Objective: To determine the level and influencing factors of informal caregiver burden in gynaecological oncology inpatients receiving chemotherapy. Methods: This cross-sectional study enrolled gynaecological oncology patients and their informal caregivers between May 2018 and November 2018 and measured the caregivers’ burden using the Caregiver Burden Inventory. The influencing factors were evaluated with univariate regression analysis and multivariate linear stepwise regression analysis. Results: A total of 138 patients and their informal caregivers completed the questionnaire. The mean ± SD total informal caregiver burden score was 53.18 ± 10.97. The highest mean ± SD score was recorded in the dimension of time-dependent burden (14.28 ± 2.74), followed by developmental burden (13.65 ± 2.15), physical burden (10.52 ± 2.07), social burden (7.61 ± 2.58) and emotional burden (7.12 ± 1.43). Multivariate analysis showed that the informal caregiver’s sex, relationship to the patient, daily duration of care, presence of chronic health problems and the duration of the patient’s disease were factors influencing the level of caregiver burden. Conclusions: The informal caregivers of gynaecological cancer patients hospitalized for chemotherapy experience a moderate level of burden. Nursing measures should be considered to reduce informal caregiver burden and improve the quality of lives of both patients and their caregivers.

Background: Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide. Although postsurgical cardiac interventions have improved survival rates, effective management during recovery remains challenging. Traditional informational support systems often provide generic guidance that does not account for individualized medical histories or psychosocial factors. Recently, artificial intelligence (AI)–based large language models (LLM) tools have emerged as promising interventions to deliver personalized health information to post-MI patients. Objective: We aim to explore the user experiences and perceptions of an AI-based LLM tool (iflyhealth) with integrated personal health record functionality in post-MI care, assess how patients and their family members engaged with the tool during recovery, identify the perceived benefits and challenges of using the technology, and to understand the factors promoting or hindering continued use. Methods: A purposive sample of 20 participants (12 users and 8 nonusers) who underwent MI surgery within the previous 6 months was recruited between July and August 2024. Data were collected through semistructured, face-to-face interviews conducted in a private setting, using an interview guide to address participants’ first impressions, usage patterns, and reasons for adoption or nonadoption of the iflyhealth app. The interviews were audio-recorded, transcribed verbatim, and analyzed using Colaizzi method. Results: Four key themes revealed included: (1) participants’ experiences varied based on digital literacy, prior exposure to health technologies, and individual recovery needs; (2) users appreciated the app’s enhanced accessibility to professional health information, personalized advice tailored to their clinical conditions, and the tool’s responsiveness to health status changes; (3) challenges such as difficulties with digital literacy, usability concerns, and data privacy issues were significant barriers; and (4) nonusers and those who discontinued use primarily cited complexity of the interface and perceived limited relevance of the advice as major deterrents. Conclusions: iflyhealth, an LLM AI app with a built-in personal health record functionality, shows significant potential in assisting post-MI patients. The main benefits reported by iflyhealth users include improved access to personalized health information and an enhanced ability to respond to changing health conditions. However, challenges such as digital literacy, usability, and privacy and security concerns persist. Overcoming the barriers may further enhance the use of the iflyhealth app, which can play an important role in patient-centered, personalized post-MI management.

Background: Intraperitoneal (i.p.) models that accurately mimic the feature behavior of human ovarian cancer are required to investigate the pathology and therapeutics of the disease. However, established i.p. models which are well-characterized and reliable are few. The purposes of this study are to establish a representative mice i.p. model of the disease and to analyze the consequent pathology. Methods: Fresh tumor cells fiom the ascites of patient were injected into female NOD/SCID mice intraperitoneally. Histology, Cytogenetic, immunohistochemistry,tumor markers of CA125,AFP, CA-199 and CEA were used to analyze the model. Results: The mice developed marked abdominal distention within 6 months after inoculated with tumor cells from a patient with epithelial ovarian carcinoma. The mice developed clinically evident intraperitoneal tumors and massive ascites containing numerous tumor cells in clumps. CA125 level in our model was high in both serum and ascites supernatants, while levels of other tumor markers, such as AFP, CA-199 and CEA, were normal. Cytogenetic analysis and immunohistochemical staining confirmed its characteristics resembling human epithelial ovarian tumor. Conclusions: The model described in this paper accurately mimics the features of ovarian tumor, which may be useful for evaluation of new therapeutics.

Objectives To explore influencing factors for postoperative urinary retention (POUR) in cervical cancer patients and construct and validate a POUR prediction model. Design A prospective cohort study. Setting A large tertiary hospital specialised in child and maternal healthcare in Southwest China. Participants 1101 patients undergoing cervical cancer surgery at our hospital were enrolled in the analytic cohort between 1 July 2022 and 31 July 2023. Another 205 patients were enrolled in the external validation cohort between 1 August 2023 and 31 October 2023. Demographics and disease-related information were collected to construct a risk prediction model by logistic regression. Univariate analyses and a multivariate logistic regression analysis were conducted to determine possible influencing factors. The discrimination and accuracy of the model were assessed by the area under the curve (AUC) and the concordance index, respectively. Results Univariate analysis identified duration of surgery, intraoperative bleeding, presence of diabetes, hypertension, ureteral adhesion, wound healing classification, preoperative radio/chemotherapy, category of Body Mass Index, history of urinary diseases, history of caesarean section, postoperative urinary infection and use of analgesia pumps as potential influencing factors (p<0.05). Diabetes, wound healing classification, presurgery radio/chemotherapy, postoperative urinary infection, use of analgesia pumps and pain numerical rating score were founded to be significant factors influencing the occurrence of POUR in cervical cancer patients (p<0.05). A POUR prediction model constructed using the factors demonstrated excellent prediction power, with an AUC of 0.897 (95% CI, 0.877 to 0.916, p<0.001). The sensitivity of the model at the optimal threshold was 0.591, with specificity being 0.747. The receiver operating characteristic curve indicated a good performance of the model. Conclusions Presence of diabetes mellitus, wound healing classification, presurgery radio/chemotherapy, postoperative urinary infection, use of analgesia pumps and pain numerical rating score are factors influencing occurrence of POUR in cervical cancer patients. The POUR prediction model developed demonstrates good predictive power and is promising for clinical utility.

BACKGROUND The growing number of survivors of cancer in China has created an increasing need for survivorship care as many survivors face ongoing physical, psychological, and social challenges after treatment. Mobile health (mHealth) services, which are delivered through mobile devices and apps, have emerged as potential tools to support self-management, facilitate access to care, and improve quality of life. However, evidence on the prevalence, use patterns, and determinants of mHealth adoption among Chinese survivors of cancer remains limited. OBJECTIVE This study aimed to examine the prevalence and patterns of mHealth use among adult survivors of cancer in China and identify sociodemographic and clinical factors associated with adoption. METHODS We conducted a multicenter cross-sectional survey study between February 13, 2024, and September 21, 2024, at 4 tertiary cancer care centers in China. Adult survivors of cancer were recruited using convenience sampling. Data were collected through structured face-to-face questionnaires assessing sociodemographic and clinical characteristics, mHealth use, perceived needs, concerns, and user experience. Missing data were limited and handled using complete-case analysis after the Little test supported consistency with missing completely at random. Descriptive analyses summarized participant characteristics and mHealth-related variables. Group differences were examined using chi-square tests and 2-tailed independent-sample t tests. Significant variables in univariate analyses were entered into a multivariable logistic regression model. RESULTS Of 1152 participants, 364 (31.6%) reported prior mHealth use. Use was concentrated in practical functions, particularly appointment booking (301/364, 82.7%), online consultation (244/364, 67%), and viewing examination or laboratory reports (215/364, 59.1%), with WeChat-based platforms being the most commonly used access channel (244/364, 67%). Participants reported high demand for clinical guidance (917/1152, 79.6%) and direct communication with health care professionals (901/1152, 78.2%), whereas common concerns included leakage of private information (694/1152, 60.2%) and inaccurate illness judgment (633/1152, 54.9%). In multivariable analysis, mHealth use was significantly associated with younger age, higher educational level, annual household income of at least ¥100,000 (US $14,527.90), widowed or divorced marital status, living alone, and treatment dissatisfaction; cancer type and time since diagnosis were not significant predictors. CONCLUSIONS mHealth use among adult survivors of cancer in China is established but uneven, with use concentrated in practical service functions rather than comprehensive survivorship support. Uptake was significantly associated with age, socioeconomic position, social circumstances, and treatment experience but not with clinical characteristics. Survivors reported a strong demand for clinically relevant and communication-oriented functions but also expressed substantial concerns about privacy, accuracy, reimbursement, and physician authenticity. Future survivorship mHealth services should prioritize clinical relevance, trust, integration with formal care, and equitable implementation to achieve broader and more meaningful use.

BACKGROUND Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide. Although postsurgical cardiac interventions have improved survival rates, effective management during recovery remains challenging. Traditional informational support systems often provide generic guidance that does not account for individualized medical histories or psychosocial factors. Recently, artificial intelligence (AI)–based large language models (LLM) tools have emerged as promising interventions to deliver personalized health information to post-MI patients. OBJECTIVE We aim to explore the user experiences and perceptions of an AI-based LLM tool (iflyhealth) with integrated personal health record functionality in post-MI care, assess how patients and their family members engaged with the tool during recovery, identify the perceived benefits and challenges of using the technology, and to understand the factors promoting or hindering continued use. METHODS A purposive sample of 20 participants (12 users and 8 nonusers) who underwent MI surgery within the previous 6 months was recruited between July and August 2024. Data were collected through semistructured, face-to-face interviews conducted in a private setting, using an interview guide to address participants’ first impressions, usage patterns, and reasons for adoption or nonadoption of the iflyhealth app. The interviews were audio-recorded, transcribed verbatim, and analyzed using Colaizzi method. RESULTS Four key themes revealed included: (1) participants’ experiences varied based on digital literacy, prior exposure to health technologies, and individual recovery needs; (2) users appreciated the app’s enhanced accessibility to professional health information, personalized advice tailored to their clinical conditions, and the tool’s responsiveness to health status changes; (3) challenges such as difficulties with digital literacy, usability concerns, and data privacy issues were significant barriers; and (4) nonusers and those who discontinued use primarily cited complexity of the interface and perceived limited relevance of the advice as major deterrents. CONCLUSIONS iflyhealth, an LLM AI app with a built-in personal health record functionality, shows significant potential in assisting post-MI patients. The main benefits reported by iflyhealth users include improved access to personalized health information and an enhanced ability to respond to changing health conditions. However, challenges such as digital literacy, usability, and privacy and security concerns persist. Overcoming the barriers may further enhance the use of the iflyhealth app, which can play an important role in patient-centered, personalized post-MI management. CLINICALTRIAL