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Strabismus, misalignment of the eyes arising from central nervous system dysregulation and extraocular muscles imbalance, commonly manifests in childhood, leading to amblyopia, binocular vision dysfunction, torticollis and other developmental and psychological disorders. This exerts a negative impact on individuals, families and society. Timely diagnosis and intervention are crucial to prevent permanent damage to vision and stereopsis. Presently, strabismus diagnosis is reliant on the ophthalmologists′ evaluations which results in a lack of efficiency and coverage. However, routine school screening proves inadequate in assessing strabismus degree with low accuracy. Therefore, how to improve the efficiency of strabismus screening is an issue of great importance. This paper delves into the present landscape of strabismus diagnosis and treatment, considering both local and global research advancements. It focuses on the evolution of artificial intelligence technology, illuminating the utilization of artificial intelligence models and algorithms in strabismus. By pinpointing and exploring their strengths and limitations, it offers valuable insights, paving the way for future investigations into artificial intelligence-assisted strabismus diagnosis and treatment.

Abstract Background Hospice and Palliative Care (HPC) is in high demand in China; however, the country is facing the shortage of qualified HPC nurses. A well-suited competence framework is needed to promote HPC human resource development. Nevertheless, existing unstandardized single-structured frameworks may not be sufficient to meet this need. This study aimed at constructing a comprehensive multi-structured HPC competence framework for nurses. Methods This study employed a mixed-method approach, including a systematic review and qualitative interview for HPC competence profile extraction, a two-round Delphi survey to determine the competences for the framework, and a cross-sectional study for framework structure exploration. The competence profiles were extracted from publications from academic databases and interviews recruiting nurses working in the HPC field. The research team synthesized profiles and transferred them to competences utilizing existing competence dictionaries. These synthesized competences were then subjected to Delphi expert panels to determine the framework elements. The study analyzed theoretical structure of the framework through exploratory factor analysis (EFA) based on a cross-sectional study receiving 491 valid questionnaires. Results The systematic review involved 30 publications from 10 countries between 1995 and 2021, while 13 nurses from three hospitals were interviewed. In total, 87 and 48 competence profiles were respectively extracted from systematic review and interview and later synthesized into 32 competences. After the Delphi survey, 25 competences were incorporated into the HPC competence framework for nurses. The EFA found a two-factor structure, with factor 1 comprising 18 competences namely Basic Competences; factor 2 concluding 7 competences namely Developmental Competences. Conclusions The two-factor HPC competence framework provided valuable insights into the need and directions of Chinese HPC nurses’ development.

Abstract Health behaviors before, during and after pregnancy can have lasting effects on maternal and infant health outcomes. Although digital health interventions (DHIs) have potential as a pertinent avenue to deliver mechanisms for a healthy behavior change, its success is reliant on addressing the user needs. Accordingly, the current study aimed to understand DHI needs and expectations of women before, during and after pregnancy to inform and optimize future DHI developments. Forty-four women (13 pre-, 16 during and 15 postpregnancy; age range = 21–40 years) completed a 60-minute, semistructured, qualitative interview exploring participant’s experience in their current phase, experience with digital health tools, and their needs and expectations of DHIs. Interviews were audio-recorded, transcribed verbatim and thematically analyzed. From the interviews, two core concepts emerged—personalization and localization of DHI. Between both concepts, five themes and nine subthemes were identified. Themes and subthemes within personalization cover ideas of two-way interactivity, journey organization based on phases and circumstances, and privacy trade-off. Themes and subthemes within localization cover ideas of access to local health-related resources and information, and connecting to local communities through anecdotal stories. Here we report, through understanding user needs and expectations, the key elements for the development and optimization of a successful DHI for women before, during and after pregnancy. To potentially empower downstream DHI implementation and adoption, these insights can serve as a foundation in the initial innovation process for DHI developers and be further built upon through a continued co-design process.

Abstract Background We examined the risk of severe life-threatening morbidity in pregnant patients with Covid-19 infection. Methods We conducted a population-based study of 162,576 pregnancies between March 2020 and March 2022 in Quebec, Canada. The main exposure was Covid-19 infection, including the severity, period of infection (antepartum, peripartum), and circulating variant (wildtype, alpha, delta, omicron). The outcome was severe maternal morbidity during pregnancy up to 42 days postpartum. We estimated risk ratios (RR) and 95% confidence intervals (CI) for the association between Covid-19 infection and severe maternal morbidity using adjusted log-binomial regression models. Results Covid-19 infection was associated with twice the risk of severe maternal morbidity compared with no infection (RR 2.02, 95% CI 1.76–2.31). Risks were elevated for acute renal failure (RR 3.01, 95% CI 1.79–5.06), embolism, shock, sepsis, and disseminated intravascular coagulation (RR 1.35, 95% CI 0.95–1.93), and severe hemorrhage (RR 1.49, 95% CI 1.09–2.04). Severe antepartum (RR 13.60, 95% CI 10.72–17.26) and peripartum infections (RR 20.93, 95% CI 17.11–25.60) were strongly associated with severe maternal morbidity. Mild antepartum infections also increased the risk, but to a lesser magnitude (RR 3.43, 95% CI 2.42–4.86). Risk of severe maternal morbidity was around 3 times greater during circulation of wildtype and the alpha and delta variants, but only 1.2 times greater during omicron. Conclusions Covid-19 infection during pregnancy increases risk of life-threatening maternal morbidity, including renal, embolic, and hemorrhagic complications. Severe Covid-19 infection with any variant in the antepartum or peripartum periods all increase the risk of severe maternal morbidity.

AbstractObjective The present study aims to investigate the clinical and histopathological features of peritoneal endometriosis (PEM) and deep infiltrating endometriosis (DIE).Methods A total of 100 patients with PEM and DIE admitted to Dalian Women and Children’s Hospital/Dalian Women and Children’s Medical Center between October 2018 and December 2021 were selected as the study subjects. One hundred and thirty-one PEM specimens and 37 DIE were collected, 22 cases of these patients’ eutopic endometrium were used as control (15 in PEM, seven in DIE). The present study mainly analysed the pelvic distribution, the histopathological and immunohistochemical features and peritoneal invasion of PEM and DIE.Results The main distribution of PEM and DIE was located in the posterior pelvic cavity (p