Patient Outcomes Research Articles

A sales force effectiveness framework for enhancing healthcare access through pharmaceutical sales and training programs

This paper introduces a conceptual framework aimed at enhancing sales force effectiveness within the pharmaceutical industry, specifically focusing on improving healthcare access through optimized sales training programs and customer relationship management (CRM) systems. The proposed framework recognizes the critical role that pharmaceutical sales teams play in facilitating healthcare delivery, particularly during public health crises. It emphasizes the integration of effective training strategies and advanced CRM tools to enhance engagement with healthcare professionals, thereby improving product distribution and patient outcomes. The framework comprises three essential components Sales Force Training Enhancement, which focuses on developing targeted training programs that equip sales representatives with the necessary skills and knowledge to effectively communicate the value of pharmaceutical products to healthcare providers. This includes understanding clinical data, addressing healthcare providers’ concerns, and fostering a consultative selling approach. Secondly, CRM System Implementation, which underscores the significance of adopting robust CRM systems that streamline interactions between sales teams and healthcare professionals. By leveraging data analytics and customer insights, pharmaceutical companies can tailor their approach to meet the specific needs of healthcare providers, thereby enhancing relationship-building and communication. Thirdly, engagement and Outcome Measurement, which highlights the importance of measuring engagement levels and patient outcomes resulting from effective sales strategies. This component advocates for the use of key performance indicators (KPIs) to assess the impact of training and CRM initiatives on product distribution and healthcare access. The framework ultimately aims to foster a more collaborative environment between pharmaceutical sales teams and healthcare professionals, ensuring that patients receive timely access to essential medications and services. By focusing on the strategic integration of training and CRM systems, this framework presents a comprehensive approach to improving sales force effectiveness in the pharmaceutical sector. Future research and practical applications of this framework can further explore its implications for enhancing healthcare access, particularly in times of public health challenges.

The impact and correlation of lymphadenectomy with outcomes in patients with uterine sarcoma: A systematic review

Background: Sarcomas are an uncommon type of smooth muscle tumors in the uterus. The most popular form of treatment for uterine sarcomas is surgery. There is disagreement, nonetheless, over the best course of action for initial surgery. The aim of this systematic review was to examine the potential association between patient outcomes and the effects of LAD in individuals diagnosed with uterine sarcoma. Method: We conducted a comprehensive search of the PubMed database to identify publications assessing the impact of LAD on patients with uterine sarcoma that were published between 2009 and 2024. Based on the references in the studies that we included, we also conducted a search of the PubMed database. To find more relevant studies, the bibliographies of each pertinent paper were thoroughly reviewed. This review only includes studies that were published in English. Results: We included 7 papers in this systematic review, totaling 7896 patients. Four publications examined ESS, four studies examined LMS, two articles examined adenosarcoma, one study examined carcinosarcoma, and one study examined rhabdomyosarcoma. For individuals with low-grade ESS or adenosarcoma, LAD was not linked to a better prognosis. The survival rate was lower for patients with leiomyosarcoma who had LAD. Those who had LAD and had high-grade undifferentiated ESS fared better. LAD did not significantly correlate with RFS or OS in uterine sarcoma patients. Conclusion: Resection of lymph nodes or LAD has minimal potential benefit in terms of prognosis and clinical value.

Comparative cross-sectional study of comorbid depression in patients with irritable bowel syndrome and chronic gastritis at an urban outpatient clinic

This comparative cross-sectional study evaluates the prevalence of comorbid depression in patients with Irritable Bowel Syndrome (IBS) and chronic gastritis (CG) at an urban outpatient clinic. A total of 178 patients—89 diagnosed with IBS and 89 with CG—were assessed for depressive symptoms using the Beck Depression Inventory-II (BDI-II) and the 21-item Hamilton Depression Rating Scale (HDRS-21). Our findings indicate a significantly higher prevalence of depression in IBS patients, with 71.9% meeting criteria for depression based on the BDI-II and 70.8% based on the HDRS-21, compared to 10.1% in CG patients. The study highlights that retardation symptoms, including depressed mood and loss of interest, are central to depression and contribute to substantial impairment in daily activities and quality of life. Additionally, anxiety and somatization were prominent in the IBS cohort. This study emphasizes the need for integrated care approaches that address both gastrointestinal and psychological symptoms, fostering improved patient management and outcomes. Our results underscore the importance of regular psychological assessments in IBS patients, particularly those without overt depressive symptoms, to enhance their overall well-being.

Cost minimization analysis of chronic kidney disease management: Evaluating economic strategies for early intervention and treatment optimization

Chronic kidney disease is a common global health condition characterized by progressive loss of kidney function and significant cost burden on the healthcare system worldwide. The disease often coexists with conditions such as diabetes and hypertension, further complicates its management and increasing associated costs. Effective economic management, particularly in the early stages of CKD, is crucial for reducing long-term healthcare expenses and improving patient outcomes. This review examines the utility of cost minimization analysis (CMA) as a strategic tool for evaluating economic options in CKD management. CMA is an economic evaluation method that compares the costs of different therapeutic interventions that have demonstrated equivalent clinical effectiveness, thereby aiding healthcare providers and policymakers in identifying the most cost-efficient options to optimize resource utilization. This review highlights the economic impact of CKD, focusing on the increasing costs linked to advanced disease management and the essential role of early diagnosis and intervention in reducing these costs and explores various cost-effective strategies for CKD management, including pharmacological treatments, lifestyle modifications, and patient education, to identify approaches that are both clinically and economically advantageous. Additionally, the broader implications of cost minimization for healthcare policy, clinical decision-making, and resource allocation are discussed for their significance in a resource-limited healthcare setting. Evidence indicates that prioritizing cost-effective CKD management through early intervention and preventive care can significantly lower healthcare expenditures, slow the disease progression, and enhance patient’s quality of life. By adopting such strategies, healthcare systems can achieve better clinical outcomes and ensure financial sustainability.

Advancing prostate cancer detection: High-accuracy urine test distinguishes between high and low-risk cancers

Prostate cancer remains one of the leading causes of cancer-related deaths among men. The standard method for early detection involves measuring Prostate-specific antigen (PSA) levels in the blood, with elevated PSA levels prompting further diagnostic procedures, such as a prostate biopsy. While effective, biopsies can be painful and carry risks of complications such as fever and urinary tract infections. To reduce unnecessary biopsies, researchers have sought alternative diagnostic methods. In recent years, a urine test was developed for early-stage prostate cancer detection. However, this test struggled to differentiate between aggressive and slow-growing cancers, the latter of which often require minimal treatment and are managed through active surveillance. To address this limitation, scientists at Wunderkind University have significantly enhanced the urine test. By analysing the genomes of thousands of prostate cancer patients, they identified a panel of 16 genes detectable in urine that can effectively distinguish between high-risk and low-risk cancers. When applied to individuals with elevated PSA levels, this improved test demonstrated an impressive 97% accuracy in identifying advanced prostate cancer. This advancement holds great promise for reducing unnecessary biopsies and improving patient outcomes by accurately identifying those needing aggressive treatment versus those suitable for observation. This review paper will explore the development, validation, and clinical implications of this novel urine test, highlighting its potential to transform prostate cancer diagnostics and patient management.

Pharmacists in global primary healthcare systems: A comprehensive model for community health empowerment

Pharmacists play an increasingly important role in global primary healthcare systems, evolving from traditional dispensers to active participants in patient-centered care. This paper advocates for the stronger integration of pharmacists into healthcare teams to improve patient outcomes, medication adherence, and overall healthcare delivery. Through a comprehensive review, we examine the evolving role of pharmacists, the key challenges to their full integration, and the substantial opportunities they present, particularly in managing chronic diseases, providing preventative care, and enhancing patient education. We propose a model for empowering pharmacists to contribute more effectively to community health, especially in underserved areas. The paper also highlights the potential global impact of pharmacists in reducing healthcare costs, improving disease management, and promoting public health initiatives. Lastly, recommendations for future policy reforms, education enhancements, and research directions are offered to support the continued expansion of pharmacists' roles in primary healthcare systems worldwide.

The Impact of Big Data and Predictive Analytics on U.S. Healthcare Delivery: Opportunities, Challenges, and Future Directions

Healthcare, like many other industries, has been significantly influenced by big data and predictive analytics. The vast volume, velocity, and variety of information within big data sets have transformed the way we approach patient care and medical innovation. From predicting disease outbreaks to delivering personalized treatment plans, big data analytics offers immense potential for revolutionizing healthcare. This review explores the significant impact of big data and predictive analytics on the U.S. healthcare system. It delves into hospitals' ability to effectively leverage complex information and assess the potential benefits they may reap from successful implementation. The introduction defines big data and predictive analytics in the context of healthcare and outlines the paper's objectives. It further discusses the evolution of big data and analytics in healthcare, its key applications, and the challenges it presents. Additionally, we explore potential solutions and ethical considerations surrounding big data analytics in healthcare. Overall, this paper underscores the transformative power of big data and predictive analytics in revolutionizing U.S. healthcare delivery and improving patient outcomes.

Design and development of an ai-driven communication model for stimulating memory recall in patients with Alzheimer’s and other neurodegenerative diseases

This study presents the development and evaluation of an AI-driven communication tool designed to stimulate memory recall in patients with Alzheimer’s disease and other neurodegenerative disorders. The tool integrates machine learning algorithms, including Random Forest and Long Short-Term Memory (LSTM) networks, with mass communication theories to deliver personalized and adaptive communication strategies. The AI models were trained and tested on patient interaction data, enabling the tool to dynamically adjust its prompts based on real-time cognitive states. Performance metrics such as accuracy, precision, and AUC-ROC were used to assess model efficacy, with the Random Forest model achieving the highest performance across all metrics. Clinical trials demonstrated significant improvements in memory recall and emotional engagement in patients using the AI tool compared to standard cognitive stimulation therapy. Caregivers also reported higher satisfaction with the tool's usability and impact on patient interaction quality. These findings highlight the potential of AI-driven, personalized communication tools to revolutionize Alzheimer's care, offering scalable, adaptive interventions that improve cognitive and emotional health. Academically, this work advances the integration of AI and health communication, emphasizing culturally sensitive and patient-centered approaches. In healthcare, it presents a promising non-pharmacological intervention that can be easily scaled for widespread use, potentially reducing caregiver burden and improving patient outcomes.

Assessment of drug therapy problems in pediatric patients using Cipolle/Morley/strand classification: A hospital based study

Drug plays an important role in treating diseases and improving health. However, inappropriate drug use can lead to the occurrence of drug-Therapy problems (DTPs). According to Cipolle/Morley/Strand classification DTPs are any undesirable event experienced by the patient that involves or is suspected to involve drug therapy and that actually or potentially interferes with a desired patient outcome. The purpose of this study was to assess the drug therapy problems in a pediatric ward of Srinivas institute of medical science and research centre and to identify associated factors for drug therapy problems. A retrospective observational study was carried out to assess drug therapy problems (DTPs) in paediatric patients using Cipolle/Morley/Strand classification from January to June 2024. Ethical approval was obtained, and informed consent was signed by each study participant's parent before the commencement of the study. All paediatric out patients and patients admitted to the paediatric ward during the study period were included in the study. Data were collected through medical record reviews of patients and using semi structured questionnaire. Data was analysed using Microsoft Excel. The participants mean age was 2.32 years. Among 150 patients, 116 (77.33%) of them had at least one drug therapy problem per patient which indicates that the prevalence of the drug therapy problem was substantially high. On the other hand, Adverse drug reaction was the least 4 (3.38%) drug therapy problem. Hence to ensure optimum care in paediatric patients, a targeted approach is essential. Thus, the present study concluded that pharmacists can be helpful in the identification of the drug therapy problems, their causes and the risk factors involved and thus lead to a better health care outcome.

The critical role of quality-of-life data in managing patients with central nervous system disorders

Quality of life (QoL) is an essential metric in evaluating the overall well-being of patients with central nervous system (CNS) disorders. Understanding QoL data is crucial for tailoring treatment approaches, enhancing patient outcomes, and assessing the effectiveness of therapeutic interventions. This paper discusses the importance of QoL assessments in CNS disorders, including the impact on treatment decisions, patient satisfaction, and long-term health outcomes. It also explores the challenges and methodologies for collecting and analyzing QoL data, emphasizing the need for integrating these metrics into clinical practice. Ultimately, enhancing QoL is paramount in the comprehensive management of CNS disorders.

Telehealth innovations for cardiovascular disease management

Telehealth innovations and wearable technologies are revolutionizing the management of cardiovascular diseases (CVD), providing patients with more accessible and continuous care. The integration of remote monitoring tools and mobile health applications allows for real-time tracking of vital signs, such as heart rate, blood pressure, and oxygen levels, enabling early detection of abnormalities and timely medical interventions. These technologies empower patients to actively participate in their own health management, encouraging adherence to prescribed lifestyle changes, such as increased physical activity and improved dietary habits, which are essential for preventing and controlling CVD. Wearable devices, such as smartwatches and fitness trackers, play a critical role in the continuous monitoring of cardiovascular health. Combined with telehealth platforms, they offer personalized insights and facilitate communication between patients and healthcare providers, enhancing care coordination. This leads to improved patient outcomes, particularly in managing chronic conditions like hypertension and heart failure, where early intervention is key to preventing complications. Additionally, telehealth solutions reduce the need for frequent in-person visits, cutting healthcare costs and making care more convenient and accessible for patients in remote or underserved areas. As telehealth and wearable technologies continue to advance, they hold the potential to further transform cardiovascular care, contributing to more efficient healthcare delivery and better management of cardiovascular risk factors.

Impacts of Hospital Volume and Patient-Hospital Distances on Outcomes of Older Adults Receiving Percutaneous Microaxial Ventricular Assist Devices for Cardiogenic Shock.

Background: Percutaneous microaxial ventricular assist devices (pVAD) have the potential to reduce mortality of patients with cardiogenic shock (CS). However, the association between the distribution of pVAD-performing centers and outcomes of CS has not been explored. Methods: This observational study included Medicare fee-for-service beneficiaries aged 65-99 years treated with pVAD for CS from 2016 to 2020 and examined the associations between patient outcomes and two exposure variables: hospitals' procedure volumes of pVAD and patient-hospital distances (in quintiles [Qn]). We developed Cox proportional hazard regression for 180-day mortality and heart failure (HF) readmission rates and multivariable logistic regression for in-hospital outcomes, adjusting for patient demographics, comorbidities, concomitant treatments, and hospital characteristics, including CS volume, teaching status, and the ability to perform extracorporeal membrane oxygenation. Results: A total of 6,637 patients with CS underwent pVAD at 1,041 hospitals, with the annualized hospital volume ranging widely from 0.3 to 55.6 cases/year. Patients treated at higher-volume centers experienced lower 180-day mortality compared with patients treated at lower-volume centers (Qn1=reference; Qn2: adjusted hazard ratio [aHR], 0.88; 95% confidence interval [CI], 0.79-0.97; Qn3: aHR, 0.88; 95% CI, 0.79-0.98; Qn4: aHR, 0.88; 95% CI, 0.78-0.99; Qn5: aHR, 0.84; 95% CI, 0.74-0.95; p-for-trend, 0.026), while we found no evidence that patient-hospital distances were associated with mortality (Qn1=reference; Qn2: adjusted hazard ratio [aHR], 0.99; 95% confidence interval [CI], 0.89-1.09; Qn3: aHR, 0.94; 95% CI, 0.85-1.04; Qn4: aHR, 1.01; 95% CI, 0.92-1.11; Qn5: aHR, 0.91; 95% CI, 0.82-1.01; p-for-trend, 0.160). We found no evidence that the hospital volume and patient-hospital distances were associated with in-hospital bleeding, intracranial hemorrhage, or renal replacement therapy initiation. Conclusions: Hospital volume was more strongly associated with mortality than patient-hospital distances, suggesting that rational distribution of pVAD-performing centers while ensuring adequate procedure volumes may optimize patient mortality.

Computational Analysis of Amine Functionalization in Zwitterionized Polyether Sulfone Dialysis Membranes

Hemodialysis is a critical treatment for patients with end-stage renal disease (ESRD) who lack kidney transplant options. The compatibility of hemodialysis membranes is vital, as incompatibility can trigger inflammation, coagulation, and immune responses, potentially increasing morbidity and mortality among patients with ESRD. This study employed molecular dynamics simulation (MDS) and molecular docking to assess the hemocompatible properties of Polyether Sulfone (PES) membranes modified via two distinct amine functionalization techniques. The molecular docking results demonstrated that side amine functionalization exhibited a lower affinity energy (−7.6) for fibrinogen compared to the middle amine functionalization (−8.2), suggesting enhanced antifouling properties and superior hemocompatibility. Additionally, side amine functionalization formed hydrogen bonds with four amino acids, enhancing its resistance to protein adhesion compared to three amino acids in the middle amine structure. Furthermore, the molecular dynamics simulations revealed differences in water mobility, with the side amine functionalized membranes showing a lower mobility value (9.74 × 10−7) than those treated with the middle amine method (9.85 × 10−7), indicating higher water stability and potentially better patient outcomes. This study’s findings contribute to the design of more efficient and safer hemodialysis treatments by optimizing membrane materials.

Low-Value Clinical Practices in Pediatric Trauma Care.

Reducing low-value care has the potential to improve patient experiences and outcomes and decrease the unnecessary use of health care resources. Research suggests that low-value practices (ie, the potential for harm exceeds the potential for benefit) in adult trauma care are frequent and subject to interhospital variation; evidence on low-value practices in pediatric trauma care is lacking. To estimate the incidence of low-value practices in pediatric trauma care and evaluate interhospital practice variation. A retrospective multicenter cohort study in a Canadian provincial trauma system was conducted. Children younger than 16 years admitted to any of the 59 provincial trauma centers from April 1, 2016, to March 31, 2022, were included. Low-value practices were identified from systematic reviews of clinical practice guidelines on pediatric trauma. The frequencies of low-value practices were evaluated by estimating incidence proportions and cases per 1000 admissions (low if ≤10% and ≤10 cases, moderate if >10% or >10 cases, and high if >10% and >10 cases) were identified. Interhospital variation with intraclass correlation coefficients (ICCs) were assessed (low if <5%, moderate if 5%-20%, and high if >20%). A total of 10 711 children were included (mean [SD] age, 7.4 [4.9] years; 6645 [62%] boys). Nineteen low-value practices on imaging, fluid resuscitation, hospital/intensive care unit admission, specialist consultation, deep vein thrombosis prophylaxis, and surgical management of solid organ injuries were identified. Of these, 14 (74%) could be evaluated using trauma registry data. Five practices had moderate to high frequencies and interhospital variation: head computed tomography in low-risk children (7.1%; 33 per 1000 admissions; ICC, 8.6%), pretransfer computed tomography in children with a clear indication for transfer (67.6%; 4 per 1000 admissions; ICC, 5.7%), neurosurgical consultation in children without clinically important intracranial lesions (11.6%; 13 per 1000 admissions; ICC, 15.8%), hospital admission in isolated mild traumatic brain injury (38.8%; 98 per 1000 admissions; ICC, 12.4%), and hospital admission in isolated minor blunt abdominal trauma (10%; 5 per 1000 admissions; ICC, 31%). In this cohort study, low-value practices appeared to be frequent and subject to interhospital variation. These practices may represent priority targets for deimplementation interventions, particularly as they can be measured using routinely collected data.

Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.

Acute kidney injury (AKI) is a significant health challenge associated with adverse patient outcomes and substantial economic burdens. Many authors have sought to prevent and predict AKI. Here, we comprehensively review recent advances in the use of artificial intelligence (AI) to predict AKI, and the associated challenges. Although AI may detect AKI early and predict prognosis, integration of AI-based systems into clinical practice remains challenging. It is difficult to identify AKI patients using retrospective data; information preprocessing and the limitations of existing models pose problems. It is essential to embrace standardized labeling criteria and to form international multi-institutional collaborations that foster high-quality data collection. Additionally, existing constraints on the deployment of evolving AI technologies in real-world healthcare settings and enhancement of the reliabilities of AI outputs are crucial. Such efforts will improve the clinical applicability, performance, and reliability of AKI Clinical Support Systems, ultimately enhancing patient prognoses.

Effects of two non-drug interventions on pain and anxiety in the nursing process of burn patients: a literature review with meta-analysis

BackgroundBurns are a global health issue causing significant mortality and high medical costs. Non-pharmacological interventions such as music therapy and virtual reality (VR) therapy have shown potential in alleviating pain and anxiety in burn patients. This study systematically evaluates the impact of these interventions using a network meta-analysis.MethodsA systematic review and network meta-analysis were conducted according to PRISMA 2020 guidelines and registered in PROSPERO (CRD42024566536). Searches in PubMed, Cochrane Library, Web of Science, and Embase up to November 22, 2023, identified randomized controlled trials (RCTs) involving music therapy or VR therapy in burn patients. The Cochrane Risk of Bias Tool (2.0) assessed study quality. Data were analyzed using StataMP-64 software.ResultsSeventeen RCTs with 1,119 burn patients were included. Both music therapy and VR therapy significantly reduced pain and anxiety compared to control groups. Music therapy was more effective for pain reduction (SUCRA: 85.4%), while VR therapy was superior for anxiety relief (SUCRA: 79.5%).ConclusionMusic therapy and VR therapy effectively reduce pain and anxiety in burn patients. Integrating these interventions into burn care can enhance patient outcomes. Further research is needed to confirm these findings and optimize individualized treatment plans.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, PROSPERO (CRD42024566536).

Artificial Intelligence-based Enhanced Brain Tumor Prediction: A Comprehensive Investigation of Machine Learning and Deep Learning

Brain tumors pose a significant threat to patients’ quality of life and survival rates, with traditional diagnostic methods often falling short due to their time-consuming nature and susceptibility to high misdiagnosis rates. Recent progress in Artificial Intelligence (AI), especially in Machine Learning (ML) and Deep Learning (DL), offer promising alternatives for the prediction and diagnosis of brain tumors. AI models, including Support Vector Machine (SVM), Random Forests, Logistic Regression, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models networks, have demonstrated superior performance in processing complex medical data, thereby enhancing predictive accuracy and aiding clinical decision-making. This review systematically evaluates the application of these AI techniques in brain tumor prediction, highlighting their strengths and limitations, as well as the challenges faced in terms of model interpretability, data applicability, and patient privacy. Furthermore, this paper explored future prospects for improving model interpretability, transfer learning and domain adaptation for enhancing model applicability, and federated learning for preserving patient privacy. By addressing these issues, Artificial Intelligence can significantly advance brain tumor diagnosis and treatment, ultimately improving patient outcomes.

Voice Assessment in Patients with Amyotrophic Lateral Sclerosis: An Exploratory Study on Associations with Bulbar and Respiratory Function

Background: Speech production is a possible way to monitor bulbar and respiratory functions in patients with amyotrophic lateral sclerosis (ALS). Moreover, the emergence of smartphone-based data collection offers a promising approach to reduce frequent hospital visits and enhance patient outcomes. Here, we studied the relationship between bulbar and respiratory functions with voice characteristics of ALS patients, alongside a speech therapist’s evaluation, at the convenience of using a simple smartphone. Methods: For voice assessment, we considered a speech therapist’s standardized tool—consensus auditory-perceptual evaluation of voice (CAPE-V); and an acoustic analysis toolbox. The bulbar sub-score of the revised ALS functional rating scale (ALSFRS-R) was used, and pulmonary function measurements included forced vital capacity (FVC%), maximum expiratory pressure (MEP%), and maximum inspiratory pressure (MIP%). Correlation coefficients and both linear and logistic regression models were applied. Results: A total of 27 ALS patients (12 males; 61 years mean age; 28 months median disease duration) were included. Patients with significant bulbar dysfunction revealed greater CAPE-V scores in overall severity, roughness, strain, pitch, and loudness. They also presented slower speaking rates, longer pauses, and higher jitter values in acoustic analysis (all p &lt; 0.05). The CAPE-V’s overall severity and sub-scores for pitch and loudness demonstrated significant correlations with MIP% and MEP% (all p &lt; 0.05). In contrast, acoustic metrics (speaking rate, absolute energy, shimmer, and harmonic-to-noise ratio) significantly correlated with FVC% (all p &lt; 0.05). Conclusions: The results provide supporting evidence for the use of smartphone-based recordings in ALS patients for CAPE-V and acoustic analysis as reliable correlates of bulbar and respiratory function.

AN ATYPICAL CASE OF INFECTIOUS MONONUCLEOSIS – CASE REPORT

Aims: Infectious mononucleosis, caused by the Epstein-Barr virus (EBV), typically presents with fever, exudative pharyngitis and lymphadenopathy. However, atypical presentations can complicate diagnosis and treatment. The objective of this paper is to illustrate the atypical progression of infectious mononucleosis and to delineate the differential diagnosis that should be considered. Additionally, it outlines the therapeutic approaches for managing symptoms associated with the disease. Case Description: A 25-year-old woman presented at a GP's office with eyelid edema, fever, and significant weight loss. Initial tests and treatments were inconclusive, leading to further investigations. Despite typical treatment for viral infections, the patient’s condition worsened, displaying dyspnoea, acute hepatitis, rash, significantly elevated ESR, hemolytic anemia, leukopenia, and thrombocytopenia. Hospitalization was required for comprehensive care, including steroid therapy and fluid management. Conclusions: This case highlights the diagnostic challenges posed by atypical presentations of infectious mononucleosis and underscores the importance of considering EBV infection in the differential diagnosis. Early recognition and appropriate management are crucial for optimal patient outcomes.

Tracer-Separator: A Deep Learning Model for Brain PET Dual-Tracer (18F-FDG and Amyloid) Separation.

Multiplexed PET imaging revolutionized clinical decision-making by simultaneously capturing various radiotracer data in a single scan, enhancing diagnostic accuracy and patient comfort. Through a transformer-based deep learning, this study underscores the potential of advanced imaging techniques to streamline diagnosis and improve patient outcomes. The research cohort consisted of 120 patients spanning from cognitively unimpaired individuals to those with mild cognitive impairment, dementia, and other mental disorders. Patients underwent various imaging assessments, including 3D T1-weighted MRI, amyloid PET scans using either 18F-florbetapir (FBP) or 18F-flutemetamol (FMM), and 18F-FDG PET. Summed images of FMM/FBP and FDG were used as proxy for simultaneous scanning of 2 different tracers. A SwinUNETR model, a convolution-free transformer architecture, was trained for image translation. The model was trained using mean square error loss function and 5-fold cross-validation. Visual evaluation involved assessing image similarity and amyloid status, comparing synthesized images with actual ones. Statistical analysis was conducted to determine the significance of differences. Visual inspection of synthesized images revealed remarkable similarity to reference images across various clinical statuses. The mean centiloid bias for dementia, mild cognitive impairment, and healthy control subjects and for FBP tracers is 15.70 ± 29.78, 0.35 ± 33.68, and 6.52 ± 25.19, respectively, whereas for FMM, it is -6.85 ± 25.02, 4.23 ± 23.78, and 5.71 ± 21.72, respectively. Clinical evaluation by 2 readers further confirmed the model's efficiency, with 97 FBP/FMM and 63 FDG synthesized images (from 120 subjects) found similar to ground truth diagnoses (rank 3), whereas 3 FBP/FMM and 15 FDG synthesized images were considered nonsimilar (rank 1). Promising sensitivity, specificity, and accuracy were achieved in amyloid status assessment based on synthesized images, with an average sensitivity of 95 ± 2.5, specificity of 72.5 ± 12.5, and accuracy of 87.5 ± 2.5. Error distribution analyses provided valuable insights into error levels across brain regions, with most falling between -0.1 and +0.2 SUV ratio. Correlation analyses demonstrated strong associations between actual and synthesized images, particularly for FMM images (FBP: Y = 0.72X + 20.95, R2 = 0.54; FMM: Y = 0.65X + 22.77, R2 = 0.77). This study demonstrated the potential of a novel convolution-free transformer architecture, SwinUNETR, for synthesizing realistic FDG and FBP/FMM images from summation scans mimicking simultaneous dual-tracer imaging.