Objective: To understand the epidemiological characteristics and disease burden of influenza like illness (ILI) among student populations, so as to provide data support for policy formulation and optimal allocation of health resources. Methods: From January 2024 to February 2025, a questionnaire survey was conducted among parents of kindergarten, primary school, junior and senior high school students in 9 districts of Shenzhen, including Longhua, Futian, Bao an, Longgang, Luohu, Nanshan, Guangming, Pingshan and Yantian. Parents were asked to complete the questionnaire based on whether their children had fever, cough, vomiting, diarrhea, rash and other common symptoms in 2024. A total of 3 537 parents were investigated, and 444 ILI cases were included as study subjects. The epidemiological burden, including incidence rate of influenza, visitation rate, years lived with disability (YLDs) and economic burden (including direct economic burden, indirect economic burden and intangible burden) were analyzed. Results: The incidence rate of influenza among students in Shenzhen in 2024 was 12.55%. The ILI incidence rates in kindergarten, primary school, junior and senior high school were 14.01%, 11.69% and 5.23%, respectively, with a statistically significant difference ( χ 2= 45.20, P <0.01). The ILI consultation rate among students was 85.36%, and the consultation rates in kindergarten (87.36%) and primary school students (84.62%) were higher than those in junior and senior high school students ( 56.52 %) ( χ 2=16.47, P <0.01). A total of 78.88% of cases did not receive etiological detection.The median total economic burden per ILI case was 2 354.62 yuan, including direct medical costs of 300.00 yuan, direct non medical costs of 212.50 yuan, indirect costs of 1 000.00 yuan, and intangible burden of 500.00 yuan. Conclusions Schools are high risk environment for influenza, and younger students are a high risk group for ILI. The disease burden caused by student ILI remains substantial.

Objective To summarize the clinical characteristics and analyze prognostic factors of neuroblastoma (NB) in infants (≤12 months) at a single center. Methods A retrospective analysis was conducted on the clinical data of infant patients (≤12 months) diagnosed with NB and treated between January 2014 and December 2022. Clinical features were analyzed, and comparisons between two sample rates were performed using the χ² test. Univariate prognostic analysis was conducted using the log-rank test, and survival outcomes were analyzed using the Kaplan-Meier method. Results A total of 42 infants (≤12 months) with NB were enrolled. Low-risk patients underwent surgical resection alone; intermediate-risk patients received surgery combined with chemotherapy with or without maintenance therapy; high-risk patients were treated with surgery and chemotherapy with or without maintenance therapy or radiotherapy. The 5-year event-free survival (EFS) rate was (92.7±4.9)%, and the 5-year overall survival rate was (95.2±3.6)%. Only two patients died because of tumor recurrence or progression. Univariate analysis identified MYCN amplification and the initial lactate dehydrogenase (LDH) level ≥ five times the upper limit of the normal were significantly associated with poor prognosis (5-year EFS: 33.3% vs. 97.4% and 60.0% vs. 97.3%, P<0.0001 and P=0.0035). Conclusion Infant NB has a favorable overall prognosis. MYCN amplification and markedly elevated initial LDH are associated with poor outcomes.

Acute lung injury （ALI） is a critical respiratory emergency caused by various non-cardiogenic factors， including severe pulmonary and extrapulmonary infections， lung contusion， and sepsis. Its pathological features are centered on alveolar-capillary barrier dysfunction， uncontrolled inflammatory responses， and impaired alveolar fluid clearance， manifesting as refractory hypoxemia and respiratory distress， with a poor clinical prognosis. Modern studies have revealed that mitochondrial dysfunction and glucose metabolic reprogramming in macrophages， characterized by hyperactivated glycolysis， drive pro-inflammatory polarization and represent a key mechanism leading to excessive inflammatory responses and disease progression in ALI. Therefore， targeting macrophage glycolysis may be a potential therapeutic strategy for ALI. The Plain Questions： Special Discussion on Channels and Vessels in Huangdi's Internal Classic states that "The spleen disseminates essence upward to the lungs". When the spleen fails in transport and transformation， the distribution of refined nutrients becomes disordered， resulting in the endogenous generation of turbid pathogens that obstruct the pulmonary collaterals， causing failure of lung dispersion and descent and leading to dyspneic reversal. This forms the dynamic pathological pattern of ''deficiency in the root with excess in the manifestation''， thereby proposing the core pathogenesis of ALI as ''spleen deficiency with transport dysfunction and turbid pathogens obstructing the lungs''. This process is intrinsically associated with the inflammatory cascade triggered by disordered macrophage glucose metabolism. Taking the theory of ''spleen failing to disperse essence'' as the entry point， the present study systematically elucidates the progressive pathological mechanism of "energy metabolism disorder-cytokine storm'' in ALI from the perspectives of the ''spleen deficiency-turbid obstruction'' pathogenesis and macrophage glycolysis-pro-inflammatory polarization. In addition， we summarize Chinese medicine compound formulas and monomeric components that ''restore spleen transport and eliminate turbid pathogens''， and explore their potential mechanisms in the treatment of ALI by regulating macrophage metabolic phenotypes to improve the state of ''spleen failing to disperse essence''. This provides a molecular basis for elucidating the ''spleen-lung'' energy metabolism dialogue， deepens understanding of the multi-target regulatory mechanisms of herbal prescriptions， and promotes the deep integration of classical theory with modern medicine.

Objective To investigate the correlation between spread through air space (STAS) of sub-centimeter non-small cell lung cancer and clinical characteristics and radiological features, constructing a nomogram risk prediction model for STAS to provide a reference for the preoperative planning of sub-centimeter non-small cell lung cancer patients. Methods The data of patients with sub-centimeter non-small cell lung cancer who underwent surgical treatment in Nanjing Drum Tower Hospital from January 2022 to October 2023 were retrospectively collected. According to the pathological diagnosis of whether the tumor was accompanied with STAS, they were divided into a STAS positive group and a STAS negative group. The clinical and radiological data of the two groups were collected for univariate logistic regression analysis, and the variables with statistical differences were included in the multivariate analysis. Finally, independent risk factors for STAS were screened out and a nomogram model was constructed. The sensitivity and specificity were calculated based on the Youden index, and area under the curve (AUC), calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the model. Results A total of 112 patients were collected, which included 17 patients in the STAS positive group, consisting of 11 males and 6 females, with a mean age of (59.0±10.3) years. The STAS negative group included 95 patients, with 30 males and 65 females, and a mean age of (56.8±10.3) years. Univariate logistic regression analysis showed that male, anti-GAGE7 antibody positive, mean CT value and spiculation were associated with the occurrence of STAS (P＜0.05). Multivariate regression analysis showed that associations between STAS and male (OR=5.974, 95%CI 1.495 to 23.872), anti-GAGE7 antibody positive (OR=11.760, 95%CI 1.619 to 85.408) and mean CT value (OR=1.008, 95%CI 1.004 to 1.013) were still significant (P＜0.05), while the association between STAS and spiculation was not significant anymore (P=0.438). Based on the above three independent predictors, a nomogram model of STAS in sub-centimeter non-small cell lung cancer was constructed. The AUC value of the model was 0.890, the sensitivity was 76.5%, and the specificity was 91.6%. The calibration curve was well fitted, suggesting that the model had a good prediction efficiency for STAS. The DCA plot showed that the model had a good clinically utility. Conclusion Male, anti-GAGE7 antibody positive and mean CT value are independent predictors of STAS positivity of sub-centimeter non-small cell lung cancer, and the nomogram model established in this study has a good predictive value and provides reference for preoperative planning of patients.

[Objective] To validate and optimize the platelet transfusion refractoriness (PTR) prediction model for patients with hematological disorders established by our center. [Methods] The data of patients with hematological diseases who received platelet transfusions from December 2021 to December 2022 were used as the training set, and data from January 2023 to December 2023 as the validation set. The validation set data was used to validate the predictive model constructed on the training set. Relevant risk factors for PTR were collected through literature review and preliminary studies。 The patients were divided into effective and ineffective groups according to the corrected count increment (CCI) of platelet counts. Predictive factors were screened using univariate and multivariate logistic regression. The calibration of the model were assessed via calibration curves, while discrimination, accuracy, sensitivity, and specificity were evaluated using receiver operating characteristic (ROC) curves Clinical utility was further analyzed with decision curve analysis (DCA). [Results] The Hosmer-Lemeshow (H-L) goodness-of-fit test for the validation set yielded S: P=0.000, indicating that the original model needs optimization. Baseline comparisons and logistic regression identified the number of red blood cell units (RBCU) and platelet units (PLT-U) transfused as key predictors for the optimized model. The H-L goodness-of-fit test S: P values for the training and validation sets were 0.930 and 0.056, respectively; the ROC areas were 0.793 5 and 0.809 4, specificities 90.95% and 84.21%, sensitivities 59.26% and 70.04%, and accuracies 78.14% and 74.10%, respectively. DCA demonstrated clinical net benefit within a prediction probability threshold range of 0.2-0.8. [Conclusion] Transfusion volumes of RBC-U and PLT-U were inversely associated with PTR in hematological patients. The resulting PTR prediction model exhibits moderate predictive efficacy and clinical benefit.

ObjectiveTo establish the multi-technique characteristic profiles of Alumen by X-ray diffraction（XRD）， Fourier-transform infrared spectroscopy（FTIR） and thermogravimetric-differential thermal analysis（TG-DTA）， and to explore the spectral characteristics for rapid identification of Alumen and its potential adulterant， Ammonium alum. MethodsA total of 27 batches of Alumen samples from 8 production regions were collected for preliminary identification based on visual characteristics. The PDF standard cards of XRD were used to differentiate Alumen from A. alum， and the XRD characteristic profiles of Alumen were established， and then the common peaks were screened. Based on hierarchical clustering analysis（HCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， the characteristic information that could be used for identification of Alumen was selected with variable importance in the projection（VIP） value>1. FTIR characteristic profiles of Alumen were established， and key wavenumbers for identification were screened by HCA and OPLS-DA with VIP value>1. Meanwhile， the thermogravimetric differences between Alumen and A. alum were analyzed by TG-DTA， and the thermogravimetric traits that could be used for identification were screened. ResultsAlumen and A. alum could not be effectively distinguished by traits alone. However， by comparing the PDF standard cards of XRD， 15 batches of Alumen and 12 batches of A. alum could be distinguished. In the XRD profiles， 10 characteristic peaks were confirmed， corresponding to diffraction angles of 14.560°， 24.316°， 12.620°， 32.122°， 17.898°， 34.642°， 27.496°， 46.048°， 40.697° and 21.973°. In the FTIR profiles， 4 wavenumber ranges（399.193-403.050， 1 186.010-1 471.420， 1 801.190-2 620.790， 3 612.020-3 997.710 cm^-1） and 12 characteristic wavenumbers（1 428.994， 1 430.922， 1 432.851， 1 434.779， 1 436.708， 1 438.636， 1 440.565， 1 442.493， 1 444.422， 1 446.350， 1 448.279， 1 450.207 cm^-1） were identified. In the TG-DTA profiles， there were characteristic decomposition peaks of ammonium ion and mass reduction features near 555.34 ℃ for A. alum. These characteristics could serve as important criteria for distinguishing the authenticity of Alumen. ConclusionXRD， FTIR and TG-DTA can be used to rapidly detect Alumen and A. alum， and combined with the discriminant features selected through chemometrics， the rapid and accurate identification of Alumen and A. alum can be achieved. The research findings provide new approaches for the rapid identification of Alumen.

ObjectiveTo analyze the "compatibility" relationship of sugars and glycosides and the heat-clearing and blood-cooling effect of the roots of four varieties of Rehmannia glutinosa and provide a basis for research on the pharmacodynamic material basis and quality control of R. glutinosa. MethodsThe content of sugars and glycosides in the roots of four varieties of R. glutinosa was determined during the growth period. The principal component analysis （PCA）， orthogonal partial least squares-discriminant analysis （OPLS-DA）， and the "compatibility" relationship of active components were employed to screen out the differential samples. A rat model of bleeding due to blood heat was used to verify the pharmacodynamic differences and the potential active components of differential samples. ResultsThe content and proportion characteristics of various components in roots of the four varieties of R. glutinosa during the expansion stage and the maturity stage had obvious differences. The proportion of phenylethanoid glycosides at the maturity stage was higher than that at the expansion stage. The R. glutinosa variety 85-5 had special quality characteristics among the tested varieties. All the samples alleviated the symptoms in the rat model. The effect of clearing heat and cooling blood was different between the maturity stage and the expansion stage， as well as between 85-5 samples at the maturity stage and other samples. The effect of clearing heat and cooling blood of R. glutinosa roots was the result of the combined action of multiple components in R. glutinosa roots and might be related to the high proportions of polysaccharides， iridoid glycosides， and phenylethanoid glycosides. ConclusionThe growth stage and variety affect the quality of R. glutinosa roots. The effect of clearing heat and cooling blood of R. glutinosa roots was related to the content and proportions of various components. The study can provide a basis for the basic research on the active components and quality control of R. glutinosa.

ObjectiveTo establish the multi-technique characteristic profiles of Alumen by X-ray diffraction（XRD）， Fourier-transform infrared spectroscopy（FTIR） and thermogravimetric-differential thermal analysis（TG-DTA）， and to explore the spectral characteristics for rapid identification of Alumen and its potential adulterant， Ammonium alum. MethodsA total of 27 batches of Alumen samples from 8 production regions were collected for preliminary identification based on visual characteristics. The PDF standard cards of XRD were used to differentiate Alumen from A. alum， and the XRD characteristic profiles of Alumen were established， and then the common peaks were screened. Based on hierarchical clustering analysis（HCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， the characteristic information that could be used for identification of Alumen was selected with variable importance in the projection（VIP） value>1. FTIR characteristic profiles of Alumen were established， and key wavenumbers for identification were screened by HCA and OPLS-DA with VIP value>1. Meanwhile， the thermogravimetric differences between Alumen and A. alum were analyzed by TG-DTA， and the thermogravimetric traits that could be used for identification were screened. ResultsAlumen and A. alum could not be effectively distinguished by traits alone. However， by comparing the PDF standard cards of XRD， 15 batches of Alumen and 12 batches of A. alum could be distinguished. In the XRD profiles， 10 characteristic peaks were confirmed， corresponding to diffraction angles of 14.560°， 24.316°， 12.620°， 32.122°， 17.898°， 34.642°， 27.496°， 46.048°， 40.697° and 21.973°. In the FTIR profiles， 4 wavenumber ranges（399.193-403.050， 1 186.010-1 471.420， 1 801.190-2 620.790， 3 612.020-3 997.710 cm^-1） and 12 characteristic wavenumbers（1 428.994， 1 430.922， 1 432.851， 1 434.779， 1 436.708， 1 438.636， 1 440.565， 1 442.493， 1 444.422， 1 446.350， 1 448.279， 1 450.207 cm^-1） were identified. In the TG-DTA profiles， there were characteristic decomposition peaks of ammonium ion and mass reduction features near 555.34 ℃ for A. alum. These characteristics could serve as important criteria for distinguishing the authenticity of Alumen. ConclusionXRD， FTIR and TG-DTA can be used to rapidly detect Alumen and A. alum， and combined with the discriminant features selected through chemometrics， the rapid and accurate identification of Alumen and A. alum can be achieved. The research findings provide new approaches for the rapid identification of Alumen.

ObjectiveTo analyze the "compatibility" relationship of sugars and glycosides and the heat-clearing and blood-cooling effect of the roots of four varieties of Rehmannia glutinosa and provide a basis for research on the pharmacodynamic material basis and quality control of R. glutinosa. MethodsThe content of sugars and glycosides in the roots of four varieties of R. glutinosa was determined during the growth period. The principal component analysis （PCA）， orthogonal partial least squares-discriminant analysis （OPLS-DA）， and the "compatibility" relationship of active components were employed to screen out the differential samples. A rat model of bleeding due to blood heat was used to verify the pharmacodynamic differences and the potential active components of differential samples. ResultsThe content and proportion characteristics of various components in roots of the four varieties of R. glutinosa during the expansion stage and the maturity stage had obvious differences. The proportion of phenylethanoid glycosides at the maturity stage was higher than that at the expansion stage. The R. glutinosa variety 85-5 had special quality characteristics among the tested varieties. All the samples alleviated the symptoms in the rat model. The effect of clearing heat and cooling blood was different between the maturity stage and the expansion stage， as well as between 85-5 samples at the maturity stage and other samples. The effect of clearing heat and cooling blood of R. glutinosa roots was the result of the combined action of multiple components in R. glutinosa roots and might be related to the high proportions of polysaccharides， iridoid glycosides， and phenylethanoid glycosides. ConclusionThe growth stage and variety affect the quality of R. glutinosa roots. The effect of clearing heat and cooling blood of R. glutinosa roots was related to the content and proportions of various components. The study can provide a basis for the basic research on the active components and quality control of R. glutinosa.

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*