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Simple Summary: HER2-positive breast cancer occurs in 15–30% of cases and has a poor prognosis. Digital image analysis of HER2 is promising, but its implementation in real clinical practice remains unclear. This systematic review evaluates the effectiveness of digital image analysis algorithms for HER2 in breast cancer and their performance, with a focus on testing them in real-world clinical settings. The authors aim to assess the applicability of these algorithms in practical clinical scenarios. By analyzing 25 papers from the period 2013–2024 and emphasizing mostly deep learning approaches, the review underscores the importance of standardized evaluation criteria, study designs tailored for clinical applications, and clinical validation. While direct evidence of clinical application was not found, the findings aim to guide future research and the implementation of digital image analysis in breast cancer diagnosis within clinical settings, potentially impacting the research community by advancing algorithmic applications in real clinical practice. This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer. [ABSTRACT FROM AUTHOR]

As global populations continue to undergo demographic aging, the role of caregivers in providing essential support and assistance to older adults has become increasingly prominent. This demographic shift has led to a growing reliance on informal caregivers, often family members, who take on the responsibilities of caring for older adults. This not only affects immediate family dynamics but also holds broader implications for societal sustainability. The primary objective of this bibliometric analysis is to comprehensively examine the worldwide research output related to the quality of life and caregiver burden among individuals providing care to older adults. By understanding the worldwide research output related to caregivers and their quality of life and burden, we can assess the long-term sustainability of caregiving practices. We retrieved studies with titles containing the terms "caregivers", "burden", "quality of life", and "aged" from the Web of Science (WOS) database. The collected publications were then subjected to analysis using the "bibliometric" package in the R programming environment. A total of 44 publications from 2006–2023 were included in the analysis. Spain emerged as the leading contributor in terms of the number of publications, accounting for 21.9%, followed by the USA at 16.5% and China at 13.6%. The most prolific institution was Kaohsiung Medical University, Taiwan, responsible for 25% of the publications. Among the authors, Cura-Gonzalez I.D. had the highest number of articles, contributing four publications, or 9.1% of the total output. An analysis of co-occurring keywords revealed that the predominant focus of the research revolved around caregiver burden, quality of life, health, care, stress, and impact, reflecting enduring areas of interest within this field. This bibliometric analysis may serve as a tool to provide insights into the current state of research on caregiver burden and quality of life among those caring for older adults. The results of this study can contribute to the assessment of research strategies and the encouragement of global cooperation in the field of care for older adults. By considering the multidimensional nature of caregiving challenges and promoting international cooperation, strides can be made towards sustainable caregiving practices that ensure the wellbeing of both caregivers and the aging population, thus safeguarding the sustainability of healthcare systems worldwide. [ABSTRACT FROM AUTHOR]

The aim of this study was to describe temporal trends in socio-demographic and lifestyle characteristics among delivering women in two Northern provinces of Russia from 1973 to 2017. Totally 161,730 births were registered in three birth registries. Changes in the distribution of maternal age, education, marital status, smoking during pregnancy were studied using Pearson's chi-squared tests and one-way ANOVA. The logistic regression models were used to assess factors, contributing to the variations in the prevalence of maternal smoking. The mean age of primiparous mothers increased from 22.1 years in 1973–1980 to 25.4 years in 2012–2017 (p < 0.001). The proportion of primiparous mothers with higher education increased from 26.2% in 2006 to 38.3% in 2017 (p < 0.001). The proportion of cohabiting primiparous women increased from 5.0% to 15.2% over the study period (p < 0.001). The proportion of mothers smoking during pregnancy decreased from 18.9% in 2006–2011 to 14.8% in 2012–2017 (p < 0.001). Downward in the prevalence of smoking was revealed in 2012–2017 compared to 2006–2011 (OR = 137.76; 95%CI:71.62–264.96, OR = 183.74; 95%CI:95.52–353.41, respectively). Over the past decades, women postpone childbearing until receiving higher education, continue living in cohabitation during pregnancy and smoke less. [ABSTRACT FROM AUTHOR]