OBJECTIVE To study the clinical characteristics and potential risk factors for infection in patients with multiple myeloma （MM） following treatment with bortezomib. METHODS Clinical data were retrospectively collected from MM patients who received bortezomib-based treatment regimens at the Department of Hematology， the Second Affiliated Hospital of Soochow University， from October 2021 to February 2025. The collected data primarily included demographic characteristics， disease characteristics of MM， treatment regimens， occurrence of infections and corresponding management measures， and prophylactic medication use. Univariate and multivariate Logistic regression analyses were conducted to identify potential risk factors for MM complicated with infection. RESULTS Among the 284 MM patients treated with bortezomib， 132 patients （46.5%） experienced at least one infection. The predominant types of infections were respiratory tract infections and gastrointestinal infections. Univariate analysis showed that age at initial diagnosis， pathological classification， and grade of myelosuppression were influencing factors for infection in MM patients （ P ＜0.05）. Further analysis of influencing factors for the two main types of infections revealed that sex， age at initial diagnosis， pathological classification， treatment regimen， and smoking history were influencing factor s for respiratory tract infections in MM patients （ P ＜0.05）； BMI， pathological classification， treatment regimen， and grade of myelosuppression were influencing factors for gastrointestinal infections in MM patients （ P ＜0.05）. Multivariate Logistic regression analysis indicated that age≥70 years and the presence of grade Ⅳ myelosuppression before treatment were risk factors for infection in MM patients， while the IgG-λ type was a protective factor against infection （ P ＜0.05）. CONCLUSIONS The incidence of infection is relatively high in MM patients receiving bortezomib-based treatment regimens， with respiratory and gastrointestinal infections being the most common. Age at initial diagnosis， grade of myelosuppression， and pathological classification are influencing factors for infection in MM patients.

OBJECTIVE To systematically evaluate risk prediction models for chemotherapy-induced myelosuppression in pediatric patients with malignant tumors， evaluate their modeling strategies， key predictors， and predictive performance， and provide evidence-based references for clinical decision-making and research. METHODS A literature search was conducted across 11 databases， including CNKI， Wanfang Data， and PubMed， for relevant studies published before April 2025. Two reviewers independently performed literature screening and data extraction， and the risk of bias and applicability of the models were evaluated using the PROBAST tool. RESULTS Ultimately， seven studies were selected， of which four were English articles and three were Chinese articles， involving 12 risk prediction models. Although model discrimination was good （AUC 0.748-0.981）， only two models underwent external validation； furthermore， calibration was inadequately reported in three studies. PROBAST indicated that all models exhibited a high risk of bias， with major issues including a predominance of retrospective designs， inadequate sample representativeness， and lack of blinding. However， in terms of applicability， all models received favorable evaluations. In terms of modeling methods， most studies employed traditional logistic regression approaches to construct models， while only a minority introduced machine learning algorithms and conducted systematic comparisons among multiple algorithms. Models developed using machine learning methods significantly outperformed those constructed with traditional statistical methods. CONCLUSIONS The existing risk prediction models for myelosuppression after chemotherapy in children with malignant tumors demonstrate potential in clinical risk early warning. However， they generally suffer from design and methodological limitations， such as a predominance of retrospective single-center designs， few events per variable， opaque handling of missing data， and inconsistent reporting of model coefficients. Future studies should adopt prospective designs， incorporate machine learning with key clinical predictors， and follow TRIPOD reporting guidelines to enhance scientific rigor and clinical utility.

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 explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

In recent years, artificial intelligence(AI),a branch of computer science based on deep learning and machine learning, has advanced rapidly in the screening and clinical management of ophthalmic diseases. Its application scope has gradually expanded from the posterior segment to the anterior segment of the eye. Automated detection and interpretation frameworks incorporating multimodal imaging modalities, including slit-lamp photography and anterior and posterior segment optical coherence tomography(OCT), have demonstrated considerable potential in the identification, early diagnosis, and clinical decision support of common anterior segment diseases such as keratitis, dry eye disease, pterygium, and glaucoma. As a fundamental instrument for anterior segment examination, the slit-lamp microscope remains indispensable in routine ophthalmic practice. This review summarizes recent progress in AI-assisted anterior segment research and discusses the prospects of integrating AI with slit-lamp imaging in clinical ophthalmology.

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 explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveTo evaluate the antitumor activity of Periplaneta americana extract（PAE） against human-derived lung adenocarcinoma organoids（LUAD-PDOs） and to elucidate its potential mechanism based on transcriptomics. MethodsFresh tumor and adjacent normal tissues from patients with LUAD were collected to construct LUAD-PDOs and normal lung organoid（Nor-PDOs） models using 3D organoid culture technology. The effective intervention concentration of PAE was determined using the cell counting kit-8（CCK-8） assay. Experimental groups included the model group（LUAD-PDOs）， normal group， model administration group（LUAD-PDOs+PAE）， and normal administration group（Nor-PDOs+PAE）. Hematoxylin-eosin（HE） staining was used to observe the pathological structures of PDOs， immunohistochemistry（IHC） was performed to detect the expressions of the proliferation marker Ki-67 and lung adenocarcinoma differentiation markers cytokeratin-7（CK-7） and Napsin A， TUNEL staining was applied to detect cell apoptosis. RNA sequencing（RNA-Seq） was conducted to identify differentially expressed genes（DEGs）， followed by Gene Ontology（GO）， Kyoto Encyclopedia of Genes and Genomes（KEGG）， and Gene Set Enrichment Analysis（GSEA）， alongside protein-protein interaction（PPI） network analysis to screen core mechanisms. Finally， key targets were validated by integrating external database analysis with immunofluorescence（IF）. ResultsNor-PDOs and LUAD-PDOs that highly recapitulated the pathological characteristics of the primary tissues were successfully established. The CCK-8 assay determined that the effective intervention concentration of PAE was 16 g·L^-1. Morphological observation showed that Nor-PDOs exhibited lumen-forming structures， whereas LUAD-PDOs displayed dense， solid structures. CCK-8 and TUNEL assays revealed that， compared with the model group， PAE intervention inhibited the proliferation of LUAD-PDOs and promoted apoptosis in LUAD cells， while showing no significant effect on the viability of Nor-PDOs. Transcriptomic analysis identified 719 DEGs that were significantly reversed after PAE intervention（347 up-regulated and 372 down-regulated）（P<0.05）. GO enrichment analysis indicated that DEGs in the model administration group were significantly enriched in biological processes related to cell cycle regulation compared to the model group. KEGG pathway analysis revealed that PAE affected pathways related to proliferation and metabolism， including pathways in cancer and the p53 signaling pathway. GSEA further confirmed that PAE significantly enhanced the activity of the p53 signaling pathway（P<0.05）. PPI network analysis indicated that breast cancer type 1 susceptibility protein（BRCA1） and checkpoint kinase 1（CHEK1） were the core down-regulated targets in the p53 pathway. IF verified the high expression of BRCA1 and CHEK1 in LUAD-PDOs and their significant downregulation after PAE intervention（P<0.05）. Furthermore， survival analysis based on The Cancer Genome Atlas（TCGA） database indicated that low expression of BRCA1 and CHEK1 was significantly associated with prolonged overall survival in patients with LUAD（P<0.05）. ConclusionPAE effectively inhibits proliferation of LUAD-PDOs and promotes their apoptosis， its anti-tumor mechanism is potentially associated with the activation of the p53 signaling pathway， with BRCA1 and CHEK1 genes likely serving as key downstream targets for the effects of PAE.

Functional constipation （FC） is a clinically common functional bowel disorder characterized by a protracted course and associations with various chronic disorders and psychological abnormalities. Although not life-threatening， FC significantly impairs patients' quality of life. FC subtypes include slow-transit constipation （STC）， defecatory disorder （DD）， and normal-transit constipation （NTC）. The pathological mechanisms underlying FC have not been fully elucidated， and overall clinical efficacy remains unsatisfactory. Animal models of FC serve as essential tools for the study of disease mechanisms and the development of novel therapeutics. This article systematically reviews the current state of research on the animal models of FC and identifies that rodents， particularly rats and mice， are the most commonly used species. Dogs and pigs are also employed in complex intervention studies due to their physiological similarities to humans， though their use is limited by housing challenges and ethical considerations. Induction methods vary across different FC subtypes. STC models are primarily established with chemical agents such as loperamide or compound diphenoxylate. DD modeling often involves low-fiber diets combined with methylene blue injection or rectal narrowing. NTC modeling mainly relies on low-fiber dietary interventions. In addition， disease-syndrome combination models based on traditional Chinese medicine （TCM） theory have been developed， encompassing excess patterns such as heat accumulation， cold accumulation， and Qi stagnation， as well as deficiency patterns including Qi deficiency， blood deficiency， Yin deficiency， and Yang deficiency. These are achieved through an approach of disease model + syndrome induction， enabling the integration of mechanisms from both Western and TCM perspectives. Models are evaluated from two aspects： disease and syndrome manifestations （e.g.， colonic transit， secretory function， and TCM syndrome indicators such as mental state and body weight） and disease mechanisms （e.g.， enteric nervous system， interstitial cells of Cajal， smooth muscle cells， gut microbiota， and metabolites）. However， current research still faces challenges such as poor consistency in some models， non-specific interference in mechanism interpretation， insufficient studies on NTC， and lack of TCM tongue and pulse diagnosis in evaluation. Future efforts should focus on optimizing model stability and specificity to provide a more reliable experimental basis for investigating the pathological mechanisms of FC and developing therapeutic agents.

ObjectiveTo construct a scale for the diagnosis of chronic atrophic gastritis （CAG） with turbid toxin accumulating in the stomach. MethodsFirst， a research group was established to construct the scale framework. Relevant literature of CAG with syndrome of turbid toxin accumulating in the stomach was searched in CNKI， Wanfang Database （WF）， and VIP Database （CQVIP） from April 1， 2003 to April 1， 2023， and items were preliminarily selected after standardization of terms. Through clinical investigation， the discrete trend method， correlation coefficient method， Cronbach's coefficient method， and factor analysis method were used to screen symptom items， and the frequency method was used to screen signs， tongue coating， and pulse conditions. Three rounds of Delphi expert consultation were conducted to determine the items of the scale. The weight of each item was obtained by the analytic hierarchy process. ResultsA total of 49 articles were included， and 45 items were obtained after primary screening， including 28 symptoms， 2 signs， 10 tongue coatings， and 5 pulse conditions. After clinical investigation， 15 symptoms were retained， and 8 signs and pulse conditions of tongue coating were retained. The positive coefficients of experts in three rounds of Delphi expert consultation were 100%， 96.67%， and 100%， respectively. The expert authority coefficients were 0.86， 0.87， and 0.87， respectively， and the coordination coefficients were 0.18， 0.25， and 0.30. After core group discussion， Delphi method investigation， and AHP weight assignment， the diagnostic scale items of CAG with turbid toxin accumulating in stomach syndrome were finally established， namely， dark red or purplish tongue proper with yellow greasy （or dry） coating （30 points）， epigastric stuffiness and fullness or pain （15 points）， sticky and unsmooth defecation （10 points）， taste disturbance （sticky mouth， fetid breath， bitter taste， 7 points）， heartburn or acid regurgitation （6 points）， dizziness and clouding （5 points）， general heaviness and fatigue （5 points）， slippery， string‑slippery， or slippery‑rapid pulse （5 points）， dysuria （or yellow or deep yellow urine， 4 points）， poor appetite （4 points）， dull complexion （3 points）， sticky， greasy， and fetid secretions （3 points）， and poor sleep （3 points）. ConclusionBased on the establishment， screening， confirmation， and weighting of an item pool， combined with subjective and objective approaches as well as qualitative and quantitative methods， a diagnostic scale for CAG with the syndrome of turbid toxin accumulating in the stomach was successfully constructed.