OBJECTIVE To establish the fingerprint of Pinghuo tea standard decoction and a method for determination of multi-component to clarify the transfer relationship of quantities and quality from pieces and standard decoction. METHODS Fifteen batches of Pinghuo tea standard decoction were prepared and the extract rate was determined； the fingerprint of the preparation was established by using high-performance liquid chromatography（HPLC）； the similarity evaluation and the determination of common peaks were performed， and chemometric analysis was performed； the same method was used to determine the content of indicator components and the transfer rate was calculated. The chromatographic column was Venusil C18 column with mobile phase consisted of acetonitrile-0.1% phosphoric acid solution （gradient elution）； the column temperature was 30 ℃， and the detection wavelengths were 238 nm （0-37 min， 85-102 min） and 330 nm （37-85 min） at a flow rate of 1.0 mL/min with an injection volume of 10 μL. RESULTS The similarity of HPLC fingerprints for 15 batches of Pinghuo tea standard decoction was not lower than 0.968. A total of 24 common peaks were calibrated and 9 peaks were recognized， which were as follows neochlorogenic acid （peak 3）， chlorogenic acid （peak 6）， geniposide （peak 9）， glycyrrhizin （peak 10）， galuteolin （peak 11）， isochlorogenic acid A （peak 14）， luteolin （peak 21）， kaempferol （peak 23） and glycyrrhizic acid （peak 24）. Cluster analysis， principal component analysis and orthogonal partial least squares discriminant analysis showed consistent results， all of which could classify the 15 batches of samples into three categories. The linear range of indicator components in 15 batches of Pinghuo tea standard decoction， such as geniposide， luteolin， isochlorogenic acid A， glycyrrhizin， and glycyrrhizic acid， were 0.020 580-0.411 600， 0.001 617-0.080 850， 0.006 076-0.607 600， 0.005 125-0.071 740， and 0.017 288-0.432 200 mg/mL， respectively； RSDs of precision， repeatability， stability and recovery rate tests were all not higher than 4% （n＝6）. The mass fractions ranged 3.227 9-10.002 2， 0.297 4-0.554 6， 3.350 1-6.159 6， 0.720 6-1.073 3， 2.003 1-3.030 1 mg/g； transfer rates from the pieces and standard decoction were 19.762 8%-35.840 5%， 12.123 3%-21.254 0%， 46.097 2%-82.869 4%， 58.708 8%-91.629 6%， 39.114 3%-63.710 6%. The transfer rates of the extract from 15 batches of Pinghuo tea standard decoction ranged from 61.15%-84.68%. CONCLUSIONS Established HPLC fingerprint and content determination methods in this study are simple and accurate， which can provide reference for the quantitative value transfer study， quality control， clinical application and the development of subsequent formulations of Pinghuo tea standard decoction.

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.

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*

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Although enteric glial cell (EGC) abnormal activation is reported to be involved in the pathogenesis of Parkinson's disease (PD), and inhibition of EGC gliosis alleviated gut and dopaminergic neuronal dysfunction was verified in our previous study, the potential role of gut microbiota on EGC function in PD still need to be addressed. In the present study, fecal microbiota transplantation revealed that EGC function was regulated by gut microbiota. By employing 16S rRNA and metabolomic analysis, we identified that 3-indolepropionic acid (IPA) was the most affected differential microbial metabolite that regulated EGC gliosis. The protective effects of IPA on PD were validated in rotenone-stimulated EGCs and rotenone (30 mg/kg i.g. for 4 weeks)-induced PD mice, as indicated by decreased inflammation, improved intestinal and brain barrier as well as dopaminergic neuronal function. Mechanistic study showed that IPA targeted pregnane X receptor (PXR) in EGCs, and inhibition of IL-13Rα1 involved cytokine-cytokine receptor interaction pathway, leading to inactivation of downstream JAK1-STAT6 pathway. Our data not only provided evidence that EGC gliosis was critical in spreading intestinal damage to brain, but also highlighted the potential role of microbial metabolite IPA in alleviating PD pathological damages through gut-brain axis.

Hepatic stellate cells (HSCs) are the primary fibrogenic cells in the liver, and their activation plays a crucial role in the development and progression of hepatic fibrosis. Here, we report that retinoid X receptor-alpha (RXRα), a unique member of the nuclear receptor superfamily, is a key modulator of HSC activation and liver fibrosis. RXRα exerts its effects by modulating calcium/calmodulin-dependent protein kinase kinase β (CaMKKβ)-mediated activation of AMP-activated protein kinase-alpha (AMPKα). In addition, we demonstrate that K-80003, which binds RXRα by a unique mechanism, effectively suppresses HSC activation, proliferation, and migration, thereby inhibiting liver fibrosis in the CCl₄ and amylin liver NASH (AMLN) diet animal models. The effect is mediated by AMPKα activation, promoting mitophagy in HSCs. Mechanistically, K-80003 activates AMPKα by inducing RXRα to form condensates with CaMKKβ and AMPKα via a two-phase process. The formation of RXRα condensates is driven by its N-terminal intrinsic disorder region and requires phosphorylation by CaMKKβ. Our results reveal a crucial role of RXRα in liver fibrosis regulation through modulating mitochondrial activities in HSCs. Furthermore, they suggest that K-80003 and related RXRα modulators hold promise as therapeutic agents for fibrosis-related diseases.

[This corrects the article DOI: 10.1016/j.apsb.2025.02.029.].

Following the principles of evidence-based medicine,in accordance with the structure and drafting rules of standardized documents,based on literature research,according to the characteristics of chronic cough in children and issues that need to form a consensus,the TCM Guidelines for Diagnosis and Treatment of Chronic Cough in Children was formulated based on the Delphi method,expert discussion meetings,and public solicitation of opinions.The guideline includes scope of application,terms and definitions,eti-ology and diagnosis,auxiliary examination,treatment,prevention and care.The aim is to clarify the optimal treatment plan of Chinese medicine in the diagnosis and treatment of this disease,and to provide guidance for improving the clinical diagnosis and treatment of chronic cough in children with Chinese medicine.

Traditional spine surgery frequently encounters difficulties with inadequate surgical visualization and high risk.Robot-assisted spine surgery is quickly evolving,particularly in screw placement,providing three-dimensional imaging and precise positioning to optimize the surgical process. Robot-assisted systems can increase surgical precision,reduce operating time and radiation exposure,and improve patient prognosis. They also have strong image recognition and analysis capabilities,reducing intraoperative instability and fatigue and allowing remote manipulation.While robot-assisted spine surgery has demonstrated noteworthy advantages in regards to screw placement accuracy and reduced radiation exposure,its effects on operative time remain subject to debate,with cost being a significant hindrance to widespread implementation.Long-term clinical validation and studies of outcomes are necessary for the extensive use of robotic-assisted spine surgery.Future priorities include the enhancement of surgical navigation and imaging,integration of artificial intelligence,improvement of telesurgical capabilities,expansion of robotic functionality,and the development of policy guidance and clinical guidelines to accompany the growth of technology.Robot-assisted spine surgery enhances accuracy and safety,and is anticipated to assume an increasingly crucial role in spine surgery as technology advances and becomes more widely available.