilitating the evaluation of material properties in a quick and cost-effective manner.

This study aimed to expand the knowledge on the application of the most common industrial byproduct, i.e., fly ash, as a supplementary cementitious material. The characteristics of cement-based composites containing fly ash as supplementary cementitious material were discussed. This research evaluated the mechanical, durability, and microstructural properties of FA-based concrete. Additionally, the various factors affecting the aforementioned properties are discussed, as well as the limitations associated with the use of FA in concrete. The addition of fly ash as supplementary cementitious material has a favorable impact on the material characteristics along with the environmental benefits; however, there is an optimum level of its inclusion (up to 20%) beyond which FA has a deleterious influence on the composite’s performance. The evaluation of the literature identified potential solutions to the constraints and directed future research toward the application of FA in higher amounts. The delayed early strength development is one of the key downsides of FA use in cementitious composites. This can be overcome by chemical activation (alkali/sulphate) and the addition of nanomaterials, allowing for high-volume use of FA. By utilizing FA as an SCM, sustainable development may promote by lowering CO2 emissions, conserving natural resources, managing waste effectively, reducing environmental pollution, and low hydration heat.

One of the main targets of future 5G cellular networks is enlarging the Internet of Things (IoT) devices’ connectivity while facing the challenging requirements of the available bandwidth, power and the restricted delay limits. Unmanned aerial vehicles (UAVs) have been recently used as aerial base stations (BSs) to empower the line of sight (LoS), throughput and coverage of wireless networks. Moreover, non-orthogonal multiple access (NOMA) has become a bright multiple access technology. In this paper, NOMA is combined with UAV for establishing a high-capacity IoT uplink multi-application network, where the resource allocation problem is formulated with the objective of maximizing the system throughput while minimizing the delay of IoT applications. Moreover, power allocation was investigated to achieve fairness between users. The results show the superiority of the proposed algorithm, which achieves 31.8% delay improvement, 99.7% reliability increase and 50.8% fairness enhancement when compared to the maximum channel quality indicator (max CQI) algorithm in addition to preserving the system sum rate, spectral efficiency and complexity. Consequently, the proposed algorithm can be efficiently used in ultra-reliable low-latency communication (URLLC).

Mango fruit quality plays a significant role in fruit storage. It also directly affects the economic value of fruit in the national and international markets. However, deterioration of soil health due to low organic matter is a major hurdle for mango growers. Scientists suggest incorporation of organic matter. However, high temperature and low precipitation lead to oxidation of organic residues in soil. On the other hand, biochar is gaining the attention of growers due to its resistance against decomposition. It can improve soil physicochemical attributes. Limited literature is available regarding biochar effects on the quality attributes of mango. Therefore, the current study was planned to investigate the effects of acidified biochar on mango quality and yield attributes in alkaline soil. Five levels of biochar, i.e., 0, 5, 10, 20 and 40 Mg/ha, were applied in a randomized complete block design (RCBD). Results showed that 20 and 40 Mg/ha acidified biochar significantly enhanced fruit retention, sugar contents, ash contents and TSS of mango compared to control. A significant increase in mango fruit weight and yield per plant validated the efficacious role of 40 Mg/ha acidified biochar over control. Furthermore, the maximum significant decrease in fruit juice acidity signified the imperative functioning of 40 Mg/ha acidified biochar in alkaline soil. In conclusion, 40 Mg/ha acidified biochar application can improve mango quality and yield attributes in alkaline soil. More investigations on different soil types, climatic zones and mango varieties are recommended to declare 40 Mg/ha acidified biochar as the best treatment for improvement in the quality and yield of mango fruit in alkaline soils.

Herpes stromal keratitis (HSK) is a disease that commonly affects the cornea and external eye and is caused by Herpes Simplex Virus type 1 (HSV-1). This virus infects approximately 66% of people worldwide; however, only a small portion of these people will develop symptoms in their lifetime. There is no cure or vaccine available for HSV-1; however, there are treatments available that aim to control the inflammation caused by the virus and prevent its recurrence. While these treatments are beneficial to those suffering with HSK, there is a need for more effective treatments to minimise the need for topical steroids, which can have harmful effects, and to prevent bouts of disease reactivation, which can lead to progressive corneal scarring and visual impairment. This review details the current understanding of HSV-1 infection and discusses potential novel treatment options including microRNAs, TLRs, mAbs, and aptamers.

Polymeric hydrogels are currently at the center of research due to their particular characteristics. They have tunable physical, chemical, and biological properties making them a material of choice for a large range of applications. Polymer-composite and nanocomposite hydrogels were developed to enhance the native hydrogel’s properties and to include numerous functionalities. In this work, alginate/gelatin-methacryloyl-based interpenetrating polymer network hydrogels were prepared with different alginate concentrations and investigated before and after the functionalization with nanoliposomes. The multiscale analysis was obtained through Fourier transform infrared spectroscopy and proton nuclear magnetic resonance. The results show interactions between two polymers as well as between the nanoliposomes and biopolymer.

A paired-dominating set of a graph G without isolated vertices is a dominating set of vertices whose induced subgraph has perfect matching. The minimum cardinality of a paired-dominating set of G is called the paired-domination number γpr(G) of G. The paired-domination subdivision number sdγpr(G) of G is the minimum number of edges that must be subdivided (each edge in G can be subdivided at most once) in order to increase the paired-domination number. Here, we show that, for each tree T ≠ P5 of order n ≥ 3 and each edge e ∉ E(T), sdγpr(T) + sdγpr(T + e) ≤ n + 2.

In order to increase the paired-domination number of a graph G, the minimum number of edges that must be subdivided (where each edge in G can be subdivided no more than once) is called the paired-domination subdivision number sdγpr(G) of G. It is well known that sdγpr(G+e) can be smaller or larger than sdγpr(G) for some edge e∉E(G). In this note, we show that, if G is an isolated-free graph different from mK2, then, for every edge e∉E(G), sdγpr(G+e)≤sdγpr(G)+2Δ(G).

A vertex v of a graph G = ( V , E ) , ve-dominates every edge incident to v, as well as every edge adjacent to these incident edges. A set S ⊆ V is a double vertex-edge dominating set if every edge of E is ve-dominated by at least two vertices of S. The double vertex-edge domination number γ d v e ( G ) is the minimum cardinality of a double vertex-edge dominating set in G. A subset S ⊆ V is a total dominating set (respectively, a 2-dominating set) if every vertex in V has a neighbor in S (respectively, every vertex in V − S has at least two neighbors in S). The total domination number γ t ( G ) is the minimum cardinality of a total dominating set of G, and the 2-domination number γ 2 ( G ) is the minimum cardinality of a 2-dominating set of G . Krishnakumari et al. (2017) showed that for every triangle-free graph G , γ d v e ( G ) ≤ γ 2 ( G ) , and in addition, if G has no isolated vertices, then γ d v e ( G ) ≤ γ t ( G ) . Moreover, they posed the problem of characterizing those graphs attaining the equality in the previous bounds. In this paper, we characterize all trees T with γ d v e ( T ) = γ t ( T ) or γ d v e ( T ) = γ 2 ( T ) .

We address here organellar genetic regulation and intercompartment genome coordination. We developed earlier a strategy relying on a tRNA-like shuttle to mediate import of nuclear transgene-encoded custom RNAs into mitochondria in plants. In the present work, we used this strategy to drive trans-cleaving hammerhead ribozymes into the organelles, to knock down specific mitochondrial RNAs and analyze the regulatory impact. In a similar approach, the tRNA mimic was used to import into mitochondria in Arabidopsis thaliana the orf77, an RNA associated with cytoplasmic male sterility in maize and possessing sequence identities with the atp9 mitochondrial RNA. In both cases, inducible expression of the transgenes allowed to characterise early regulation and signaling responses triggered by these respective manipulations of the organellar transcriptome. The results imply that the mitochondrial transcriptome is tightly controlled by a “buffering” mechanism at the early and intermediate stages of plant development, a control that is released at later stages. On the other hand, high throughput analyses showed that knocking down a specific mitochondrial mRNA triggered a retrograde signaling and an anterograde nuclear transcriptome response involving a series of transcription factor genes and small RNAs. Our results strongly support transcriptome coordination mechanisms within the organelles and between the organelles and the nucleus.

Let k be a positive integer, and set [ k ] : = { 1 , 2 , … , k } . For a graph G, a k-rainbow dominating function (or kRDF) of G is a mapping f : V ( G ) → 2 [ k ] in such a way that, for any vertex v ∈ V ( G ) with the empty set under f, the condition ⋃ u ∈ N G ( v ) f ( u ) = [ k ] always holds, where N G ( v ) is the open neighborhood of v. The weight of kRDF f of G is the summation of values of all vertices under f. The k-rainbow domination number of G, denoted by γ r k ( G ) , is the minimum weight of a kRDF of G. In this paper, we obtain the k-rainbow domination number of grid P 3 □ P n for k ∈ { 2 , 3 , 4 } .

The number of babies in Qatar being exclusively breastfed is significantly lower than the global target set by the World Health Organization. The purpose of this study was to assess knowledge, attitude, and practice (KAP), selected barriers, and professional support as well as their association with continued breastfeeding at one year of age. A sample of Qatari and non-Qatari mothers (N = 195) who attended a well-baby clinic held at primary health care centers in Qatar completed a self-administered questionnaire. Descriptive analysis, the Pearson Chi-squared test, and logistic regression were performed. Around 42% of the mothers stopped breastfeeding when their child was aged between 0 and 11 months old. Mothers who had only one or female child stopped breastfeeding between the ages of 0 and 6 months (p = 0.025, 0.059). The more optimal the breastfeeding practices followed by the mothers, the older the age of the infant when they stopped breastfeeding (p = 0.001). The following factors were inversely associated with breastfeeding duration: the mother’s perceptions that she “did not know how to breastfeed,” or “wasn’t making enough milk,” and the need “to return to work/school”, with p = 0.022, 0.004, and 0.022, respectively. These findings present factors that should be considered when planning for health education and promotion programs to prolong breastfeeding duration in Qatar.

Annona muricata, also known as soursop, has been reported to show cytotoxic activities against cancer cell lines. In this study, we evaluated the antiproliferative effect of Annona muricata leaf extract on the triple negative breast cancer cell line (MDA-MB-231). The antiproliferative effects of leaf extracts on MDA-MB-231cell line were evaluated by means of cell proliferation assay with XTT Reagent (Biological Industries, Israel). The assay was a colorimetric test based on the reduction tetrazolium salt, XTT to colored formazan products by mitochondria of live cells. In brief, cells were seeded into 96-well microtiter plates (Greiner) at a concentration of 5.0 × 104 cells/well and incubated for 72 h in medium containing horizontal dilutions of leaf extract. In each plate, the assay was performed with a column of blank medium control and a cell control column. Then, XTT reagent was added and the soluble product was measured at 500 nm with Biotek Elisa Reader, 96-well plate reader. IC50 value was calculated 320 µg/mL on MDA-MB-231 cells. In conclusion, these results may suggest that Annona muricata is a promising candidate for metastatic breast cancer treatment.

St. John’s wort (Hypericum perforatum L.) is a medicinal plant used for many diseases. Hypericum perforatum has strong historical background about the therapeutic potential. In the present study, we have investigated the cytotoxic activity of the Hypericum perforatum against cultured cervix adenocarcinoma (HeLa) cell line. Antiproliferative effects of Hypericum perforatum on HeLa cells were evaluated by means of the XTT Cell Proliferation Kit (Biological Industries, Israel) according to manufacturer's instructions, and then the IC50 value was calculated. IC50 value of Hypericum perforatum was found as 120 μg/mL. Therefore, this plant may be a potential natural antitumor drug for cervix adenocarcinoma.

Translational health research is an interdisciplinary field aimed at bridging the gap between basic science studies, preventative studies, and clinical practice to improve health-related outcomes. Qualitative research methods provide a unique perspective on the emotional, social, cultural, and contextual factors that influence health and healthcare and thus are recognized as valuable tools for translational health research. This approach can be embedded within a mixed method design which complements the quantitative findings. This methodological paper aims to provide a comprehensive review of the fundamental concepts and methodologies used in qualitative research, emphasizing their utilization and significance in translational health research. Several approaches to qualitative research methodology are discussed in this review, including ethnography, phenomenology, grounded theory, case study, and action research. Theoretical frameworks such as the social-ecological model, intersectionality, and participatory action research are also examined to provide a structure for understanding and interpreting complex health issues. This methodological paper also reviews commonly used sampling techniques such as purposive, snowball, convenience, theoretical, and maximum variation sampling, along with data collection methods such as in-depth interviews, focus groups, observation, document analysis, and participatory methods. Moreover, data analysis techniques such as thematic analysis, grounded theory, content analysis, narrative analysis, and reflexive analysis, are discussed in the context of translational health. Overall, this review highlights the challenges and opportunities of using qualitative methods in current practice, while also discussing future directions and providing valuable guidance and insights to researchers interested in conducting qualitative research in translational health.

Datura , a genus of medicinal herb from the Solanaceae family, is credited with toxic as well as medicinal properties. The different plant parts of Datura sp., mainly D. stramonium L., commonly known as Datura or Jimson Weed, exhibit potent analgesic, antiviral, anti-diarrheal, and anti-inflammatory activities, owing to the wide range of bioactive constituents. With these pharmacological activities, D. stramonium is potentially used to treat numerous human diseases, including ulcers, inflammation, wounds, rheumatism, gout, bruises and swellings, sciatica, fever, toothache, asthma, and bronchitis. The primary phytochemicals investigation on plant extract of Datura showed alkaloids, carbohydrates, cardiac glycosides, tannins, flavonoids, amino acids, and phenolic compounds. It also contains toxic tropane alkaloids, including atropine, scopolamine, and hyoscamine. Although some studies on D. stramonium have reported potential pharmacological effects, information about the toxicity remains almost uncertain. Moreover, the frequent abuse of D. stramonium for recreational purposes has led to toxic syndromes. Therefore, it becomes necessary to be aware of the toxic aspects and the potential risks accompanying its use. The present review aims to summarize the phytochemical composition and pharmacological and toxicological aspects of the plant Datura .

Cancer patients are at high risk of antibiotic resistant bacterial urinary tract infections (UTIs). In this study, we assessed the bacterial profile and antibiotic resistance among cancer patients suspected of UTI in B.P. Koirala Memorial Cancer Hospital in Nepal through a cross-sectional study with routinely collected data. All cancer patients who had a recorded urine culture between July 2018-June 2019 were included in the study. Out of 308 patients who had undergone culture, 73 (24%) of samples had bacterial growth. The most common organisms isolated were E. coli (58%), Staphylococcus (11%) and Klebsiella (10%). These bacteria had undergone susceptibility testing to 27 different antibiotics in various proportions. Of the limited antibiotic testing levels, nitrofurantoin (54/66, 82%) and amikacin (30/51, 59%) were the most common. Among those tested, there were high levels of resistance to antibiotics in the "Access" and "Watch" groups of antibiotics (2019 WHO classification). In the "Reserve" group, both antibiotics showed resistance (polymyxin 15%, tigecycline 8%). Multidrug resistance was seen among 89% of the positive culture samples. This calls for urgent measures to optimize the use of antibiotics in UTI care at policy and health facility levels through stewardship to prevent further augmentation of antibiotic resistance among cancer patients.