Objective: To develop a dual-branch deep learning framework for accurate multi-label classification of fundus diseases, addressing the key limitations of insufficient complementary feature extraction and inadequate cross-modal feature fusion in existing automated diagnostic methods. Methods: The fundus multi-label classification dataset with 12 disease categories (FMLC-12) dataset was constructed by integrating complementary samples from Ocular Disease Intelligent Recognition (ODIR) and Retinal Fundus Multi-Disease Image Dataset (RFMiD), yielding 6 936 fundus images across 12 retinal pathology categories, and the framework was validated on both FMLC-12 and ODIR. Inspired by the holistic multi-regional assessment principle of the Five Wheels theory in traditional Chinese medicine (TCM) ophthalmology, the dual-branch multi-label network (DBMNet) was developed as a novel framework integrating complementary visual feature extraction with pathological correlation modeling. The architecture employed a TransNeXt backbone within a dual-branch design: one branch processed red-green-blue (RGB) images to capture color-dependent features, such as vascular patterns and lesion morphology, while the other processed grayscale-converted images to enhance subtle textural details and contrast variations. A feature interaction module (FIM) effectively integrated the multi-scale features from both branches. Comprehensive ablation studies were conducted to evaluate the contributions of the dual-branch architecture and the FIM. The performance of DBMNet was compared against four state-of-the-art methods, including EfficientNet Ensemble, transfer learning-based convolutional neural network (CNN), BFENet, and EyeDeep-Net, using mean average precision (mAP), F1-score, and Cohen's kappa coefficient. Results: The dual-branch architecture improved mAP by 15.44 percentage points over the single-branch TransNeXt baseline, increasing from 34.41% to 44.24%, and the addition of FIM further boosted mAP to 49.85%. On FMLC-12, DBMNet achieved an mAP of 49.85%, a Cohen’s kappa coefficient of 62.14%, and an F1-score of 70.21%. Compared with BFENet (mAP: 45.42%, kappa: 46.64%, F1-score: 71.34%), DBMNet outperformed it by 4.43 percentage points in mAP and 15.50 percentage points in kappa, while BFENet achieved a marginally higher F1-score. On ODIR, DBMNet achieved an F1-score of 85.50%, comparable to state-of-the-art methods. Conclusion DBMNet effectively integrates RGB and grayscale visual modalities through a dual-branch architecture, significantly improving multi-label fundus disease classification. The framework not only addresses the issue of insufficient feature fusion in existing methods but also demonstrates outstanding performance in balancing detection across both common and rare diseases, providing a promising and clinically applicable pathway for standardized, intelligent fundus disease classification.

OBJECTIVE To establish the UPLC fingerprint and the method for multi-component content determination in Jingangteng capsules， and to evaluate its quality by combining chemical pattern recognition analysis. METHODS An UPLC method was established. Separation was performed on a Zorbax SB-C 18 Rapid Resolution HD column， with acetonitrile-0.1% formic acid as the mobile phase for gradient elution.Using the Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicines （2012 edition）， UPLC fi ngerprints were established for 10 batches of Jingangteng capsules， and similarity was evaluated. SPSS 22.0 and SIMCA 14.1 software were used to perform hierarchial-cluster analysis and orthogonal partial least squares discriminant analysis （OPLS-DA）， respectively. The same UPLC method was employed to determine the contents of chlorogenic acid， 3，5-dihydroxy-2-methylbenzoic acid-3- O -glucoside （M1）， caffeic acid， astilbin， oxyresveratrol， quercitrin and resveratrol in the 10 batches of samples. RESULTS A total of 17 common peaks were identified in UPLC fingerprints of the 10 batches of samples， of which 7 were identified as chlorogenic acid， M1， caffeic acid， astilbin， oxyresveratrol， quercitrin， and resveratrol. The similarities of 10 batches of samples ranged from 0.820 to 0.985. The results of hierarchial-cluster analysis showed that 10 batches of samples were grouped into four categories： S1-S4 formed one group， S5 and S6 formed another， S7， S8 and S10 formed a third， and S9 formed a fourth， consistent with the OPLS-DA results； the variable importance projection values for peaks 7， 10， 2， 16 （resveratrol）， 13 （oxyresveratrol）， 11， 6 （caffeic acid）， 5 （M1） and 15 （quercitrin） were ＞1. Quantitative analysis results showed that the contents of chlorogenic acid， M1， caffeic acid， astilbin， oxyresveratrol， quercitrin， and resveratrol were 1.650 8-4.213 7， 0.636 2-2.161 7， 0.031 0-0.086 5， 0.239 1-1.069 3， 0.211 9-1.104 0， 0.488 8-2.399 2， and 0.164 0-0.699 8 mg/g， respectively. CONCLUSIONS UPLC fingerprint and content determination methods established in this study are simple to operate， accurate， reliable and reproducible； when combined with chemical pattern recognition analysis， they can be used to evaluate the quality of Jingangteng capsules. Nine components， such as resveratrol， oxyresveratrol， caffeic acid， M1 and quercitrin， may serve as markers of quality variation.

The hydroxyl value and conjugated linoleic acid content of dehydrated castor oils are two important indicators that reflect its properties.Thus,monitoring the two indicators can better realize the industrial production of high-quality dehydrated castor oils.However,traditional chemical measurement methods have many disadvantages in determination of the two indictors,including large reagent consumption,long determination time,and cannot achieve rapid monitoring.Fourier transform infrared spectrometry(FTIR)is a new and non-destructive detection method that is low-cost and can achieve rapid detection.In this work,FTIR technique was employed to collect spectral information of dehydrated castor oils and analyze the relationship between FTIR spectral information and hydroxyl value and conjugated linoleic acid content,and a rapid detection method for detecting the property indicators of dehydrated castor oils was thus established.FTIR scanning was performed on dehydrated castor oils with a hydroxyl value of 21.9-161.4 mg KOH/g and a conjugated linoleic acid content of less than 37.5%.Among different preprocessing methods,orthogonal scatter correction(OSC)could improve the prediction accuracy of the resulting model,by which the optimal modeling data segments for hydroxyl value and conjugated linoleic acid content were 3200-3800 cm-1 and 800-1200 cm-1,respectively,and the optimal modeling method was partial least squares(PLS).The coefficients of determination of the optimal models for hydroxyl value and conjugated linoleic acid content were all above 0.99.

Lymphoma is a highly heterogeneous malignancy characterized by complex molecular regulatory mechanisms that result in significant differences in aggressiveness and prognosis across its subtypes.Gene copy number alteration(CNA)analysis,an emerging technology,has become a pivotal tool in the precision re-search and management of lymphoma.By detecting DNA deletions,amplifications,and chromosomal copy number changes,CNA analysis addresses the limitations of traditional cytogenetic techniques,enhances the ac-curacy of subtype classification,and aids in evaluating tumor heterogeneity and disease progression.This re-view provides a comprehensive summary of CNA detection methods and their applications in lymphoma,with a focus on recent advancements in the field.It offers a comparative analysis of CNA detection techniques and discusses their role in precision diagnosis,subtype classification,monitoring disease progression,predicting therapeutic resistance,and assessing prognosis.Additionally,the review explores the potential applications of CNA analysis in uncovering molecular regulatory mechanisms,optimizing therapeutic strategies,and impro-ving patient survival outcomes.

ObjectiveTo observe the clinical effectiveness of horticultural therapy involving the planting of Chinese medicinal herbs （mint and lily potted plants） combined with intradermal needling therapy for generalized anxiety disorder （GAD） of liver depression transforming into fire syndrome under transcranial magnetic stimulation and basic psychological therapy， and to explore the possible mechanisms of action. MethodsA total of 180 patients with GAD of liver depression transforming into fire syndrome were randomly divided into three groups， horticultural therapy group， intradermal needling group， and horticultural therapy+intradermal needling group， with 60 patients in each. All groups received basic treatment including basic psychological therapy and transcranial magnetic stimulation. The horticultural therapy group received horticultural therapy in addition to the basic treatment； the intradermal needling group received intradermal needling therapy once a week for 8 weeks in addition to the basic treatment； the horticultural therapy+intradermal needling group received both horticultural therapy and intradermal needling therapy， following the same procedures and duration. Hamilton Anxiety Rating Scale （HAMA）， Self-Rating Anxiety Scale （SAS）， and Pittsburgh Sleep Quality Index （PSQI） scores were assessed at baseline and after 2， 4， 6， and 8 weeks of treatment. Serum levels of adrenocorticotropic hormone （ACTH） and corticosterone （CORT） were measured before treatment and after 8 weeks of treatment. Motor-evoked potential （MEP） baseline levels were recorded before treatment， and MEP amplitude ratios were compared after 1 week and 8 weeks of treatment. Clinical effectiveness and safety were evaluated after 8 weeks of treatment. Pearson correlation analysis was used to examine the relationships between serum ACTH and CORT levels， MEP amplitude， and anxiety. ResultsIn the horticultural therapy group and intradermal needling group， HAMA， SAS and PSQI scores after 4， 6， and 8 weeks treatment were lower than baseline scores （P＜0.05）. In the horticultural therapy+intradermal needling group， these scores showed a significant decline starting after 2 weeks treatment and continuing through 8 weeks after treatment （P＜0.05）. The HAMA， SAS， and PSQI scores in the horticultural therapy+intradermal needling group were significantly lower than those in the other two groups after 2， 4， 6， and 8 weeks treatment （P＜0.05）. After 8 weeks of treatment， serum CORT and ACTH levels in the horticultural therapy+intradermal needling group were significantly lower than baseline levels （P＜0.05） and were also lower than those in the horticultural therapy group and intradermal needling group at the same time point （P＜0.01）. When comparing the level after 8 weeks treatment to that after 1 week treatment， under PAS10 stimulation， the MEP amplitude ratio in the intradermal needling group decreased at 30 minutes， while in the horticultural therapy+intradermal needling group， the MEP amplitude ratio decreased at all time points （P＜0.05 or P＜0.001）； under PAS25 stimulation， the MEP amplitude ratio in the horticultural therapy group increased at 20 minutes， and in the intradermal needle group at 10 minutes （P＜0.05）. In the horticultural therapy+intradermal needling group， the MEP amplitude ratio increased significantly at all time points after treatment （P＜0.001）. The cure rate in the horticultural therapy+intradermal needling group （74.14%， 43/58） was significantly higher than that in the horticultural therapy group （30.00%， 18/60） and the intradermal needling group （48.28%， 28/58， P＜0.05）. Correlation analysis revealed that serum ACTH and CORT levels were positively correlated with HAMA scores （r = 0.488， P＜0.01； r = 0.428， P＜0.01）. Following PAS10 intervention， the MEP amplitude ratio was positively correlated with HAMA scores （r = 0.458， P＜0.01）， whereas after PAS25 intervention， the MEP amplitude ratio was negatively correlated with HAMA scores （r = -0.562， P＜0.01）. ConclusionHorticultural therapy combined with intradermal needling treatment， under transcranial magnetic stimulation and basic psychological therapy， demonstrates significant clinical effectiveness in patients with GAD of liver depression transforming into fire syndrome. Its mechanism of action may be related to the regulation of hyperactivation of the hypothalamic-pituitary-adrenal （HPA） axis and the reduction of cortical excitability.

Natural arabinogalactan， an important polysaccharide， has a wide range of sources， a complex structure， various biological activities， and great application potential. Natural arabinogalactan is mainly rich in plants and microorganisms， and its structure varies due to different sources， including types Ⅰ， type Ⅱ， type Ⅱ-related types， and new configurations. Natural arabinogalactan has shown a variety of biological activities， such as anti-tumor， anti-oxidation， anti-coagulation， anti-aging， blood glucose-lowering， intestinal health-maintaining， anti-inflammatory， and immunomodulatory activities. In addition， natural arabinogalactan shows good biocompatibility and low toxicity， serving as a potential material in the biomedical field. Natural arabinogalactan has been designed as a carrier in the drug delivery system to effectively improve drug stability and targeting. Natural arabinogalactan is often added to skin care products to help delay skin aging and enhance skin barrier function because of their moisturizing and antioxidant properties. Additionally， natural arabinogalactan acts as a thickener， stabilizer， and emulsifier to improve the texture and taste while enhancing the nutritional value of food products. The review of latest research reports is helpful to further understand the relationship between the structure， biological activity， and functional application of natural arabinogalactan and provides an important reference for future research and development.

[Objective] To investigate the infection status and characteristics of HEV among voluntary blood donors in Ningbo, and to provide a basis for improving the blood screening strategy. [Methods] A total of 12 227 blood samples from voluntary blood donors in Ningbo from June 2022 to May 2023 were tested for HEV serology, enzymology, and nucleic acid testing. Furthermore, HEV gene sequencing was performed for genotyping analysis, and donors with reactive nucleic acid testing results were followed up to confirm their infection status. [Results] The reactivity rate of HEV Ag, anti-HEV IgM and anti-HEV IgG was 0.098%, 0.899% and 29.198%, respectively. There was no difference in the reactivity of anti-HEV IgM and anti-HEV IgG between genders, donation frequencies and donation types (P>0.05). The reactivity rate increased significantly with age (P<0.05). The rate of ALT disqualification (ALT>50U/L) was significantly higher than that in non-reactive samples (P<0.05). The HEV Ag reactivity rate (0.098%) was not correlated with gender, donation frequency, donation type or age. One HEV RNA positive case was found, with a positive rate of 0.008%(1/12 227). It was confirmed to be hepatitis E virus genotype 3 by sequencing analysis. Apart from HEV Ag reactivity, all other blood safety screening items were non-reactive, suggesting this case might be in the acute infection phase. The follow-up results showed that all indicators of the donor's previous blood donation were non-reactive. [Conclusion] Pre-donation ALT detection can reduce the risk of transfusion-transmitted HEV (TT-HEV) to a certain extent, and the effective way to prevent TT-HEV is to detect HEV RNA and serology of donor blood.

OBJECTIVE To analyze chemical components of Yao ethnic medicine Pittosporum pauciflorum， establish its fingerprint and investigate the spectrum-effect relationship of its anti-inflammatory effect. METHODS UHPLC-Q-Orbitrap-MS technology was used to analyze the chemical components of P. pauciflorum （batch S6）. The fingerprints for 10 batches of P. pauciflorum from different producing areas in Guangxi Province （batches S1-S10） were established by HPLC， and similarity assessment and chemometric pattern recognition analysis were conducted. RAW264.7 inflammatory cell model was induced by lipopolysaccharide， and the anti-inflammatory activity of P. pauciflorum was investigated. Using inhibition rates of nitric oxide （NO）， tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6） and IL-1β as efficacy indicators， grey relational analysis and partial least squares regression analysis were adopted to evaluate the spectrum-effect relationship of the anti-inflammatory effect of P. pauciflorum. RESULTS There were 60 chemical components， including flavonoids， phenolic acids， lipids， etc.， identified in P. pauciflorum. The fingerprints for 10 batches of P. pauciflorum showed 14 common peaks，with similarity values ranging from 0.883 to 0.991. Three common peaks were assigned neochlorogenic acid （peak 5）， chlorogenic acid （peak 7）， and syringaldehyde （peak 10）. The classification results of the systematic clustering analysis and the principal component analysis were basically consistent. Batches S1 to S10 of P. pauciflorum significantly reduced the levels of NO， TNF-α， IL-6 （except for batch S5） and IL-1β in the cell supernatant （P＜0.05 or P＜0.01）. Inhibition rates of above inflammatory indexes were 10.26%-39.96%， 14.96%-31.36%， 1.38%-21.27%， 18.54%-28.00%， respectively. The contents of neochlorogenic acid， syringaldehyde， as well as the components corresponding to peaks 1， 3， 9， 12 and 14，exhibited a strong correlation with the anti-inflammatory effects of P. pauciflorum. CONCLUSIONS The present study has analyzed the chemical components of P. pauciflorum and established HPLC fingerprints for 10 batches of P. pauciflorum. Each batch of medicinal herbs demonstrates certain anti- inflammatory activities， among which neochlorogenic acid， syringaldehyde， and the components corresponding to peaks 1， 3， 9， 12 and 14 are likely to be the active anti-inflammatory components.

Objective:To investigate the influencing factors for sarcopenia in hospitalized patients,to construct a risk prediction model for sarcopenia in elderly hospitalized patients,and to provide a quantitative tool for early screening and intervention of sarcopenia based on the integration of multi-dimensional indicators.Methods:A retrospective analysis was performed for the data of 2105 elderly patients who were hospitalized in The First Affiliated Hospital of Chongqing Medical University from March 2016 to June 2023.The least absolute shrinkage and selection operator analysis was used for the screening of variables,and the logistic regression analysis was used to investigate the influencing factors for sarcopenia.A predictive model was constructed,and internal and external validation was performed.The Shapley additive explanations model was used to analyze feature contributions,and a nomogram model was constructed to visualize and interpret the results.Results:The 1259 patients from March 2016 to December 2020 were randomly divided into a training set with 882 patients and an internal test set with 377 patients at a ratio of 7∶3,and the 846 patients from January 2021 to June 2023 were established as the external validation set.A total of 489 cases of sarcopenia(55.44%)were detected in the training set.The logistic regression analysis based on the training set showed that asthenia,dependence on Activity of Daily Living,malnutrition,and in-creasing age were risk factors for sarcopenia(odds ratio[OR]>1,P<0.05),and male sex,normal body mass index,and overweight were protective factors against sarcopenia(OR<1,P<0.05).The model had an AUC of 0.876(95%CI=0.854-0.899)in the training set,0.883(95%CI=0.849-0.918)in the internal test set,and 0.750(95%CI=0.717-0.783)in the external validation set,suggesting that the model had good performance.The decision curve analysis showed that the nomogram model had a good clinical value.Conclu-sion:The predictive model for sarcopenia has good performance and holds promise for clinical application.