Network meta-analysis of the efficacy and safety of monoclonal antibodies and traditional conventional dichotomous agents for chronic obstructive pulmonary disease.

Introduction: Monoclonal antibodies (mAbs) against cytokines and chemokines or their receptors promise to be a potential therapeutic option to address chronic obstructive pulmonary disease (COPD). We aim to provide a comprehensive literature review of the improvement in FEV1 and safety when comparing mAbs with conventional dichotomous agents. Methods: We systematically searched 3 electronic databases (PubMed, EMBASE, and CENTRAL) up to August 1, 2023 to collect eligible randomized controlled trials (RCTs). A frequentist network meta-analysis using a random-effects model was deployed to calculate mean differences (MD) for FEV1, relative risk (RR) of treatment-emergent adverse events (TEAEs), and estimate the surface under cumulative rankings (SUCRA). A higher SUCRA indicates a better outcome. Results: This study included 23 RCTs involving a total of 20,853 patients. Overall, except for Dupilumab, mAbs did not significantly improve FEV1 compared to traditional conventional dichotomous agents. Among all the interventions included, Aclidinium bromide/Formoterol (AB/FF) (SUCRA 97.7%) ranked highest, followed by Umeclidinium/vilanterol (UMEC/VI) (SUCRA 93.5%), and Glycopyrrolate Formoterol Fumarate (GFF) (SUCRA 84.7%). Dupilumab (SUCRA 66.9%) ranked the fourth among all interventions but ranked the first among all the mAbs. Importantly, all mAbs demonstrated a good safety profile compared with placebo. Conclusion: Considering the improvement in FEV1 and its safety, the development of mAbs for COPD still holds significant clinical potential. Systematic review registration: PROSPERO, CRD42023452714.

A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning.

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Developing a risk prediction model to identify high-risk patients for UTIs in those with T2DM and assisting clinical decision-making can help reduce the incidence of UTIs in T2DM patients. To construct the predictive model, potential relevant variables were first selected from the reference literature, and then data was extracted from the Hospital Information System (HIS) of the Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital for analysis. The data set was split into a training set and a test set in an 8:2 ratio. To handle the data and establish risk warning models, four imputation methods, four balancing methods, three feature screening methods, and eighteen machine learning algorithms were employed. A 10-fold cross-validation technique was applied to internally validate the training set, while the bootstrap method was used for external validation in the test set. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the models. The contributions of features were interpreted using the SHapley Additive ExPlanation (SHAP) approach. And a web-based prediction platform for UTIs in T2DM was constructed by Flask framework. Finally, 106 variables were identified for analysis from a total of 119 literature sources, and 1340 patients were included in the study. After comprehensive data preprocessing, a total of 48 datasets were generated, and 864 risk warning models were constructed based on various balancing methods, feature selection techniques, and a range of machine learning algorithms. The receiver operating characteristic (ROC) curves were used to assess the performances of these models, and the best model achieved an impressive AUC of 0.9789 upon external validation. Notably, the most critical factors contributing to UTIs in T2DM patients were found to be UTIs-related inflammatory markers, medication use, mainly SGLT2 inhibitors, severity of comorbidities, blood routine indicators, as well as other factors such as length of hospital stay and estimated glomerular filtration rate (eGFR). Furthermore, the SHAP method was utilized to interpret the contribution of each feature to the model. And based on the optimal predictive model a user-friendly prediction platform for UTIs in T2DM was built to assist clinicians in making clinical decisions. The machine learning model-based prediction system developed in this study exhibited favorable predictive ability and promising clinical utility. The web-based prediction platform, combined with the professional judgment of clinicians, can assist to make better clinical decisions.

Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms.

Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) are key indicators reflecting blood glucose control in type 2 diabetes mellitus (T2DM) patients. The purpose of this study is to establish a predictive model for blood glucose changes in T2DM patients after 3 months of treatment, achieving personalized treatment.A retrospective study was conducted on type 2 diabetes mellitus real-world medical data from 4 cities in Sichuan Province, China from January 2015 to December 2020. After data preprocessing, data inputting, data sampling, and feature screening, 16 kinds of machine learning methods were used to construct prediction models, and 5 prediction models with the best prediction performance were screened respectively. A total of 100,000 cases were included to establish the FBG model, and 2,169 cases were established to establish the HbA1c model. The best prediction model both of FBG and HbA1c finally obtained are realized by ensemble learning and modified random forest inputting, the AUC values are 0.819 and 0.970, respectively. The most important indicators of the FBG and HbA1c prediction model were FBG and HbA1c. Medication compliance, follow-up outcome, dietary habits, BMI, and waist circumference also had a greater impact on FBG levels. The prediction accuracy of the models of the two blood glucose control indicators is high and has certain clinical applicability.HbA1c and FBG are mutually important predictors, and there is a close relationship between them.

Effect of Frying Process on Nutritional Property, Physicochemical Quality, and in vitro Digestibility of Commercial Instant Noodles.

The effects of frying process on the nutritional property, physicochemical quality, and in vitro digestibility of instant noodle products are investigated in this study. Scanning electron microscope (SEM) and Fourier transform infrared spectrometer (FT-IR) were also used to explore the changes in the microstructure and protein transformation. Noodles, after the frying process, showed a lower proportion of carbohydrate, protein, fiber, and also total starch and digestible starch, but higher content of fat and resistant starch in the proximate analysis. The frying process was also considered to improve the texture, surface color, and sensory properties of instant noodle products, accompanied by better cooking quality, including shorter cooking time and lower cooking loss during the rehydration. The honeycomb-like, porous, and less uniformed structure, and also the higher levels of ß-sheets and ß-turns, and the lower proportion of α-helixes of protein structure from fried instant noodle was also observed. The in vitro digestibility of starch and protein were downregulated in the fried group (81.96% and 81.31, respectively, on average) compared with the non-fried group (97.58% and 88.78, respectively, on average). Thus, the frying process lowered the glycemic index and regulated protein secondary structure by inhibiting continuous digesting enzyme activity, generating starch-lipid complexes, and changing the levels of protein transformation. In conclusion, our findings will provide an innovative evaluation of the frying process on instant noodles and even other various starch-based prepared food products.

Differential regulation and preventive mechanisms of green tea powder with different quality attributes on high-fat diet-induced obesity in mice.

Tea powder has been reported to have some physiological functions. However, there is no report on whether there are differences in the active ingredients of tea powder with different qualities and whether there are different prebiotic mechanisms. This study was aimed to investigate the effects of different qualities of tea powder on preventing obesity from different aspects, namely antioxidation, inflammation, lipid-lowering, and intestinal flora, using an obesity mouse model. The results showed that all three types of tea powder with different qualities could reduce body weight and decrease serum TC, TG, and LDL-C. However, tea powder with different quality attributes exhibited diverse modulatory effects and mechanisms. Tender tea powder contained more tea polyphenols, and it had a better effect on improving oxidative stress. Tender tea powder significantly decreased the abundances of Blautia, Bilophila, and Oscillibacter, and increased the abundances of Alloprevotella, Lachnoclostridium, Romboutsia, and Ruminococcaceae_UCG-004. Coarse tea powder contained more dietary fiber, and had a better effect on reducing the food intake and improving lipid metabolism, which could reduce lipid synthesis and increase lipid ß-oxidation. Coarse tea powder significantly decreased the abundance of Dubosiella and increased the abundances of the Lachnospiraceae_NK4A136 group and Coriobacteriaceae_UCG-002. Our findings provide a theoretical reference for the comprehensive utilization of tea powder.

Current Advances in Structure-Function Relationships and Dose-Dependent Effects of Human Milk Oligosaccharides.

HMOs (human milk oligosaccharides) are the third most important nutrient in breast milk. As complex glycans, HMOs play an important role in regulating neonatal intestinal immunity, resisting viral and bacterial infections, displaying anti-inflammatory characteristics, and promoting brain development. Although there have been some previous reports of HMOs, a detailed literature review summarizing the structure-activity relationships and dose-dependent effects of HMOs is lacking. Hence, after introducing the structures and synthetic pathways of HMOs, this review summarizes and categorizes identified structure-function relationships of HMOs. Differential mechanisms of different structural HMOs utilization by microorganisms are summarized. This review also emphasizes the recent advances in the interactions between different health benefits and the variance of dosage effect based on in vitro cell tests, animal experiments, and human intervention studies. The potential relationships between the chemical structure, the dosage selection, and the physiological properties of HMOs as functional foods are vital for further understanding of HMOs and their future applications.

2'-Fucosyllactose Ameliorates Oxidative Stress Damage in d-Galactose-Induced Aging Mice by Regulating Gut Microbiota and AMPK/SIRT1/FOXO1 Pathway.

The imbalance of reactive oxygen species is the main cause in aging, accompanied by oxidative stress. As the most abundant in human milk oligosaccharides (HMOs), 2'-Fucosyllactose (2'-FL) has been confirmed to have great properties in immunity regulation and anti-inflammatory. The research on 2'-FL is focused on infants currently, while there is no related report of 2'-FL for the elderly. A d-galactose-induced accelerated aging model was established to explore the protective effect of 2'-FL on the intestines and brain in mice. In this study, 2'-FL significantly reduced oxidative stress damage and inflammation in the intestines of aging mice, potentially by regulating the sirtuin1 (SIRT1)-related and nuclear factor E2-related factor 2 (Nrf2) pathways. In addition, 2'-FL significantly improved the gut mucosal barrier function and increased the content of short-chain fatty acids (SCFAs) in the intestine. The gut microbiota analysis indicated that 2'-FL mainly increased the abundance of probiotics like Akkermansia in aging mice. Moreover, 2'-FL significantly inhibited apoptosis in the brains of aging mice, also increasing the expression of SIRT1. These findings provided a basis for learning the benefits of 2'-FL in the aging process.

Six Oligosaccharides' Variation in Breast Milk: A Study in South China from 0 to 400 Days Postpartum.

This study investigated the variation in oligosaccharide levels in the breast milk of south Chinese mothers in a prolonged breastfeeding period of up to 400 days postpartum. A total of 488 breast milk samples were collected from 335 healthy mothers at five different time points: 0-5 days, 10-15 days, 40-45 days, 200-240 days, and 300-400 days postpartum. A high-performance anion-exchange chromatography-pulsed amperometric detector (HPAEC-PAD) was used to quantify 2'-fucosyllactose (2'-FL), 3-fucosyllactose (3-FL), lacto-N-tetraose (LNT), lacto-N-neotetraose (LNnT), 3'-sialyllactose (3'-SL) and 6'-sialyllactose (6'-SL). In this study, we found six oligosaccharides that were present in breast milk from 0 to 400 days postpartum. The median value ranges of individual oligosaccharide components in this study were 1013-2891 mg/L 2'-FL, 193-1421 mg/L 3-FL, 314-1478 mg/L LNT, 44-255 mg/L LNnT, 111-241 mg/L 3'-SL, and 23-602 mg/L6'-SL. HMO levels decreased over the lactation periods, except for 3-FL, which increased throughout lactation. The predominant fucosylated and sialylated HMOs were 2'-FL and 6'-SL at 40-45 days postpartum and changed to 3-FL and 3'-SL at 200-240 days postpartum. Results from this study showed that lactating women continue to provide their offspring with a high level of 2'-FL one year after delivery, suggesting that 2'-FL may play an important role for infants in early life. Our findings also provide further evidence in support of breastfeeding after one-year postpartum.

[Clinical study on applying brainstem auditory evoked potential to monitor anesthesia depth and awaken in children].

OBJECTIVES: To study applying auditory brainstem reaction (ABR) to monitor the general anesthesia depth and awaken degree in children as an objective guide. METHODS: Forty-five infant or children patients, American Society of Anesthesiologists(ASA) graded I-II, with normal audition, were selected and divided into 3 groups (15 cases per group) to receive propofal venous anesthesia, fentanyl venous anesthesia, or isoflurane anesthesia, respectively. During anesthesia, peak latencies (PL) of I, III, V wave, and interpeak latencies (IPL) of I-III, III-V, I-V were monitored and recorded after short sound stimulation of 90 db which accumulated 1,000 times with Madsen. The changing of PL and IPL with time passing and anesthesia dosage was studied to discuss the feature of ABR in each anesthesia procedure as above. RESULTS: There is a significant positive correlation between PL and IPL of ABR waves as above and the dosage of propofol or the concentration of isoflurane. The dosage or the concentration increased, and PL and IPL prolonged. It is important of PL of wave I to reflect anesthesia degree. It is wave V with the best stability and correlation to monitor anesthesia degree. After anesthesia period or patients almost awaken, PL and IPL of some ABR waves were still more than normal values, which is detention reaction. For the fentanyl anesthesia, ABR is not sensible enough, i.e. has little correlation. CONCLUSION: The changing of PL and IPL of ABR waves could conclude anesthesia degree in children and be treated as reference of whether awaken or not in some certain, but have possibility of detention reaction.