Comprehensive Cardiovascular

Abstract Enterovirus D68 (EV-D68) is a reemerging respiratory pathogen that is increasingly associated with severe respiratory disease and occasional neurological complications, yet its circulation in Portugal remains poorly documented. In the present study, we conducted molecular surveillance in southern Portugal (Algarve region) from October 2024 to February 2025. Screening of 150 respiratory samples from patients (115 from children and 35 from adults), all positive for rhinovirus/enterovirus, revealed eight EV-D68–positive cases, corresponding to a detection rate of 5.44%, spanning a broad age range from infancy to the elderly. Clinical manifestations ranged from mild respiratory illness to severe conditions, including pneumonia and acute respiratory distress syndrome. In addition, neurological involvement such as complex febrile seizures and encephalomyelitis was observed. Some cases involved coinfections, while others presented with EV-D68 as the sole detected pathogen. Genomic characterization from one isolate revealed a VP1 fragment belonging to clade B3, and a complete genome from another isolate clustered within clade A2. Phylogenetic analysis confirmed clustering with contemporary European isolates. These findings highlight the circulation of multiple EV-D68 clades in Portugal, underscore the clinical diversity of infections, and reinforce the importance of genomic surveillance to monitor viral evolution and potential public health impact.

This paper introduces the theoretical foundations of data-driven models for cadre appointment decision-making and elaborates on the policy and legal frameworks that govern their application. It explains key components such as the integration of multi-source heterogeneous data and the construction of a comprehensive cadre profiling system. The study also addresses precise demand analysis and matching, performance feedback during the tenure period, and related processes. Furthermore, it reviews current progress and existing limitations, and discusses future development trends in data-driven cadre management and decision-making.

Study identifies GDF15 as a biomarker-accurately distinguishing PVOD from other PAH forms and predicting outcomes across PVOD, IPAH, and HPAH-advancing earlier diagnosis and personalized treatment.

Breast cancer (BC) is the most prevalent malignancy in women worldwide. Despite most cases being diagnosed in the early stages, patients typically require a multimodal treatment approach. This typically involves a combination of surgery, radiotherapy, systemic treatments (including chemotherapy or immunotherapy), targeted therapy, and endocrine therapy, depending on the disease subtype and the risk of recurrence. Moreover, patients with BC and germline mutations in the breast cancer genes 1 or 2 (BRCA1/BRCA2), (gBRCAm), who are typically young women, often require more aggressive therapeutic interventions. These mutations present unique characteristics that necessitate a distinct treatment approach, potentially influencing the side effect profiles of patients with BC. Regardless of the clear benefit observed with these treatments in terms of reduced recurrence and mortality rates, long-term, treatment-related adverse events occur that negatively affect the health-related quality of life (HRQoL) of BC survivors. Thus, long-term adverse events need to be factored into the treatment decision algorithm of patients with early BC (eBC). Physical, functional, emotional, and psychosocial adverse events can occur and represent a significant concern and a challenge for clinicians, patients, and their families. This review article provides an overview of the various long-term adverse events that patients with eBC may experience, including their associated risk factors, as well as management and prevention strategies. We also explore the evidence of the long-term impact of treatment on the HRQoL of patients with gBRCAm. By providing a comprehensive overview of current evidence and recommendations regarding patients' HRQoL, we aim to equip clinicians with scientific and clinical knowledge and provide guidance to optimize care and improve long-term outcomes.

Abstract Introduction Management of OSA by Remote-Patient-Monitoring (RPM) methods has been gaining momentum, mainly utilizing the data obtained from PAP devices providing a metric represent an Apnea-Hypopnea-Index (pAHI). The REST-Tracker, a ring-based RPM platform, provides FDA-cleared methodology by SleepImage rendering AHI metrics (‘ahi_3%’ and ‘ahi_4%’) vai cardiopulmonary-coupling analysis. This study evaluates the REST Tracker AHI metrics to the ResMed PAP pAHI metric from patients utilizing both systems. Methods Data from 70 patients on PAP with at least 30 nights of simultaneous data from both REST-Tracker and PAP device. Nights with < 20% difference in usage time between devices were included, resulting in 7,835 nights of data for statistical analysis. Metrics included ‘ahi_3%’ and ‘ahi_4%’ (REST Tracker / SleepImage) and ‘pap_ahi’ (ResMed-AirView). Statistical analyses, including T-test and Bland-Altman plots, were used to assess agreement between the methods. Results The average AHI values and standard deviations were as follows: ‘ahi_3%’ had a mean of 10.67 (±8.45), ‘ahi_4%’ had a mean of 5.38 (±5.69), and ‘pap_ahi’ had a mean of 4.21 (±6.73). Average usage times were 7.26 (±1.43) hours for the REST Tracker and 7.60 (±1.40) hours for the PAP device, demonstrating similar nightly usage. T-tests revealed significant differences between the REST Tracker metrics and PAP AHI values, with t-statistics of 52.99 (p < 0.001) for ‘ahi_3%’ vs ‘pap_ahi’ and 11.72 (p < 0.001) for ‘ahi_4%’ vs ‘pap_ahi’. Bland-Altman analysis showed mean differences of 6.47 and 1.17 for ‘ahi_3%’ and ‘ahi_4%’ vs ‘pap_ahi’, respectively, with wide 95% limits of agreement, indicating variability and non-equivalence. Conclusion Our findings demonstrate that PAP AHI measurements are not equivalent to REST Tracker / SleepImage metrics, as demonstrated by Bland-Altman analysis and significant t-test results. The ResMed AHI was less sensitive than the REST Tracker’s ring-based (SleepImage) platform. Additional limitations of PAP-based RPM approaches include no data when the PAP device is not in use, further limiting the value of this approach. As an alternative to the current trend of utilizing PAP-based metrics for RPM, implementing FDA-cleared wearables with better sensitivity of OSA characterization should be embraced as these approaches will raise the standard of care. Support (if any)