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: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.

Objective:This study aims to evaluate the quality and explore the preliminary clinical applications of a domestically developed hepatitis D virus nucleic acid quantification reagent (abbreviated as"domestic HDV RNA reagent").Methods:The sensitivity and accuracy of the reagent were evaluated in accordance with the WHO HDV RNA international standard, employing the Bio-Rad CFX Opus 96 real-time fluorescence quantitative PCR analysis system. Serial dilutions of pseudo-viruses or cell culture-derived virus were used to determine the linear range of the domestic HDV RNA reagent. Specificity was assessed using positive samples of HAV, HBV, HCV infection, and HEV national reference materials. Precision was evaluated with samples at both high and low concentrations. In a comparative analysis, 30 HDV IgG positive samples were tested using both the domestic HDV RNA reagent and the RoboGene HDV RNA kit based on the ABI 7500 FAST DX system. The Pearson correlation coefficient (r) was used to examine the correlation between the two reagents.Results:The domestic HDV RNA reagent demonstrated a high sensitivity of up to 6 IU/ml, consistent with that of the comparator reagent. The calibration curve for WHO HDV RNA standards had a slope of -3.286, with an amplification efficiency of 101.6%. The linear detection range spanned from 10 to 10 8 IU/ml for eight HDV genotypes. The domestic HDV RNA reagent exhibited exceptional specificity, without cross-reactivity observed with HAV, HBV, HCV, or HEV. Accuracy assessments at five concentration levels met the required standards, with intra-assay precision coefficient of variation ( CV) ranging from 1.20% to 4.20%, and inter-assay precision CV from 1.20% to 7.90%. The detection results for HDV IgG positive samples were highly correlated with the comparator reagent ( r=0.984, P<0.001), achieving a diagnostic accuracy of 100% compared to sequencing results. Conclusion:In this study, the domestic HDV RNA reagent possesses excellent specificity, accuracy, precision, and a broad linear range, attaining a sensitivity level on par with international reagents of the same type.

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

Objective:To investigate the value of implantable cardiac monitor (ICM) in the diagnosis and treatment of patients over 60 years old with unexplained syncope.Methods:This was a multi-center, prospective cohort study. Between June 2018 and April 2021, patients over the age of 60 with unexplained syncope at Beijing Hospital, Fuwai Hospital, Beijing Anzhen Hospital and Puren Hospital were enrolled. Patients were divided into 2 groups based on their decision to receive ICM implantation (implantation group and conventional follow-up group). The endpoint was the recurrence of syncope and cardiogenic syncope as determined by positive cardiac arrhythmia events recorded at the ICM or diagnosed during routine follow-up. Kaplan‐Meier survival analysis was used to compare the differences of cumulative diagnostic rate between the 2 groups. A multivariate Cox regression analysis was performed to determine independent predictors of diagnosis of cardiogenic syncope in patients with unexplained syncope.Results:A total of 198 patients with unexplained syncope, aged (72.9±8.25) years, were followed for 558.0 (296.0,877.0) d, including 98 males (49.5%). There were 100 (50.5%) patients in the implantation group and 98 (49.5%) in the conventional follow-up group. Compared with conventional follow-up group, patients in the implantation group were older, more likely to have comorbidities, had a higher proportion of first degree atrioventricular block indicated by baseline electrocardiogram, and had a lower body mass index (all P<0.05). During the follow-up period, positive cardiac arrhythmia events were recorded in 58 (58.0%) patients in the ICM group. The diagnosis rate (42.0% (42/100) vs. 4.1% (4/98), P<0.001) and the intervention rate (37.0% (37/100) vs. 2.0% (2/98), P<0.001) of cardiogenic syncope in the implantation group were higher than those in the conventional follow-up group (all P<0.001). Kaplan-Meier survival analysis showed that the cumulative diagnostic rate of cardiogenic syncope was significantly higher in the implantation group than in the traditional follow-up group ( HR=11.66, 95% CI 6.49-20.98, log-rank P<0.001). Multivariate analysis indicated that ICM implantation, previous atrial fibrillation, diabetes mellitus or first degree atrioventricular block in baseline electrocardiogram were independent predictors for cardiogenic syncope (all P<0.05). Conclusions:ICM implantation improves the diagnosis and intervention rates in patients with unexplained syncope, and increases diagnostic efficiency in patients with unexplained syncope.

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.