Efficacy and Clinical Outcomes of mRNA COVID-19 Vaccine in Pregnancy: A Systematic Review and Meta-Analysis.

BACKGROUND: The world has witnessed one of the largest pandemics, dubbed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of December 2020, the USA alone reported 98,948 cases of coronavirus disease 2019 (COVID-19) infection during pregnancy, with 109 related maternal deaths. Current evidence suggests that unvaccinated pregnant women infected with SARS-CoV-2 are at a higher risk of experiencing complications related to COVID-19 compared to nonpregnant women. This review aimed to provide healthcare workers and non-healthcare workers with a comprehensive overview of the available information regarding the efficacy of vaccines in pregnant women. SUMMARY: We performed a systematic review and meta-analysis following PRISMA guidelines. The search through the database for articles published between December 2019 and October 2021 was performed. A comprehensive search was performed in PubMed, Scopus, and EMBASE databases for research publications published between December 2019 and October 2021. We focused on original research, case reports, case series, and vaccination side effect by authoritative health institutions. Phrases used for the Medical Subject Heading [MeSH] search included ("COVID-19" [MeSH]) or ("Vaccine" [MeSH]) and ("mRNA" [MeSH]) and ("Pregnant" [MeSH]). Eleven studies were selected and included, with a total of 46,264 pregnancies that were vaccinated with mRNA-containing lipid nanoparticle vaccine from Pfizer/BioNTech and Moderna during pregnancy. There were no randomized trials, and all studies were observational (prospective, retrospective, and cross-sectional). The mean maternal age was 32.2 years, and 98.7% of pregnant women received the Pfizer COVID-19 vaccination. The local and systemic adverse effects of the vaccination in pregnant women were analyzed and reported. The local adverse effects of the vaccination (at least 1 dose) such as local pain, swelling, and redness were reported in 32%, 5%, and 1%, respectively. The systemic adverse effects such as fatigue, headaches, new onset or worsening of muscle pain, chills, fever, and joint pains were also reported in 25%, 19%, 18%, 12%, 11%, and 8%, respectively. The average birthweight was 3,452 g. Among these pregnancies, 0.03% were stillbirth and 3.68% preterm (<37 weeks) births. KEY MESSAGES: The systemic side effect profile after administering the COVID-19 mRNA vaccine to pregnant women was similar to that in nonpregnant women. Maternal and fetal morbidity and mortality were lowered with the administration of either one or both the doses of the mRNA COVID-19 vaccination.

Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI's GPT-4 model.

The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness applications are becoming more common, facilitating the opportunity for individualised exercise prescription. However, the use of AI carries the risk of inadequate expert supervision, and the efficacy and validity of such applications have not been thoroughly investigated, particularly in the context of diverse health conditions. The aim of the study was to critically assess the efficacy of exercise prescriptions generated by OpenAI's Generative Pre-Trained Transformer 4 (GPT-4) model for five example patient profiles with diverse health conditions and fitness goals. Our focus was to assess the model's ability to generate exercise prescriptions based on a singular, initial interaction, akin to a typical user experience. The evaluation was conducted by leading experts in the field of exercise prescription. Five distinct scenarios were formulated, each representing a hypothetical individual with a specific health condition and fitness objective. Upon receiving details of each individual, the GPT-4 model was tasked with generating a 30-day exercise program. These AI-derived exercise programs were subsequently subjected to a thorough evaluation by experts in exercise prescription. The evaluation encompassed adherence to established principles of frequency, intensity, time, and exercise type; integration of perceived exertion levels; consideration for medication intake and the respective medical condition; and the extent of program individualization tailored to each hypothetical profile. The AI model could create general safety-conscious exercise programs for various scenarios. However, the AI-generated exercise prescriptions lacked precision in addressing individual health conditions and goals, often prioritizing excessive safety over the effectiveness of training. The AI-based approach aimed to ensure patient improvement through gradual increases in training load and intensity, but the model's potential to fine-tune its recommendations through ongoing interaction was not fully satisfying. AI technologies, in their current state, can serve as supplemental tools in exercise prescription, particularly in enhancing accessibility for individuals unable to access, often costly, professional advice. However, AI technologies are not yet recommended as a substitute for personalized, progressive, and health condition-specific prescriptions provided by healthcare and fitness professionals. Further research is needed to explore more interactive use of AI models and integration of real-time physiological feedback.

Real world evidence of epidemiological trends, clinical presentation, and prognostic outcomes of multiple myeloma (2007-2021).

Background: Multiple myeloma (MM) is one of the most common hematological malignancies globally, and it is projected to increase in the coming years. It occurs more frequently in males and affects older individuals. Presenting symptoms can range from being asymptomatic to severely debilitating. The objective of this study was to determine the epidemiology, clinical features, and prognostic outcomes of patients with MM in the only tertiary cancer hospital in Qatar. Methods: Patients with symptomatic myeloma diagnosed at the National Center for Cancer Care and Research in Qatar between 2007 and 2021 were included. Data on demographics, laboratory work, bone marrow analysis, radiology, and given treatment were collected. Descriptive statistics, survival curves, and multivariable cox regression were used to identify independent mortality risk factors. Results: During the study period of 15 years, a total of 192 patients were diagnosed with MM. The incident rate of myeloma cases in 2021 was 8 patients per million. The median age of patients was 57 years [range 22-88], with 68% being above the age of 50 years at diagnosis. The majority of patients were male (71%) and (85%) were expats. At the time of diagnosis, most patients [n = 169 (88%)] had bone lesions, and 27% had extramedullary plasmacytoma. Anemia, hypercalcemia, and spinal cord compression were reported in 53%, 28%, and 7% of patients, respectively, at presentation. The monoclonal immunoglobulin subtypes were IgG, IgA, and free light chain in 52%, 16%, and 26% of patients, respectively. The overall median survival was 103 months (95% CI 71-135 months). In a multivariate cox-regression analysis for risk factors, only high serum calcium (≥ 2.7 mmol/L) was associated with increased mortality (HR: 2.54, 95% C.I.: 1.40-4.63, p = 0.002). Patients who received an autologous stem cell transplant (ASCT) had significantly better overall survival. Conclusion: In this comprehensive study of patients with MM treated in a country with a small and young general population, centralized hematology care, and free cancer care, we found a low but increasing incidence of MM and a good overall survival. Hypercalcemia was confirmed as a negative risk factor. ASCT had a significant positive impact on survival and should be provided to all patients eligible for this treatment, even in the era of novel agents.

Harnessing artificial intelligence for advancing early diagnosis in hidradenitis suppurativa.

This perspective delves into the integration of artificial intelligence (AI) to enhance early diagnosis in hidradenitis suppurativa (HS). Despite significantly impacting Quality of Life, HS presents diagnostic challenges leading to treatment delays. We present a viewpoint on AI-powered clinical decision support system designed for HS, emphasizing the transformative potential of AI in dermatology. HS diagnosis, primarily reliant on clinical evaluation and visual inspection, often results in late-stage identification with substantial tissue damage. The incorporation of AI, utilizing machine learning and deep learning algorithms, addresses this challenge by excelling in image analysis. AI adeptly recognizes subtle patterns in skin lesions, providing objective and standardized analyses to mitigate subjectivity in traditional diagnostic approaches. The AI integration encompasses diverse datasets, including clinical records, images, biochemical and immunological data and OMICs data. AI algorithms enable nuanced comprehension, allowing for precise and customized diagnoses. We underscore AI's potential for continuous learning and adaptation, refining recommendations based on evolving data. Challenges in AI integration, such as data privacy, algorithm bias, and interpretability, are addressed, emphasizing the ethical considerations of responsible AI deployment, including transparency, human oversight, and striking a balance between automation and human intervention. From the dermatologists' standpoint, we illustrate how AI enhances diagnostic accuracy, treatment planning, and long-term follow-up in HS management. Dermatologists leverage AI to analyze clinical records, dermatological images, and various data types, facilitating a proactive and personalized approach. AI's dynamic nature supports continuous learning, refining diagnostic and treatment strategies, ultimately reshaping standards of care in dermatology.

Dissecting bloodstream infections in febrile neutropenic patients with hematological malignancies, a decade-long single center retrospective observational study (2009-2019).

BACKGROUND: The use of ill-suited antibiotics is a significant risk factor behind the increase in the mortality, morbidity, and economic burden for patients who are under treatment for hematological malignancy (HM) and bloodstream infections (BSI). Such unfitting treatment choices intensify the evolution of resistant variants which is a public health concern due to possible healthcare-associated infection spread to the general population. Hence, this study aims to evaluate antibiograms of patients with BSI and risk factors associated with septicemia. METHODS: A total of 1166 febrile neutropenia episodes (FNE) among 513 patients with HM from the National Center for Cancer Care and Research (NCCCR), Qatar, during 2009-2019 were used for this study. The socio-demographic, clinical, microbial, and anti-microbial data retrieved from the patient's health records were used. RESULTS: We analyzed the sensitivity of gram-negative and gram-positive bacilli reported in HM-FN-BSI patients. Out of the total 512 microorganisms isolated, 416 (81%) were gram-negative bacteria (GNB), 76 (15%) were gram-positive bacteria (GPB) and 20 (4%) were fungi. Furthermore, in 416 GNB, 298 (71.6%) were Enterobacteriaceae sp. among which 121 (41%) were ESBL (Extended Spectrum Beta-Lactamase) resistant to Cephalosporine third generation and Piperacillin-Tazobactam, 54 (18%) were Carbapenem-resistant or multidrug-resistant organism (MDRO). It's noteworthy that the predominant infectious agents in our hospital include E. coli, Klebsiella species, and P. aeruginosa. Throughout the study period, the mortality rate due to BSI was 23%. Risk factors that show a significant correlation with death are age, disease status, mono or polymicrobial BSI and septic shock. CONCLUSION: Decision pertaining to the usage of antimicrobials for HM-FN-BSI patients is a critical task that relies on the latest pattern of prevalence, treatment resistance, and clinical outcomes. Analysis of the antibiogram of HM-FN-BSI patients in Qatar calls for a reconsideration of currently followed empirical antibiotic therapy towards better infection control and antimicrobial stewardship.

Novel Tetrahydro-[1,2,4]triazolo[3,4-a]isoquinoline Chalcones Suppress Breast Carcinoma through Cell Cycle Arrests and Apoptosis.

Chalcones are interesting anticancer drug candidates which have attracted much interest due to their unique structure and their extensive biological activity. Various functional modifications in chalcones have been reported, along with their pharmacological properties. In the current study, novel chalcone derivatives with the chemical base of tetrahydro-[1,2,4]triazolo[3,4-a]isoquinolin-3-yl)-3-arylprop-2-en-1-one were synthesized, and the structure of their molecules was confirmed through NMR spectroscopy. The antitumor activity of these newly synthesized chalcone derivatives was tested on mouse (Luc-4T1) and human (MDA-MB-231) breast cancer cell lines. The antiproliferative effect was evaluated through SRB screening and the MTT assay after 48 h of treatment at different concentrations. Interestingly, among the tested chalcone derivatives, chalcone analogues with a methoxy group were found to have significant anticancer activity and displayed gradient-dependent inhibition against breast cancer cell proliferation. The anticancer properties of these unique analogues were examined further by cytometric analysis of the cell cycle, quantitative PCR, and the caspases-Glo 3/7 assay. Chalcone methoxy derivatives showed the capability of cell cycle arrest and increased Bax/Bcl2 mRNA ratios as well as caspases 3/7 activity. The molecular docking analysis suggests that these chalcone methoxy derivatives may inhibit anti-apoptotic proteins, particularly cIAP1, BCL2, and EGFRK proteins. In conclusion, our findings confirm that chalcone methoxy derivatives could be considered to be potent drug candidates against breast cancer.

In silico screening of the effectiveness of natural compounds from algae as SARS-CoV-2 inhibitors: molecular docking, ADMT profile and molecular dynamic studies.

Marine species are known as rich sources of metabolites largely involved in the pharmaceutical industry. This study aimed to evaluate in silico the effect of natural compounds identified in algae on the SARS-CoV-2 Main protease, RNA-dependent-RNA polymerase activity (RdRp), endoribonuclease (NSP15) as well as on their interaction with viral spike protein. A total of 45 natural compounds were screened for their possible interaction on SARS-CoV-2 target proteins using Maestro interface for molecular docking, molecular dynamic (MD) simulation to estimate compounds binding affinities. Among the algal compounds screened in this study, three (Laminarin, Astaxanthin and 4'-chlorostypotriol triacetate) exhibited the lowest docking energy and best interaction with SARS-CoV-2 viral proteins (Main protease, RdRp, Nsp15, and spike protein). The complex of the main protease with laminarin shows the most stable RMSD during a 150 ns MD simulation time. Which indicates their possible inhibitory activity on SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

ChatGPT is not ready yet for use in providing mental health assessment and interventions.

Background: Psychiatry is a specialized field of medicine that focuses on the diagnosis, treatment, and prevention of mental health disorders. With advancements in technology and the rise of artificial intelligence (AI), there has been a growing interest in exploring the potential of AI language models systems, such as Chat Generative Pre-training Transformer (ChatGPT), to assist in the field of psychiatry. Objective: Our study aimed to evaluates the effectiveness, reliability and safeness of ChatGPT in assisting patients with mental health problems, and to assess its potential as a collaborative tool for mental health professionals through a simulated interaction with three distinct imaginary patients. Methods: Three imaginary patient scenarios (cases A, B, and C) were created, representing different mental health problems. All three patients present with, and seek to eliminate, the same chief complaint (i.e., difficulty falling asleep and waking up frequently during the night in the last 2°weeks). ChatGPT was engaged as a virtual psychiatric assistant to provide responses and treatment recommendations. Results: In case A, the recommendations were relatively appropriate (albeit non-specific), and could potentially be beneficial for both users and clinicians. However, as complexity of clinical cases increased (cases B and C), the information and recommendations generated by ChatGPT became inappropriate, even dangerous; and the limitations of the program became more glaring. The main strengths of ChatGPT lie in its ability to provide quick responses to user queries and to simulate empathy. One notable limitation is ChatGPT inability to interact with users to collect further information relevant to the diagnosis and management of a patient's clinical condition. Another serious limitation is ChatGPT inability to use critical thinking and clinical judgment to drive patient's management. Conclusion: As for July 2023, ChatGPT failed to give the simple medical advice given certain clinical scenarios. This supports that the quality of ChatGPT-generated content is still far from being a guide for users and professionals to provide accurate mental health information. It remains, therefore, premature to conclude on the usefulness and safety of ChatGPT in mental health practice.

Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling.

Outpatient Chemotherapy Appointment (OCA) planning and scheduling is a process of distributing appointments to available days and times to be handled by various resources through a multi-stage process. Proper OCAs planning and scheduling results in minimizing the length of stay of patients and staff overtime. The integrated consideration of the available capacity, resources planning, scheduling policy, drug preparation requirements, and resources-to-patients assignment can improve the Outpatient Chemotherapy Process's (OCP's) overall performance due to interdependencies. However, developing a comprehensive and stochastic decision support system in the OCP environment is complex. Thus, the multi-stages of OCP, stochastic durations, probability of uncertain events occurrence, patterns of patient arrivals, acuity levels of nurses, demand variety, and complex patient pathways are rarely addressed together. Therefore, this paper proposes a clustering and stochastic optimization methodology to handle the various challenges of OCA planning and scheduling. A Stochastic Discrete Simulation-Based Multi-Objective Optimization (SDSMO) model is developed and linked to clustering algorithms using an iterative sequential approach. The experimental results indicate the positive effect of clustering similar appointments on the performance measures and the computational time. The developed cluster-based stochastic optimization approaches showed superior performance compared with baseline and sequencing heuristics using data from a real Outpatient Chemotherapy Center (OCC).

A Bibliometric Analysis and Visualization of Decision Support Systems for Healthcare Referral Strategies.

BACKGROUND: The referral process is an important research focus because of the potential consequences of delays, especially for patients with serious medical conditions that need immediate care, such as those with metastatic cancer. Thus, a systematic literature review of recent and influential manuscripts is critical to understanding the current methods and future directions in order to improve the referral process. METHODS: A hybrid bibliometric-structured review was conducted using both quantitative and qualitative methodologies. Searches were conducted of three databases, Web of Science, Scopus, and PubMed, in addition to the references from the eligible papers. The papers were considered to be eligible if they were relevant English articles or reviews that were published from January 2010 to June 2021. The searches were conducted using three groups of keywords, and bibliometric analysis was performed, followed by content analysis. RESULTS: A total of 163 papers that were published in impactful journals between January 2010 and June 2021 were selected. These papers were then reviewed, analyzed, and categorized as follows: descriptive analysis (n = 77), cause and effect (n = 12), interventions (n = 50), and quality management (n = 24). Six future research directions were identified. CONCLUSIONS: Minimal attention was given to the study of the primary referral of blood cancer cases versus those with solid cancer types, which is a gap that future studies should address. More research is needed in order to optimize the referral process, specifically for suspected hematological cancer patients.

Home Cancer Care Research: A Bibliometric and Visualization Analysis (1990-2021).

Home cancer care research (HCCR) has accelerated, as considerable attention has been placed on reducing cancer-related health costs and enhancing cancer patients' quality of life. Understanding the current status of HCCR can help guide future research and support informed decision-making about new home cancer care (HCC) programs. However, most current studies mainly detail the research status of certain components, while failing to explore the knowledge domain of this research field as a whole, thereby limiting the overall understanding of home cancer care. We carried out bibliometric and visualization analyses of Scopus-indexed papers related to home cancer care published between 1990-2021, and used VOSviewer scientometric software to investigate the status and provide a structural overview of the knowledge domain of HCCR (social, intellectual, and conceptual structures). Our findings demonstrate that over the last three decades, the research on home cancer care has been increasing, with a constantly expanding stream of new papers built on a solid knowledge base and applied to a wide range of research themes.

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.

BACKGROUND: Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management. OBJECTIVE: This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient's cancer stage to determine future research directions in blood cancer. METHODS: We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model. RESULTS: Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review. CONCLUSIONS: The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient's pathway to treatment requires a prior prediction of the malignancy based on the patient's symptoms or blood records, which is an area that has still not been properly investigated.

Bibliometric analysis of cancer care operations management: current status, developments, and future directions.

Around the world, cancer care services are facing many operational challenges. Operations management research can provide important solutions to these challenges, from screening and diagnosis to treatment. In recent years, the growth in the number of papers published on cancer care operations management (CCOM) indicates that development has been fast. Within this context, the objective of this research was to understand the evolution of CCOM through a comprehensive study and an up-to-date bibliometric analysis of the literature. To achieve this aim, the Web of Science Core Collection database was used as the source of bibliographic records. The data-mining and quantitative tools in the software Biblioshiny were used to analyze CCOM articles published from 2010 to 2021. First, a historical analysis described CCOM research, the sources, and the subfields. Second, an analysis of keywords highlighted the significant developments in this field. Third, an analysis of research themes identified three main directions for future research in CCOM, which has 11 evolutionary paths. Finally, this paper discussed the gaps in CCOM research and the areas that require further investigation and development.

Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care.

Reliable and rapid medical diagnosis is the cornerstone for improving the survival rate and quality of life of cancer patients. The problem of clinical decision-making pertaining to the management of patients with hematologic cancer is multifaceted and intricate due to the risk of therapy-induced myelosuppression, multiple infections, and febrile neutropenia (FN). Myelosuppression due to treatment increases the risk of sepsis and mortality in hematological cancer patients with febrile neutropenia. A high prevalence of multidrug-resistant organisms is also noted in such patients, which implies that these patients are left with limited or no-treatment options amidst severe health complications. Hence, early screening of patients for such organisms in their bodies is vital to enable hospital preparedness, curtail the spread to other weak patients in hospitals, and limit community outbreaks. Even though predictive models for sepsis and mortality exist, no model has been suggested for the prediction of multidrug-resistant organisms in hematological cancer patients with febrile neutropenia. Hence, for predicting three critical clinical complications, such as sepsis, the presence of multidrug-resistant organisms, and mortality, from the data available from medical records, we used 1166 febrile neutropenia episodes reported in 513 patients. The XGboost algorithm is suggested from 10-fold cross-validation on 6 candidate models. Other highlights are (1) a novel set of easily available features for the prediction of the aforementioned clinical complications and (2) the use of data augmentation methods and model-scoring-based hyperparameter tuning to address the problem of class disproportionality, a common challenge in medical datasets and often the reason behind poor event prediction rate of various predictive models reported so far. The proposed model depicts improved recall and AUC (area under the curve) for sepsis (recall = 98%, AUC = 0.85), multidrug-resistant organism (recall = 96%, AUC = 0.91), and mortality (recall = 86%, AUC = 0.88) prediction. Our results encourage the need to popularize artificial intelligence-based devices to support clinical decision-making.

Inhibitory activity of marine sponge metabolites on SARS-CoV-2 RNA dependent polymerase: virtual screening and molecular dynamics simulation.

Marine species are known as rich sources of metabolites involved mainly in the pharmaceutical industry. This study aimed to evaluate the effect of biologically active compounds in the marine sponge on the SARS-CoV-2 RNA-dependent-RNA polymerase protein (RdRp) using the in-silico method. A total of 51 marine compounds were checked for their possible interaction with SARS-CoV-2 RdRp using Maestro interface for molecular docking, molecular dynamic (MD) simulation, and MM/GBSA method to estimate compounds binding affinities. Among the 51 compounds screened in this study, two (mycalamide A, and nakinadine B) exhibited the lowest docking energy and best interaction. Among these compounds, mycalamide A was identified as a potent inhibitor of SARS-CoV-2 RdRp that showed the best and stable interaction during molecular dynamic simulation, with residues (Asp760 and Asp761) found in the catalytic domain of RdRp. The analysis through MM/GBSA for molecular dynamic simulation results revealed binding energy -59.7 ± 7.18 for Mycalamide A and -56 ± 10.55 for Nakinadine B. These results elucidate the possible use of mycalamide A for treating coronavirus disease.Communicated by Ramaswamy H. Sarma.

Prognostic value of E-Cadherin and its tumor suppressor role in Saudi women with advanced epithelial ovarian cancer.

The extracellular matrix (ECM) disruption and cytoskeleton reorganization are crucial events in tumor proliferation and invasion. E-Cadherin (E-CAD) is a member of cell adhesion molecules involved in cell-cell junctions and ECM stability. The loss of E-CAD expression is associated with cancer progression and metastasis. This retrospective study aimed to assess E-CAD protein expression in ovarian cancer (OC) tissues and to evaluate its prognostic value. PATIENTS AND METHODS: 143 formalin-fixed and paraffin-embedded (FFPE) blocks of primary advanced stages OC were retrieved and used to construct Tissue microarrays. Automated immunohistochemistry technique was performed to evaluate E-CAD protein expression patterns in OC. RESULTS: E-CAD protein expression was significantly correlated with OC histological subtype (p < 0.0001), while borderline significant correlations were observed with both tumor grade (p = 0.06) and stage (p = 0.07). Interestingly, Kaplan-Meier survival analysis showed that OC patients with membranous E-CAD expression survived longer than those with no E-CAD expression mainly those at advanced stages (p < 0.009). Further in silico analysis confirms the key roles of E-CAD in OC molecular functions. CONCLUSION: we reported a prognosis value of membranous E-CAD in advanced stage OC patients. Further validation using larger cohorts is recommended to extract clinically relevant outcomes towards better OC management and individualized oncology.

Drying methodology effect on the phenolic content, antioxidant activity of Myrtus communis L. leaves ethanol extracts and soybean oil oxidative stability.

In this study, different drying methodologies (convective air, oven and microwave) of Myrtus communis L. (M. communis L.) leaves were conducted to investigate their effects on the levels of phenolic compounds, antioxidant capacity of ethanolic extracts (EEs) as well as the soybean oil oxidative stability. Drying methodology significantly influenced the extractability of phenolic compounds. Microwave drying led to an increase in the amounts of total phenols, flavonoids and proanthocyanidins followed by oven drying at 70 °C. Higher temperature of drying (100 and 120 °C) led to a significant reduction of their amounts (p < 0.05). An ultra-performance liquid chromatography method combined with high resolution mass spectroscopic detection was used to analyze the phenolic fraction of extracts. Higher amounts of the identified compounds were observed when leaves were heat treated. Furthermore, the evaluation of the antioxidant activity showed that the studied extracts possess in general high antioxidant capacities, significantly dependent on the employed drying methodology. The incorporation of the different extracts at 200 ppm in soybean oil showed that its oxidative stability was significantly improved. Extracts from leaves treated with microwave (EE_MW) and at 70 °C (EE_70) have better effect than BHT. The results of the present study suggest that microwave drying could be useful to enhance the extractability of phenolic compounds and the antioxidant capacity of M. communis L. leaf extract.

Attitudes Toward Psychological Disorders and Alternative Medicine in Saudi Participants.

Background: This study was designed to investigate Saudis' attitudes toward mental distress and psychotropic medication, attribution of causes, expected side effects, and to analyze participants' expectations toward alternative or complementary medicine using aromatic and medicinal plants, through a survey. Method: The study included 674 participants (citizens and residents in Saudi Arabia) who were randomly contacted via email and social media and gave their consent to complete a questionnaire dealing with 39 items that can be clustered in six parts. Descriptive statistics and Chi-square for cross-tabulation were generated using SPSS. Results: Among the 664 participants, 73.4% believed that there are some positive and negative outcomes of psychotropic medication. Participants (72.0%) think that the most important reason leading to psychological disorders is mainly due to the loss of a relative or beloved person, and 73.9% considered psychic session as one of the possible treatments of psychological disorders. Surprisingly, only 18.8% of the participants agreed that medicinal and aromatic plants could be a possible treatment of the psychological disorder. Participants (82%) consider that physicians are the most trustful and preferred source of information about alternative and complementary medicine.