ObjectiveBased on traditional quality evaluation of Gardeniae Fructus（GF） recorded in historical materia medica， this study systematically compared the quality differences between wild and cultivated GF from morphological characteristics， microscopic features， and contents of primary and secondary metabolites. MethodsVernier calipers and analytical balances were used to measure the length， diameter and individual fruit weight of wild and cultivated GF， and the aspect ratio was calculated. A colorimeter was used to determine the chromaticity value of wild and cultivated GF， and the paraffin sections of them were prepared by safranin-fast green staining and examined under an optical microscope to observe their microstructure. Subsequently， the contents of water-soluble and alcohol-soluble extracts of wild and cultivated GF were detected by hot immersion method under the general rule 2201 in volume Ⅳ of the 2020 edition of the Pharmacopoeia of the People's Republic of China， the starch content was measured by anthrone colorimetric method， the content of total polysaccharides was determined by phenol-sulfuric acid colorimetric method， the sucrose content was determined by high performance liquid chromatography coupled with evaporative light scattering detection（HPLC-ELSD）， and the contents of representative components in them were measured by ultra-performance liquid chromatography（UPLC）. Finally， correlation analysis was conducted between quality traits and phenotypic traits， combined with multivariate statistical analysis methods such as principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， key differential components between wild and cultivated GF were screened. ResultsIn terms of traits， the wild GF fruits were smaller， exhibiting reddish yellow or brownish red hues with significant variation between batches. While the cultivated GF fruits are larger， displaying deeper orange-red or brownish red. The diameter and individual fruit weight of cultivated GF were significantly greater than those of wild GF， while the blue-yellow value（b^*） of wild GF was significantly higher than that of cultivated GF. In the microstructure， the mesocarp of wild GF contained numerous scattered calcium oxalate cluster crystals， while the endocarp contained stone cell class round， polygonal or tangential prolongation， undeveloped seeds were visible within the fruit. In contrast， the mesocarp of cultivated GF contained few calcium oxalate cluster crystals， or some batches exhibited extremely numerous cluster crystals. The stone cells in the endocarp were predominantly round-like， with the innermost layer arranged in a grid pattern. Seeds were basically mature， and only a few immature seeds existed in some batches. Regarding primary metabolite content， wild GF exhibited significantly higher total polysaccharide level than cultivated GF（P<0.01）. In category-specific component content， wild GF exhibited significantly higher levels of total flavonoids and total polyphenols compared to cultivated GF（P<0.01）. Analysis of 12 secondary metabolites revealed that wild GF exhibited significantly higher levels of Shanzhiside， deacetyl asperulosidic acid methyl ester， gardenoside and chlorogenic acid compared to cultivated GF（P<0.01）. Conversely， the contents of genipin 1-gentiobioside， geniposide and genipin were significantly lower in wild GF（P<0.01）. ConclusionThere are significant differences between wild and cultivated GF in terms of traits， microstructure， and contents of primary and secondary metabolites. At present， the quality evaluation system of cultivated GF remains incomplete， and this study provides a reference for guiding the production of high-quality GF medicinal materials.

Background: Influenza A virus (IAV) is a negative-sense RNA virus of the Orthomyxoviridae family and is the etiological agent of a highly contagious acute respiratory disease that can lead to acute lung injury. Objective: To elucidate the molecular mechanisms of IAV infection, an integrative research approach combining gene expression profiling, multinetwork analysis, and in vivo experimental validations was employed. Methods: First, a series of network-based analyses were performed, including protein-protein interaction network construction, weighted gene co-expression network analysis, and subsequent gene set enrichment analysis, to identify the major underlying mechanisms of IAV infection. Following gene expression analysis, core targets, both direct and indirect regulators, were screened. An IAV (H1N1) strain A/PR/8/34-induced acute lung injury mouse model was constructed for in vivo validations. Batch one included two groups to evaluate findings from the multi-network analysis: Mock (n = 10; 5 males and 5 females) and IAV (n = 10; 5 males and 5 females). Batch two included three groups to assess the role of toll-like receptor 3 (TLR3) in IAV infection: Mock (n = 6; 3 males and 3 females), IAV (n = 6; 3 males and 3 females), and TLR3 inhibitor (n = 6; 3 males and 3 females). Body weight was measured on days 0, 3, and 5 after infection. On day 5, lung tissues were collected to assess viral load and histopathological changes. Key targets were examined using enzyme-linked immunosorbent assay, Western blotting, and immunofluorescence staining, both in sera and lung tissues. Results: IAV infection was significantly associated with dysregulation of the immune-inflammation system, such as the LTR, nucle-otide-binding oligomerization domain-(NOD) like receptor, retinoic acid-inducible gene I-like receptor, and nuclear factor kappa-B signaling pathways. Gene set enrichment analysis further indicated that the TLR and necroptosis signaling pathways played crucial roles in the progression of IAV infection (TLR signaling pathway normalized enrichment score = 2.3941, P = 1.00 × 10 ⁻¹⁰; necroptosis normalized enrichment score = 1.9421, P = 6.21 × 10 ⁻⁷). Among the core targets, TLR3 and mixed lineage kinase domain-like protein (MLKL) may regulate gene expression at the transcriptional level (all P < 0.05). In vivo validation using an IAV (PR8) infected acute lung injury mouse model demonstrated increased viral load and lung index, alveolar structural damage, and inflammatory cell infiltration. Immunofluorescence staining exhibited large gaps in Lamin B1 staining and breaches in Emerin signals following IAV-PR8 infection. Expression levels of TLR3, p-receptor-interacting serine/threonine-protein kinase 3 (RIPK3)/RIPK3, and p-mixed lineage kinase domain-like protein (MLKL)/MLKL proteins in lung tissues, as well as proinflammatory factors and mediators in sera, were significantly elevated after IAV infection. Moreover, enhanced neutrophil infiltration (myeloperoxidase) and citrullinated histone H3 (a neutrophil extracellular trap-specific marker), both established indicators of neutrophil extracellular trap formation, were observed. Notably, treatment with a TLR3 inhibitor significantly ameliorated IAV-induced acute lung injury by regulating necroptosis-related targets. Conclusion: Our study provides network-based in vivo evidence that TLR3-receptor-interacting serine/threonine-protein kinase 3-MLKL-mediated necroptosis may underlie IAV-induced acute lung injury and could serve as a potential therapeutic target in severe influenza cases.

Objective To develop and validate a prediction model for in-hospital recurrent intensive care unit (ICU) admission in critically ill patients with ischemic stroke (IS) based on machine learning (ML) algorithms. Methods Clinical data from 2 929 IS patients were included from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Least absolute shrinkage and selection operator (LASSO) regression was used to identify predictive factors, and the synthetic minority over-sampling technique (SMOTE) was employed to create a derivation cohort comprising 2 583 patients. These patients were randomly divided into a training set (n=2 066) and a test set (n=517) at an 8:2 ratio. Five ML algorithms, including decision tree, random forest, adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), and support vector machine (SVM), were performed to construct prediction models. Five-fold cross-validation was used to evaluate the performance of the model in the training set. The area under the receiver operating characteristic curve (ROC-AUC) and decision curve analysis (DCA) were used to assess and compare the models in the testing set. The best-performing model was interpreted by shapley additive explanations (SHAP). Results Among the 2 929 patients included, 704 (24.0%) experienced in-hospital recurrent ICU admission. Among the five ML models, the random forest model demonstrated the best predictive performance, with an AUC of 0.839 (95%CI 0.801–0.877). Feature importance analysis identified five most significant features affecting model prediction, including APS Ⅲ score, albumin, age, heart rate, and SOFA score. Conclusions ML-based models can effectively predict the risk of in-hospital recurrent ICU admission in critically ill patients with IS. The random forest model showed superior predictive performance, which may have potential applications in early clinical risk stratification and intervention.

ObjectiveTo analyze the current status of traditional Chinese medicine （TCM） prediction model research for emerging acute infectious diseases， in order to provide reference for subsequent model construction and optimization. MethodsEight databases， including PubMed， Cochrane Library， and Embase， were systematically searched to comprehensively collect Chinese and English literature on TCM prediction models for emerging acute infectious diseases. The search period ranged from database inception to July 5， 2024. Reporting quality compliance was evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis—Traditional Chinese Medicine （TRIPOD-TCM） guideline. Risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool （PROBAST）. ResultsA total of 21 studies were included， comprising 11 early warning model studies， 5 diagnostic model studies， 3 prognostic model studies， and 2 prescription prediction model studies， involving 84 models in total. The diseases involved were mainly viral infectious diseases such as coronavirus disease 2019 （COVID-19） and hepatitis B. The most frequently applied TCM theory was the "five movements and six climates" theory. The most commonly used modeling method was neural network-based approaches. The most frequently reported predictive factors were meteorological variables， including daily mean temperature， wind speed， precipitation， relative humidity， and vapor pressure. In terms of reporting quality， the included studies showed poor item-level compliance， with only 13 sub-items achieving compliance rates above 50%. Overall compliance ranged from 27% to 59% across studies. Although the included studies showed relatively good applicability， all were assessed as having a high risk of bias. The main sources of bias included inappropriate handling of missing data， absence of calibration methods， and unreasonable validation strategies， indicating deficiencies across multiple stages of model development. ConclusionCurrent research on TCM prediction models for emerging acute infectious diseases exhibits distinctive modeling characteristics. These models are based on traditional TCM theories such as the "five movements and six climates"， incorporate TCM-specific features as predictive variables， and use TCM syndromes as outcomes. However， they generally suffer from low reporting compliance and a high risk of bias. Future studies should systematically design model development and validation processes， adhere to TRIPOD-TCM reporting standards， and apply PROBAST or develop dedicated bias risk assessment tools for TCM prediction models to improve methodological quality and promote their translation into clinical practice.

Objective To genetically investigate the protective effects of statins on breast cancer. Methods Instrumental variables for the statin target gene HMGCR and five other cholesterol-regulated genes (LDLR, PCSK9, ABCG8, APOB, and NPC1L1) were obtained from previous expression quantitative trait locus (eQTL) studies. Cholesterol-regulated genes predicted by these instrumental variables served as the exposure factors. Mendelian randomization based on pooled data (SMR) was conducted to explore the genetic effects of exposure factors on the incidence risk of all breast cancers, ER+ breast cancer, and ER-breast cancer. Instrumental variables for total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C) were derived from a previous human genome-wide association study and restricted to be chromosomally located within 100 kb of the above cholesterol regulatory genes; the instrumental variables could predict TC, LDL-C, or non-HDL-C levels under the regulation of the abovementioned cholesterol-associated genes which were used as exposure factors. Two-sample Mendelian randomization (IVW, MR-PRESSO, and MR-Egger) was used to explore the genetic effects of exposure factors on the risk of all breast cancers, ER+ breast cancer, and ER− breast cancer. Results SMR analysis reported that elevated HMGCR expression was significantly associated with the increased incidence risk of all breast cancers and ER+ breast cancer (P=0.044 and P=0.039, respectively) but not with the change in incidence risk of ER− breast cancer (P=0.190); the other five regulatory genes were not significantly correlated with the change in incidence risk of all breast cancers, ER+ breast cancer, and ER− breast cancer (all P>0.05). IVW analysis reported that under the regulation of HMGCR, elevated levels of peripheral TC, LDL-C, and non-HDL-C significantly increased the incidence risk of all breast cancers (P=1.160e-05, P=1.248e-05, and P=1.869e-05) and the incidence risk of ER+ breast cancer (P=3.181e-04, P=2.231e-04, and P=3.520e-04), but they were not associated with a change in the incidence risk of ER− breast cancer (P=0.062, P=0.133, and P=0.055). The results of MR-PRESSO and MR-Egger analyses supported the IVW results. Conclusion Statins could reduce the incidence risk of ER+ breast cancer at the genetic level, but there is no such protective effects on ER− breast cancer.

Diabetes of the exocrine pancreas(DEP)used to be called pancreatic diabetes,pancreatogenic diabetes or type 3c diabetes mellitus.Currently,the incidence of DEP is higher than that of type 1 diabetes mellitus.The pathogenesis and clinical manifestations of DEP are related to primary pancreatic diseases.In terms of management,we need to consider both pancreatic endocrine and exocrine functions,and comprehensively treat diabetes mellitus and primary pancreatic diseases.By now,there has been no guideline related to DEP;its diagnostic criteria,differentiation with type 2 diabetes mellitus,and selection of hypoglycemic programs are challenges in clinical practice.This article reviews the clinical studies related to DEP,and summarizes the evolution of its terminology,pathogenesis,clinical manifestations,complications,diagnosis,treatment and management.

Objective To develop a machine learning model integrating preoperative chest CT radiomic features with clinical data for predicting 5-year postoperative recurrence risk in stage Ⅰ non-small cell lung cancer(NSCLC)patients undergoing surgical resection.Methods A total of 217 patients with pathologically confirmed stage Ⅰ NSCLC(selected from 778 initially screened cases based on our inclusion and exclusion criteria)treated in Army Medical Center of PLA between January 2014 and December 2019 were retrospectively enrolled,including 53 recurrence cases and 164 non-recurrence cases within 5-year follow-up.They were randomly divided into a training set(n=173)and a validation set(n=44)in a ratio of 8:2.Radiomic models were established based on extracted features from tumor-dominant regions of interest(ROI)on CT images,while clinical models were developed using demographic characteristics and preoperative laboratory examinations.A combined model was further constructed by integrating both feature sets,and model performance was compared to identify the optimal predictive model.Results This study screened the features from non-contrast CT images and ultimately selected 7 radiomic features for constructing radiomic model.Among 6 machine learning algorithms,the adaptive boosting(Adaboost)model demonstrated the best overall predictive performance,with an area under the curve(AUC)of 0.866(95%CI:0.808～0.923;accuracy:0.832,specificity:0.884)in the training set and of 0.806(95%CI:0.630～0.983;accuracy:0.795,specificity:0.971)in the validation set.Univariate and multivariate logistic regression analyses identified 4 clinical features for clinical model construction.The clinical model achieved an AUC value of 0.874(95%CI:0.821～0.928;accuracy:0.827,specificity:0.891)in the training set and 0.813(95%CI:0.677～0.948;accuracy:0.636,specificity:0.600)in the validation set.By integrating the 7 radiomic features and 4 clinical features using a feature-level fusion strategy,the combined model exhibited further improved predictive performance,with an AUC value of 0.953(95%CI:0.924～0.983;accuracy:0.884,specificity:0.860)and 0.852(95%CI:0.729～0.976;accuracy:0.682,specificity:0.629),respectively in the training set and the validation set.Conclusion The combined model integrating preoperative CT radiomic features with clinical risk factors may provide an evidence-based framework for evaluating 5-year postoperative recurrence risk in stage Ⅰ NSCLC patients.

As a serine protease,thrombin can convert soluble fibrinogen into insoluble fibrin and plays a pivotal role in the coagulation cascade.Therefore,the accurate quantitative assay of thrombin levels is of great value in the evaluation of coagulation function,clinical screening and prognostic monitoring of coagulation-related diseases,and screening of drugs for targeted therapy.Existing methods for thrombin detection can be divided into two categories,e.g.,the assay of concentration levels using nucleic acid aptamers as the affinity elements and the assay of activity levels based on the hydrolytic cleavage of substrate peptides.In recent years,electrochemical biosensors have attracted much attention in thrombin detection due to high sensitivity,high selectivity,simple instrument,fast response,and good portability.In this review,the latest research progress in methods for electrochemical detection of thrombin was summarized,focusing on the detection principles and the applied signal amplification strategies of related electrochemical biosensors.In addition,the challenges with respect to the practical use of electrochemical thrombin biosensors and the prospects were discussed.

Objective To verify the efficacy of a domestic human DNA quantification kit based on real-time fluorescence quantitative PCR in detecting the total human DNA concentration,male DNA concen-tration in mixed male/female DNA samples,the degree of DNA degradation and inhibitor tolerance.Methods Samples with different concentrations,different male/female ratios,different concentrations of inhibitors,and different degradation degrees were tested using the domestic human DNA quantification kit based on real-time fluorescence quantitative PCR.This kit was compared with a similar product on the market and was applied to the detection of DNA from real cases.Results This human DNA quan-tification kit can effectively detect human DNA as low as 0.001 65 ng/μL,and 6.25 pg/μL of male DNA in mixed samples with a male-to-female ratio of 1∶15 000.Even when the sample contains as high as 400 ng/μL of humic acid or 1 000 μmol/L of hemin alone,the DNA concentration can still be accurately detected.The degradation index can effectively characterize the degradation degree of the sample.This kit has been successfully applied in forensic practice.Conclusion This human DNA quan-tification kit is accurate and reliable in detection.It can accurately reflect the degradation of DNA and inhibitor tolerance.It has good performance in quantitative accuracy,determination of the male/female ratio in mixed samples,and inhibitor tolerance.It has application potential in forensic case examination.

Aim To investigate the effect of p-AMPK activity on autophagy in different subtypes of MDA-MB-231(triple-negative breast cancer cells)and MCF-7(estrogen receptor-positive cells)and its regulatory mechanism.Methods MDA-MB-231 cells were trea-ted with EBSS,Baf-A1,and EBSS+Baf-A1 for four hours,and MCF-7 cells for eight hours.The effects of autophagy on cell proliferation and apoptosis were ob-served,mitochondrial morphology was examined,and the expression of autophagy markers LC3B,P62,LAMP1,TOM20,AMPK,p-AMPK,ULK1,and Bec-lin1/VPS34 proteins was detected.The autophagy pathway was validated by inhibiting AMPK activity.Results Breast cancer cells underwent autophagy af-ter starvation induction(EBSS),with inconsistent au-tophagy processes observed in different subtypes of breast cancer cells.Autophagy inhibited cell prolifera-tion.In MDA-MB-231 cells,autophagy led to an in-crease in p-AMPK levels and a decrease in ULK1 lev-els,initiating autophagy through p-AMPK activation of ULK1.In MCF-7 cells,both p-AMPK and ULK1 levels decreased after autophagy,suggesting that autophagy might not be mediated by p-AMPK activation.Conclu-sions MDA-MB-231 cells primarily initiate autophagy by directly activating ULK1 by p-AMPK,independent of the MTOR pathway.In MCF-7 cells autophagy might be triggered by inhibiting MTOR through AMPK activity or directly activating MTOR through other up-stream factors.Regulating p-AMPK activity based on the autophagy pathways in different cell subtypes could enable more precise targeting and treatment of different types of breast cancer.