Analyze and simulate an individualized nutritional computing system for multidisciplinary teams in nutritional support therapy.

To explore the efficiency of nutritional support therapy . Pharmacists led the construction of an individualized nutritional computing system and were involved in the process of treatment. After obtaining relevant professional knowledge and instruction on how to operate the system, MDT members intervened in the incorrect treatment process during nutritional support therapy. The Department of Radiation Oncology and the Intensive Care Unit (ICU) were selected as pilot departments to compare and analyze the rationality of nutrition risk screening and the use of enteral nutrition (EN) and parenteral nutrition (PN) in treatment before and after intervention. The individualized nutritional computing system significantly improved work efficiency, promoted nutrition risk screening, and saved 10-15 minutes in the treatment of each patient. After intervention in the Department of Radiation Oncology, the use rate of Total Nutrient Admixture (TNA) increased by 7.17%, and the single-bottle infusion rate of PN preparation decreased by 17.94% in patients at risk of malnutrition. The use rate of EN and single-bottle infusion rate of PN preparation in patients without risk of malnutrition decreased by 15.17% and 20.81%, respectively. Overall, 98.75% of ICU patients were at risk of malnutrition. The use rates of EN and TNA increased by 12.79% and 12.14%, respectively, and the single-bottle infusion rate of PN preparation decreased by 10.06%. Streamlined and mobile MDT, the use of an individualized nutritional computing system, and the effective work of pharmacists in the process significantly improved the efficiency and rationality of nutritional support therapy .

A machine-learning approach for predicting the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome.

The present study aimed to explore the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome (PCOS) and predict an appropriate dosage schedule using a machine-learning approach. Data were obtained from literature mining and the rates of body weight change from the initial values were selected as the therapeutic index. The maximal effect (Emax) model was built up as the machine-learning model. A total of 242 patients with PCOS were included for analysis. In the machine-learning model, the Emax of carnitine supplementation on body weight was -3.92%, the ET50 was 3.6 weeks, and the treatment times to realize 25%, 50%, 75%, and 80% (plateau) Emax of carnitine supplementation on body weight were 1.2, 3.6, 10.8, and 14.4 weeks, respectively. In addition, no significant relationship of dose-response was found in the dosage range of carnitine supplementation used in the present study, indicating the lower limit of carnitine supplementation dosage, 250 mg/day, could be used as a suitable dosage. The present study first explored the effect of carnitine supplementation on body weight in patients with PCOS, and in order to realize the optimal therapeutic effect, carnitine supplementation needs 250 mg/day for at least 14.4 weeks.

Molecular Mechanism of Jinchan Oral Liquid in the Treatment of Children with Respiratory Syncytial Virus Pneumonia Based on Network Pharmacology and Molecular Docking Technology.

OBJECTIVE: Exploration of the underlying molecular mechanism of Jinchan Oral Liquid (JOL) in treating children with the respiratory syncytial virus (RSV) pneumonia to provide new evidence for the clinical application. METHODS: The active components and target genes of JOL were screened by the TCMSP database. The targets of RSV pneumonia were obtained from the GeneCards, OMIM, DrugBank, and PharmGKB database. Then, we constructed the active component-target network and screened the core genes. The overlaps were screened for PPI network analysis, GO analysis, and KEGG analysis. Finally, result validation was performed by molecular docking. RESULTS: According to the screening criteria of the ADME, 74 active compounds of JOL were obtained; after removing redundant targets, we selected 180 potential targets. By screening the online database, 893 RSV pneumonia-related targets were obtained. A total of 82 overlapping genes were chosen by looking for the intersection. The STRING online database was used to acquire PPI relationships, and 16 core genes were obtained. GO and KEGG analyses showed that the main pathways of JOL in treating RSV pneumonia include TNF signaling pathway and IL17 signaling pathway. The molecular docking results showed that the active compounds of JOL had a good affinity with the core genes. CONCLUSION: In this study, we preliminarily discussed the main active ingredients, related targets, and pathways of JOL and predicted the pharmacodynamic basis and the potential therapeutic mechanisms of RSV pneumonia. In summary, the network pharmacology strategy may be helpful for the discovery of multitarget drugs against complex diseases.

Quantitative efficacy of L-carnitine supplementation on glycemic control in type 2 diabetes mellitus patients.

OBJECTIVES: This study aimed to explore the quantitative efficacy of L-carnitine supplementation on glycemic control in type 2 diabetes mellitus patients using model-based meta-analysis (MBMA). METHODS: Literatures were retrieved from the public database and data from these trials were extracted. The quantitative efficacy of L-carnitine on fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c) in type 2 diabetes mellitus patients were evaluated by maximal effect (Emax) models with nonlinear mixed effects modeling (NONMEM). RESULTS: In the model of FPG, Emax and treatment duration to reach half of the maximal effects (ET50) were -9.8% and 36.1 weeks, respectively. In the model of HbA1c, Emax and ET50 were -19.6% and 106 weeks, respectively. In addition, the durations for achieving 25%, 50%, 75%, 80%, and 90% Emax of L-carnitine on FPG were 13, 36.1, 118, 160, and 390 weeks, respectively. The durations for achieving 25%, 50%, 75%, 80%, and 90% Emax of L-carnitine on HbA1c were 38, 106, 334, 449, and 1058 weeks, respectively. CONCLUSIONS: It was the first time to provide valuable quantitative information for efficacy of L-carnitine supplementation on glycemic control in type 2 diabetes mellitus patients.