OBJECTIVES: To explore the risk factors of feeding intolerance (FI) in critically ill children receiving enteral nutrition (EN) and to construct a prediction nomogram model for FI. METHODS: A retrospective study was conducted to collect data from critically ill children admitted to the Pediatric Intensive Care Unit of Xiangya Hospital, Central South University, between January 2015 and October 2020. The children were randomly divided into a training set (346 cases) and a validation set (147 cases). The training set was further divided into a tolerance group (216 cases) and an intolerance group (130 cases). Multivariate logistic regression analysis was used to screen for risk factors for FI in critically ill children receiving EN. A nomogram was constructed using R language, which was then validated on the validation set. The model's discrimination, calibration, and clinical net benefit were evaluated using receiver operating characteristic curves, calibration curves, and decision curves. RESULTS: Duration of bed rest, shock, gastrointestinal decompression, use of non-steroidal anti-inflammatory drugs, and combined parenteral nutrition were identified as independent risk factors for FI in critically ill children receiving EN (P<0.05). Based on these factors, a nomogram prediction model for FI in critically ill children receiving EN was developed. The area under the receiver operating characteristic curve for the training set and validation set was 0.934 (95%CI: 0.906-0.963) and 0.852 (95%CI: 0.787-0.917), respectively, indicating good discrimination of the model. The Hosmer-Lemeshow goodness-of-fit test showed that the model had a good fit (χ 2=12.559, P=0.128). Calibration curve and decision curve analyses suggested that the model has high predictive efficacy and clinical application value. CONCLUSIONS Duration of bed rest, shock, gastrointestinal decompression, use of non-steroidal anti-inflammatory drugs, and combined parenteral nutrition are independent risk factors for FI in critically ill children receiving EN. The nomogram model developed based on these factors exhibits high predictive efficacy and clinical application value.
Humans ; Critical Illness ; Enteral Nutrition/adverse effects* ; Male ; Risk Factors ; Female ; Child, Preschool ; Infant ; Nomograms ; Retrospective Studies ; Child ; Logistic Models

The anterior cingulate cortex (ACC) has recently been proposed as a key player in the representation of itch stimuli. However, to date, little is known about the contribution of specific ACC interneuron populations to itch processing. Using c-Fos immunolabeling and in vivo Ca²⁺ imaging, we reported that both histamine and chloroquine stimuli-induced acute itch caused a marked enhancement of vasoactive intestinal peptide (VIP)-expressing interneuron activity in the ACC. Behavioral data indicated that optogenetic and chemogenetic activation of these neurons reduced scratching responses related to histaminergic and non-histaminergic acute itch. Similar neural activity and modulatory role of these neurons were seen in mice with chronic itch induced by contact dermatitis. Together, this study highlights the importance of ACC VIP⁺ neurons in modulating itch-related affect and behavior, which may help us to develop novel mechanism-based strategies to treat refractory chronic itch in the clinic.
Animals ; Pruritus/physiopathology* ; Vasoactive Intestinal Peptide/metabolism* ; Interneurons/metabolism* ; Gyrus Cinguli/metabolism* ; Mice ; Male ; Mice, Inbred C57BL ; Histamine ; Chloroquine ; Optogenetics ; Mice, Transgenic

Objective:To compare the lung shunt fraction (LSF) of 90Y imaging after 90Y-selective internal radiation therapy (SIRT) and preoperative 99Tc m-macroaggregated albumin (MAA) imaging in patients with liver malignant tumors, and compare the volume and visual score of intrahepatic distribution of both nucleins on SPECT/CT images. Methods:A total of 91 patients with liver malignant tumors (78 males, 13 females; age (56.7±13.7)years; 99 cases) who underwent 90Y-SIRT in the Second Affiliated Hospital of Guangzhou Medical University from November 2022 to June 2024 were retrospectively collected. All patients underwent preoperative 99Tc m-MAA simulation and postoperative 90Y distribution verification by whole-body planar scintigraphy and hepatic SPECT/CT imaging. ROIs of the liver and lungs under the anterior-posterior position were delineated on the planar scintigraphy and LSF of 99Tc m-MAA and 90Y were calculated. The volume of interest (VOI) was drawn on the SPECT/CT images to calculate the nuclide distribution volume of both 99Tc m-MAA and 90Y within the liver. Wilcoxon signed rank test was used to compare the difference between two groups. In addition, the liver was divided into five lobes, namely left lateral lobe, left medial lobe, caudate lobe, right anterior lobe and right posterior lobe. Visual assessment of 90Y and 99Tc m-MAA radioactive distribution was performed ( 90Y and 99Tc m-MAA uptakes were graded on a scale of 0-3, where 0 indicated no nuclide accumulation and 3 indicated heavy accumulation). Kappa consistency test was used to analyze the scores of the corresponding lobes between two groups. Results:LSF for 99Tc m-MAA was 11.60%(4.27%, 15.03%), and LSF for 90Y was 11.80%(9.70%, 13.30%), without significant difference ( Z=-1.50, P=0.134). The distribution volume of 99Tc m-MAA within the liver was 542.63(204.00, 818.00)ml, which was significantly different from that of 90Y (688.69(287.00, 954.00)ml; Z=-7.37, P<0.001). Kappa values of the score of each lobe between 99Tc m-MAA imaging and 90Y imaging were 0.469-0.740 (all P<0.001). Conclusions:99Tc m-MAA simulation is reliable for assessing LSF for 90Y-SIRT. The distribution volume of 99Tc m-MAA is generally smaller than that of 90Y, but the consistency of the visual score of radioactive distribution is high. Overall, 99Tc m-MAA may well simulate the distribution pattern of 90Y-SIRT.

BACKGROUND: Preclinical studies have indicated that Angong Niuhuang Pills (ANP) reduce cerebral infarct and edema volumes. This study aimed to investigate whether ANP safely reduces cerebral infarct and edema volumes in patients with moderate to severe acute ischemic stroke. METHODS: This randomized, double-blind, placebo-controlled pilot trial included patients with acute ischemic stroke with National Institutes of Health Stroke Scale (NIHSS) scores ranging from 10 to 20 in 17 centers in China between April 2021 and July 2022. Patients were allocated within 36 h after onset via block randomization to receive ANP or placebo (3 g/day for 5 days). The primary outcomes were changes in cerebral infarct and edema volumes after 14 days of treatment. The primary safety outcome was severe adverse events (SAEs) for 90 days. RESULTS: There were 57 and 60 patients finally included in the ANP and placebo groups, respectively for modified intention-to-treat analysis. The median age was 66.0 years, and the median NIHSS score at baseline was 12.0. The changes in cerebral infarct volume at day 14 were 0.3 mL and 0.4 mL in the ANP and placebo groups, respectively (median difference: -7.1 mL; interquartile range [IQR]: -18.3 to 2.3 mL, P = 0.30). The changes in cerebral edema volume of the ANP and placebo groups on day 14 were 11.4 mL and 4.0 mL, respectively ( median difference: 3.0 mL, IQR: -1.3 to 9.9 mL, P = 0.15). The rates of SAE within 90 days were similar in the ANP (3/57, 5%) and placebo (7/60, 12%) groups ( P = 0.36). Changes in serum mercury and arsenic concentrations were comparable. In patients with large artery atherosclerosis, ANP reduced the cerebral infarct volume at 14 days (median difference: -12.3 mL; IQR: -27.7 to -0.3 mL, P = 0.03). CONCLUSIONS: ANP showed a similar safety profile to placebo and non-significant tendency to reduce cerebral infarct volume in patients with moderate-to-severe stroke. Further studies are warranted to assess the efficacy of ANP in reducing cerebral infarcts and improving clinical prognosis. TRAIL REGISTRATION Clinicaltrials.gov , No. NCT04475328.
Aged ; Female ; Humans ; Male ; Middle Aged ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Ischemic Stroke/drug therapy* ; Pilot Projects ; Stroke/drug therapy* ; Treatment Outcome

Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

Four cases of typical faults of Siemens MOBILETT XP Digital bedside DR were introduced.The causes for the problems were analyzed,and some countermeasures were put forward accordingly.References were provided for medical engineers to treat similar faults.[Chinese Medical Equipment Journal,2025,46(3):118-120]

Four cases of typical faults of Siemens MOBILETT XP Digital bedside DR were introduced.The causes for the problems were analyzed,and some countermeasures were put forward accordingly.References were provided for medical engineers to treat similar faults.[Chinese Medical Equipment Journal,2025,46(3):118-120]

Objective:To compare the lung shunt fraction (LSF) of 90Y imaging after 90Y-selective internal radiation therapy (SIRT) and preoperative 99Tc m-macroaggregated albumin (MAA) imaging in patients with liver malignant tumors, and compare the volume and visual score of intrahepatic distribution of both nucleins on SPECT/CT images. Methods:A total of 91 patients with liver malignant tumors (78 males, 13 females; age (56.7±13.7)years; 99 cases) who underwent 90Y-SIRT in the Second Affiliated Hospital of Guangzhou Medical University from November 2022 to June 2024 were retrospectively collected. All patients underwent preoperative 99Tc m-MAA simulation and postoperative 90Y distribution verification by whole-body planar scintigraphy and hepatic SPECT/CT imaging. ROIs of the liver and lungs under the anterior-posterior position were delineated on the planar scintigraphy and LSF of 99Tc m-MAA and 90Y were calculated. The volume of interest (VOI) was drawn on the SPECT/CT images to calculate the nuclide distribution volume of both 99Tc m-MAA and 90Y within the liver. Wilcoxon signed rank test was used to compare the difference between two groups. In addition, the liver was divided into five lobes, namely left lateral lobe, left medial lobe, caudate lobe, right anterior lobe and right posterior lobe. Visual assessment of 90Y and 99Tc m-MAA radioactive distribution was performed ( 90Y and 99Tc m-MAA uptakes were graded on a scale of 0-3, where 0 indicated no nuclide accumulation and 3 indicated heavy accumulation). Kappa consistency test was used to analyze the scores of the corresponding lobes between two groups. Results:LSF for 99Tc m-MAA was 11.60%(4.27%, 15.03%), and LSF for 90Y was 11.80%(9.70%, 13.30%), without significant difference ( Z=-1.50, P=0.134). The distribution volume of 99Tc m-MAA within the liver was 542.63(204.00, 818.00)ml, which was significantly different from that of 90Y (688.69(287.00, 954.00)ml; Z=-7.37, P<0.001). Kappa values of the score of each lobe between 99Tc m-MAA imaging and 90Y imaging were 0.469-0.740 (all P<0.001). Conclusions:99Tc m-MAA simulation is reliable for assessing LSF for 90Y-SIRT. The distribution volume of 99Tc m-MAA is generally smaller than that of 90Y, but the consistency of the visual score of radioactive distribution is high. Overall, 99Tc m-MAA may well simulate the distribution pattern of 90Y-SIRT.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.