Current status of vitamin D2 deficiency among children in a region of China.

Background: The aim of this study was to explore the current status of vitamin D2 (VD2) deficiency in hospitalized children in a region of China. Methods: The instances of detection of vitamin D (VD) and VD2 in children who visited the hospital from January 2022 to May 2023 were analyzed retrospectively. Additionally, the relationships between VD2 level and gender and age were further analyzed. Furthermore, for departments with a high frequency of VD detection, the VD2 deficiencies in children with different diseases were further analyzed. Results: Among the different age groups, children aged 11-15 years exhibited the most severe VD2 deficiency, followed by those aged 7-10 years, 0-1 years, and 2-6 years. Moreover, 25(OH)D2 levels were significantly lower in children aged 7-10 years and 11-15 years compared with 2-6 years. Gender did not have an impact on the level of 25(OH)D2. When analyzing the orthopedics, dermatology, thoracic surgery, and nephroimmunology departments' data on children's levels of 25(OH)D2, it was found that an average of approximately 76.56% had levels below <1.5 ng/ml compared to individuals with levels between >15 ng/ml and 100 ng/ml. The average ratio between individuals with <1.5 ng/ml vs. those with <15 ng/ml was found to be 91.22%. Conclusions: Children who came to the hospital were severely deficient in VD2. The degree of deficiency was related to age, but there was no gender difference. The phenomenon of VD2 deficiency was reflected in children with both skeletal and non-skeletal diseases.

A rapid and simple HPLC-MS/MS method for the therapeutic drug monitoring of six special-grade antimicrobials in pediatric patients.

Meropenem, linezolid, fluconazole, voriconazole, posaconazole, and vancomycin are six important antimicrobials used for severe infections in critically ill patients listed in special-grade antimicrobials in China. The six antimicrobials' highly variable pharmacodynamics and pharmacokinetics in critically ill pediatric patients present significant challenges to clinicians in ensuring optimal therapeutic targets. Therefore, therapeutic drug monitoring of these antimicrobials in human plasma is necessary to obtain their plasma concentration. A rapid, simple, and sample-saving high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was developed, which could simultaneously determine all six antimicrobials. It required only 10 µL of plasma and a one-step protein precipitation process. Chromatographic separation was achieved on a reversed-phase column (C18, 30 × 2.1 mm, 2.6 µm) via gradient elution using water and acetonitrile containing 0.1 % formic acid as mobile phase. The injection volume was 2 µL, and the total run time was only 2.5 min. Detection was done using a Triple Quad™ 4500MD tandem mass spectrometer coupled with an electrospray ionization (ESI) source in positive mode. The calibration curves ranged from 0.5 to 64 µg/mL for meropenem and fluconazole, 0.2-25.6 µg/mL for linezolid and voriconazole, 0.1-12.8 µg/mL for posaconazole and 1-128 µg/mL for vancomycin, with the coefficients of correlation all greater than 0.996. Furthermore, the method was validated rigorously according to the European Medicines Agency (EMA) guidelines, demonstrating excellent accuracy (from 93.0 % to 110.6 %) and precision (from 2.0 % to 12.8 %). Moreover, its applicability to various matrices (including serum, hemolytic plasma, and hyperlipidemic plasma) was evaluated. Thus, this method was successfully applied to routine therapeutic drug monitoring for critically ill pediatric patients and other patients in need.

Effect and Mechanisms of Huangqi-Shanzhuyu in the Treatment of Diabetic Nephropathy based on Network Pharmacology and In Vitro Experiments.

BACKGROUND: Huangqi-Shanzhuyu (HS), a classic combination of Chinese herbal formulae, has been widely used for the treatment of diabetic nephropathy (DN). However, its pharmacological mechanism of action is still unclear. METHODS: The active ingredients of HS and their potential targets were identified through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the DN-related targets were determined from GeneCards, Online Mendelian Inheritance in Man (OMIM), PharmGkb, and Therapeutic Target Database (TTD). The Cytoscape software was used to construct a herb-disease-target network and screen core genes. STRING was employed to generate a protein-protein interaction (PPI) network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to predict the mechanism of action of HS in DN. Animal experiments and molecular docking were used to verify the potential mechanism. RESULTS: In total, 40 active ingredients and 180 effective targets of HS in DN were identified and 1115 DN-related targets were retrieved. From the PPI network, VEGFA, AKT1, IL6, IL1B, TP53, MMP9, PTGS2, CASP3, EGF and EGFR were identified as core genes. The anti-DN mechanism mainly involved multiple signaling pathways such as AGEs-RAGE. Animal experiments and molecular docking analysis confirmed that HS downregulated the expression of IL-1 and IL-6 via kaempferol-mediated inhibition of JNK1 phosphorylation. CONCLUSIONS: HS exhibits a therapeutic effect in DN through its multiple ingredients that act on several targets and multiple signaling pathways, including AGEs-RAGE.

Deep fusion of multi-modal features for brain tumor image segmentation.

Accurate segmentation of pathological regions in brain magnetic resonance images (MRI) is essential for the diagnosis and treatment of brain tumors. Multi-modality MRIs, which offer diverse feature information, are commonly utilized in brain tumor image segmentation. Deep neural networks have become prevalent in this field; however, many approaches simply concatenate different modalities and input them directly into the neural network for segmentation, disregarding the unique characteristics and complementarity of each modality. In this study, we propose a brain tumor image segmentation method that leverages deep residual learning with multi-modality image feature fusion. Our approach involves extracting and fusing distinct and complementary features from various modalities, fully exploiting the multi-modality information within a deep convolutional neural network to enhance the performance of brain tumor image segmentation. We evaluate the effectiveness of our proposed method using the BraTS2021 dataset and demonstrate that deep residual learning with multi-modality image feature fusion significantly improves segmentation accuracy. Our method achieves competitive segmentation results, with Dice values of 83.3, 89.07, and 91.44 for enhanced tumor, tumor core, and whole tumor, respectively. These findings highlight the potential of our method in improving brain tumor diagnosis and treatment through accurate segmentation of pathological regions in brain MRIs.

A cross-sectional study exploring the relationship between the dietary inflammatory index and hyperlipidemia based on the National Health and Nutrition Examination Survey (2005-2018).

BACKGROUND: Hyperlipidemia is closely associated with dietary patterns and inflammation. However, the relationship between hyperlipidemia and the inflammatory potential of diets remains unexplored. The research was conducted to examine the relationship between hyperlipidemia and dietary inflammatory index (DII). METHODS: The data utilized in the research were acquired from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. The information on dietary intake was gathered by conducting 24-h dietary recall interviews. Restricted cubic spline (RCS) and Survey-weighted logistic regression were utilized to determine the association between DII and hyperlipidemia. Furthermore, stratification analysis was carried out. RESULTS: This study included 8982 individuals with and 3458 without hyperlipidemia. Participants with hyperlipidemia exhibited higher DII scores than those without hyperlipidemia. Following adjustment for gender, age, race, education level, marital status, poverty, drinking status, diabetes, hypertension, smoking status, body mass index (BMI), chronic kidney disease (CKD), cardiovascular disease (CVD), and hemoglobin (Hb), the association between the prevalence of hyperlipidemia and DII remained significant. The RCS data demonstrated that the hyperlipidemia prevalence did not exhibit an increase until the DII score was approximately 2.78. Stratification analysis revealed that the association between DII and hyperlipidemia persisted in all subgroups. CONCLUSIONS: DII was associated with hyperlipidemia, and the threshold DII score for the risk of hyperlipidemia was 2.78.

Deep convolutional neural network for hippocampus segmentation with boundary region refinement.

Accurately segmenting the hippocampus from magnetic resonance (MR) brain images is a crucial step in studying brain disorders. However, this task is challenging due to the low signal contrast of hippocampal images, the irregular shape, and small structural size of the hippocampi. In recent years, several deep convolutional networks have been proposed for hippocampus segmentation, which have achieved state-of-the-art performance. These methods typically use large image patches for training the network, as larger patches are beneficial for capturing long-range contextual information. However, this approach increases the computational burden and overlooks the significance of the boundary region. In this study, we propose a deep learning-based method for hippocampus segmentation with boundary region refinement. Our method involves two main steps. First, we propose a convolutional network that takes large image patches as input for initial segmentation. Then, we extract small image patches around the hippocampal boundary for training the second convolutional neural network, which refines the segmentation in the boundary regions. We validate our proposed method on a publicly available dataset and demonstrate that it significantly improves the performance of convolutional neural networks that use single-size image patches as input. In conclusion, our study proposes a novel method for hippocampus segmentation, which improves upon the current state-of-the-art methods. By incorporating a boundary refinement step, our approach achieves higher accuracy in hippocampus segmentation and may facilitate research on brain disorders.

Population Pharmacokinetics and Dosing Optimization of Ceftazidime in Infants.

Ceftazidime, a third-generation cephalosporin, can be used for the treatment of adults and children with infections due to susceptible bacteria. To date, the pediatric pharmacokinetic data are limited in infants, and therefore we aimed to evaluate the population pharmacokinetics of ceftazidime in infants and to define the appropriate dose to optimize ceftazidime treatment. Blood samples were collected from children treated with ceftazidime, and concentrations of the drug were quantified by high-performance liquid chromatography with UV detection (HPLC-UV). A population pharmacokinetic analysis was performed using NONMEM software (version 7.2.0). Fifty-one infants (age range, 0.1 to 2.0 years) were included. Sparse pharmacokinetic samples (n = 90) were available for analysis. A one-compartment model with first-order elimination showed the best fit with the data. A covariate analysis identified that body weight and creatinine clearance (CLCR) were significant covariates influencing ceftazidime clearance. Monte Carlo simulation demonstrated that the currently used dosing regimen of 50 mg/kg twice daily was associated with a high risk of underdosing in infants. In order to reach the target of 70% of the time that the free antimicrobial drug concentration exceeds the MIC (fT>MIC), 25 mg/kg every 8 h (q8h) and 50 mg/kg q8h were required for MICs of 4 and 8 mg/liter, respectively. The population pharmacokinetic characteristics of ceftazidime were evaluated in infants. An evidence-based dosing regimen was established based on simulation.

[A randomized controlled study on the efficacy of inhaled nitric oxide in treatment of neonates with meconium aspiration syndrome].

OBJECTIVE: Meconium aspiration syndrome (MAS) is a disease of the term and near-term infant that is associated with considerable respiratory morbidity. The purpose of this study was to investigate effects of inhaled nitric oxide (iNO) in oxygenation and outcome of newborns with MAS. METHODS: Eligible patients diagnosed as severe MAS admitted consecutively to the neonatal intensive care unit (NICU) of Hebei Children's Hospital from January 2004 to June 2006 were included in the study. The patients with an oxygenation index (OI) > or = 15 were randomized in a nonblinded manner to receive either iNO (NO group, n = 21) or no NO (control group, n = 25). Patients with an OI > or = 15 after enrollment were treated with iNO at 15 ppm initially. The response to iNO was assessed according to the increase in arterial PaO(2) and oxygen saturation (SpO(2)) after exposure to the starting concentration for 60 minutes. A response of 10 mm Hg (1 mm Hg = 0.133 kPa) increase in PaO(2) and a 10% increase in SpO(2) was assessed responsive to iNO. All patients were treated in the same neonatal unit and received the same standard therapy throughout the study period. Arterial blood gas tensions, pulmonary arterial pressure and systemic arterial blood pressures were recorded at baseline, 1 hour, and 24 hours in all patients. Methemoglobin levels were obtained at 12 - 24 hours after inhaled NO treatment. Parameters of fraction of inspired oxygen (FiO(2)), OI, mortality, ventilation time, and incidence of intraventricular hemorrhage (IVH, grade III-IV) were recorded. Informed consent was obtained from parents before enrollment. The protocol and the informed consent forms were approved by the ethic committee of the hospital before patient enrollment. RESULTS: There was no significant difference in gestational age, birth weight, gender ratio, age at admission in hours, c-section delivery between the two groups, and no significant difference was found in respiratory mechanics parameters between the two groups at baseline. The duration of iNO was 34.90 +/- 16.41 hours. At the beginning of the treatment, no significant differences were detected in the OI and PAP between the two groups. One hour later, OI and PAP of NO group decreased significantly (OI, F = 35.27, P < 0.01, PAP, F = 24.30, P < 0.01), while in control group the difference was not found until 24 hours (OI, F = 20.16, P < 0.01, PAP, F = 101.22, P < 0.01). There were significant differences in PAP at 1, 24 hours between the two groups (1 h, t = 2.41, P < 0.05; 24 h, t = 3.11, P < 0.01). The methemoglobin levels were normal. Compared to the controls, hospital stay (t = 2.86, P < 0.05), duration of the need for oxygen supplement (t = 2.53, P < 0.05) and ventilation time were shorter (t = 2.41, P < 0.05), whereas mortality (chi(2) = 0.21, P > 0.05) and incidence of IVH (chi(2) = 0.00, P > 0.05) were not significantly different between the groups. CONCLUSIONS: iNO could effectively improve the oxygenation and shorten the ventilation time and hospital stay without augmentation of risk of IVH and pneumothorax in these neonatal patients.