Background: The World Health Organization published the 2019 Integrated Care for Older People (ICOPE) framework to guide, assess, and promote the intrinsic capacity (IC) of older adults, referring to their physical and mental health. This study aims to investigate the relationship between IC and frailty among older adults. Methods: This cross-sectional study was conducted in a medical center in Taiwan in 2021. Two hundred ten patients over 65 admitted to the geriatric ward were invited to participate. The questionnaire included an IC measure, Fried Frailty Scale, and demographic items. The IC measure was ascertained using the six domains of ICOPE (cognition, mobility, nutrition, visual, hearing, and depressive symptoms). The Fried Frailty Scale was used to categorize participants as robust (Fried Frailty Scale=0), prefrail (Fried Frailty Scale=1-2), or frail (Fried Frailty Scale ≥3). Multinomial logistic regression was used to analyze the association between individual ICOPE domains and frailty stages, while adjusting for confounders. Results: Among the participants, 39.0% were prefrail, and 28.6% were frail. Limited mobility and depressive symptoms were significantly associated with prefrail (adjusted odds ratio [aOR]=4.44, 95% confidence interval [CI] 1.82–10.82; aOR=8.41, 95% CI 1.75–40.37) and frail (aOR=11.57, 95% CI 3.63–36.93; aOR=13.77, 95% CI 2.62–72.49) individuals, respectively. Malnutrition (aOR=4.01, 95% CI 1.18–13.62) and hearing loss (aOR=4.37, 95% CI 1.09–19.66) were significantly associated with frail older adults. Conclusion Limited mobility and depressive symptoms occurring at the prefrail stage could be used as assessment items for early detection of prefrail.

ObjectiveTo address the key challenges in breast electrical impedance tomography (EIT) reconstruction including the severe ill‑posedness of the inverse problem, limited measurement information from the electrodes, low contrast of small lesions, as well as the common drawbacks of traditional reconstruction methods such as boundary blurring, low spatial resolution, and weak small-target detection capability, and to overcome the limitations of existing deep learning approaches namely low training efficiency, insufficient physical consistency, and substantial information loss during the low‑to‑high dimensional mapping process, this study proposes a multi‑scale attention and dynamic sparse mask fusion network (MADS‑Net). The proposed method aims to enhance high‑resolution reconstruction accuracy, small-lesion detectability, noise robustness, and computational efficiency in breast‑screening-oriented EIT, thereby providing a feasible solution for the application of EIT in breast functional imaging. MethodsThe proposed network adopts a strategy of “locate first, reconstruct later”. It realizes adaptive allocation of computational resources through a dynamic mask module, alleviates information loss in the low-to-high dimensional mapping process via a multi-scale attention mechanism, and embeds physical constraints to improve the physical rationality of reconstruction results. The performance of the proposed method was validated through systematic numerical simulations, agar phantom experiments, and porcine tissue experiments. Comparisons were made with mainstream methods including the Gauss-Newton algorithm, convolutional neural networks, U-Net, and Deep D-Bar. The image correlation coefficient (ICC), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) were adopted as quantitative evaluation metrics. Furthermore, lesion‑level evaluation metrics were introduced to assess the capability of different methods in reconstructing abnormal target boundaries and small‑sized target regions. ResultsExperimental results demonstrate that MADS-Net outperforms all comparison methods across all test scenarios. In numerical simulations, it achieves higher accuracy in target morphology and spatial location recovery as well as better background suppression. In agar phantom experiments, its detection and imaging reliability for small targets is significantly superior to the comparison methods. In porcine tissue experiments, the model maintains good generalization performance and can adapt to complex biological tissue imaging scenarios. Quantitative results show that MADS-Net achieves an average ICC of 0.917 6±0.048 8, an RMSE as low as0.060 6±0.031 6, and a PSNR of (23.893 9±2.685 3) dB. It successfully detects tiny targets as small as 5 mm within a 100 mm diameter sensing field, reduces the total training duration by 23.5% compared with the baseline method, and maintains stable reconstruction performance even under strong noise interference. ConclusionThe results demonstrate that MADS-Net achieves favorable EIT reconstruction performance in numerical simulations, agar phantom experiments, and porcine tissue experiments, even under common practical imaging challenges including low signal-to-noise ratio and blurred tissue boundaries. This study provides a methodological reference for further research on EIT in breast-screening-related scenarios and other medical functional imaging tasks, and also offers a low-cost, radiation-free alternative for breast lesion screening that aligns with the clinical demand for non-invasive diagnostic tools. It should be noted that real clinical breast data have not yet been used for validation in this study, and the clinical applicability of the proposed method requires further investigation. Future work will prioritize collecting multi-center real clinical breast datasets, and further optimize the model’s generalization ability to adapt to individual differences in breast tissue composition to promote its clinical application.

Under the background of carbon peaking and carbon neutrality goals, the Ministry of Ecology and Environment, together with 15 national ministries and commissions, has formulated the Implementation Plan on Establishing a Carbon Footprint Management System, and it is urgent for traditional Chinese medicine(TCM) pharmaceutical enterprises to carry out research on carbon footprint accounting methods of related products. Based on the life cycle assessment(LCA) theory, taking mulberry leaf extract produced by a certain enterprise as an example, this study analyzed the carbon footprint of TCM extracts during the life cycle. The results show that for every 1 kg of product produced, the carbon emissions from the stages of raw material acquisition, transportation, and extract production are-20.569, 1.205, and 173.577 kgCO_2eq(CO_2 equivalent), respectively. The carbon footprint of the product is 154.213 kgCO_2eq·kg~(-1). In addition, the carbon emission is the highest in the production stage, in which the consumption of ethanol solvents makes the greatest contribution to the carbon footprint, accounting for 25.71%, more than one-fourth of the total carbon footprint. The second contribution was from the treatment process of TCM residues, accounting for 19.67%, closely followed by wastewater treatment(17.71%), the consumption of hot steam(17.43%), and drinking water(16.90%). The consumption of electric power and packaging materials has a smaller carbon emission of 2.58%. In particular, the carbon emission caused by the consumption of packaging materials is only 0.04%, which is negligible. The results of the study are expected to provide a reference for TCM enterprises to carry out research on the carbon footprint of products, offer ideas for collaborative innovation in reducing pollution and carbon emissions throughout the entire industry chain of TCM, and develop new quality productivity of modern TCM industry based on green and low-carbon manufacturing.
Morus/chemistry* ; Plant Leaves/chemistry* ; Carbon Footprint ; Drugs, Chinese Herbal/chemistry* ; Plant Extracts/analysis* ; Medicine, Chinese Traditional

The seed kernel of Momordica cochinchinensis, i.e., Momordicae Semen, is used for medicinal purposes, but to date, no research has been reported on its chemical constituents. In this study, the chemical constituents of Momordicae Semen were investigated for the first time using silica gel column chromatography, semi-preparative HPLC, HR-MS, and NMR. As a result, eight compounds were isolated and identified as: p-hydroxybenzoic acid-7-O-trehaloside(mubeside A, 1), 2,6-dimethoxyphenol-O-β-D-apiosyl-(1→2)-β-D-glucoside(mubeside B, 2), 1-O-p-methoxybenzoyl-1,4-benzenediol-4-O-β-D-apiosyl-(1→2)-β-D-glucoside(mubeside C, 3), 1-O-p-hydroxybenzoyl-1,4-benzenediol-4-O-β-D-apiosyl-(1→2)-β-D-glucoside(mubeside D, 4), gypsogenin-3-O-β-D-galactosyl-(1→2)-β-D-glucuronoside(5), quillaic acid-3-O-β-D-galactosyl-(1→2)-β-D-glucuronoside(6), violanthin(7), and kaempferitrin(8). Compounds 1-4 are new compounds, while compounds 5-8 were isolated from Momordicae Semen for the first time.
Glycosides/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification* ; Molecular Structure ; Magnetic Resonance Spectroscopy ; Chromatography, High Pressure Liquid

Immunotherapy has been widely used in cancer treatment in recent years and functions by stimulating the immune system to kill tumor cells. Radiation therapy (RT) uses radiation to induce DNA damage and kill tumor cells. However, this activates the body's immune system, promoting the release of tumor-related antigens from inactive dendritic cells, which stimulates the recurrence and metastasis of tumors in immune system tissues. The combination of RT and immunotherapy has been increasingly evaluated in recent years, with studies confirming the synergistic effect of the two antitumor therapies. Particularly, the combination of RT by dose adjustment with different immunotherapies has positive implications on antitumor immunity as well as disease prognosis compared with respective monotherapies. This review summarizes the current research status, progress, and prospects of RT combined with immunotherapy in cancer treatment. It additionally discusses the prevalent concerns regarding the dose, time window, and toxicity of this combination therapy.
Humans ; Neoplasms/radiotherapy* ; Immunotherapy/methods* ; Combined Modality Therapy ; Radiotherapy/methods*

Objective:This study aimed to evaluate the prognostic factors of clinical and histopathological parameters,including age,gender,tumor stage,tumor grade,tumor differentiation,lymph node metastasis(LNM),tumor frequency,and tumor count,in patients undergoing radical cystectomy(RC)combined with Mainz Pouch Ⅱ bladder reconstruction.Methods:A total of 237 bladder cancer patients(198 male and 39 female)who underwent RC combined with Mainz Pouch Ⅱ bladder reconstruction without chemotherapy or radiotherapy,from January 2004 to January 2023,were included in this study.Kaplan-Meier and Cox regression analyses were performed to assess the impact of age,tumor grade,tumor stage,tumor differentiation,LNM,tumor frequency,and tumor count on 5-year overall survival(OS)and 5-year cancer-specific survival(CSS).Results:The mean age at diagnosis was 59.8 years,with 198 male and 39 female patients.The mean follow-up duration was 47.8 months.In univariate analysis,patients younger than 65 years had significantly higher 5-year OS and 5-year CSS compared to those aged≥65 years.Patients with urothelial carcinoma showed better 5-year OS than those with non-urothelial carcinoma.Additionally,tumor stage,tumor grade,and LNM were negatively associated with 5-year OS and 5-year CSS.On multivariate analysis,only tumor grade and LNM remained statistically significant(P<0.05).Conclusions:Tumor grade and LNM were identified as independent prognostic risk factors for 5-year OS and 5-year CSS following RC combined with Mainz PouchⅡ bladder reconstruction.Moreover,the application of RC combined with Mainz Pouch Ⅱ bladder reconstruction should consider the patient's preferences and physical condition.

Objective To observe the clinical effect of abdominal full-thickness skin graft combined with adipose stem cells in repairing soft tissue defects after resection of facial benign tumors.Methods A total of 180 patients with soft tissue defects after resection of facial benign tumor in our hospital from January 2019 to June 2022 were selected,the study was designed by a double-blind method,and patients were divided into the observation group and the control group by a random number table method,with 90 cases in each group.Patients in the control group were repaired by abdominal full-thickness skin graft treatment,while these in the observation group were repaired by a combination of abdominal full-thickness skin graft and adipose stem cells.The scar status scores in the surgical area,clinical efficacy,incidence of complications and satisfaction of patients in the two groups were compared.Results Compared with preoperative results,the scores of color and thickness of scars,vascular distribution,and softness in face 6 months after surgery in both groups decreased,with statistically significant differences(P<0.05).Six months after surgery,the observation group had significantly lower scores on scar color and thickness,vascular distribution,and softness compared to the control group(P<0.05);there was no difference in the effective rate of the forehead,nose,eyelids,lips,or cheeks between the observation group and the control group(P>0.05);while the observation group had higher overall effective rate than the control group(P<0.05).There was no statistically significant difference in the incidence of complica-tions between the two groups(P>0.05).There was statistically significant difference in the distribution of the satisfaction of patients between the two groups(P<0.05),and the observation group showed significantly higher satisfaction of patients than the control group(P<0.05).Conclusion Abdominal full-thickness skin graft combined with adipose stem cells can improve facial scars in repairing soft tissue defects after resection of facial benign tumors,and enhance the repair effects and satisfaction of patients,with high safety.

Objective To introduce how to import and analyze data using the Research Electronic Data Capture(REDCap)system,taking a multi-center randomized controlled clinical research of total knee arthroplasty as an example.Methods Various tools within the REDCap system,including data import tools,data export functions,reports and statistics,project dashboards,and coding manuals,were used to systematically process and analyze the multi-center randomized controlled clinical trial data for total knee arthroplasty.Initially,electronically collected clinical data were adjusted and standardized,then uploaded in bulk to the system using the REDCap data import tool.Subsequently,the data were organized through REDCap's data export feature,and basic descriptive statistical analysis was performed using its reporting and statistical functions to ensure data quality and completeness.Results An electronic data collection and management platform for clinical research on knee osteoarthritis wase successfully created by the REDCap system.The platform enabled real-time data collection from multiple centers,and ensured data accuracy and consistency through built-in data management and quality control mechanisms.With the statistical analysis features of REDCap,the research team could monitor the progress of data in real time,conduct effective quality assessments,and perform dynamic analysis for further in-depth statistical evaluations.Conclusion The REDCap system can be used not only to build a new clinical research project,but also to import and analyze data that has been previously digitized of ongoing clinical researches into the system,which improved the scientificity of data management and research efficiency.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.