CD34+ cell yield among healthy donors: Large-scale model development and validation.

BACKGROUND: Successful engraftment in hematopoietic stem cell transplantation necessitates the collection of an adequate dose of CD34+ cells. Thus, the precise estimation of CD34+ cells harvested via apheresis is critical. Current CD34+ cell yield prediction models have limited reproducibility. This study aims to develop a more reliable and universally applicable model by utilizing a large dataset, enhancing yield predictions, optimizing the collection process, and improving clinical outcomes. MATERIALS AND METHODS: A secondary analysis was conducted using the Center for International Blood and Marrow Transplant Research database, involving data from over 17 000 healthy donors who underwent filgrastim-mobilized hematopoietic progenitor cell apheresis. Linear regression, gradient boosting regressor, and logistic regression classification models were employed to predict CD34+ cell yield. RESULTS: Key predictors identified include pre-apheresis CD34+ cell count, weight, age, sex, and blood volume processed. The linear regression model achieved a coefficient of determination (R2) value of 0.66 and a correlation coefficient (r) of 0.81. The gradient boosting regressor model demonstrated marginally improved results with an R2 value of 0.67 and an r value of 0.82. The logistic regression classification model achieved a predictive accuracy of 96% at the 200 × 106 CD34+ cell count threshold. At thresholds of 400, 600, 800, and 1000 × 106 CD34+ cell count, the accuracies were 88%, 83%, 83%, and 88%, respectively. The model demonstrated a high area under the receiver operator curve scores ranging from 0.90 to 0.93. CONCLUSION: This study introduces advanced predictive models for estimating CD34+ cell yield, with the logistic regression classification model demonstrating remarkable accuracy and practical utility.

Healthcare Providers' knowledge, attitudes and practice in relation to drug hypersensitivity reactions at King Abdulaziz Medical City in Riyadh.

Background: Drug hypersensitivity reactions (DHRs) are immune-mediated responses triggered by exposure to a drug. DHRs are responsible for serious adverse drug reactions (ADRs) and are considered the fifth leading cause of death. This study aims to assess and evaluate the knowledge, practice, and attitudes of healthcare providers (HCPs) towards DHRs. Methods: A cross-sectional survey was conducted at King Abdulaziz Medical City (KAMC) in Riyadh, Saudi Arabia. Healthcare providers, including pharmacists, physicians, and nurses, were recruited using a convenience sampling method to complete the survey. The survey comprised three domains: knowledge (14 items), attitudes (5 items), and practices (6 items), utilizing a standardized self-administered questionnaire. Results: The survey was completed by 373 healthcare providers. The respondents were predominantly female (72.1 %) with a mean age of 33.8 ± 7.8 years. Of the respondents, 64 % were nurses, 25 % pharmacists, and 11.3 % physicians. Educational levels varied, with 53 % holding a bachelor's degree, 22 % an associate degree, and 25 % a master's degree or higher. The median knowledge score was 48. Female healthcare providers, those with advanced levels of education, and physicians had higher knowledge scores compared to male and nurse participants (p < 0.05). One-third of the respondents (33 %) were satisfied with their knowledge of DHRs, and 42 % believed HCPs should receive more advanced training in DHR management. Less than a quarter of HCPs reported inquiring about patients' histories of hypersensitivity reactions. Conclusions: The study revealed that healthcare workers had a relatively low level of knowledge about drug hypersensitivity reactions and lacked a consensus on DHR management. While displaying a positive attitude towards DHRs, they often did not translate this attitude into consistent clinical practice.

Revolutionizing healthcare: the role of artificial intelligence in clinical practice.

INTRODUCTION: Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. RESEARCH SIGNIFICANCE: This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. MATERIALS AND METHODS: The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. RESULTS: Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. CONCLUSION: AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

Sound-Based Localization Using LSTM Networks for Visually Impaired Navigation.

In this work, we developed a prototype that adopted sound-based systems for localization of visually impaired individuals. The system was implemented based on a wireless ultrasound network, which helped the blind and visually impaired to navigate and maneuver autonomously. Ultrasonic-based systems use high-frequency sound waves to detect obstacles in the environment and provide location information to the user. Voice recognition and long short-term memory (LSTM) techniques were used to design the algorithms. The Dijkstra algorithm was also used to determine the shortest distance between two places. Assistive hardware tools, which included an ultrasonic sensor network, a global positioning system (GPS), and a digital compass, were utilized to implement this method. For indoor evaluation, three nodes were localized on the doors of different rooms inside the house, including the kitchen, bathroom, and bedroom. The coordinates (interactive latitude and longitude points) of four outdoor areas (mosque, laundry, supermarket, and home) were identified and stored in a microcomputer's memory to evaluate the outdoor settings. The results showed that the root mean square error for indoor settings after 45 trials is about 0.192. In addition, the Dijkstra algorithm determined that the shortest distance between two places was within an accuracy of 97%.

Distinct Mechanisms of Cytotoxicity in Novel Nitrogenous Heterocycles: Future Directions for a New Anti-Cancer Agent.

Electron-rich, nitrogenous heteroaromatic compounds interact more with biological/cellular components than their non-nitrogenous counterparts. The strong intermolecular interactions with proteins, enzymes, and receptors confer significant biological and therapeutic properties to the imidazole derivatives, giving rise to a well-known and extensively used range of therapeutic drugs used for infections, inflammation, and cancer, to name a few. The current study investigates the anti-cancer properties of fourteen previously synthesized nitrogenous heterocycles, derivatives of imidazole and oxazolone, on a panel of cancer cell lines and, in addition, predicts the molecular interactions, pharmacokinetic and safety profiles of these compounds. METHOD: The MTT and CellTiter-Glo® assays were used to screen the imidazole and oxazolone derivatives on six cancer cell lines: HL60, MDA-MB-321, KAIMRC1, KMIRC2, MCF-10A, and HCT8. Subsequently, in vitro tubulin staining and imaging were performed, and the level of apoptosis was measured using the Promega ApoTox-Glo® triplex assay. Furthermore, several computational tools were utilized to investigate the pharmacokinetics and safety profile, including PASS Online, SEA Search, the QikProp tool, SwissADME, ProTox-II, and an in silico molecular docking study on tubulin to identify the critical molecular interactions. RESULTS: In vitro analysis identified compounds 8 and 9 to possess the most significant potent cytotoxic activity on the HL60 and MDA-MB-231 cell lines, supported by PASS Online anti-cancer predictions with pa scores of 0.413 and 0.434, respectively. In addition, compound 9 induced caspase 3/7 dependent-apoptosis and interfered with tubulin polymerization in the MDA-MB-231 cell line, consistent with in silico docking results, identifying binding similarity to the native ligand colchicine. All the derivatives, including compounds 8 and 9, had acceptable pharmacokinetics; however, the safety profile was suboptimal for all the tested derivates except compound 4. CONCLUSION: The imidazole derivative compound 9 is a promising anti-cancer agent that switches on caspase-dependent apoptotic cell death and modulates microtubule function. Therefore, it could be a lead compound for further drug optimization and development.

Interaction between Na-K-ATPase and Bcl-2 proteins BclXL and Bak.

In silico analysis predicts interaction between Na-K-ATPase (NKA) and Bcl-2 protein canonical BH3- and BH1-like motifs, consistent with NKA inhibition by the benzo-phenanthridine alkaloid chelerythrine, a BH3 mimetic, in fetal human lens epithelial cells (FHLCs) (Lauf PK, Heiny J, Meller J, Lepera MA, Koikov L, Alter GM, Brown TL, Adragna NC. Cell Physiol Biochem 31: 257-276, 2013). This report establishes proof of concept: coimmunoprecipitation and immunocolocalization showed unequivocal and direct physical interaction between NKA and Bcl-2 proteins. Specifically, NKA antibodies (ABs) coimmunoprecipitated BclXL (B-cell lymphoma extra large) and BAK (Bcl-2 antagonist killer) proteins in FHLCs and A549 lung cancer cells. In contrast, both anti-Bcl-2 ABs failed to pull down NKA. Notably, the molecular mass of BAK1 proteins pulled down by NKA and BclXL ABs appeared to be some 4-kDa larger than found in input monomers. In silico analysis predicts these higher molecular mass BAK1 proteins as alternative splicing variants, encoding 42 amino acid (aa) larger proteins than the known 211-aa long canonical BAK1 protein. These BAK1 variants may constitute a pool separate from that forming mitochondrial pores by specifically interacting with NKA and BclXL proteins. We propose a NKA-Bcl-2 protein ternary complex supporting our hypothesis for a special sensor role of NKA in Bcl-2 protein control of cell survival and apoptosis.

Ferroptosis targeting natural compounds as a promising approach for developing potent liver cancer agents.

Liver cancer is the second leading cause of cancer-related death worldwide. However, treatment options, including surgical resection, transplantation, and molecular drug therapies, are of limited effectiveness. Recent studies have demonstrated that suppressing ferroptosis might be a pivotal signal for liver cancer initiation, thus providing a new way to combat liver cancer. Ferroptosis is a distinct form of controlled cell death that differs from conventional cell death routes like apoptosis, necrosis, and pyroptosis. It results from intracellular iron overload, which raises iron-dependent reactive oxygen species. This, in turn, leads to the accumulation of lipid peroxides that further result in oxidative damage to cell membranes, disrupt normal functioning, and ultimately speed up the ferroptosis phenomenon. Ferroptosis regulation is intricately linked to cellular physiological processes, encompassing iron metabolism, lipid metabolism, and the equilibrium between oxygen-free radical reactions and lipid peroxidation. This review intends to summarize the natural compounds targeting ferroptosis in liver cancer to offer new therapeutic ideas for liver cancer. Furthermore, it serves as the foundation for identifying and applying chemical medicines and natural chemicals that target ferroptosis to treat liver cancer efficiently.

Abelacimab: A leap forward in anticoagulation with FXI and FXIa Inhibition.

Direct Oral Anticoagulants (DOACs) have revolutionized the treatment of thromboembolic disorders, offering targeted, effective, and safer alternatives to traditional anticoagulants like heparins and vitamin K antagonists (VKAs). Despite their benefits, DOACs have drawbacks, including an increased risk of gastrointestinal bleeding and unsuitability for patients with mechanical heart valves. Recent research has highlighted Factor XI (FXI) as a promising anticoagulation target due to its significant role in pathological thrombosis and minor involvement in normal hemostasis. Abelacimab, an antibody that inhibits FXI, has shown potential in transforming anticoagulation therapy by sparing hemostasis. This review provides a comprehensive analysis of abelacimab, examining its clinical pharmacology and its pharmacokinetic and pharmacodynamic properties. It scrutinizes abelacimab's safety profile and key monitoring parameters. The current evidence supporting its use and potential future research strengthening its position in anticoagulant therapy is also discussed. The objective is to enhance understanding and contribute to discussions around developing safer anticoagulants, particularly for patients at risk for thrombosis.

Adherence to Advisory Committee on Immunization Practices in diabetes mellitus patients in Saudi Arabia: A multicenter retrospective study.

BACKGROUND: Patients with diabetes mellitus (DM) are predisposed to an increased risk of infection signifying the importance of vaccination to protect against its potentially severe complications. The Centers for Disease Control and Prevention/Advisory Committee on Immunization Practices (CDC/ACIP) issued immunization re-commendations to protect this patient population. AIM: To assess the adherence of patients with DM to the CDC/ACIP immunization recommendations in Saudi Arabia and to identify the factors associated with the vaccine adherence rate. METHODS: An observational retrospective study conducted in 2023 was used to collect data on the vaccination records from 13 diabetes care centers in Saudi Arabia with 1000 eligible patients in phase I with data collected through chart review and 709 patients in phase II through online survey. RESULTS: Among participants, 10.01% (n = 71) had never received any vaccine, while 85.89% (n = 609) received at least one dose of the coronavirus disease 2019 (COVID-19) vaccine, and 34.83% (n = 247) had received the annual influenza vaccine. Only 2.96% (n = 21), 2.11% (n = 15), and 1.12% (n = 8) received herpes zoster, tetanus, diphtheria, and pertussis (Tdap), and human papillomavirus (HPV) vaccines, respectively. For patients with DM in Saudi Arabia, the rate of vaccination for annual influenza and COVID-19 vaccines was higher compared to other vaccinations such as herpes zoster, Tdap, pneumococcal, and HPV. Factors such as vaccine recommendations provided by family physicians or specialists, site of care, income level, DM-related hospitalization history, residency site, hemoglobin A1c (HbA1c) level, and health sector type can significantly influence the vaccination rate in patients with DM. Among non-vaccinated patients with DM, the most reported barriers were lack of knowledge and fear of side effects. This signifies the need for large-scale research in this area to identify additional factors that might facilitate adherence to CDC/ACIP vaccine recommendations in patients with DM. CONCLUSION: In Saudi Arabia, patients with DM showed higher vaccination rates for annual influenza and COVID-19 vaccines compared to other vaccinations such as herpes zoster, Tdap, pneumococcal, and HPV. Factors such as vaccine recommendations provided by family physicians or specialists, the site of care, income level, DM-related hospitalization history, residency site, HbA1c level, and health sector type can significantly influence the vaccination rate in patients with DM.

Surface-enhanced Raman spectroscopy (SERS) tracking of chelerythrine, a Na(+)/K(+) pump inhibitor, into cytosol and plasma membrane fractions of human lens epithelial cell cultures.

BACKGROUND/AIMS: The quaternary benzo-phenanthridine alkaloid (QBA) chelerythrine (CET) is a pro-apoptotic drug and Na(+)/K(+) pump (NKP) inhibitor in human lens epithelial cells (HLECs). In order to obtain further insight into the mechanism of NKP inhibition by CET, its sub-cellular distribution was quantified in cytosolic and membrane fractions of HLEC cultures by surface-enhanced Raman spectroscopy (SERS). METHODS: Silver nanoparticles (AgNPs) prepared by the Creighton method were concentrated, and size-selected using a one-step tangential flow filtration approach. HLECs cultures were exposed to 50 µM CET in 300 mOsM phosphate-buffered NaCl for 30 min. A variety of cytosolic extracts, crude and purified membranes, prepared in lysing solutions in the presence and absence of a non-ionic detergent, were incubated with AgNPs and subjected to SERS analysis. Determinations of CET were based on a linear calibration plot of the integrated CET SERS intensity at its 659 cm(-1) marker band as a function of CET concentration. RESULTS: SERS detected chemically unaltered CET in both cytosol and plasma membrane fractions. Normalized for protein, the CET content was some 100 fold higher in the crude and purified plasma membrane fraction than in the soluble cytosolic extract. The total free CET concentration in the cytosol, free of membranes or containing detergent-solubilized membrane material, approached that of the incubation medium of HLECs. CONCLUSION: Given a negative membrane potential of HLECs the data suggest, but do not prove, that CET may traverse the plasma membrane as a positively charged monomer (CET(+)) accumulating near or above passive equilibrium distribution. These findings may contribute to a recently proposed hypothesis that CET binds to and inhibits the NKP through its cytosolic aspect.

Adefovir Dipivoxil as a Therapeutic Candidate for Medullary Thyroid Carcinoma: Targeting RET and STAT3 Proto-Oncogenes.

Aberrant gene expression is often linked to the progression of various cancers, making the targeting of oncogene transcriptional activation a potential strategy to control tumor growth and development. The RET proto-oncogene's gain-of-function mutation is a major cause of medullary thyroid carcinoma (MTC), which is part of multiple endocrine neoplasia type 2 (MEN2) syndrome. In this study, we used a cell-based bioluminescence reporter system driven by the RET promoter to screen for small molecules that potentially suppress the RET gene transcription. We identified adefovir dipivoxil as a transcriptional inhibitor of the RET gene, which suppressed endogenous RET protein expression in MTC TT cells. Adefovir dipivoxil also interfered with STAT3 phosphorylation and showed high affinity to bind to STAT3. Additionally, it inhibited RET-dependent TT cell proliferation and increased apoptosis. These results demonstrate the potential of cell-based screening assays in identifying transcriptional inhibitors for other oncogenes.

Hybrid Radiant Disinfection: Exploring UVC and UVB Sterilization Impact on the Mechanical Characteristics of PLA Materials.

This study explores the impact of disinfection techniques on the mechanical properties of poly(lactic acid) (PLA), a crucial material in the production of medical implants, tissue engineering, orthopedic devices and drug delivery systems, owing to its biocompatibility and ease of manufacturing. The focus is on evaluating the effectiveness of ultraviolet (UV) type C (254 nm wavelength) and the combined use of type C and B (310 nm wavelength) disinfection methods. Fifteen tensile test specimens (ASTM D638) and fifteen compression test specimens (ASTM D695) were utilized to assess PLA's mechanical properties, including yield strength, ultimate strength, and fracture strength. The investigation involved subjecting the specimens to the specified disinfection methods and evaluating these properties both before and after the disinfection process. In the tensile test, a statistically significant difference (p = 0) in yield displacement was observed among the three groups. Additionally, a notable difference (p = 0.047) in fracture displacement was identified between the untreated group and the UVC and UVB combination group. No discernible impact on yield or fracture forces was noted. In the compression test, there was a significant difference (p = 0.04) in yield displacement and a clear difference (p = 0.05) in fracture force between the untreated group and the UVC and UVB combination group. The hybrid combination of UVC and UVB disinfection techniques did not affect yield force in both tensile and compression tests. However, it demonstrated a clear impact on displacement, suggesting its potential as a promising disinfection technique in the medical field.

Radiant Reinforcement: Enhancing Composite Polymer Magnet Materials Mechanical Properties with UVC Medical Disinfection.

Magnetic polymer composites have recently attracted considerable interest, primarily because of their promising applications, especially in the biomedical industry. The aim of this study is to investigate the impact of ultraviolet C (UVC) irradiation as a disinfection method on the mechanical characteristics of composite polymer magnets. Tensile and compression tests were conducted following the standards set by ASTM D3039 and ASTM D3410, respectively. In addition, energy dispersive spectroscopy (EDS) was used to determine the effect of the disinfection method on the amount of carbon, oxygen, and iron within the surface of the composite polymer magnet material. The UVC's irradiation impact was statistically assessed by a t-test. The results of the tensile tests demonstrated a significant increase in the transition force, measuring 0.41 kN and 0.58 kN before and after UVC exposure, respectively. Similarly, the outcomes of the compression tests showed a notable increase in yield force, registering 4.9 kN and 6 kN before and after UVC treatment. This suggests that the composite magnetic material has gained a higher capacity to withstand compressive loads than tensile loads. Finally, the EDS analysis revealed the carbon mass percentage was 71.69% prior to UVC radiation exposure, with it increasing to 78.56%, following exposure. This suggests that the composite material exhibited improved hardness. These findings highlight that UVC irradiation has a beneficial impact on both the mechanical and chemical properties of the composite magnet material, which support its use as a disinfection method in clinical settings.

The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research.

Artificial Intelligence (AI) has revolutionized various domains, including education and research. Natural language processing (NLP) techniques and large language models (LLMs) such as GPT-4 and BARD have significantly advanced our comprehension and application of AI in these fields. This paper provides an in-depth introduction to AI, NLP, and LLMs, discussing their potential impact on education and research. By exploring the advantages, challenges, and innovative applications of these technologies, this review gives educators, researchers, students, and readers a comprehensive view of how AI could shape educational and research practices in the future, ultimately leading to improved outcomes. Key applications discussed in the field of research include text generation, data analysis and interpretation, literature review, formatting and editing, and peer review. AI applications in academics and education include educational support and constructive feedback, assessment, grading, tailored curricula, personalized career guidance, and mental health support. Addressing the challenges associated with these technologies, such as ethical concerns and algorithmic biases, is essential for maximizing their potential to improve education and research outcomes. Ultimately, the paper aims to contribute to the ongoing discussion about the role of AI in education and research and highlight its potential to lead to better outcomes for students, educators, and researchers.

Practical Guide for Anticoagulant and Antiplatelet Reversal in Clinical Practice.

In recent years, anticoagulant and antiplatelet use have increased over the past years for the prevention and treatment of several cardiovascular conditions. Due to the rising use of antithrombotic medications and the complexity of specific clinical cases requiring such therapies, bleeding remains the primary concern among patients using antithrombotics. Direct oral anticoagulants (DOACs) include rivaroxaban, apixaban, edoxaban, and betrixaban. Direct thrombin inhibitors (DTIs) include argatroban, bivalirudin, and dabigatran. DOACs are associated with lower rates of fatal, life-threatening, and significant bleeding risks compared to those of warfarin. The immediate reversal of these agents can be indicated in an emergency setting. Antithrombotic reversal recommendations are still in development. Vitamin K and prothrombin complex concentrate (PCCs) can be used for warfarin reversal. Andexanet alfa and idarucizumab are specific reversal agents for DOACs and DTIs, respectively. Protamine sulfate is the solely approved reversal agent for unfractionated heparin (UFH) and low-molecular-weight heparin (LMWH). However, there are no specific reversal agents for antiplatelets. This article aims to provide a practical guide for clinicians regarding the reversal of anticoagulants and antiplatelets in clinical practice based on the most recent studies.

Assessing the influence of COVID-19 on influenza prevalence: A multicenter time series study.

BACKGROUND: During the COVID-19 pandemic, countries around the world implemented various interventions to manage the spread of respiratory illnesses, including influenza. However, there is a lack of studies that have assessed the influence of COVID-19 on influenza prevalence in Saudi Arabia. In this study, we aimed to evaluate the prevalence of positive influenza cases before and during the COVID-19 pandemic in relation to the mitigation measures and policy initiatives in Saudi Arabia. METHODS: A multicenter, time-series cross-sectional study was conducted to evaluate influenza prevalence before and during the COVID-19 pandemic between 01/01/2017 and 31/12/2021. This study included all patients who were screened for influenza infection at healthcare facilities across Saudi Arabia using polymerase chain reaction (PCR). The primary outcome was to determine the prevalence of influenza infections before and during the COVID-19 pandemic, while the secondary outcome was to describe the demographic data and comorbidities of the included patients in both periods. RESULTS: During the study period, 5238 cases were identified based on a positive PCR result for influenza virus. The yearly number of influenza cases in the pre-COVID-19 period was 1123 (2.03 %), 1075 (1.63 %), and 1883 (2.20 %) cases in 2017, 2018, and 2019, respectively. On the other hand, the number of cases during the COVID-19 pandemic was 417 (0.63 %) and 740 (1.27 %) in 2020 and 2021, respectively, with a comparable number of performed tests. Patients infected with the influenza virus between 2020 and 2021 were older than patients who were infected before the COVID-19 pandemic. CONCLUSION: The study found a lower number of influenza cases during the COVID-19 pandemic, with no clear peak during November and December 2020 and 2021.

Real-world analysis of integration of sacubitril/valsartan into clinical practice in Saudi Arabia.

Despite the demonstrated advantages of angiotensin receptor/neprilysin inhibitors in the management of heart failure, the pivotal Angiotensin-Neprilysin Inhibition versus Enalapril in Heart Failure (PARADIGM-HF) trial, which explored this class of medications, did not include individuals from Saudi Arabia. Recognizing that different nations and ethnic groups may exhibit unique characteristics, this study aimed to compare the demographics and outcomes of patients in Saudi Arabia who received sacubitril/valsartan (Sac/Val) with those enrolled in the PARADIGM-HF trial. In this retrospective, multicenter cohort study, we included all adult patients diagnosed with heart failure with reduced ejection fraction (HFrEF) within a tertiary healthcare system in Saudi Arabia between January 2018 and December 2021 and were initiated on Sac/Val. The primary objective was to compare the patient characteristics of those initiating Sac/Val treatment with the participants in the PARADIGM-HF trial. The secondary endpoints included the initiation setting, dose initiation, and titration, as well as alterations in B-type natriuretic peptide and ejection fraction at the 6-month mark. Furthermore, we reported the hospitalization and mortality event rates at the 12-month time point. The study included 400 patients with HFrEF receiving Sac/Val. Compared with the PARADIGM-HF trial, the cohort had a younger mean age and a higher prevalence of diabetes mellitus. SAC/VAL was prescribed as the initial therapy for 34% of the patients, while the remaining participants were initially treated with either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker before transitioning to Sac/Val. Approximately 75% of patients were initiated on 100 mg Sac/Val twice daily, and 90% initiated therapy in the inpatient setting. The mean ejection fraction significantly improved from 26.5 ±â€…8.4% to 30.5 ±â€…6.4% at 6 months (P < .001), while the median B-type natriuretic peptide level change was not significant (P = .39). Our study revealed notable disparities in the baseline characteristics of patients with HFrEF compared with those in the PARADIGM-HF trial. These findings offer valuable real-world insights into the prescription patterns and outcomes of Sac/Val in patients with HFrEF in Saudi Arabia, an aspect not previously represented in the PARADIGM-HF study.

Deep Transfer Learning Based Unified Framework for COVID19 Classification and Infection Detection from Chest X-Ray Images.

The presentation of the COVID19 has endangered several million lives worldwide causing thousands of deaths every day. Evolution of COVID19 as a pandemic calls for automated solutions for initial screening and treatment management. In addition to the thermal scanning mechanisms, findings from chest X-ray imaging examinations are reliable predictors in COVID19 detection, long-term monitoring and severity evaluation. This paper presents a novel deep transfer learning based framework for COVID19 detection and segmentation of infections from chest X-ray images. It is realized as a two-stage cascaded framework with classifier and segmentation subnetwork models. The classifier is modeled as a fine-tuned residual SqueezeNet network, and the segmentation network is implemented as a fine-tuned SegNet semantic segmentation network. The segmentation task is enhanced with a bioinspired Gaussian Mixture Model-based super pixel segmentation. This framework is trained and tested with two public datasets for binary and multiclass classifications and infection segmentation. It achieves accuracies of 99.69% and 99.48% for binary and three class classifications, and a mean accuracy of 83.437% for segmentation. Experimental results and comparative evaluations demonstrate the superiority of this unified model and signify potential extensions for biomarker definition and severity quantization.

Selective Antitumor Activity of Datelliptium toward Medullary Thyroid Carcinoma by Downregulating RET Transcriptional Activity.

Medullary thyroid carcinoma (MTC) is a rare aggressive form of thyroid cancer with high rates of metastasis. Sporadic and hereditary MTC are strongly driven by somatic and germline mutations, respectively, in the transmembrane REarranged during Transfection (RET) proto-oncogene, which encodes a receptor tyrosine kinase. Our previous study identified datelliptium as a novel RET transcription inhibitor, which stabilizes the RET G-quadruplex structures and suppresses RET oncogene transcription. The present study aimed to elucidate the effect of datelliptium on the suppression of epithelial-to-mesenchymal transition (EMT) and metastasis-related behaviors of MTC cells, including cell migration and formation of cancer stem cells (CSCs). Our results demonstrated that datelliptium downregulated the expression of the mesenchymal markers, including N-cadherin, vimentin, slug, snail, and claudin-1. Compared to untreated cells, datelliptium significantly decreased the migration of TT cells in a dose-dependent manner in a wound healing assay. Additionally, datelliptium significantly reduced the size of preformed spheroids from TT cells over the time course. Finally, datelliptium inhibited approximately 75% of MTC xenograft growth with minimal systemic toxicity. In conclusion, datelliptium exerts its antitumor activity against MTC cells by reducing the EMT program, migratory ability, and self-renewal capacity of TT cells, thus preventing invasive and metastatic behavior of MTC.

A review on advances in green treatment of glycerol waste with a focus on electro-oxidation pathway.

Over the past decades, research efforts are being devoted into utilizing the biomass waste as a major source of green energy to maintain the economic, environmental, and social sustainability. Specifically, there is an emerging consensus on the significance of glycerol (an underutilised waste from biodiesel industry) as a cheap, non-toxic, and renewable source for valuable chemicals synthesis. There are numerous methods enacted to convert this glycerol waste to tartronic acid, mesoxalic acid, glyceraldehyde, dihydroxyacetone, oxalic acid and so on. Among these, the green electro-oxidation technique is one of the techniques that possesses potential for industrial application due to advantages such as non-toxicity process, fast response, and lower energy consumption. The current review covers the general understanding on commonly used techniques for alcohol (C1 & C2) conversion, with a specific insight on glycerol (C3) electro-oxidation (GOR). Since catalysts are the backbone of chemical reaction, they are responsible for the overall economy prospect of any processes. To this end, a comprehensive review on catalysts, which include noble metals, non-noble metals, and non-metals anchored over various supports are incorporated in this review. Moreover, a fundamental insight into the development of future electrocatalysts for glycerol oxidation along with products analysis is also presented.