The COVID-19 pandemic has significantly accelerated the adoption of telemedicine and digital health technologies, revealing their immense potential in managing cancer patients effectively. This article explores the impact of recent technological developments and widened consumer perspectives on personalised healthcare and patient awareness, particularly in oncology. Smartphones and wearable devices have become integral to daily life, promoting healthy lifestyles and supporting cancer patients through remote monitoring and health management. The widespread use of these devices presents an unprecedented opportunity to transform clinical trials and patient care by offering convenient and accessible means of collecting health data continuously and non-invasively. However, to fully harness their potential, it is crucial to establish standardised methods for measuring patient metrics to ensure data reliability and validity. This article also addresses the challenges of integrating these technologies into clinical practice, such as cost, patient and professional reluctance, and technological oversaturation. It emphasises the need for continuous innovation, the development of robust digital infrastructures, and the importance of fostering a supportive environment to integrate these advancements permanently. Ultimately, the convergence of technological innovation and personalised healthcare promises to enhance patient outcomes, improve quality of life, and revolutionise cancer management in the post-COVID era.

Background: Human Immunodeficiency Virus (HIV) is a virus that progressively impairs immune function by depleting CD4 + T-lymphocytes, ultimately leading to acquired immunodeficiency syndrome (AIDS). People living with HIV face a higher risk of developing various bone disorders, such as osteopenia, osteoporosis, and osteonecrosis. The aim of this study was to evaluate the bone mineral density (BMD) status, to determine the prevalence of osteopenia/osteoporosis and to identify the risk factors for low BMD in patients living with HIV undergoing antiretroviral treatment (ART), registered in Craiova Regional Center. Methods: A retrospective study was conducted between June 2024 and January 2025, including HIV-infected subjects aged over 18 years. Results: The study group included 106 patients. Dual-energy X-ray absorptiometry (DEXA) showed that 87 patients had low BMD, 51% having osteopenia and 31.1% having osteoporosis. We found a statistically significant correlation between low BMD and older age, higher levels HIV viremia, CD4 nadir < 200 cells/mm³, prolonged ART exposure and tenofovir disoproxil fumarate containing regimens. Conclusions: These findings support the inclusion of routine bone health monitoring in the standard care of patients with HIV, as well as the need for reevaluation.

Measles remains an important cause of morbidity and mortality worldwide. Most HIV-infected adults are immune against measles, so titres of measles antibodies should be determined prior to vaccination. A measles vaccine can be administered to HIV-positive patients who do not have protective antibody levels and who have a CD4 lymphocyte count ≥ 200 cells/mm³. We describe the case of an HIV-infected patient, diagnosed with complicated measles at our Infectious Diseases Hospital in April 2024.

Background: Vitamin D deficiency (VDD) is highly prevalent among people living with human immunodeficiency virus (HIV), with reported rates of insufficiency and deficiency substantially higher than in many general-population cohorts. This study aims to assess the prevalence of vitamin D deficiency and to investigate the risk factors contributing to its occurrence among people living with HIV who are receiving antiretroviral therapy (ART) and are registered at the Craiova Regional Center (CRC). Methods: A retrospective study was conducted from May 2024 to August 2024, including individuals with HIV aged 18 years and older who were registered at the CRC. Results: A total of 138 patients were included in the study. The prevalence of vitamin D deficiency (<20 ng/mL) and vitamin D insufficiency (20–29.9 ng/mL) was 36.2% and 33.3%, respectively, with an average vitamin D level of 26.4 ± 9.9 ng/mL. Vitamin D deficiency was associated with obesity (p = 0.0013), high HIV viral load (p = 0.043), low CD4 nadir (<200 cells/mm³, p = 0.006), prolonged ART exposure (p = 0.002), and the use of tenofovir disoproxil fumarate or protease inhibitor-containing regimens (p = 0.034 and p = 0.016, respectively). Conclusions: These findings indicate that monitoring vitamin D levels could be particularly relevant for patients with HIV with higher-risk profiles. However, our study included a relatively small number of participants, so further research in larger cohorts is needed to better understand these patterns.

Early risk stratification of COVID-19 severity in oncology patients is critical for improving clinical outcomes and optimizing hospital resource allocation. This study proposes a rule-based clinical decision support system (CDSS) designed for integration into digital triage workflows. In practical terms, the score is intended to be applied at hospital admission or triage, where demographic and comorbidity information is routinely available. The computed score can automatically flag high-risk oncology patients for intensified monitoring or early ICU evaluation, supporting rapid resource allocation while preserving clinician decision-making. Using retrospective clinical data from hospitalized oncological patients with confirmed SARS-CoV-2 infection, we developed a scoring algorithm based on four common comorbidities: age ≥ 70, obesity, diabetes mellitus, and hypertension. Each factor was assigned a weighted contribution to a cumulative score ranging from 0 to 7. Patients were classified into three risk levels (low, moderate, high), correlating with observed rates of ICU admission and mortality. The system is built for low-complexity implementation in electronic health records (EHRs) or web-based triage dashboards and includes a software logic model with pseudocode. Results indicate that the score effectively distinguishes patient risk levels with statistical significance (p < 0.01), and can function as an early triage mechanism. The proposed model does not require laboratory data or imaging, making it particularly suitable for rapid deployment in both hospital and remote settings. This work demonstrates a pragmatic, interpretable, and scalable approach to clinical decision support in pandemic contexts involving vulnerable populations such as cancer patients.

Introduction: This study aims to construct a predictive model that classifies the severity of coronavirus disease 2019 (COVID-19) in cancer patients by analyzing clinical, radiological, and demographic data. The goal is to assist healthcare providers in identifying high-risk patients and ensuring optimal resource allocation. Materials and methods: Data from 237 cancer patients diagnosed with COVID-19 was utilized to forecast disease severity based on various predictors. Key factors included cancer type and stage, intensive care unit admission, radiological assessments, ventilation status, obesity, and systemic inflammatory response syndrome (SIRS). The performance of multiple machine learning (ML) models, including Error-Correcting Output Codes (ECOC) frameworks built on Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Naive Bayes, Discriminant Analysis and an ensemble bagging method, was evaluated using 10-fold cross-validation. Model accuracy and receiver operating characteristic (ROC) curve scores served as the primary evaluation metrics. Results: KNN and ensemble bagging emerged as the most effective models, achieving accuracy rates of 100% and 98.3%, respectively, along with high area under the curve (AUC) values. These models excelled at identifying severe cases associated with intensive care unit (ICU) admission, ventilation, and metastatic cancer. Decision Trees demonstrated satisfactory performance with an accuracy of 82.55%, while SVM and Discriminant Analysis yielded moderate accuracy (64.26% and 65.11%). Naive Bayes underperformed, achieving only 40% accuracy, largely due to its assumption of feature independence. Conclusions: KNN and ensemble bagging models successfully predicted severe COVID-19 outcomes in cancer patients by capturing intricate relationships between factors such as ICU stays and ventilator support. Decision Trees also showed promise, but Naive Bayes was less reliable due to its simplified approach. The findings underscore the importance of non-linear models for predicting complex clinical outcomes. Despite robust evaluation through 10-fold cross-validation, the possibility of overfitting—particularly with KNN’s flawless accuracy—remains a concern. Additionally, the absence of external validation constrains the broader applicability of these findings to different patient populations.