Effects of different natural products in patients with non-alcoholic fatty liver disease-A network meta-analysis of randomized controlled trials.

Due to a scarcity of appropriate therapeutic approaches capable of ameliorating or eliminating non-alcoholic fatty liver disease (NAFLD), many researchers have come to focus on natural products based on traditional medicine that can be utilized to successfully treat NAFLD. In this study, we aimed to evaluate the effects exerted by seven natural products (curcumin, silymarin, resveratrol, artichoke leaf extract, berberine, catechins, and naringenin) on patients with NAFLD. For this purpose, PubMed, Embase, Cochrane Library, and Web of Science, were searched for randomized controlled trials (RCTs) exclusively. The selected studies were evaluated for methodological quality via the Cochrane bias risk assessment tool, and data analysis software was used to analyze the data accordingly. The RCTs from the earliest available date until September 2022 were collected. This process resulted in 37 RCTs with a total sample size of 2509 patients being included. The results of the network meta-analysis showed that artichoke leaf extract confers a relative advantage in reducing the aspartate aminotransferase (AST) levels (SUCRA: 99.1%), alanine aminotransferase (ALT) levels (SUCRA: 88.2%) and low-density lipoprotein cholesterol (LDL-C) levels (SUCRA: 88.9%). Naringenin conferred an advantage in reducing triglyceride (TG) levels (SUCRA: 97.3%), total cholesterol (TC) levels (SUCRA: 73.9%), and improving high-density lipoprotein cholesterol (HDL-C) levels (SUCRA: 74.9%). High-density catechins significantly reduced body mass index (BMI) levels (SUCRA: 98.5%) compared with the placebo. The Ranking Plot of the Network indicated that artichoke leaf extract and naringenin performed better than the other natural products in facilitating patient recovery. Therefore, we propose that artichoke leaf extract and naringenin may exert a better therapeutic effect on NAFLD. This study may help guide clinicians and lead to further detailed studies.

Utilization and performance of long-term care system for older people with disabilities and dementia in Zhejiang Province, China.

Introduction: To explore changes in performance, weaknesses, and utilization of the long-term care (LTC) system for older people with disabilities and dementia (OPWDD) in Zhejiang Province, China, thereby providing a reference for decision-making amid a progressively aging population. Methods: A performance evaluation model of the LTC system for OPWDD was constructed using three dimensions: input, process, and outcome. Performance indicators and trends were calculated based on data collected from statistical yearbooks, documents, and work reports of the Bureau of Statistics and other government departments in Zhejiang Province, China, published in 2015-2021. Results: Significant improvements were observed in most LTC performance indicators for OPWDD, such as input, process, and outcome, with notable enhancements in fairness, accessibility, and affordability of LTC services. By 2021, there were 6.20 nursing and rehabilitation beds in medical institutions and 3.77 general practitioners per 1,000 people aged 65 and above, up 144.14% and 13.73%, respectively, from 2015. The rate of health management for older people was 70.91%, representing a 10.33% increase from 2015. The actual reimbursement ratio of hospitalization expenses covered by basic medical insurance for older people rose 7.05%, from 72.76% in 2015 to 77.89% in 2021. Social security satisfaction rose 12.4%, from 71.3% in 2015 to 83.7% in 2021. Certain indicators, however, showed no significant improvement and tended to decline, such as the number of beds at older care institutions and caregivers per 1,000 people aged 65 and over. Discussion: It is imperative to further balance the allocation of care resources, using a people-centered and integrated LTC system. The proportion of rehabilitation and nursing beds for older people should be consistently increased to effectively alleviate the shortage of care beds. Furthermore, a talent incentive policy should be improved to train caregivers and provide whole-person and whole-life course care based on OPWDD needs.

Constructing a nomogram based on the distribution of thyroid nodules and suspicious lateral cervical lymph nodes in fine-needle aspiration biopsies to predict metastasis in papillary thyroid carcinoma.

Purpose: Elevated concentrations of thyroglobulin eluent is a risk factor for lateral cervical lymph node metastasis (LLNM) in patients with papillary thyroid cancer (PTC). We aimed to develop a practical nomogram based on the distribution of thyroid nodules and the presence of suspicious lateral cervical lymph nodes in fine-needle aspiration biopsies (LN-FNABs), including the cytopathology and the suspicious lateral cervical lymph node (LLN) thyroglobulin eluent (Tg), to predict the possibility of LLNM preoperatively in patients with PTC. Methods: The clinical data of PTC patients who were admitted to the Third Affiliated Hospital of Soochow University from January 2022 to May 2023 to undergo fine-needle aspiration biopsy (FNAB) were included in this study. A total of 208 patients in 2022 served as the training set (70%), and 89 patients in 2023 served as the validation set (30%). The clinical characteristics and LN-FNAB results were collected to determine the risk factors of LLNM. A preoperative nomogram was developed for predicting LLNM based on the results of the univariate and multivariate analyses. Internal calibration, external calibration, and decision curve analysis (DCA) were performed for these models. Results: The multivariate logistic regression analysis showed that the maximum thyroid nodule diameter (Odds Ratio (OR) 2.323, 95% CI 1.383 to 3.904; p = 0.001), Tg level (OR 1.007, 95% CI 1.005 to 1.009; p = 0.000), Tg divided by serum thyroglobulin, (Tg/sTg) [odds ratio (OR) 1.005, 95% CI 1.001 to 1.008; p = 0.009], and cytopathology (OR 9.738, 95% CI 3.678 to 25.783; p = 0.000) (all p < 0.05) had a significant impact on the LLNM of patients with suspicious LLNs. The nomogram showed a better predictive value in both the training cohort [area under the curve, (AUC) 0.937, 95% CI 0.895 to 0.966] and the validation cohort (AUC 0.957, 95% CI 0.892 to 0.989). The nomogram also showed excellent internal and external calibration in predicting LLNM. According to the DCA, the diagnostic performance of this model was dependent on the following variables: maximum thyroid nodule diameter, Tg level, Tg/sTg, and cytopathology. Conclusion: Based on the aforementioned risk factors, we believe that it is necessary to establish a personalized LLNM model for patients with PTC. Using this practical nomogram, which combines clinical and Tg risk factors, surgeons could accurately predict the possibility of LLNM preoperatively. The nomogram will also help surgeons to establish personalized treatment plans before surgery.