OBJECTIVE To evaluate the efficacy， safety and cost-effectiveness of dupilumab in the treatment of severe asthma with rapid health technology assessment （HTA）， and to provide evidence-based evidence for clinical treatment. METHODS Retrieved from PubMed，Cochrane Library，CNKI，Wanfang， VIP database， and other related websites of HTA. HTA reports， systematic review/meta-analysis， and economic studies of dupilumab in the treatment of severe asthma were collected. Researchers independently identified literature， extracted data， and assessed the quality of included studies. Qualitative description was performed. RESULTS A total of 15 pieces of literature were included， involving 9 systematic reviews/meta-analyses and 6 economic studies. In terms of effectiveness， dupilumab was significantly better than placebo. Compared with other biological drugs， in the patients with severe asthma aged 12 years and above， for those with eosinophil （EOS） ≥300 cells/μL， dupilumab ranked first in improving forced expiratory volume in one second （FEV1）， outperforming tezepelumab， benralizumab， and mepolizumab. It ranked second in reducing acute exacerbations， surpassed only by tezepelumab， while its effect on improving asthma control questionnaire score was relatively lower， being better only than benralizumab. For those with 150 cells/μL ≤EOS＜300 cells/μL， dupilumab was superior to mepolizumab in reducing asthma exacerbation， while the effect on FEV1 was weaker than benralizumab and mepolizumab. In terms of safety， there was no significant difference in the incidence of adverse events and serious adverse events between dupilumab and placebo or other biological drugs， while the incidence of injection site reactions of dupilumab was significantly higher than placebo. In terms of cost-effectiveness， the research results of different countries were not consistent， and there was a lack of research data from China. CONCLUSIONS Dupilumab is an effective and safe choice in the treatment of severe asthma， and its cost-effectiveness requires further research based on China’s medical environment to be determined.

Background Protein tyrosine phosphatase non-receptor type II (PTPN2) is essential for the regulation of inflammation and immunity, but the specific mechanism of action of Ptpn2 in silicosis is unknown. Objective To investigate the regulatory role of overexpression of Ptpn2 in SiO₂-mediated inflammatory response in alveolar type II epithelial cells based on transcriptome sequencing. Methods This study was an in vitro study. A negative control group (vector transferred) and an overexpression of Ptpn2 group of mouse lung epithelial cell line MLE-12 cells were firstly constructed. Transcriptome sequencing was performed to detect differentially expressed genes (DEGs), differentially expressed mRNAs, and differentially expressed ncRNAs in the two groups of MLE-12 cells, and then the DEGs were analyzed by the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Constructed MLE-12 cells and A549 cells were stimulated using SiO₂ suspension, and divided into a negative control group (vector transferred), an overexpression of Ptpn2 group, a negative control + SiO₂ group, and an overexpression of Ptpn2 + SiO₂ group, respectively. Protein expressions of tumor necrosis factor-α (TNF-α) and interleukin (IL)-17A, IL-2, IL-1β were detected by Western blot. Positive TNF-α expression was detected by immunofluorescence staining. Results The results of Western blot showed that the protein expression level of PTPN2 was up-regulated in the overexpressed Ptpn2 group compared with the negative control group (P < 0.05). The volcano plot and clustering heat map showed that there were 1122 DEGs, 3681 differentially expressed mRNAs, and 474 differentially expressed ncRNAs in the overexpressed Ptpn2 group compared with the negative control group. The results of GO enrichment showed that, in terms of the biological process, the DEGs were mainly related to the regulation of metal ion transport, extracellular matrix organization, and extracellular structural organization; in terms of cellular composition, the DEGs were mainly related to collagen-containing extracellular matrix, receptor complexes, and basement membranes; in terms of molecular function, the DEGs were mainly related to the extracellular matrix structural constituents, glycosaminoglycan binding, and semaphorin receptor binding. The results of KEGG enrichment showed that the DEGs were closely related to cytokin-cytokine receptor interaction, IL-17 signaling pathway, TNF signaling pathway, and chemokine signaling pathway. In MLE-12 and A549 cells, the results of Western blot showed that the protein expression levels of TNF-α , IL-17A, IL-2, and IL-1β were up-regulated in the negative control + SiO₂ group compared with the negative control group (P < 0.05), and the protein expression levels of TNF-α, IL-17A, IL-2, and IL-1β were down-regulated in the overexpression of Ptpn2 + SiO₂ group compared with the negative control + SiO₂ group (P < 0.05). The immunofluorescence staining showed that the expression of TNF-α was increased in the negative control + SiO₂ group compared with the negative control group, and decreased in the overexpression of Ptpn2 + SiO₂ group compared with the negative control + SiO₂ group. Conclusion Overexpression of Ptpn2 inhibits SiO₂-mediated inflammatory response in MLE-12 cells and A549 cells.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

BackgroundNon-suicidal self-injury （NSSI） behaviors are prevalent among adolescents， significantly affecting their physical and mental well-being. Understanding the risk factors associated with adolescent NSSI is crucial for prevention. Previous studies have identified mobile phone dependence as a risk factor for NSSI in adolescents. However， as a key form of mobile phone dependence， the evidence regarding the impact of mobile phone social media dependence on adolescent NSSI behavior remains insufficient. ObjectiveTo explore the impact of mobile phone social media dependence and its associated factors on adolescent NSSI behavior， so as to provide references for intervention strategies targeting NSSI in adolescents. MethodsA total of 100 adolescents diagnosed with NSSI according to the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5）， and receiving treatment at Tongde Hospital of Zhejiang Province from January 2022 to December 2023 were included in the study group. Concurrently， 100 age- and sex-matched students from Hangzhou were recruited as the control group. Assessments were conducted using Ottawa Self-injury Inventory（OSI） Function Subscale and Addiction Features Subscale， Adolescents Self-Harm Scale（ASHS）， and Mobile Phone Social Media Dependence Questionnaire. Multiple linear regression was used to analyze the factors influencing NSSI behaviors. ResultsThe research group had a total of 99 patients （99.00%） who completed the study， while the control group consisted of 97 （97.00%） adolescents who finished this research.The study group had statistically significantly higher total scores on the Mobile Phone Social Media Dependence Questionnaire， as well as higher scores on the conflict and withdrawal dimensions， compared with control group（t=-3.061， -2.874， -2.368， P<0.05 or 0.01）. The study group also scored significantly higher on the OSI Function Subscale for internal emotion regulation， social influence， external emotion regulation， and sensation-seeking factors， as well as on the OSI Addiction Features Subscale scores， compared to the control group（t=-22.249， -8.854， -17.968， -10.591， -20.157， P<0.01）. OSI Function Subscale scores were positively correlated with Mobile Phone Social Media Dependence Questionnaire scores （r=0.321， P<0.01）， and OSI Addiction Features Subscale scores were positively correlated with Mobile Phone Social Media Dependence Questionnaire scores （r=0.282， P<0.01）. ASHS scores were positively correlated with Mobile Phone Social Media Dependence Questionnaire scores （r=0.145， P<0.05）. Multiple linear regression analysis showed that compulsivity （β=0.416， P<0.01） and conflict （β=0.256， P<0.05） were significant predictors for adolescent NSSI behaviors. ConclusionAdolescent NSSI behaviors are associated with mobile phone social media dependence. The compulsivity and conflict dimension of mobile phone social media dependence are influencing factors for adolescent NSSI behaviors. The higher level of the compulsivity and conflict are associated with an increased risk of the NSSI behaviors in adolescents. ［Funde by Zhejiang Medical and Health Science and Technology Plan Project in 2022 （number， 2022KY704］

Locomotion, a fundamental motor function encompassing various forms such as swimming, walking, running, and flying, is essential for animal survival and adaptation. The mesencephalic locomotor region (MLR), located at the midbrain-hindbrain junction, is a conserved brain area critical for controlling locomotion. This review highlights recent advances in understanding the MLR’s structure and function across species, from lampreys to mammals and birds, with a particular focus on insights gained from optogenetic studies in mammals. The goal is to uncover universal strategies for MLR-mediated locomotor control. Electrical stimulation of the MLR in species such as lampreys, salamanders, cats, and mice initiates locomotion and modulates speed and patterns. For example, in lampreys, MLR stimulation induces swimming, with increased intensity or frequency enhancing propulsive force. Similarly, in salamanders, graded stimulation transitions locomotor outputs from walking to swimming. Histochemical studies reveal that effective MLR stimulation sites colocalize with cholinergic neurons, suggesting a conserved neurochemical basis for locomotion control. In mammals, the MLR comprises two key nuclei: the cuneiform nucleus (CnF) and the pedunculopontine nucleus (PPN). Both nuclei contain glutamatergic and GABAergic neurons, with the PPN additionally housing cholinergic neurons. Optogenetic studies in mice by selectively activating glutamatergic neurons have demonstrated that the CnF and PPN play distinct roles in motor control: the CnF drives rapid escape behaviors, while the PPN regulates slower, exploratory movements. This functional specialization within the MLR allows animals to adapt their locomotion patterns and speed in response to environmental demands and behavioral objectives. Similar to findings in lampreys, the CnF and PPN in mice transmit motor commands to spinal effector circuits by modulating the activity of brainstem reticular formation neurons. However, they achieve this through distinct reticulospinal pathways, enabling the generation of specific behaviors. Further insights from monosynaptic rabies viral tracing reveal that the CnF and PPN integrate inputs from diverse brain regions to produce context-appropriate behaviors. For instance, glutamatergic neurons in the PPN receive signals from other midbrain structures, the basal ganglia, and medullary nuclei, whereas glutamatergic neurons in the CnF rarely receive inputs from the basal ganglia but instead are strongly influenced by the periaqueductal grey and inferior colliculus within the midbrain. These differential connectivity patterns underscore the specialized roles of the CnF and PPN in motor control, highlighting their unique contributions to coordinating locomotion. Birds exhibit exceptional flight capabilities, yet the avian MLR remains poorly understood. Comparative studies suggest that the pedunculopontine tegmental nucleus (PPTg) in birds is homologous to the mammalian PPN, which contains cholinergic neurons, while the intercollicular nucleus (ICo) or nucleus isthmi pars magnocellularis (ImC) may correspond to the CnF. These findings provide important clues for identifying the avian MLR and elucidating its role in flight control. However, functional validation through targeted experiments is urgently needed to confirm these hypotheses. Optogenetics and other advanced techniques in mice have greatly advanced MLR research, enabling precise manipulation of specific neuronal populations. Future studies should extend these methods to other species, particularly birds, to explore unique locomotor adaptations. Comparative analyses of MLR structure and function across species will deepen our understanding of the conserved and evolved features of motor control, revealing fundamental principles of locomotion regulation throughout evolution. By integrating findings from diverse species, we can uncover how the MLR has been adapted to meet the locomotor demands of different environments, from aquatic to aerial habitats.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.