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.

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.

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.

Objective:To observe and evaluate the clinical efficacy and safety of albumin-bound paclitaxel as first-line treatment for patients with advanced pancreatic cancer in China, and to explore the prognosis-related molecules in pancreatic cancer based on next-generation sequencing (NGS) of tumor tissues.Methods:From December 2018 to December 2020, patients with locally advanced or metastatic pancreatic cancer were recruited to accept albumin-bound paclitaxel as first-line treatment in the oncology departments of 24 hospitals in East China. The primary endpoints were overall survival (OS) and treatment related adverse events, and the secondary endpoint was progression-free survival (PFS). Adverse effects were graded using Common Terminology Criteria for Adverse Events 5.0 (CTCAE 5.0). NGS sequencing on the primary or metastatic tissue samples of pancreatic cancer obtained through surgical resection or biopsy was performed.Results:This study recruited 229 patients, including 70 patients with locally advanced pancreatic cancer (LAPC) and 159 patients with metastatic pancreatic cancer (mPC). The disease control rate was 79.9% and the objective response rate is 36.3%.The common adverse effects during treatment were anaemia (159 cases), leucopenia (170 cases), neutropenia (169 cases), increased aminotransferases (110 cases), and thrombocytopenia (95 cases), and the incidence of grade 3-4 neutropenia is 12.2% (28/229). The median follow-up time was 21.2 months (95% CI: 18.5-23.1 months). The median PFS (mPFS) was 5.3 months (95% CI: 4.37-4.07 months) and the median OS (mOS) was 11.2 months (95% CI: 9.5-12.9 months). The mPFS of patients with LAPC was 7.4 months (95% CI: 6.6-11.2 months), and their mOS was 15.5 months (95% CI: 12.6-NA months). The mPFS of patients with mPC was 3.9 months (95% CI: 3.4-5.1 months), and their mOS was 9.3 months (95% CI: 8.0-10.8 months). Multivariate Cox regression analysis showed that clinical stage ( HR=1.47, 95% CI: 1.06-2.04), primary tumor site ( HR=0.64, 95% CI: 0.48-0.86), Eastern Cooperative Oncology Group Performance Status (ECOG PS) score ( HR=2.66, 95% CI: 1.53-4.65), and whether to combine radiotherapy ( HR=0.65, 95% CI: 0.42-1.00) were independent influencing factors for the PFS of these patients. The primary tumor site ( HR=0.68, 95% CI: 0.48-0.95), ECOG score ( HR=5.82, 95% CI: 3.14-10.82), and whether to combine radiotherapy ( HR=0.58, 95% CI: 0.35-0.96) were independent influencing factors of the OS of these patients. The most frequent gene mutations in these advanced stage pancreatic patients were KRAS (89.66%), TP53 (77.01%), CDKN2A (32.18%), and SMAD4 (21.84%) by NGS of tumor tissues from 87 pancreatic cancer patients with sufficient specimens. Further analysis revealed that mutations in CDKN2B, PTEN, FGF6, and RBBP8 genes were significantly associated with an increased risk of death ( P＜0.05). Conclusion:Albumin-bound paclitaxel as first-line treatment demonstrated feasible anti-tumor efficacy and manageable safety for patients with advanced pancreatic cancer in China.

Objective:To observe and evaluate the clinical efficacy and safety of albumin-bound paclitaxel as first-line treatment for patients with advanced pancreatic cancer in China, and to explore the prognosis-related molecules in pancreatic cancer based on next-generation sequencing (NGS) of tumor tissues.Methods:From December 2018 to December 2020, patients with locally advanced or metastatic pancreatic cancer were recruited to accept albumin-bound paclitaxel as first-line treatment in the oncology departments of 24 hospitals in East China. The primary endpoints were overall survival (OS) and treatment related adverse events, and the secondary endpoint was progression-free survival (PFS). Adverse effects were graded using Common Terminology Criteria for Adverse Events 5.0 (CTCAE 5.0). NGS sequencing on the primary or metastatic tissue samples of pancreatic cancer obtained through surgical resection or biopsy was performed.Results:This study recruited 229 patients, including 70 patients with locally advanced pancreatic cancer (LAPC) and 159 patients with metastatic pancreatic cancer (mPC). The disease control rate was 79.9% and the objective response rate is 36.3%.The common adverse effects during treatment were anaemia (159 cases), leucopenia (170 cases), neutropenia (169 cases), increased aminotransferases (110 cases), and thrombocytopenia (95 cases), and the incidence of grade 3-4 neutropenia is 12.2% (28/229). The median follow-up time was 21.2 months (95% CI: 18.5-23.1 months). The median PFS (mPFS) was 5.3 months (95% CI: 4.37-4.07 months) and the median OS (mOS) was 11.2 months (95% CI: 9.5-12.9 months). The mPFS of patients with LAPC was 7.4 months (95% CI: 6.6-11.2 months), and their mOS was 15.5 months (95% CI: 12.6-NA months). The mPFS of patients with mPC was 3.9 months (95% CI: 3.4-5.1 months), and their mOS was 9.3 months (95% CI: 8.0-10.8 months). Multivariate Cox regression analysis showed that clinical stage ( HR=1.47, 95% CI: 1.06-2.04), primary tumor site ( HR=0.64, 95% CI: 0.48-0.86), Eastern Cooperative Oncology Group Performance Status (ECOG PS) score ( HR=2.66, 95% CI: 1.53-4.65), and whether to combine radiotherapy ( HR=0.65, 95% CI: 0.42-1.00) were independent influencing factors for the PFS of these patients. The primary tumor site ( HR=0.68, 95% CI: 0.48-0.95), ECOG score ( HR=5.82, 95% CI: 3.14-10.82), and whether to combine radiotherapy ( HR=0.58, 95% CI: 0.35-0.96) were independent influencing factors of the OS of these patients. The most frequent gene mutations in these advanced stage pancreatic patients were KRAS (89.66%), TP53 (77.01%), CDKN2A (32.18%), and SMAD4 (21.84%) by NGS of tumor tissues from 87 pancreatic cancer patients with sufficient specimens. Further analysis revealed that mutations in CDKN2B, PTEN, FGF6, and RBBP8 genes were significantly associated with an increased risk of death ( P＜0.05). Conclusion:Albumin-bound paclitaxel as first-line treatment demonstrated feasible anti-tumor efficacy and manageable safety for patients with advanced pancreatic cancer in China.