"Pattern identification by formula" originates from the formula-pattern correspondence theory in traditional Chinese medicine （TCM）， with its core being the reverse inference of the pattern attributes of models through pharmacodynamic responses after formula intervention. With the advancement of TCM modernization， stable and reliable pattern models have become an important bridge connecting basic research and clinical practice， and "pattern identification by formula" has provided a feasible approach to enhance the authenticity and clinical translational value of these models. In recent years， notable progress has been achieved in its application to single-pattern models （such as phlegm-stasis pattern， Yang deficiency pattern， and spleen-Qi deficiency pattern） as well as disease-pattern combination models involving the respiratory， digestive， circulatory， and nervous systems. It has not only verified the pattern attributes of the models but also revealed the pattern differentiation regularity of "different diseases with the same pattern" and "the same pattern treated with different formulas". On this basis， this study further conducts a commonality analysis of pattern models， summarizes them into two major categories （deficiency patterns and excess patterns）， and embodies the pathological connotation of "deficiency results from essence-Qi consumption， and excess arises from pathogen abundance". This not only reveals the internal connections between different models but also provides a basis for the summary of formula-pattern rules and research on pattern standardization. However， current studies still suffer multiple problems such as non-standardized naming of patterns， lack of rationality in modeling methods， simplified design of control experiments， and insufficient description of macroscopic patterns in evaluation indicators， which affect the scientific rigor and reproducibility of the research. Future research needs to promote the unification of nomenclature and evaluation criteria of patterns， optimize modeling methods， strengthen the design of control experiments， and establish a multi-dimensional model evaluation system integrating macro- and micro-level indicators. This aims to improve the rigor and strength of evidence for "pattern identification by formula" studies， strengthen interdisciplinary integration and clinical feedback， and promote the development of TCM pattern models towards standardization and scientification. This study not only provides theoretical reference and methodological support for improving the system of TCM pattern animal models but also offers an important pathway for the experimental interpretation and modernization of TCM theories.

Objective:To develop a predictive model for the risk of post-traumatic hydrocephalus (PTH) in patients with severe traumatic brain injury (sTBI) and validate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 580 sTBI patients admitted to the 904th Hospital of the Joint Logistics Support Force of the PLA between January 2016 and December 2023, including 413 males and 167 females, aged 18-88 years [(54.3±14.6)years]. Patients were stratified into PTH group ( n=195) and non-PTH group ( n=385), based on the presence of PTH within 6 months after injury. Data collected from the two groups such as general baseline indicators, TBI-related clinical indicators (including surgical data), laboratory findings, and radiological features. Except for the data collected during the operation, all the above data are the results of the first examination at admission. Univariate analysis and Lasso regression analysis were used to screen predictors for the risk of PTH in sTBI patients. Subsequent multivariate Logistic regression was employed to identify predictors and construct a regression equation. Based on this equation, a nomogram prediction model was developed using the R language. Model discrimination was estimated through the receiver operating characteristic (ROC) curve, and calibration performance via the Hosmer-Lemeshow (H-L) goodness-of-fit test and calibration curve. Moreover, decision curve analysis (DCA) and clinical impact curve (CIC) were used for evaluating the clinical utility of the model. Results:Univariate analysis revealed statistically significant differences in 37 variables between the two groups, including age, age group, heart rate, oxygen saturation, Glasgow coma scale (GCS) score, left pupil size, right pupil size, pupillary light reflex, intracranial pressure (ICP) monitoring, type of decompressive craniectomy, neutrophil count, lymphocyte count, monocyte count, red blood cell count, platelet count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), neutrophil-lymphocyte-platelet ratio (N/LP), lymphocyte-to-monocyte ratio (LMR), systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), fibrinogen (FIB), D-dimer, D-dimer-to-fibrinogen ratio (DFR), serum albumin, prognostic nutritional index, blood glucose, status of basal cisterns, midline shift, degree of midline shift, cerebral herniation, epidural hematoma (EDH), subdural hematoma (SDH), intraventricular hemorrhage (IVH), subarachnoid hemorrhage (SAH), modified Fisher grade, and skull fracture ( P<0.05). Lasso regression analysis identified 24 potential predictors for PTH, including age, GCS score, pupillary light reflex, type of decompressive craniectomy, monocyte count, platelet count, NLR, PLR, N/LP, LMR, SII, D-dimer, DFR, serum albumin, prognostic nutritional index, blood glucose, status of basal cisterns, degree of midline shift, cerebral herniation, EDH, SDH, IVH, modified Fisher grade and skull fracture. Multivariate Logistic regression analysis demonstrated that age, unilateral pupillary light reflex, absent pupillary light reflex, bilateral decompressive craniectomy, monocyte count, PLR, cerebral herniation, SDH, IVH, linear skull fracture and depressed skull fracture were independent risk factors for PTH. In contrast, serum albumin was identified as an independent protective factor for PTH ( P<0.05). The regression equation derived from these factors was: Logit[ P/(1- P)]=0.05×"age"+1.65×"unilateral pupillary light reflex"+2.79×"absent pupillary light reflex"+1.60×"bilateral decompressive craniectomy"+1.90×"monocyte count"+0.02×"PLR"-0.12×"serum albumin"+2.07×"cerebral herniation"+2.59×"SDH"+2.23×"IVH"+1.24×"linear skull fracture"+ 1.66×"depressed skull fracture"-22.61. The prediction model built upon this equation achieved an area under the ROC curve (AUC) of 0.95(95% CI 0.93, 0.97), with a sensitivity of 91.79%, specificity of 85.97%, and Youden′s index of 0.78. The H-L goodness-of-fit test indicated good calibration ( χ2=7.90, P=0.545). DCA results showed that the bias-corrected curve closely aligned with the actual curve and approximated the ideal curve, indicating a high clinical net benefit. Furthermore, CIC results demonstrated that with threshold probabilities greater than 60%, the number of patients identified as high-risk by the model highly corresponded with the actual number of patients who developed PTH. Conclusion:The prediction model incorporating age, unilateral pupillary light reflex, absent pupillary light reflex, bilateral decompressive craniectomy, monocyte count, PLR, serum albumin, cerebral herniation, SDH, IVH, linear skull fracture and depressed skull fracture exhibits robust predictive performance for PTH in sTBI patients.

Acute lung injury (ALI) has been a kind of acute and severe disease that is mainly characterized by systemic uncontrolled inflammatory response to the production of huge amounts of reactive oxygen species (ROS) in the lung tissue. Given the critical role of ROS in ALI, a Fe₃O₄ loaded bovine serum albumin (BSA) nanocluster (BF) was developed to act as a nanomedicine for the treatment of ALI. Combining with NIR irradiation, it exhibited excellent ROS scavenging capacity. Significantly, it also displayed the excellent antioxidant and anti-inflammatory functions for lipopolysaccharides (LPS) induced macrophages (RAW264.7), and Sprague Dawley rats via lowering intracellular ROS levels, reducing inflammatory factors expression levels, inducing macrophage M2 polarization, inhibiting NF-κB signaling pathway, increasing CD4⁺/CD8⁺ T cell ratios, as well as upregulating HSP70 and CD31 expression levels to reprogram redox homeostasis, reduce systemic inflammation, activate immunoregulation, and accelerate lung tissue repair, finally achieving the synergistic enhancement of ALI immunotherapy. It finally provides an effective therapeutic strategy of BF + NIR for the management of inflammation related diseases.

Objective To provide a scientific basis for the quality control of Chrysanthemi Indici Flos medicinal materials and the sustainable development of this resource,the germplasm differences,tissue distribution,and developmental stage changes of flavonoid content in Chrysanthemum indicum was explored.Methods High-performance liquid chromatography(HPLC)was used to quantitatively analyze the five main flavonoid compounds,including linarin,tilianin,cynaroside,apigenin,and acacetin.Real-time fluorescence quantitative PCR(qPCR)technology was used to evaluate the relative expression levels of the rhamnosyltransferase(RhaT)gene at different developmental stages.Results The study found significant differences in flavonoid accumulation among different Chrysanthemum indicum germplasms,with the highest content of linarin observed in the Hubei diploid.Flavonoid content was significantly higher in leaves,flower buds,and flowers compared to stems and roots.Linarin and tilianin peaked at the flower bud stage and then gradually decreased,while the contents of apigenin and acacetin increased with flower development.The qPCR analysis further showed that the expression level of the RhaT gene was highly consistent with the accumulation pattern of linarin.Conclusion The results reveal that flavonoid content in Chrysanthemum indicum is significantly affected by germplasm,tissue type,and developmental stage,providing important data support for the quality evaluation of Chrysanthemum indicum medicinal materials and the rational development of this resource.

Objective To provide a scientific basis for the quality control of Chrysanthemi Indici Flos medicinal materials and the sustainable development of this resource,the germplasm differences,tissue distribution,and developmental stage changes of flavonoid content in Chrysanthemum indicum was explored.Methods High-performance liquid chromatography(HPLC)was used to quantitatively analyze the five main flavonoid compounds,including linarin,tilianin,cynaroside,apigenin,and acacetin.Real-time fluorescence quantitative PCR(qPCR)technology was used to evaluate the relative expression levels of the rhamnosyltransferase(RhaT)gene at different developmental stages.Results The study found significant differences in flavonoid accumulation among different Chrysanthemum indicum germplasms,with the highest content of linarin observed in the Hubei diploid.Flavonoid content was significantly higher in leaves,flower buds,and flowers compared to stems and roots.Linarin and tilianin peaked at the flower bud stage and then gradually decreased,while the contents of apigenin and acacetin increased with flower development.The qPCR analysis further showed that the expression level of the RhaT gene was highly consistent with the accumulation pattern of linarin.Conclusion The results reveal that flavonoid content in Chrysanthemum indicum is significantly affected by germplasm,tissue type,and developmental stage,providing important data support for the quality evaluation of Chrysanthemum indicum medicinal materials and the rational development of this resource.

Objective Systematic identification and analysis of the cytochrome P450(CYP450)gene family members and their functions in Chrysanthemum indicum L.to provide a basis for further studies on their roles in the flavonoid synthesis pathway.Methods Based on the genomic data of a diploid Chrysanthemum indicum L.,the members of the P450 gene family were identified and analyzed using bioinformatics methods.Moreover,the transcriptome and quasi-targeted metabolome analysis were combined to screen the flavone synthase Ⅱ(FNSII)genes from the CYP93 family,and their expression levels were tested in different tissues of C.indicum using real-time fluorescence quantitative PCR(qPCR).Results A total of 460 P450 genes were identified from the C.indicum genome,belonging to 43 families within 8 family clans.The encoded proteins ranged from 336 to 1538 amino acids in length,with a relative molecular mass between 38.01 to 175.00 kDa,and an isoelectric point between 5.61 to 9.71.Chromosome localization analysis indicated that these 460 P450 genes were unevenly distributed on nine chromosomes,with the highest number of light-responsive elements in the promoter regions.Expression pattern analysis showed that the expression levels of two FNSII genes(CindChr2G00102820.1 and CindChr8G00552890.1)were significantly higher in leaf of C.indicum than in flower.Conclusion The comprehensive analysis of the P450 gene family in C.indicum is helpful to further elucidate the function of the P450 gene family in flavonoid biosynthesis.

Objective The three UGT genes involved in the synthesis of flavonoid glycosides in the wild chrysanthemum were cloned,fused and expressed in prokaryotic system,which provided a basis for further study of the functions of these genes in the synthesis of flavonoid compounds.Methods three candidate UDP-glycosyltransferase(UGT)family genes were successfully cloned from flower tissue cDNA of diploid Chrysanthemum indicum.L in Hubei,namely:CiUGT1(1425 bp),CiUGT2(1311 bp)and CiUGT3(1314 bp)encoded opal fraction of 51.72,48.08 and 48.28 kDa,respectively.After the physical and chemical properties of the protein were analyzed,the recombinant plasmid was constructed by seamless Klon technology,and the recombinant proteins of CiUGT2 and CiUGT3 were successfully expressed in Escherichia coli.Results SDS-PAGE analysis showed that the purified CiUGT2 and CiUGT3 protein samples were about 0.2 mg/mL and 1.4 mg/mL,respectively.Conclusion Two candidate UGTs were obtained to catalyze the synthesis of flavonoid 7-O-glucosides in Chrysanthemum indicum.L,which provide the theoretical basis for the molecular mechanism of flavonoid glycosides biosynthesis in the capitulum of Chrysanthemum indicum.L.

Objective Systematic identification and analysis of the cytochrome P450(CYP450)gene family members and their functions in Chrysanthemum indicum L.to provide a basis for further studies on their roles in the flavonoid synthesis pathway.Methods Based on the genomic data of a diploid Chrysanthemum indicum L.,the members of the P450 gene family were identified and analyzed using bioinformatics methods.Moreover,the transcriptome and quasi-targeted metabolome analysis were combined to screen the flavone synthase Ⅱ(FNSII)genes from the CYP93 family,and their expression levels were tested in different tissues of C.indicum using real-time fluorescence quantitative PCR(qPCR).Results A total of 460 P450 genes were identified from the C.indicum genome,belonging to 43 families within 8 family clans.The encoded proteins ranged from 336 to 1538 amino acids in length,with a relative molecular mass between 38.01 to 175.00 kDa,and an isoelectric point between 5.61 to 9.71.Chromosome localization analysis indicated that these 460 P450 genes were unevenly distributed on nine chromosomes,with the highest number of light-responsive elements in the promoter regions.Expression pattern analysis showed that the expression levels of two FNSII genes(CindChr2G00102820.1 and CindChr8G00552890.1)were significantly higher in leaf of C.indicum than in flower.Conclusion The comprehensive analysis of the P450 gene family in C.indicum is helpful to further elucidate the function of the P450 gene family in flavonoid biosynthesis.

Objective The three UGT genes involved in the synthesis of flavonoid glycosides in the wild chrysanthemum were cloned,fused and expressed in prokaryotic system,which provided a basis for further study of the functions of these genes in the synthesis of flavonoid compounds.Methods three candidate UDP-glycosyltransferase(UGT)family genes were successfully cloned from flower tissue cDNA of diploid Chrysanthemum indicum.L in Hubei,namely:CiUGT1(1425 bp),CiUGT2(1311 bp)and CiUGT3(1314 bp)encoded opal fraction of 51.72,48.08 and 48.28 kDa,respectively.After the physical and chemical properties of the protein were analyzed,the recombinant plasmid was constructed by seamless Klon technology,and the recombinant proteins of CiUGT2 and CiUGT3 were successfully expressed in Escherichia coli.Results SDS-PAGE analysis showed that the purified CiUGT2 and CiUGT3 protein samples were about 0.2 mg/mL and 1.4 mg/mL,respectively.Conclusion Two candidate UGTs were obtained to catalyze the synthesis of flavonoid 7-O-glucosides in Chrysanthemum indicum.L,which provide the theoretical basis for the molecular mechanism of flavonoid glycosides biosynthesis in the capitulum of Chrysanthemum indicum.L.

Objective:To develop a predictive model for the risk of post-traumatic hydrocephalus (PTH) in patients with severe traumatic brain injury (sTBI) and validate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 580 sTBI patients admitted to the 904th Hospital of the Joint Logistics Support Force of the PLA between January 2016 and December 2023, including 413 males and 167 females, aged 18-88 years [(54.3±14.6)years]. Patients were stratified into PTH group ( n=195) and non-PTH group ( n=385), based on the presence of PTH within 6 months after injury. Data collected from the two groups such as general baseline indicators, TBI-related clinical indicators (including surgical data), laboratory findings, and radiological features. Except for the data collected during the operation, all the above data are the results of the first examination at admission. Univariate analysis and Lasso regression analysis were used to screen predictors for the risk of PTH in sTBI patients. Subsequent multivariate Logistic regression was employed to identify predictors and construct a regression equation. Based on this equation, a nomogram prediction model was developed using the R language. Model discrimination was estimated through the receiver operating characteristic (ROC) curve, and calibration performance via the Hosmer-Lemeshow (H-L) goodness-of-fit test and calibration curve. Moreover, decision curve analysis (DCA) and clinical impact curve (CIC) were used for evaluating the clinical utility of the model. Results:Univariate analysis revealed statistically significant differences in 37 variables between the two groups, including age, age group, heart rate, oxygen saturation, Glasgow coma scale (GCS) score, left pupil size, right pupil size, pupillary light reflex, intracranial pressure (ICP) monitoring, type of decompressive craniectomy, neutrophil count, lymphocyte count, monocyte count, red blood cell count, platelet count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), neutrophil-lymphocyte-platelet ratio (N/LP), lymphocyte-to-monocyte ratio (LMR), systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), fibrinogen (FIB), D-dimer, D-dimer-to-fibrinogen ratio (DFR), serum albumin, prognostic nutritional index, blood glucose, status of basal cisterns, midline shift, degree of midline shift, cerebral herniation, epidural hematoma (EDH), subdural hematoma (SDH), intraventricular hemorrhage (IVH), subarachnoid hemorrhage (SAH), modified Fisher grade, and skull fracture ( P<0.05). Lasso regression analysis identified 24 potential predictors for PTH, including age, GCS score, pupillary light reflex, type of decompressive craniectomy, monocyte count, platelet count, NLR, PLR, N/LP, LMR, SII, D-dimer, DFR, serum albumin, prognostic nutritional index, blood glucose, status of basal cisterns, degree of midline shift, cerebral herniation, EDH, SDH, IVH, modified Fisher grade and skull fracture. Multivariate Logistic regression analysis demonstrated that age, unilateral pupillary light reflex, absent pupillary light reflex, bilateral decompressive craniectomy, monocyte count, PLR, cerebral herniation, SDH, IVH, linear skull fracture and depressed skull fracture were independent risk factors for PTH. In contrast, serum albumin was identified as an independent protective factor for PTH ( P<0.05). The regression equation derived from these factors was: Logit[ P/(1- P)]=0.05×"age"+1.65×"unilateral pupillary light reflex"+2.79×"absent pupillary light reflex"+1.60×"bilateral decompressive craniectomy"+1.90×"monocyte count"+0.02×"PLR"-0.12×"serum albumin"+2.07×"cerebral herniation"+2.59×"SDH"+2.23×"IVH"+1.24×"linear skull fracture"+ 1.66×"depressed skull fracture"-22.61. The prediction model built upon this equation achieved an area under the ROC curve (AUC) of 0.95(95% CI 0.93, 0.97), with a sensitivity of 91.79%, specificity of 85.97%, and Youden′s index of 0.78. The H-L goodness-of-fit test indicated good calibration ( χ2=7.90, P=0.545). DCA results showed that the bias-corrected curve closely aligned with the actual curve and approximated the ideal curve, indicating a high clinical net benefit. Furthermore, CIC results demonstrated that with threshold probabilities greater than 60%, the number of patients identified as high-risk by the model highly corresponded with the actual number of patients who developed PTH. Conclusion:The prediction model incorporating age, unilateral pupillary light reflex, absent pupillary light reflex, bilateral decompressive craniectomy, monocyte count, PLR, serum albumin, cerebral herniation, SDH, IVH, linear skull fracture and depressed skull fracture exhibits robust predictive performance for PTH in sTBI patients.