To investigate the nutritional status of patients before and after hematopoietic stem cell transplantation (HSCT), and explore optimal methods for assessing nutritional status in patients with hematological diseases. This cohort study enrolled 170 patients who were diagnosed with hematological diseases and underwent allogeneic HSCT in the Department of Hematology, Peking University People's Hospital between May 2011 and April 2013. We used fixed-point continuous sampling and four nutritional screening tools, Nutritional Risk Screening 2002 (NRS-2002), Mini Nutritional Assessment (MNA), Subjective Global Assessment (SGA) and Malnutrition Universal Screening Tools (MUST), in combination with body measurements, to extensively screen and evaluate nutritional risks and status in patients receiving HSCT before entering and after leaving laminar air flow rooms. After HSCT, patients had significant reduction in weight, hip circumference, waist-hip ratio, calf circumference, mid-upper arm circumference, and suprailiac skinfold thickness compared with pre-HSCT measurements. Before HSCT, NRS-2002 identified that 21.2% of patients were at nutritional risks, compared with 100% after HSCT. MUST indicated that before HSCT, 11.77% of patients were at high nutritional risk, compared with 59.63% after HSCT. MNA assessed that 0.06% of patients were malnourished before HSCT, compared with 19.27% after HSCT. SGA identified that before HSCT, 1.76% of patients had mild to severe malnutrition, which increased to 83.3% after HSCT. There is a significant increase in the nutritional risk and malnutrition in patients who received HSCT. Before HSCT, some patients already had nutritional risk or nutritional deficiencies, and prompt and close nutritional screening or assessment should be performed. The nutritional status of patients after HSCT was generally deteriorated compared with that before transplantation. Body measurements should be taken more frequently during the subsequent treatment window in the laminar air flow rooms. After HSCT, it is recommended to combine MNA and SGA to fully evaluate the nutritional status, and thus provide timely and reasonable nutritional support.

Gestational diabetes mellitus (GDM) is a high-risk pregnancy complication that is associated with metabolic disorder phenotypes, such as abnormal blood glucose and obesity. The link between microbiota and diet management contributes to metabolic homeostasis in GDM. Therefore, it is crucial to understand the structure of the gut microbiota in GDM and to explore the effect of dietary management on the microbiota structure. In this study, we analyzed the composition of the gut microbiota between 27 GDM and 30 healthy subjects at two time points using Illumina HiSeq 2500 platform. The taxonomy analyses suggested that the overall bacteria clustered by diabetes status, rather than diet intervention. Of particular interest, the phylum Acidobacteria in GDM was significantly increased, and positively correlated with blood glucose levels. Moreover, Partial least-squares discriminant analysis (PLS-DA) revealed that certain genera in the phyla Firmicutes, Bacteroidetes, Proteobacteria, and Lentisphaerae characterized the GDM gut microbiota. Correlation analysis indicated that blood glucose levels and BMI index were correlated with the relative abundance of SCFAS-producing genera. Through the comparison between the GDM and healthy samples with or without diet intervention, we discovered that the role of short-term diet management in GDM processes is associated with the change in the Firmicutes/Bacteroidetes ratio and some specific taxa, rather than an alternative gut microbial pattern. Our study have important implications for understanding the beneficial effects of diet intervention on the specific gut microbiota and thus possibly their metabolism in pregnant women with GDM.

Background: Large language model (LLM) artificial intelligence (AI) tools have the potential to streamline health care administration by enhancing efficiency in document drafting, resource allocation, and communication tasks. Despite this potential, the adoption of such tools among hospital administrators remains understudied, particularly at the individual level. Objective: This study aims to explore factors influencing the adoption and use of LLM AI tools among hospital administrators in China, focusing on enablers, barriers, and practical applications in daily administrative tasks. Methods: A multicenter, cross-sectional, descriptive qualitative design was used. Data were collected through semistructured face-to-face interviews with 31 hospital administrators across 3 tertiary hospitals in Beijing, Shenzhen, and Chengdu from June 2024 to August 2024. The Colaizzi method was used for thematic analysis to identify patterns in participants’ experiences and perspectives. Results: Adoption of LLM AI tools was generally low, with significant site-specific variations. Participants with higher technological familiarity and positive early experiences reported more frequent use, while barriers such as mistrust in tool accuracy, limited prompting skills, and insufficient training hindered broader adoption. Tools were primarily used for document drafting, with limited exploration of advanced functionalities. Participants strongly emphasized the need for structured training programs and institutional support to enhance usability and confidence. Conclusions: Familiarity with technology, positive early experiences, and openness to innovation may facilitate adoption, while barriers such as limited knowledge, mistrust in tool accuracy, and insufficient prompting skills can hinder broader use. LLM AI tools are now primarily used for basic tasks such as document drafting, with limited application to more advanced functionalities due to a lack of training and confidence. Structured tutorials and institutional support are needed to enhance usability and integration. Targeted training programs, combined with organizational strategies to build trust and improve accessibility, could enhance adoption rates and broaden tool use. Future quantitative investigations should validate the adoption rate and influencing factors.

Introduction: Obesity has become a major health problem worldwide. Diet management is an important means of weight loss. The aim of this study was to explore the improvement effect of a calorie-restricted diet (CRD), 5 + 2 intermittent fasting (5 + 2 IF), and high protein diet (HPD) on weight composition and serum lipid level of overweight and obese people in a short period (3 months).Methods: Fifty-three participants aged 18–60 years and with body mass index (BMI) ranging from 24 to 35 kg/m² were screened for inclusion and exclusion criteria and were randomly divided into three groups, i.e., CRD, 5 + 2 IF, and HPD. Basic information, body composition, and venous blood samples were collected at baseline and after 3 months of intervention. Body composition was measured using a body composition analyzer (SK-V9), and serum lipids were assayed using the Roche Cobas e702 automatic biochemistry analysis system. The generalized estimating equation (GEE) was used to analyze and compare the repeated measurements of body composition and levels of serum lipids.Results: The results showed that total weight, BMI, body fat mass, muscle mass, visceral fat index, and waist and hip circumferences had significantly decreased in all three groups after 3 months of intervention, and the average weight loss was 3.3 ± 1.14, 4.12 ± 0.05, and 2.62 ± 0.13 kg in CRD, 5 + 2 IF, and HPD groups, respectively. The results of the GEE model indicated that compared with the CRD group, the 5 + 2 IF group showed a more significant decrease in weight (β = −0.272, P < 0.001), BMI (β = −0.091, P < 0.001), body fat mass (β = −0.172, P < 0.001), muscle (β = −0.043, P < 0.001), and visceral fat index (β = −0.019, P < 0.001), however, HPD has more advantages in visceral fat index loss (β = −0.011, P < 0.001) and lean body mass preserve (β = 0.229, P < 0.001).Conclusion: Our findings showed that the 5 + 2 IF may be more effective in reducing total weight and body fat, and HPD may be more helpful in preventing lean body mass loss during a short-term weight loss intervention.

Correct nutritional assessment is essential for leukemia patients after hematopoietic stem cell transplantation (HSCT). This study aimed to investigate the best nutritional assessment method for leukemia patients after HSCT, and find the possible nutritional risk of the patients during the transplantation process in order to intervene in the patients with nutritional risks and undernourished patients timely, so that the entire transplantation process could be successfully completed. A prospective study was performed in 108 leukemia patients after HSCT, and different nutritional assessment methods, including nutritional risk screening 2002 (NRS2002), mini nutritional assessment (MNA), subjective globe assessment (SGA) and malnutritional universal screening tools (MUST), were used. The associations between nutritional status of these patients and nutritional assessment methods were analyzed. A total of 108 patients completed SGA, and 99 patients completed NRS2002, MNA and MUST. During the treatment process, 85.2% of the patients lost weight, wherein, 50% lost weight greater than 5%, and 42.6% had significantly reduced food intake. For nutritional risk assessment, the positive rates of NRS2002, MNA and MUST were 100%, 74.7% and 63.6%, respectively. There was a significant difference (P30 years old (P<0.05). Patients with leukemia were in poor nutritional status during and after HSCT. The leukemia patients ≤30 years old had a greater incidence rate of nutritional risk. As nutritional risk screening tool, the specificity of NRS2002 is not high, but it can be used for evaluating nutritional deficiencies. MNA is a good nutritional risk screening tool, but not an adequate tool for nutritional assessment. If assessment of undernutrition is necessary, the combination of all these screening tools and clinical laboratory indicators should be applied to improve accuracy.

Gestational diabetes mellitus (GDM) is a high-risk pregnancy complication that is associated with metabolic disorder phenotypes, such as abnormal blood glucose and obesity. The active interface between gut microbiota and diet contributes to metabolic homeostasis in GDM. However, the contributions of gut mycobiome have been neglected. Here, we profiled the gut fungi between GDM and healthy subjects at two time points and investigate whether variations in gut mycobiome correlate with key features of host metabolism and diet management in this observational study. We identified that Hanseniaspora, Torulaspora, Auricularia, Alternaria, and Candida contributed to GDM patient clustering, indicating that these fungal taxa are associated with abnormal blood glucose levels, and the causality needs to be further explored. While Penicillium, Ganoderma, Fusarium, Chaetomium, and Heterobasidion had significant explanatory effects on healthy subject clustering. In addition, spearman analysis further indicated that blood glucose levels were negatively correlated with polysaccharide-producing genera, Ganoderma, which could be reshaped by the short-term diet. The Penicillium which was negatively correlates with metabolic parameters, also exhibited the antimicrobial attribute by the fungal-bacterial interaction analysis. These data suggest that host metabolic homeostasis in GDM may be influenced by variability in the mycobiome and could be reshaped by the diet intervention. This work reveals the potential significance of the gut mycobiome in health and has implications for the beneficial effects of diet intervention on host metabolic homeostasis through regulating gut fungal abundance and metabolites.