The Expert Consensus on Clinical Application of Qinbaohong Zhike Oral Liquid in Treatment of Acute Bronchitis and Acute Attack of Chronic Bronchitis （GS/CACM 337-2023） was released by the China Association of Chinese Medicine on December 13^th， 2023. This expert consensus was developed by experts in methodology， pharmacy， and Chinese medicine in strict accordance with the development requirements of the China Association of Chinese Medicine （CACM） and based on the latest medical evidence and the clinical medication experience of well-known experts in the fields of respiratory medicine （pulmonary diseases） and pediatrics. This expert consensus defines the application of Qinbaohong Zhike oral liquid in the treatment of cough and excessive sputum caused by phlegm-heat obstructing lung， acute bronchitis， and acute attack of chronic bronchitis from the aspects of applicable populations， efficacy evaluation， usage， dosage， drug combination， and safety. It is expected to guide the rational drug use in medical and health institutions， give full play to the unique value of Qinbaohong Zhike oral liquid， and vigorously promote the inheritance and innovation of Chinese patent medicines.

BACKGROUND:Ferroptosis is a mode of programmed cell death distinct from apoptosis,necrosis,and other novel cellular deaths,which occurs mainly due to accumulated lipid peroxidation.Ferroptosis has been shown to be involved in the pathological process following subarachnoid hemorrhage.Baicalein,serving as an adept sequestered of iron,evinces its prowess by quelling lipid peroxidative cascades.Nonetheless,the enigma lingers as to whether baicalein possesses the capacity to ameliorate neuronal ferroptosis,elicited in the wake of early brain injury after subarachnoid hemorrhage. OBJECTIVE:To investigate the effect and mechanism of baicalein on neuronal ferroptosis after subarachnoid hemorrhage. METHODS:Primary neuronal cells were extracted from C57BL/6L fetal mice at 16-17 days of gestation.Hemoglobin was used to stimulate primary neuronal cells to simulate an in vitro subarachnoid hemorrhage model.The viability of primary neuronal cells treated with baicalein at concentrations of 5,15,25,50,and 100 μmol/L for 24 hours was detected by CCK-8 assay to determine the optimal concentration of baicalein.Primary neuronal cells were divided into control group,hemoglobin group,and hemoglobin+baicalein group.The levels of reactive oxygen species and malondialdehyde in cells were detected by kits.The mRNA expressions of ferroptosis-related markers PTGS2,SLC7A11,and glutathione peroxidase 4 were detected by RT-PCR.The primary neuronal cells were further divided into control group,SLC7A11 inhibitor Erastin group,hemoglobin group,hemoglobin+baicalein group,and hemoglobin+baicalein+Erastin group.The expression of the ferroptosis related markers SLC7A11 and glutathione peroxidase 4 was detected by western blot assay. RESULTS AND CONCLUSION:(1)Baicalein(25 μmol/L)was selected as the following experimental concentration.(2)Compared with the hemoglobin group,the level of malondialdehyde and the level of reactive oxygen species were significantly decreased(P<0.05)in the hemoglobin+baicalein group.(3)Compared with the hemoglobin group,the mRNA expression of PTGS2 significantly decreased,and the mRNA expression of SLC7A11 and glutathione peroxidase 4 significantly increased(P<0.000 1)in the hemoglobin+baicalein group.(4)SLC7A11 inhibitor Erastin could reverse the baicalin-improved ferroptosis effect to a certain extent(P<0.05).(5)The results showed that baicalein could alleviate the ferroptosis of neuronal cells after subarachnoid hemorrhage through the SLC7A11/GPX4 pathway.

BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult

This article reports a child with cardioaciocutaneous syndrome (CFCS) caused by a rare microdeletion of chromosome 19p13.3, and a literature review is conducted. The child had unusual facies, short stature, delayed mental and motor development, macrocephaly, and cardiac abnormalities. Whole-exome sequencing identified a 1 040 kb heterozygous deletion in the 19p13.3 region of the child, which was rated as a "pathogenic variant". This is the first case of CFCS caused by a loss-of-function mutation reported in China, which enriches the genotype characteristics of CFCS. It is imperative to enhance the understanding of CFCS in children. Early identification based on its clinical manifestations should be pursued, and genetic testing should be performed to facilitate diagnosis.
Humans ; Chromosome Deletion ; Chromosomes, Human, Pair 19/genetics* ; Ectodermal Dysplasia/genetics* ; Facies ; Failure to Thrive/genetics* ; Heart Defects, Congenital/genetics*

The Compilation Instructions for Expert Consensus on Clinical Application of Dieda Huoxue capsules systematically expound the development methods and evidence-based basis of this consensus. In view of the weak clinical application evidence and ambiguous indications of Dieda Huoxue capsules， the Institute of Basic Research in Clinical Medicine of the China Academy of Chinese Medical Sciences and Wangjing Hospital took the lead and collaborated with 33 experts from 28 medical institutions nationwide. They strictly followed the World Health Organization （WHO） guideline-making norms and the Grading of Recommendations Assessment， Development and Evaluations （GRADE） evidence-grading system and completed the compilation through multidisciplinary cooperation. The workflow included constructing clinical questions （19 items were screened by the nominal group technique）， retrieving evidence （from Chinese and English databases and grey literature）， assessing safety （integrating drug monitoring data and clinical investigations）， and forming recommendations and consensus suggestions （3 recommendations were reached via the GRADE grid method， and 16 consensus suggestions were reached by the majority vote rule）. The results indicate that the consensus clearly states that this medicine （Dieda Huoxue capsules） is applicable to conditions like traumatic injury， blood stasis-induced pain， and sudden lumbar sprains. The recommended dose is 6 capsules each time， twice a day. Combining oral administration with external application can enhance the efficacy， and elderly patients should take the medicine at intervals. Safety monitoring suggests that it should be used with caution in people with a bleeding tendency and those with an allergic constitution. The compilation process involved three rounds of reviews by internal and external experts. Literature analysis， the Delphi method， and clinical applicability tests were employed to ensure methodological rigor. The compilation instructions comprehensively present key aspects such as project approval and registration， conflict-of-interest statements， and evidence evaluation through 12 appendices， providing methodological support for the clinical translation of the consensus. In the future， it will be continuously improved through a dynamic revision mechanism.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Based on CT scan data,a bionic model of lumbar spine injuries with high biofidelity is developed and validated through cadaver experiments.Decoupling the constraint system that affects occupants during collisions due to inertial forces and the subsequent pressure exerted by the seat upon returning to position,a simulated fall experiment is designed.The simulated outcomes are trained and predicted using deep learning algorithms,and the accuracy of the trained neural network prediction model is verified.Key parameters are analyzed for correlation using principal component analysis and cross-reverse methods.The results shows that the predicted lumbar spine injury model obtained from training has high reliability(R2>0.9).Comprehensive analysis reveals that after experiencing axial impact,the L4 vertebral body bears the highest impact load and can be used as a representative measure of lumbar spine injury.Among the environmental variables,the axial force on the L4 lumbar spine is mainly affected by torso mass and fall height,both of which have positive correlations.Torso mass,fall height,and posture angle all have positive effects on internal energy.Conversely,torso mass and fall height have negative correlations with stress.These research findings provide a scientific basis for further elucidating lumbar spine injury mechanisms in intelligent cockpit environments,devising corresponding safety protection measures,and evaluating occupant safety in automobiles.

The integration of multi-source data and the establishment of early warning indicator systems constitute pivotal elements for advancing surveillance and early warning capacities in respiratory infectious diseases. Given the multifaceted transmission mechanisms and complex contributing factors inherent in respiratory infectious diseases, surveillance datasets and associated early warning indicators demonstrate notable heterogeneity and sophisticated interrelationships. Furthermore, as surveillance and early warning requirements significantly vary across diverse epidemiological scenarios, accurate assessment of the value and applicability of distinct data types and indicators is imperative. This paper systematically reviews and synthesizes recent advancements in surveillance data and early warning indicators for respiratory infectious diseases, drawing on both domestic and international research. Particular attention is dedicated to analyzing the applicability and efficacy of various data types and indicators within multiple practical contexts, aiming to provide robust theoretical frameworks and methodological guidance to facilitate the development of resilient and efficient surveillance and early warning systems for respiratory infectious diseases.

The ventral anterior (VA) nucleus of the thalamus is a major target of the basal ganglia and is closely associated with the pathogenesis of Parkinson's disease (PD). Notably, the VA receives direct innervation from the hypothalamic histaminergic system. However, its role in PD remains unknown. Here, we assessed the contribution of histamine to VA neuronal activity and PD motor deficits. Functional magnetic resonance imaging showed reduced VA activity in PD patients. Optogenetic activation of VA neurons or histaminergic afferents significantly alleviated motor deficits in 6-OHDA-induced PD rats. Furthermore, histamine excited VA neurons via H1 and H2 receptors and their coupled hyperpolarization-activated cyclic nucleotide-gated channels, inward-rectifier K⁺ channels, or Ca²⁺-activated K⁺ channels. These results demonstrate that histaminergic afferents actively compensate for Parkinsonian motor deficits by biasing VA activity. These findings suggest that targeting VA histamine receptors and downstream ion channels may be a potential therapeutic strategy for PD motor dysfunction.
Animals ; Histamine/metabolism* ; Male ; Oxidopamine/toxicity* ; Rats ; Ventral Thalamic Nuclei/physiopathology* ; Rats, Sprague-Dawley ; Disease Models, Animal ; Parkinson Disease/metabolism* ; Neurons/physiology* ; Humans ; Optogenetics

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional