Journal of Internal Medicine最新文献

Healthcare-associated infections (HAIs) are common adverse events, and surveillance is considered a core component of effective HAI reduction programmes. Recently, efforts have focused on automating the traditional manual surveillance process by utilizing data from electronic health record (EHR) systems. Using EHR data for automated surveillance, algorithms have been developed to identify patients with (ventilator-associated) pneumonia and (catheter-related) bloodstream, surgical site, (catheter-associated) urinary tract and Clostridioides difficile infections (sensitivity 54.2%–100%, specificity 63.5%–100%). Mostly methods based on natural language processing have been applied to extract information from unstructured clinical information. Further developments in artificial intelligence (AI), such as large language models, are expected to support and improve different aspects within the surveillance process; for example, more precise identification of patients with HAI. However, AI-based methods have been applied less frequently in automated surveillance and more frequently for (early) prediction, particularly for sepsis. Despite heterogeneity in settings, populations, definitions and model designs, AI-based models have shown promising results, with moderate to very good performance (accuracy 61%–99%) and predicted sepsis within 0–40 h before onset. AI-based prediction models detecting patients at risk of developing different HAIs should be explored further. The continuous evolution of AI and automation will transform HAI surveillance and prediction, offering more objective and timely infection rates and predictions. The implementation of (AI-supported) automated surveillance and prediction systems for HAI in daily practice remains scarce. Successful development and implementation of these systems demand meeting requirements related to technical capabilities, governance, practical and regulatory considerations and quality monitoring.

Over the past decade, numerous reports have highlighted intergenerational and even transgenerational epigenetic effects resulting from parental exposure to diets, toxins, and stress. In many cases, these parentally induced phenotypes do not seem to confer an obvious benefit, making it challenging to understand the evolutionary drivers behind them. In this perspective, we discuss recent observations in humans and rodents indicating that a parental infection or vaccination can enhance the offspring's ability to cope with infections. Such parental priming of their offspring's immune system and cellular defense would provide immediate protection to the newborn, offering a clear evolutionary advantage. Here, focusing mainly on paternal effects, we propose that a parentally induced inflammatory memory in the offspring could be the underlying mechanism for many of the reported inter- and transgenerational effects. Sperm-borne RNA could be a triggering signal to initiate inflammatory pathways in early embryogenesis. This gene-regulatory state would then be maintained via epigenetic mechanisms throughout each mitosis and last for the individual's lifetime. The accumulating understanding that diet, stress, toxins, and infections affect offspring health raises important questions about public health policies. There is an urgent need to understand what consequences different exposures during sensitive time windows have on future generations.

Primary care is the ideal setting for early detection of mild cognitive impairment (MCI) and Alzheimer's disease and related dementias (ADRD), as it serves as the primary point of care for most older adults. With the growing aging population, reliance on specialists for detection and diagnosis is unsustainable, highlighting the need for primary care-led assessment. Recent research findings on successful brain health prevention strategies, AD diagnostic tools, and anti-amyloid treatments empower primary care to play a central role in early detection and intervention. Primary care-focused resources are being developed, including tools for cognitive assessments and materials designed to educate patients about brain health and initiate discussions on lifestyle modifications, thereby making early detection more feasible and efficient. Identifying risk factors early enables providers to implement interventions that can slow cognitive decline and improve outcomes for patients and caregivers. If left undetected and unmanaged, MCI and ADRD can lead to worse outcomes, including increased falls, hospitalizations, financial vulnerability, and caregiver stress. Early detection enables the identification of reversible causes of cognitive impairment, supports the management of comorbidities worsened by cognitive decline, mitigates safety risks, and can preserve quality of life. Importantly, primary care is essential for addressing ADRD-related health disparities that disproportionately affect racial minorities, rural populations, and those of lower socioeconomic status. With a focus on the United States healthcare system, this perspective addresses how implementing early detection practices into primary care can improve outcomes for patients and caregivers, reduce societal burdens, and promote health equity in ADRD care.