Explore the vast range of titles available.

ObjectiveTo review the current body of evidence surrounding non-imaging biomarkers in patients with known or suspected prostate cancer.ResultsSeveral non-imaging biomarkers have been developed and are available that aim to improve risk estimates at several clinical junctures. For patients with suspicion of prostate cancer who are considering first-time or repeat biopsy, blood- and urine-based assays can improve the prediction of harboring clinically significant disease and may reduce unnecessary biopsy. Blood- and urine-based biomarkers have been evaluated in association with prostate MRI, offering insights that might augment decision-making in the pre and post-MRI setting. Tissue-based genomic and proteomic assays have also been developed that provide independent assessments of prostate cancer aggressiveness that can complement imaging.ConclusionA growing number of non-imaging biomarkers are available to assist in clinical decision-making for men with known or suspected prostate cancer. An appreciation for the intersection of imaging and biomarkers may improve clinical care and resource utilization for men with prostate cancer.

The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics.

Dermatological conditions are primarily prevalent in humans and are primarily caused by environmental and climatic fluctuations, as well as various other reasons. Timely identification is the most effective remedy to avert minor ailments from escalating into severe conditions. Diagnosing skin illnesses is consistently challenging for health practitioners. Presently, they rely on conventional methods, such as examining the condition of the skin. State-of-the-art technologies can enhance the accuracy of skin disease diagnosis by utilizing data-driven approaches. This paper presents a Computer Assisted Diagnosis (CAD) framework that has been developed to detect skin illnesses at an early stage. We suggest a computationally efficient and lightweight deep learning model that utilizes a CNN architecture. We then do thorough experiments to compare the performance of shallow and deep learning models. The CNN model under consideration consists of seven convolutional layers and has obtained an accuracy of 87.64% when applied to three distinct disease categories. The studies were conducted using the International Skin Imaging Collaboration (ISIC) dataset, which exclusively consists of dermoscopic images. This study enhances the field of skin disease diagnostics by utilizing state-of-the-art technology, attaining exceptional levels of accuracy, and striving for efficiency improvements. The unique features and future considerations of this technology create opportunities for additional advancements in the automated diagnosis of skin diseases and tailored treatment.

The healthcare sector has faced increasing pressures to reduce costs and increase healthcare quality in recent years. Thus, managing knowledge in healthcare organizations is critical to achieving low-cost optimization of higher quality services. Also, information technology (IT) plays an essential and crucial role in providing a central database for managing patient data in the healthcare sector. The current study investigates the effects of three aspects of knowledge capabilities (i.e., individual knowledge capabilities, managerial knowledge capabilities, and collaborative organizational capabilities) on information technology governance in healthcare institutions in the Sultanate of Oman. Data were collected using a non-simple random sampling technique by distributing a questionnaire completed by (325) employees working in (13) public hospitals and medical centers in (8) governorates. The collected data were analyzed using AMOS software and structural equation modeling (SEM). This study indicates that individual knowledge capabilities significantly affect only one dimension of IT governance: IT risk management. Moreover, collaborative knowledge capabilities significantly influence all dimensions of IT governance pillars in healthcare institutions, while managerial knowledge capabilities have no significant effect. The study findings can be used to deeply understand the role of knowledge capabilities in sustaining the IT governance application in the health sector context.

Background The coronavirus disease 2019 (COVID-19) pandemic has transformed the global view of education, including graduate and postgraduate education making the development of an alternative approach in times of social isolation an academic imperative. The present review aims to investigate the challenges experienced among undergraduate and postgraduate education and the strategies adopted to address these challenges during the pandemic. Method The preferred reporting items for the systematic review and meta-analyses extension for Scoping Reviews (PRISMA-ScR) were followed. The aim was to include journal articles published in the English language that discussed the influence of the pandemic on educational processes and applied innovative approaches as a solution to educational challenges. From January to August 2020, PubMed, EMBASE, and Google Scholar were searched for articles, yielding 10,019 articles. Two groups of authors examined the retrieved articles separately to avoid any risk of bias. The title and abstract of the articles were used for scrutiny, followed by full-text screening based on the established inclusion and exclusion criteria. The facts and findings of the studies were also discussed based on per capita income, literacy rate, and Internet accessibility. Results Thirty of the obtained articles were included in the study. The selected articles were from North and South/Latin America, Asia & Pacific, South Africa, and Europe regions. Nineteen of the selected articles dealt with undergraduate education, ten with postgraduate, and one with both groups. The affordability of digital devices and the availability of Internet services were the major challenges for low- and middle-income economies. The ZOOM platform has been adopted by more than 90% of the education systems. Conclusion Means of communication, including visual media, digitized content, and other web-based platforms, have been recognized as efficient learning and training tools, but have not been fully accessible for mass application and use due to the lack of availability of resources, their cost, and insufficient training among the users. In light of this review, it is suggested that harmonized and collaborative efforts should be made to develop cost-effective and user-friendly tools to overcome the current challenges and prevent future educational crises. Systemic review registration The review was not registered.

Purposeof ReviewThe use of genomic testing for prostate cancer continues to grow; however, utilization remains institutionally dependent. Herein, we review current tissue-based markers and comment on current use with active surveillance and prostate MRI.Recent FindingsWhile data continues to emerge, several studies have shown a role for genomic testing for treatment selection. Novel testing options include ConfirmMDx, ProMark, Prolaris, and Decipher, which have shown utility in select patients.SummaryThe current body of literature on this specific topic remains very limited; prospective trials with long-term follow-up are needed to improve our understanding on how these genomic tests fit when combined with our current clinical tools. As the literature matures, it is likely that newer risk calculators that combine our classic clinical variables with genomic and imaging data will be developed to bring about standard protocols for prostate cancer decision-making.

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.

Pulmonary aspergilloma is an uncommon but potentially life-threatening condition that predominantly affects individuals with pre-existing structural lung disease and immunosuppression. Systemic lupus erythematosus (SLE), especially with long-term immunosuppressive therapy, significantly increases susceptibility to opportunistic infections, including fungal pathogens such as species. These patients are also at heightened risk for a broad range of opportunistic infections, such as species, viral infections such as herpes zoster and cytomegalovirus (CMV),  , and pneumonia (PJP). We report a case of a 50-year-old woman with long-standing SLE and poorly controlled diabetes mellitus who developed multilobar pulmonary aspergilloma while on chronic glucocorticoids and azathioprine. Despite initial improvement, she developed hemoptysis, necessitating bronchial artery embolization consideration. Due to extensive bilateral disease, surgical intervention was deferred, and she was successfully managed with prolonged voriconazole therapy. This case underscores the diagnostic and therapeutic challenges of aspergilloma in immunocompromised hosts and highlights the importance of vigilant monitoring and tailored antifungal strategies.

BackgroundPaediatric patients in acute care unit settings may be deprived of frequent assessments and monitoring. These spaced observations can put patients at risk of missed clinical deterioration that could ultimately result in unfavourable safety events. Several international guidelines encourage the use of the Paediatric Early Warning System (PEWS), which provides healthcare workers with a standardised approach to monitor patients’ clinical status and anticipate deterioration at an early stage. This study aimed to summarise the strategies used for implementing the PEWS and evaluate the impact of this tool on patient safety.MethodWe conducted a quality improvement project to implement the Bedside PEWS in 2016. Six plan-do-study-act cycles were used throughout the implementation phase. Three elements were monitored to ensure the proper utilisation of the tool: monitoring, escalation and physician review of patients based on the PEWS protocol. Outcome measures of this initiative were monitored to explore the impact of the PEWS on patient safety.ResultThe average number of unplanned paediatric intensive care unit (PICU) admissions increased by 25% in 2017, decreased by 25% in 2018 and decreased by 50% in 2019 compared with the baseline year. The average number of unplanned paediatric high-dependency unit admissions increased by 14.3% in 2017, decreased by 28.6% in 2018 and decreased by 42.9% in 2019 compared with the baseline year. The average length of stay after unplanned PICU admission remained stable in 2016 and 2017 and decreased by 50% from 2018 to 2022 relative to the baseline year. The mortality rate after unplanned PICU admission was also reduced. There was no effect on the cardiopulmonary arrest rate outside of PICUs.ConclusionContinuous staff training results in a high compliance rate with the PEWS protocol, despite persistent hospital expansion and high staff turnover. PEWS positively affects patient outcomes.

Objectives: To determine the impact of implementing a new pediatric inpatient structure--the clinical teaching unit (CTU)--on length of stay (LOS) and other patient care outcomes. Methods: A retrospective study was carried out on children admitted to the General Pediatric Inpatient Service at King Abdullah Specialized Children's Hospital, Riyadh, Saudi Arabia, between July 2015 and December 2018. The main outcome measures were median and mean LOS before and after CTU implementation. Other outcomes measured were the proportion of patients discharged on weekends, during daytime, and within 24 hours of admission, and the proportion of patients readmitted within 7 days of discharge. Results: Median LOS decreased from 2.80 to 2.63 days after CTU implementation (p<0.0001). The proportion of weekend discharges significantly increased after CTU implementation from 18% to 21.5% (p<0.0243) and daytime discharges significantly increased from 6.9% to 25.6% (p<0.0001) after CTU implementation. The improvements in LOS were sustained in the years after CTU implementation, with median LOS decreasing from 2.71 to 2.60 days during 2016-2018 (p<0.001) and mean LOS decreasing from 5.03 to 3.92 days (p=0.0031). During the same period, readmission rates remained stable at 3.5-4%. Conclusion: The implementation of a new pediatric inpatient team structure led to significant improvements in many patient care outcomes, including decreased LOS. Keywords: pediatrics, inpatients, length of stay, quality improvement [phrase omitted]