ObjectiveThis paper aims to investigate and evaluate the staged efficacy and safety of the representative empirical prescription of the “Zhu Ke Jiao” theory， Qijia Rougan prescription， combined with entecavir in the treatment of hepatic fibrosis in chronic hepatitis B. MethodsA multicenter randomized controlled clinical study was conducted， and 101 patients diagnosed with chronic hepatitis B-related hepatic fibrosis （CHB-HF） who met the diagnosis and inclusion criteria were randomly assigned to an observation group （Qijia Rougan prescription + entecavir） and a control group （entecavir）. The treatment duration was 24 weeks. Liver stiffness measurement （LSM）， fibrosis-4 index （FIB-4）， portal vein diameter， hepatitis B serology， biochemical indicators， hepatic fibrosis markers in serum ［hyaluronic acid （HA）， laminin （LN）， procollagen Ⅲ peptide （PⅢP）， and type Ⅳ collagen （Ⅳ-C）］， and traditional Chinese medicine syndrome scores were used as efficacy evaluation indicators. Efficacy assessments and explorations of different staged subgroups of Qijia Rougan prescription were conducted according to LSM values based on the Metavir pathological staging standard. ResultsA total of 98 cases were included for statistical analysis， with 49 cases in the observation group and 49 in the control group. The general data of the patients in both groups were comparable. Compared with the same group before treatment， the observation group showed a significant reduction in LSM and FIB-4 （P<0.01）， as well as notable improvements in LN， Ⅳ-C， and various TCM syndrome scores （P<0.05， P<0.01）. When compared to the control group after treatment， the observation group demonstrated significant improvements in LSM， FIB-4， and various TCM syndrome score indicators （P<0.05， P<0.01）， indicating that the observation group performed better than the control group. Subgroup analysis of the regression of hepatic fibrosis stages showed that compared to the same group before treatment， the observation group had better improvement in regression of stages F2 and F3 （P<0.05）. When compared to the control group after treatment， the observation group exhibited superior improvement in regression of stage F3 （P<0.05）. No adverse events occurred in either group during the treatment period. ConclusionCompared with entecavir alone， the combination of Qijia Rougan prescription and entecavir significantly improves the degree of hepatic fibrosis and clinical TCM symptoms in patients. The optimal intervention period is primarily during stage F3， which is a potential “interception” point of the “Zhu Ke Jiao” theory.

ObjectiveAngelicae Sinensis Radix， a commonly used medicinal herb with both medicinal and edible properties， is frequently adulterated in the market， severely affecting the clinical efficacy of preparations. While qualitative identification techniques for adulterants and counterfeits are now relatively mature， quantitative detection methods for adulterated processed products remain unexplored. Quantitative detection research of Angelicae Sinensis Radix and its primary closely related adulterant， "Tu Danggui" （Angelica gigas）， was conducted to establish a herbal quantitative molecular detection （Herb-Q） method for Angelicae Sinensis Radix and its processed products， providing a model for the establishment of quantitative detection technologies for Angelicae Sinensis Radix and related health products. MethodsThe specific single-nucleotide polymorphism （SNP） loci of Angelicae Sinensis Radix and Angelica gigas Nakai were screened based on the complete chloroplast genome sequence. The specific SNP loci of Angelicae Sinensis Radix were selected for quantitative methodological investigations （linearity， limit of quantification， limit of detection， and reproducibility） by mixing the powder of the herbs with different adulteration ratios. Huoxue Zhitong powder with three distinct adulteration ratios （15%， 25%， and 35%） was utilized to ascertain the precision of the Herb-Q method for the quantitative detection of Chinese patent medicines containing Angelicae Sinensis Radix. ResultsBy comparing the 123 chloroplast genome sequences of Angelicae Sinensis Radix， based on the principles of intraspecies conservation， interspecies specificity， and meeting the requirements of pyrophosphate high-throughput sequencing， it was determined that 9 674^th locus （A/G） in the chloroplast genome sequence NC_042826.1 and 38 592^nd locus （T/C） in the chloroplast genome sequence NC_029393.1 could be the exclusive molecular identification loci of Angelicae Sinensis Radix and Angelica gigas Nakai， respectively. The linear relationship R² of the Herb-Q method established by selecting the specific 9 674^th locus （A/G） of Angelicae Sinensis Radix was 0.997 4 （R²>0.99）， indicating an excellent linear relationship. The limits of quantification and detection were established at 2.0%， exhibiting excellent reproducibility ［relative standard deviation（RSD）<2.0%］. The established quantitative system based on the Herb-Q method detected the adulteration amount of counterfeit A. gigas in the Huoxue Zhitong powder， with an average deviation of 1.3% for three molecular quantitative replicates. ConclusionThis research demonstrates that the Herb-Q quantitative detection method established based on the 9 674^th locus （A/G） in the chloroplast genome sequence NC_042826.1 of Angelicae Sinensis Radix has good applicability， objectivity， and accuracy for Angelicae Sinensis Radix and A. gigas， and its processed products. This method has the capacity to provide technical support for the quantitative detection of commercially available Angelicae Sinensis Radix derivatives， including traditional Chinese medicinal preparations， dietary supplements， and nutraceuticals.

ObjectiveAngelicae Sinensis Radix， a commonly used medicinal herb with both medicinal and edible properties， is frequently adulterated in the market， severely affecting the clinical efficacy of preparations. While qualitative identification techniques for adulterants and counterfeits are now relatively mature， quantitative detection methods for adulterated processed products remain unexplored. Quantitative detection research of Angelicae Sinensis Radix and its primary closely related adulterant， "Tu Danggui" （Angelica gigas）， was conducted to establish a herbal quantitative molecular detection （Herb-Q） method for Angelicae Sinensis Radix and its processed products， providing a model for the establishment of quantitative detection technologies for Angelicae Sinensis Radix and related health products. MethodsThe specific single-nucleotide polymorphism （SNP） loci of Angelicae Sinensis Radix and Angelica gigas Nakai were screened based on the complete chloroplast genome sequence. The specific SNP loci of Angelicae Sinensis Radix were selected for quantitative methodological investigations （linearity， limit of quantification， limit of detection， and reproducibility） by mixing the powder of the herbs with different adulteration ratios. Huoxue Zhitong powder with three distinct adulteration ratios （15%， 25%， and 35%） was utilized to ascertain the precision of the Herb-Q method for the quantitative detection of Chinese patent medicines containing Angelicae Sinensis Radix. ResultsBy comparing the 123 chloroplast genome sequences of Angelicae Sinensis Radix， based on the principles of intraspecies conservation， interspecies specificity， and meeting the requirements of pyrophosphate high-throughput sequencing， it was determined that 9 674^th locus （A/G） in the chloroplast genome sequence NC_042826.1 and 38 592^nd locus （T/C） in the chloroplast genome sequence NC_029393.1 could be the exclusive molecular identification loci of Angelicae Sinensis Radix and Angelica gigas Nakai， respectively. The linear relationship R² of the Herb-Q method established by selecting the specific 9 674^th locus （A/G） of Angelicae Sinensis Radix was 0.997 4 （R²>0.99）， indicating an excellent linear relationship. The limits of quantification and detection were established at 2.0%， exhibiting excellent reproducibility ［relative standard deviation（RSD）<2.0%］. The established quantitative system based on the Herb-Q method detected the adulteration amount of counterfeit A. gigas in the Huoxue Zhitong powder， with an average deviation of 1.3% for three molecular quantitative replicates. ConclusionThis research demonstrates that the Herb-Q quantitative detection method established based on the 9 674^th locus （A/G） in the chloroplast genome sequence NC_042826.1 of Angelicae Sinensis Radix has good applicability， objectivity， and accuracy for Angelicae Sinensis Radix and A. gigas， and its processed products. This method has the capacity to provide technical support for the quantitative detection of commercially available Angelicae Sinensis Radix derivatives， including traditional Chinese medicinal preparations， dietary supplements， and nutraceuticals.

ObjectiveThis paper aims to investigate the differential mechanisms underlying the staged therapeutic effects of Qijia Rougan formula on liver fibrosis using proteomic technology. MethodsThe staged rat model of liver fibrosis was established by subcutaneous injection of carbon tetrachloride （CCl₄） and olive oil. One hundred and four SD rats were randomized into thirteen groups：a normal group，a two-week model group，a four-week model group，a six-week model group，an eight-week model group，a two-week Qijia Rougan formula group，a four-week Qijia Rougan formula group，a six-week Qijia Rougan formula group，an eight-week Qijia Rougan formula group，a two-week compound Biejia Ruangan tablet group，a four-week Compound Biejia Ruangan Tablet group，a six-week Compound Biejia Ruangan Tablet group，and an eight-week compound Biejia Ruangan tablet group. After two weeks of drug intervention，liver tissue and abdominal aortic blood samples were collected from the rats for testing. Hematoxylin-eosin （HE） staining，Masson staining，and Picro Sirius red staining were used to observe pathological damage and collagen fiber deposition in liver tissues. Immunohistochemistry （IHC） was employed to detect the contents of fibrosis markers in liver tissues. The contents of liver function indicators in the serum were measured using a fully automated biochemical analyzer，and the levels of liver fibrosis indicators in the serum were assessed by enzyme-linked immunosorbent assay （ELISA）. Liver tissues from the normal group，each model group，and each Qijia Rougan formula group were subjected to label-free quantitative proteomic analysis to identify differential proteins among the groups，with key proteins validated by Western blot. Finally，bioinformatics analysis was performed on the differential proteins. Results（1） The staged rat model of liver fibrosis constructed with CCl₄ and olive oil showed pathological results at the 2^nd，4^th，6^th，and 8^th weeks of modeling that were consistent with the Metavir standards for the F1，F2，F3，and F4 stages. Compared with those in the normal control group，the protein expressions of α-smooth muscle actin （α-SMA） and Collagen Ⅰ were significantly increased in each stage （P<0.05）. The levels of liver function indicators in the serum，including alanine aminotransferase （ALT），aspartate aminotransferase （AST），alkaline phosphatase （ALP），direct bilirubin （DBIL），and total bilirubin （TBil） in each model group，were significantly elevated in each stage （P<0.01）. The levels of liver fibrosis indicators in the serum，including procollagen Ⅲ peptide （PⅢP），type Ⅳ collagen（Ⅳ-C），hyaluronic acid （HA），and laminin （LN） in each model group，were significantly increased in each stage （P<0.05，P<0.01）. This study successfully established a staged rat model of liver fibrosis. （2） Compared with the model groups at each stage，the administration groups showed a reduction in hepatocyte ballooning degeneration，a more orderly arrangement of hepatocytes，and a decrease of inflammatory cell infiltration. The blue-stained collagen fibers became significantly thinner and finer，with reduced and narrowed fibrous septa. The areas of collagen fibers and Picro Sirius red staining were reduced （P<0.05）. The positive areas of α-SMA and Collagen Ⅰ expression were significantly decreased （P<0.05）. The levels of ALT，AST，ALP，DBIL，and TBil in the rats of the model groups at each stage were significantly reduced （P<0.05，P<0.01）. The levels of PⅢP，Ⅳ-C，HA，and LN in the rats of the model groups at each stage were significantly decreased （P<0.05）. Among these，the improvements in all indicators were most significant in the F3 stage （P<0.01）.（3） The proteomic results show that a total of 165 differential proteins exhibit a callback trend when comparing the model groups at four stages with the normal group，and when comparing the Qijia Rougan formula group with the model group. Western blot analysis reveals that the levels of NAD（P）H：quinone oxidoreductase 1 （NQO1），mitogen-activated protein kinase 1 （MAPK1），arginase 1 （Arg1），and glutathione S-transferase α1 （GSTA1） were consistent with the proteomic results. Bioinformatics results reveal that 165 differentially expressed proteins are enriched in multiple signaling pathways. Notably，signaling pathways such as drug metabolism-cytochrome P450，arginine biosynthesis，and the peroxisome proliferator-activated receptor （PPAR） signaling pathway were found to be closely associated with liver fibrosis，suggesting that the Qijia Rougan formula may exert its staged regulatory effects on liver fibrosis by regulating these pathways. ConclusionThe Qijia Rougan formula may achieve staged regulation of liver fibrosis by regulating drug metabolism-cytochrome P450，arginine biosynthesis，and the PPAR signaling pathway.

Purpose: Our previous study showed that circulating tumor cell (CTC) count combined with gene mutation detection might help differentiate benign and malignant pulmonary nodules (PNs). Herein, we aimed to expand the study cohort and conduct further sequencing analysis. Materials and Methods: Patients with PNs were included, and CTCs were identified before operation. Low-coverage whole-genome sequencing (LC-WGS) and lung cancer-related targeted gene sequencing were performed on CTCs. The diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve. The differences in CTC counts among subgroups classified by demographic–clinical characteristics were analyzed. LC-WGS–based copy number variation (CNV) analysis and targeted gene mutation analysis were conducted. Results: A total of 172 patients were included. CTC count of 2.5 was identified by the ROC curves as the optimal diagnostic cutoff. The sensitivity and specificity of CTC count for differentiating benign and malignant PNs were 54.2% and 78.6%, respectively. The diagnostic sensitivity and specificity of combined CTC count, radiological nodule type, and any malignant imaging features were 84.7% and 71.4%, respectively. The CTC counts were significantly greater in patients with aggressive tumors, later stage, and spread through air spaces. CTCs from malignant cases had more CNVs than those from benign cases. Conclusion CTC count can be used in identifying malignant PNs. The diagnostic efficacy can be improved if combined with computed tomography imaging characteristics. Further CNV analysis might help differential diagnosis. Greater CTC count might suggest more aggressive tumors. CTC detection can provide important information and guidance for subsequent management of PNs.

With the rapid aging of the global population, the demand for prosthodontic rehabilitation of partial edentulism continues to increase. Among the available treatment options, removable partial dentures remain an essential clinical modality for restoring partially edentulous arches and masticatory function because of their broad indications, minimal invasiveness, and cost-effectiveness. However, unlike fixed prosthodontics, which have undergone substantial digitalization, removable partial denture therapy is characterized by dual tooth-tissue support, complex biomechanics, and highly variable anatomical morphology. As a result, the digital design and fabrication of removable partial dentures have long faced major bottlenecks, including limited standardization, steep learning curves, and heavy dependence on expert experience. In recent years, artificial intelligence (AI) has undergone a paradigm shift from early expert systems to machine learning and deep learning, creating new opportunities to address the complexity and precision demands of removable partial denture design. Based on a comprehensive literature review and our team's clinical experience, this article systematically elaborates on the specific applications and normative standards of AI throughout the entire removable partial denture fabrication workflow. In the data acquisition phase, we emphasize multimodal data fusion and digital pressure impression strategies. In the intelligent diagnosis and planning phase, deep learning is utilized to achieve the automatic identification of edentulous areas and undercuts, while machine learning and expert systems assist in abutment selection and treatment plan generation. Regarding automated design logic, the article analyzes the process from the intelligent planning of the common path of insertion to parametric design based on biomechanical optimization. At the clinical operation level, a standardized human-machine collaborative workflow of “AI generation-clinician review-local fine-tuning” is proposed. In terms of ethics and accountability, the principles of algorithmic transparency and “the clinician as the ultimate responsible subject” are emphasized. Furthermore, integrating clinical practice, this article innovatively proposes a “capability grading system for AI-assisted removable partial denture design (comprising the auxiliary analysis level, the rule-based design level, and the intelligent adaptive level)”. The aim is to offer practical recommendations for dental clinicians, dental technicians, and researchers, thereby facilitating the transition of removable partial denture prosthodontics toward greater precision, intelligence, and standardization.

Prescription optimization is a crucial aspect in the study of traditional Chinese medicine （TCM） compounds. In recent years， the introduction of mathematical methods， data mining techniques， and artificial neural networks has provided new tools for elucidating the compatibility rules of TCM compounds. The study of TCM compounds involves numerous variables， including the proportions of different herbs， the specific extraction parts of each ingredient， and the interactions among multiple components. These factors together create a complex nonlinear dose-effect relationship. In this context， it is essential to identify methods that suit the characteristics of TCM compounds and can leverage their advantages for effective application in new drug development. This paper provided a comprehensive review of the cutting-edge optimization experimental design methods applied in recent studies of TCM compound compatibilities. The key technical issues， such as the optimization of source material selection， dosage optimization of compatible herbs， and multi-objective optimization indicators， were discussed. Furthermore， the evaluation methods for component effects were summarized during the optimization process， so as to provide scientific and practical foundations for innovative research in TCM and the development of new drugs based on TCM compounds.

ObjectiveThis study aims to enhance of the farnesyl pyrophosphate（FPP） pool in Saccharomyces cerevisiae by heterologously expressing different farnesyl diphosphate synthases（FPSs） from various plants， thereby increasing the production of terpenoid compounds by the engineered yeast. MethodsRNA from mixed samples of roots， stems， and leaves of seven plants including Arabidopsis thaliana， Rosa rugosa， Artemisia annua， Centella asiatica， Humulus lupulus， Medicago sativa， and Panax ginseng was extracted by column chromatography and reverse transcribed into the first strand of complementary DNA（cDNA）， and based on the transcriptome data of the seven species of plants， sequence-specific primers were designed for CaFPS， RrFPS， MsFPS， HiFPS， PgFPS， AtFPS， and AaFPS， the full-length of the genes was cloned， and the genes were analyzed for bioinformatics in order to construct a pESC yeast shuttle vector. These seven plant-derived FPSs were further heterologously expressed in the previous constructed β-elemene-producing yeast， and the yield of β-elemene was indicated for their catalytic acivities. ResultsThe coding sequences of CaFPS， RrFPS， MsFPS， HiFPS， PgFPS， AtFPS， and AaFPS were all of 1 021 bp in length and encoding 301 amino acids， all of which were similarly related to the endogenous FPS-encoding gene（ERG20） in S. cerevisiae. After heterologous expression， RrFPS was identified as the most effective in catalyzing the synthesis of FPP from isopentenyl pyrophosphate（IPP） and dimethylallyl pyrophosphate（DMAPP）. Compared to the control strains， the RrFPS overexpressed yeast strains YB-1-Rr and YB-3-Rr increased the production of β-elemene by 231.25% and 189.3%， respectively. ConclusionBy comparing the functions of FPS-encoding genes from seven different plant sources， it is determined that the protein encoded by the RrFPS from R. rugosa has the best catalytic ability， which can provide key genetic elements for the construction of engineered yeast strain constructs with high terpenoid production.

In situ bioimaging is a powerful tool for directly observing the localization, expression, and interactions of nucleic acids or protein targets within cells, providing essential insights into cell function and disease mechanisms. In recent years, the CRISPR/Cas9 system, a revolutionary gene-editing tool, has been applied to develop efficient in situ imaging techniques. This paper reviews recent CRISPR/Cas9-based imaging methods utilizing Cas9 protein, engineered single-guide RNA (sgRNA), and coupled fluorescent tags, and compares their application in living and fixed cells. It focuses on the specificity, signal amplification efficiency, and multi-modal imaging capabilities of these methods, with further discussion based on current research, aiming to offer a comprehensive overview of CRISPR/Cas9-based in situ bioimaging techniques, with some valuable reference and guidance for research in related fields.

With the increasing demand for dynamic,real-time and rapid qualitative analysis of chemical composition in areas such as emergency response and space exploration,chip-scale mass spectrometers have attracted significant attention.These devices are expected to drive the integration of mass spectrometry with micro/nano-fabrication and intelligent sensing technologies,fostering profound innovation and breakthroughs in analytical chemistry.As an excellent mass analyzer,the ion trap exhibits numerous advantages,and its miniaturization creates favorable conditions for the high-density integration of miniature mass spectrometers.However,the reduction in ion storage capacity may compromise its sensitivity and dynamic range,rendering the study of ion unidirectional ejection in highly miniaturized ion traps of significant practical importance.In this work,a research was conducted on achieving efficient ion unidirectional ejection while maintaining high mass resolution in the extremely miniature hyperbolic linear ion trap(M-HLIT)with a field radius of 1 mm,and an electric field compensation method was proposed,which combined asymmetric electrode stretching and unbalanced RF voltage to achieve high-precision optimization of the electric field composition.Simulations showed that in an ideal structure,this method achieved 100％unidirectional ejection efficiency with the mass resolution of 518,significantly outperforming traditional asymmetric structure method(365)and unbalanced voltage method(321).Following the introduction of ion ejection slots,further optimization through bidirectional stretching and electrical parameters improved the resolution to 790 while maintaining a unidirectional ejection efficiency of 93％.This method eliminated the requirement for additional excitation voltage,offering an ideal solution for the miniature mass analyzer with high detection performance of chip-level mass spectrometers.