The lung collaterals form a network that branches from the lung meridian, traversing the lung system and extending across the body′s surface. Lung collateral disease refers to the structural alterations or dysfunction in these collaterals caused by external or internal pathogens. Research into the structural and physiological functions of children′s lung collaterals, as well as the pathogenesis and syndrome differentiation for treating lung collateral diseases in children, holds significant value in guiding the prevention and treatment of pediatric respiratory conditions. Drawing on the theory of collateral disease, the clinical insights of both historical and contemporary physicians, and modern research findings—while considering the unique physiological and pathological characteristics of children′s respiratory systems—this study provides a foundational summary of the morphology and spatial distribution of children′s lung collaterals. The characteristics of these collaterals are highlighted as thin, sparse, short, narrow, brittle, and tender. From this structural understanding, the unique physiological functions of children′s lung collaterals are analyzed. The study further explores the interactions between pathogenic factors and lung collaterals, elucidating the pathogenesis and progression of children′s lung collateral diseases. It proposes treatment principles centered on "seeking treatment in the collaterals and employing the method of unblocking collaterals, "which align with the unique features of pediatric lung collaterals. Common treatment approaches, and relevant prescriptions for managing these diseases are summarized. This paper lays the foundation for a theoretical system encompassing the structure, function, pathogenesis, and syndrome differentiation for treating children′s lung collateral diseases. It offers valuable insights for the clinical diagnosis and management of pediatric respiratory diseases linked to collateral dysfunction and serves as a reference for the systematic development of a broader theoretical framework for children′s collateral diseases.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Objective:To explore the relationship between relapse tendency and childhood maltreatment in methamphetamine-dependent youths,and the role of impulsivity and quality of life in the relationship.Methods:To-tally 287 methamphetamine-dependent youths(160 females,127 males)were selected in compulsory drug rehabili-tation centers.The Relapse Tendency Questionnaire(RTQ),Childhood Trauma Questionnaire-Short Form(CTQ-SF),Barrett Impulsivity Scale(BIS-11)and Quality of Life for Drug Addicts(QOL-DA)for Drug Addicts were used to conduct the survey.SPSS macro program PROCESS was used to test the mediating role.Results:The BIS-11 total scores acted as a partial mediator between the total scores of CTQ-SF and RTQ,with an effect value of 0.03(95％CI:0.01-0.05),the QOL-DA total scores acted as a full mediator between the total scores of CTQ-SF and RTQ,with an effect value of 0.05(95％CI:0.02-0.08),and the scores of BIS-11 and QOL-DA acted as chain mediators between total scores of CTQ-SF and RTQ,with an effect value of 0.01(95％CI:0.00-0.03).Conclusion:Childhood maltreatment,impulsivity,and quality of life may be associated with relapse tendencies in methamphetamine-dependent youths.

Objective:To explore the causal relationship between human inhalation injury and circulating inflammatory proteins.Methods:This research was based on two-sample Mendelian randomization (MR) analysis. With inhalation injury as the exposure factor and circulating inflammatory proteins as the result, data on inhalation injury (216 993 samples) and 91 circulating inflammatory proteins (14 824 samples) were obtained from the genome-wide association study database, and analysis was conducted by two-sample MR analysis methods. Based on linkage disequilibrium analysis, independent site single nucleotide polymorphisms (SNPs) that were significantly associated with inhalation injury were identified as the instrumental variables. The inverse variance weighted (IVW) method was mainly used to analyze the causal relationship between inhalation injury and 91 circulating inflammatory proteins, which were further verified using the weighted median method, weighted pattern method, MR-Egger method, and simple pattern method. Based on the aforementioned IVW method analysis results, SNPs of inhalation injury conformed to the hypothesis were subjected to Cochran's Q test for heterogeneity assessment, the MR-Egger regression test and MR-PRESSO outlier test for assessment of horizontal pleiotropy, and the leave-one-out method analysis for reliability assessment.Results:Six SNPs with a significant threshold ( P<5×10 -5) were identified as representative instrumental variables of inhalation injury, with F values greater than 10, indicating strong correlated instrumental variables. Based on the 6 inhalation injury SNPs, the IVW method analysis revealed a significant causal relationship between inhalation injury and interleukin-20 (IL-20), IL-20 receptor subunit alpha (IL-20RA), IL-5, and tumor necrosis factor receptor superfamily member 9 (TNFRSF9), with odds ratios of 1.01, 1.01, 1.02, and 1.01, respectively, and 95% confidence intervals of 1.00-1.02, 1.00-1.03, 1.01-1.03, and 1.00-1.03, respectively, P<0.05. Verification through the weighted median method and MR-Egger method confirmed that the causal relationships between inhalation injury and IL-5 (with odds ratios of 1.02 and 1.03, respectively, confidence intervals of 1.00-1.04 and 1.01-1.04, respectively, P<0.05) as well as TNFRSF9 (with odds ratios of 1.02 and 1.03, respectively, confidence intervals of 1.00-1.04 and 1.01-1.04, respectively, P<0.05) were statistically significant. Conversely, verification through the weighted pattern method and simple pattern method indicated that the causal relationships between inhalation injury and IL-20, IL-20RA, IL-5, and TNFRSF9 were not statistically significant (with all P values >0.05), thus still needing IVW method results as standards. Based on the aforementioned IVW method analysis results, the Cochran's Q test demonstrated there was no significant heterogeneity in the 6 inhalation injury SNPs that had significant causal relationships with IL-20, IL-20RA, IL-5, and TNFRSF9 (with Q values of 2.67, 5.00, 5.17, and 5.29, respectively, P>0.05); assessments using the MR-Egger regression test along with MR-PRESSO outlier test showed that none of the 6 inhalation injury SNPs that had significant causal relationships with IL-20, IL-20RA, IL-5, and TNFRSF9 had significant horizontal pleiotropy (with intercepts of 0.01, <0.01, -0.02, and -0.03, respectively, RSSobs values of 3.33, 9.00, 7.88, and 7.26, respectively, P>0.05); the leave-one-out method analysis showed that the significant causal relationship between inhalation injury and IL-20, IL-20RA, IL-5, and TNFRSF9 was stable and reliable after removing the 6 inhalation injury SNPs one by one. Conclusions:Through two-sample MR analysis, it is clear that there is a significant causal relationship between inhalation injury and four circulating inflammatory proteins, namely IL-20, IL-20RA, IL-5, and TNFRSF9, suggesting the production of the above four circulating inflammatory proteins is in an increasing trend following inhalation injury.

Objective To study chest computed tomography(CT)manifestations in neonates with chronic granulomatous disease(CGD)to provide clues for early diagnosis of this disease.Methods A retrospective analysis was conducted on the clinical data and chest CT scan results of neonates diagnosed with CGD from January 2015 to December 2022 at Anhui Provincial Children's Hospital.Results Nine neonates with CGD were included,with eight presenting respiratory symptoms as the initial sign.Chest CT findings included:consolidation in all 9 cases;nodules in all 9 cases,characterized by multiple,variably sized scattered nodules in both lungs;masses in 4 cases;cavities in 3 cases;abscesses in 6 cases;bronchial stenosis in 2 cases;pleural effusion,interstitial changes,and mediastinal lymphadenopathy each in 1 case.CT enhancement scans showed nodules and masses with uneven or ring-shaped enhancement;no signs of pulmonary emphysema,lung calcification,halo signs,crescent signs,bronchiectasis,or scar lesions were observed.There was no evidence of rib or vertebral bone destruction.Fungal infections were present in 8 of the 9 cases,including 6 with Aspergillus infections;three of these involved mixed infections with Aspergillus,with masses most commonly associated with mixed Aspergillus infections(3/4).Conclusions The primary manifestations of neonatal CGD on chest CT are consolidation,nodules,and/or masses,with Aspergillus as a common pathogen.These features can serve as early diagnostic clues for neonatal CGD.

Humans ; Arthritis, Psoriatic/drug therapy* ; Interleukin-17 ; Interleukin Inhibitors ; Antirheumatic Agents/therapeutic use* ; Tumor Necrosis Factor-alpha/therapeutic use* ; Interleukin-23/therapeutic use*

Objective:This study evaluates the performance of chemiluminescence assay, which is designed to detect Hepatitis D Virus (HDV) Immunoglobulin G (IgG) antibodies.Methods:A comparative analysis was conducted among chemiluminescence anti-HDV IgG reagent, the magnetic particle-based domestic reagent A and domestic reagent B, and the Robo Gene HDV RNA kit, using 1909 HBsAg-positive plasma samples. This comparison aimed to delineate clinical specificity and detection accuracy. The anti-HDV IgG reagent precision was assessed at three different concentration levels following the Clinical Laboratory Standards Institute EP5-A2 guidelines. The specificity of the assay was validated using 200 HAV IgM positive, 545 HBsAg-positive but anti-HDV IgG-negative, 350 anti HCV positive plasma samples and 200 healthy human blood samples. Additionally, a concordance study was conducted with 545 HBsAg-positive and 37 anti-HDV IgG-positive plasma samples, comparing the anti-HDV IgG reagent against reagent A.Results:1 909 HBsAg-positive plasma samples were tested using 3 anti HDV IgG reagent and 1 HDV RNA reagent, 19 samples were identified as anti-HDV IgG-positive. The anti-HDV IgG demonstrated superior accuracy and specificity. The assay exhibited excellent precision, with intra-assay coefficient of variation (CV) values ranging from 1.57% to 4.30%, and inter-assay CV values between 1.71% and 4.67% for detecting samples at high, medium, and low concentration levels. Concordance with Reagent A showed consistent results in both positive and negative detections.Conclusion:In this study, the anti-HDV IgG reagent (chemiluminescence method) displayed outstanding specificity in detecting clinical samples and exhibited a high conformity rate with commercialized reagents, making it potentially suitable for screening anti-HDV IgG in HBsAg-positive samples.