Objective To explore a method for rapid and differential diagnosis of rejection after kidney transplantation through urine hyperspectral imaging technology. Methods Hyperspectral data information from urine samples of 118 recipients after kidney transplantation was collected, and a deep learning model was constructed to diagnose and classify the types of rejection. Results A deep learning diagnostic model based on the 34-layer residual network (ResNet-34) was constructed, and 118 patients were included and divided into the training set and the test set. Based on the pathological results of the transplanted kidney puncture, the urine samples of the patients were classified into five groups: the non-rejection group, the T-cell-mediated rejection group, the antibody-mediated rejection group, the mixed rejection group and the nephropathy recurrence group. The results showed that the diagnostic sensitivities of the model for the above five groups were 0.960, 0.980, 0.930, 0.940 and 0.943 respectively, and the diagnostic specificities were 0.983, 0.993, 0.997, 0.989 and 0.989 respectively. The overall diagnostic accuracy rate reached 95.7%. Conclusions The study provides a non-invasive, rapid and accurate auxiliary diagnostic method for the differential diagnosis of rejection after kidney transplantation.

ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

Objective: To investigate the correlation between color parameters (L^*, a^*, b^*, Eab^*) and the contents of reducing sugars, total polysaccharides, total oligosaccharides, as well as four saccharides (fructose, glucose, sucrose, and kestose) during the nine cycles of steaming and sun-drying processing of Polygonatum kingianum, and to preliminarily explore the optimal processing duration.
Methods: The color changes were objectively evaluated using a colorimeter. The anthrone-sulfuric acid method was employed to determine total polysaccharides and oligosaccharides. The 3,5-dinitrosalicylic acid (DNS) colorimetric method was used to measure total reducing sugar content. High-performance liquid chromatography coupled with charged aerosol detection (HPLC-CAD) was applied for quantitative analysis of fructose, glucose, sucrose, and kestose. Multivariate statistical analysis was conducted to assess samples from different processing stages.
Results: Significant variations in color and component contents were observed across processing stages. The herbal pieces progressively darkened with increased processing cycles: brightness (L^*) and total color difference (Eab^*) initially decreased then stabilized, while a^* (red-green) and b^* (yellow-blue) values first increased then declined. Total polysaccharides and oligosaccharides showed overall decreasing trends, whereas reducing sugars initially increased before stabilizing. Fructose and glucose levels rose continuously, while sucrose and kestose decreased progressively, becoming undetectable after five cycles.
Conclusion: The chromatic alteration and saccharide composition of P. kingianum showed significant correlation with processing duration. Both total color difference (Eab^*) and reducing sugar content stabilized after four processing cycles (12 hours), suggesting that four cycles of steaming and sun-drying may represent the optimal processing duration.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Sixteen compounds were isolated from the solid rice culture of the endophytic fungus Alternaria alstroemeriae WZ-419 derived from Pseudostellariae Radix, using silica gel, ODS, Sephadex LH-20 column chromatography, and preparative HPLC. Their structures were elucidated based on physicochemical properties and comprehensive spectroscopic analyses, and identified as alterpenoid A (1), tricycloalternarene A (2), altenusin (3), 4'-epialtenuene (4), altenuene (5), alternariol (6), altenuisol (7), 3-hydroxyalternariol-5-O-methylether (8), alternariol-9-methyl ether (9), alternariol 1′-hydroxy-9-methyl ether (10), stemphyperylenol (11), dihydroalterperylenol (12), alterinone A (13), 4-hydroxy-3-methoxy-5-(2E,6E-3,7,11-trimethyldodeca-2,6,10-trien-1-yl) benzoic acid (14), hexylitaconic acid (15), and altechromone A (16). Among them, compound 1 and 13 were new, and compounds 14 and 15 were isolated from the genus Alternaria for the first time. The antibacterial activities of compounds 1–16 were evaluated in vitro. Compounds 3, 6–8, 10, 11, 13, and 14 exhibited notable inhibitory effects against Gram-positive bacteria, with minimal inhibitory concentrations ranging from 8 to 64 µg/mL.This study isolated a series of antimicrobial metabolites from the endophytic fungi of Pseudostellariae Radix, providing new insights into their bioactive constituents and laying a foundation for further pharmacological investigations.

ObjectiveTo investigate the effects of Ditantang on metabolic dysfunction-associated fatty liver disease （MAFLD） in rats by focusing on the regulation of glycogen synthesis mediated by the uridine diphosphate glucose （UDPG）-site-1-protease （S1P）-sterol regulatory element-binding protein （SREBP） axis. MethodsMAFLD was modeled in SD rats by feeding a high-fat diet for 12 weeks. Rats were randomized into a control group， a model group， low-， medium-， and high-dose （2.68， 5.36， and 10.72 g·kg^-1·d^-1， respectively） Ditantang groups， and a positive control （Yishanfu， 0.18 g·kg^-1·d^-1） group. Starting from week 9 of modeling， each treatment group received corresponding drug interventions via gavage. After the experiment， the serum levels of total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， and high-density lipoprotein cholesterol （HDL-C） were measured. The levels of TG， non-esterified fatty acids （NEFA）， glycogen， glucose-6-phosphate （G6P）， glucose-1-phosphate （G1P）， and UDPG in the liver tissue samples were measured by biochemical assay kits. Liver pathological morphology and lipid/glycogen deposition were observed via hematoxylin-eosin （HE） staining， oil red O staining， and Periodic acid-Schiff （PAS） staining. Immunofluorescence assay was conducted to detect the expression and localization of phosphoglucomutase-1 （Pgm1） and UDP-glucose pyrophosphorylase 2 （Ugp2） in the rat liver. Real-time PCR and Western blot were employed to quantify mRNA and protein levels， respectively， of key glycogen synthesis enzymes （Pgm1 and Ugp2）， key lipogenesis-related transcription factor SREBP and its target genes ［acetyl-CoA carboxylase （ACC）， fatty acid synthetase （FASN）， and stearoyl-CoA desaturase （SCD1）］， as well as S1P. ResultsCompared with the control group， the model group exhibited elevated levels of TC， TG， LDL-C in the serum and TG and NEFA in the liver， decreased serum HDL-C level （P<0.01）， reduced hepatic levels of G1P， G6P， UDPG， and glycogen （P<0.01）， appearance of fat vacuoles of varying sizes and abundant red lipid droplets， with prominent lipid deposition and reduced areas of deep glycogen staining in the liver tissue， upregulated mRNA and protein levels of SREBP1， S1P， ACC， FASN， and SCD1 （P<0.01）， and downregulated mRNA and protein levels of Pgm1 and Ugp2 （P<0.01）. Compared with the model group， all doses of Ditantang reduced the levels of TC and TG in the serum and TG and NEFA in the liver， while elevating the serum HDL-C level. Additionally， medium- and high-dose Ditantang reducted the LDL-C level （P<0.05）. Ditantang at all doses increased the hepatic levels of G1P， G6P， UDPG， and glycogen， alleviated hepatic steatosis and lipid deposition， enhanced glycogen storage， downregulated the mRNA and protein levels of SREBP1， S1P， ACC， FASN， and SCD1 （P<0.05）， while upregulating the expression of Pgm1 and Ugp2 （P<0.01）. Immunofluorescence analysis revealed decreased fluorescence intensity of S1P and enhanced fluorescence intensity of Ugp2 （P<0.01）. ConclusionDitantang can regulate glycogen synthesis through the UDPG-S1P-SREBP axis to prevent and treat MAFLD.

Electromagnetic fields can regulate the fundamental biological processes involved in bone remodeling. As a non-invasive physical therapy, electromagnetic fields with specific parameters have demonstrated therapeutic effects on bone remodeling diseases, such as fractures and osteoporosis. Electromagnetic fields can be generated by the movement of charged particles or induced by varying currents. Based on whether the strength and direction of the electric field change over time, electromagnetic fields can be classified into static and time-varying fields. The treatment of bone remodeling diseases with static magnetic fields primarily focuses on fractures, often using magnetic splints to immobilize the fracture site while studying the effects of static magnetic fields on bone healing. However, there has been relatively little research on the prevention and treatment of osteoporosis using static magnetic fields. Pulsed electromagnetic fields, a type of time-varying field, have been widely used in clinical studies for treating fractures, osteoporosis, and non-union. However, current clinical applications are limited to low-frequency, and research on the relationship between frequency and biological effects remains insufficient. We believe that different types of electromagnetic fields acting on bone can induce various “secondary physical quantities”, such as magnetism, force, electricity, acoustics, and thermal energy, which can stimulate bone cells either individually or simultaneously. Bone cells possess specific electromagnetic properties, and in a static magnetic field, the presence of a magnetic field gradient can exert a certain magnetism on the bone tissue, leading to observable effects. In a time-varying magnetic field, the charged particles within the bone experience varying Lorentz forces, causing vibrations and generating acoustic effects. Additionally, as the frequency of the time-varying field increases, induced currents or potentials can be generated within the bone, leading to electrical effects. When the frequency and power exceed a certain threshold, electromagnetic energy can be converted into thermal energy, producing thermal effects. In summary, external electromagnetic fields with different characteristics can generate multiple physical quantities within biological tissues, such as magnetic, electric, mechanical, acoustic, and thermal effects. These physical quantities may also interact and couple with each other, stimulating the biological tissues in a combined or composite manner, thereby producing biological effects. This understanding is key to elucidating the electromagnetic mechanisms of how electromagnetic fields influence biological tissues. In the study of electromagnetic fields for bone remodeling diseases, attention should be paid to the biological effects of bone remodeling under different electromagnetic wave characteristics. This includes exploring innovative electromagnetic source technologies applicable to bone remodeling, identifying safe and effective electromagnetic field parameters, and combining basic research with technological invention to develop scientifically grounded, advanced key technologies for innovative electromagnetic treatment devices targeting bone remodeling diseases. In conclusion, electromagnetic fields and multiple physical factors have the potential to prevent and treat bone remodeling diseases, and have significant application prospects.

Microalgae can achieve sustainable CO2 fixation and biomass conversion,making them a green path for carbon capture and conversion.Monitoring extracellular carbonic anhydrase(CA)activity in microalgae is crucial for regulating carbon conversion efficiency.In this study,double-stranded DNA was designed as a capture probe to bind H+,which was produced by CA-catalyzed CO2 hydration.This interaction induced conformational changes in the DNA probe,enhancing fluorescence signals and thereby establishing a recognition and signal transduction mechanism for CA catalytic product.This approach enabled a novel method for measuring CA activity.The response of probe to H+was investigated using ultraviolet visible(UV-Vis)spectroscopy,circular dichroism sepctroscopy,and fluorescence spectroscopy.The experimental results indicated that the fluorescence intensity of the probe increased progressively with rising H+concentration.Using this probe,CA activity detection could be completed within 29 min,with a detection range of 0.35-17500 U/mL and a detection limit of 0.15 U/mL.Compared to traditional methods,this approach offered a lower detection limit and was suitable for analyzing and detecting extracellular CA activity in actual algal samples.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.