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.

OBJECTIVE To investigate the effects and mechanisms of Lycium ruthenicum Murr. in improving sleep. METHODS Network pharmacology was employed to identify the active components of L. ruthenicum and their associated disease targets， followed by enrichment analysis. A caffeine‑induced zebrafish model of sleep deprivation was established ， and the zebrafish were treated with L. ruthenicum Murr. extract （LRME） at concentrations of 0.1， 0.2 and 0.4 mg/mL， respectively； 24 h later， behavioral changes of zebrafish and pathological alterations in brain neurons were subsequently observed. The levels of inflammatory factors [interleukin-6 （IL-6）， IL-1β， IL-10， tumor necrosis factor-α （TNF-α）]， oxidative stress markers [superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione peroxidase （GSH-Px）， catalase （CAT）]， and neurotransmitters [5- hydroxytryptamine （5-HT）， γ-aminobutyric acid （GABA）， glutamic acid （Glu）， dopamine （DA）， and norepinephrine （NE）] were measured. The protein expression levels of protein kinase B1 （AKT1）， phosphorylated AKT1 （p-AKT1）， epidermal growth factor receptor （EGFR）， B-cell lymphoma 2 （Bcl-2）， sarcoma proto-oncogene，non-receptor tyrosine kinase （SRC）， and heat shock protein 90α family class A member 1 （HSP90AA1） in the zebrafish were also determined. RESULTS A total of 12 active components and 176 intersecting disease targets were identified through network pharmacology analysis. Among these， apigenin， naringenin and others were recognized as core active compounds， while AKT1， EGFR and others served as key targets； EGFR tyrosine kinase inhibitor resistance signaling pathway was identified as the critical pathway. The sleep improvement rates in zebrafish of LRME low-， medium-， and high-dose groups were 54.60%， 69.03% and 77.97%， 开发。E-mail：hjp_yft@163.com respectively， while the inhibition ratios of locomotor distance were 0.57， 0.83 and 0.95， respectively. Compared with the model group， the number of resting counts， resting time and resting distance were significantly increased/extended in LRME medium- and high-dose groups （P＜0.05）. Neuronal damage in the brain was alleviated. Additionally， the levels of IL-6， IL-1β， TNF-α， MDA， Glu， DA and NE， as well as the protein expression levels of AKT1， p-AKT1， EGFR， SRC and HSP90AA1， were markedly reduced （P＜0.05）， while the levels of IL-10， SOD， GSH-Px， CAT， 5-HT and GABA， as well as Bcl-2 protein expression， were significantly elevated （P＜0.05）. CONCLUSIONS L. ruthenicum Murr. demonstrates sleep-improving effects， and its specific mechanism may be related to the regulation of inflammatory responses， oxidative stress， neurotransmitter balance， and the EGFR tyrosine kinase inhibitor resistance signaling pathway.

Objective To understand the epidemiological characteristics of a clustered outbreak of severe fever with thrombocytopenia syndrome(SFTS)as well as the ecological tick monitoring results for Zibo City, and to provide a scientific basis for formulating prevention and control strategies. Methods A case definition was cited before epidemic investigations were performed. Epidemiological investigations were performed on the index cases and their close contacts. Blood samples were collected from cases and close contacts, and quantitative real-time RT-PCR was used to detect SFTS virus(SFTSV)nucleic acid sequences. A retrospective cohort study was conducted to analyze risk factors and develop prevention and control strategies. Results This clustered outbreak involved two index cases and six close contacts with no deaths. Case A exhibited symptom onset on July 26. SFTS was confirmed on August 2. Case B exhibited symptom onset on August 1, and SFTS was confirmed on August 3. Patient B reported a recent history of tick bites. In both index cases, the incubation period for SFTS was inferred to be 7-12 days. The time interval from symptom onset to clinical diagnosis in the two cases ranged from 2-8 days, with an average period of 5 days. SFTSV nucleic acid test result were positive for both patients, whereas all six close contacts tested negative. All captured ticks tested negative for SFTSV using quantitative real-time RT-PCR. The densities of parasitic and free-living ticks in the emergency monitoring area around the cases were 12.60 and 4.65 ticks/(flag·100 m). In 2024, the average parasitic tick index and free-living tick density index were 4.21 and 2.43 ticks/(flag ·100 m)in Yiyuan County, respectively. Conclusions No evidence of human-to-human transmission was found in the assessed SFTS clusters. The infections were likely acquired through tick bites during fieldwork, and the risk of a subsequent outbreak spreading was low.

Objective To investigate the capacity of municipal and county-level Centers for Disease Control (CDC) in Shandong Province for γ radionuclides in food as well as gross α and gross β in drinking water, and to provide evidence for strengthening radiological laboratories. Method Data regarding radioactive detection capacity were collected from 16 prefecture-level cities and 136 county-level CDCs. Chi-square test and Fisher's exact tests were used to compare capacity differences across regions and between cities with and without nuclear power plants. Correlation analysis was used to evaluate the spatial equity of gross α and gross β detection capacity for drinking water. Result The detection capacity of gross α and gross β in drinking water varied significantly among counties (χ²=81.026, P<0.001），no significant difference was found between coastal and inland cities (χ²=2.021，P>0.155); Cities with nuclear power plants showed better detection capacity (χ²=20.253，P<0.01). The detection capacity for γ radionuclides in food was primarily concentrated in the prefecture-level cities that host nuclear power plants. Conclusion The overall detection capacity for radioactivity in drinking water was acceptable but spatially uneven. The capacity for monitoring radioactive contamination in food was generally weak. It is necessary to strengthen capacity building at the grassroots level.

ObjectiveTo investigate whether the expression of Piezo1 in bulbar conjunctival fibroblasts contributes to the development of conjunctivochalasis （CCH）. MethodsPrimary bulbar conjunctival fibroblasts were obtained from CCH patients and normal control （NC） group by tissue block applanation. The cells were then purified and subjected to fibroblast identification. Immunohistochemical analysis was performed to assess Piezo1 expression in the bulbar conjunctival tissue of the NC and CCH groups. Immunofluorescence was used to detect the expression of Piezo1 in bulbar conjunctival fibroblasts in the CCH and NC groups， as well as the expression of matrix metalloproteinase-9（MMP-9） after the intervention of the Piezo1 agonist， Yoda1. qRT-PCR was used to detect the expression of mRNA of Piezo1 and MMP-9 in the CCH and NC groups， as well as the expression of mRNA of MMP-9 after the intervention of Yoda1. In addition， confocal microscopy was used to detect Ca²⁺influx in bulbar conjunctival fibroblasts in the NC group after Yoda1 intervention. Membrane clamp was used to detect the current amplitude of NC bulbar conjunctival fibroblasts after Yoda1 intervention to verify the functional expression of Piezo1. ResultsPrimary bulbar conjunctival fibroblasts were successfully cultured. Immunohistochemical analysis revealed elevated Piezo1 protein expression in the CCH group （P<0.01）. qRT-PCR analysis showed that Piezo1 mRNA expression was higher in the CCH group than in the NC group （P<0.01）， as was MMP-9 mRNA expression （P<0.01）. Furthermore， MMP-9 mRNA expression was higher in the CCH+Yoda1 group than in the CCH group （P<0.01）. Immunofluorescence staining for Piezo1 revealed enhanced fluorescence on the cell membranes of the CCH group compared with the NC group （P<0.05）， and the fluorescence of MMP-9 was enhanced by the addition of Yoda1 stimulation compared with that of the CCH group （P<0.05）. Confocal microscopy of NC group bulbar conjunctival fibroblasts after the addition of Yoda1 showed enhanced fluorescence of Piezo1 （P<0.05）. The addition of Yoda1 activated enhanced current amplitude， showed by membrane clamp detection of NC group bulbar conjunctival fibroblasts （P<0.000 1）. ConclusionMechanical damage produced by conjunctival tissues in conjunctivochalasis upregulates Piezo1 expression， and Piezo1 activation may amplify the mechanical damage caused by CCH.

Objective To propose a dynamic electrical impedance tomography imaging algorithm based on complementary information fusion network(CIFN)to enhance image quality of dynamic electrical impedance imaging.Methods There were three modules for initialization,multi-frame complementary information extraction and information fusion involved in the CIFN.Firstly,multi-frame dynamic conductivity distribution images were obtained by the initialization module;secondly,spatial complementary information was extracted from the images by using the multi-frame complementary information extraction module;finally,the fusion of lesion target distribution information and target re-reconstruction were realized by the information fusion module to aquire high-quality EIT images.With a 16-electrode multilayer cranial simulation model,the CIFN-based imaging method was compared with Tikhonov regularization algorithm,spectral constraint algorithm and U-Net algorithm in terms of imaging results of types of lesions to verify its performance.Results Compared with the Tikhonov regularization algorithm,spectral constraint algorithm and U-Net algorithm,the proposed CIFN-based algorithm exhibited the lowest mean absolute error(MAE)and the highest structural similarity(SSIM)when used to image different lesion targets,which accurately reconstructed the distribution of lesion targets and gained high imaging stability under common noise levels.Conclusion The proposed CIFN-based imaging algorithm obtains high imaging quality on a cranial simulation model and reconstruction results close to the real model distribution,which provides algorithmic support for subsequent clinical studies on electrical impedance imaging.[Chinese Medical Equipment Journal,2025,46(6):1-6]

Objective To establish a high-throughput non-targeted screening and prioritization method for emerging pollutants(EPs)in sewage using direct injection high-resolution mass spectrometry(HRMS).Methods The sewage samples were filtered by membrane filter and directly subjected to the liquid chromatography-time-of-flight mass spectrometer based on a method modified from our previous study.A C18 chromatographic column was applied for a gradient elution separation,and accurate mass and mass spectral fragment information were obtained through the MS full scan mode and MS/MS DIA data collection mode.After peak detection and alignment,the features from the raw data through open source software MZmine 3,and then high-throughput screening strategies such as MassBank and PubChem databases were used for compound annotation.Finally,the candidate features were confirmed with chemical standards by compared their retention time and mass spectrum fragmentation ion peaks.Results 13 EPs were identified,including 7 industrial chemicals,4 pharmaceuticals,1 pesticide and 1 metabolite.High detection rates were observed for metformin(86.2％),2-hydroxybenzothiazole(79.3％),1,2-benzisothiazole-3-one(72.4％),and 1,2-benzisothiazole-3-one(72.4％).The quantitative concentration range of EPs was 1.37～19.05 ng/mL,with the high concentrations observed for melamine(19.05 ng/mL)and furosemide(18.49 ng/mL).Ecological risk assessment identified 1,2-benzisothiazol-3-one,4-aminoacetophenone,creatinine,2-hydroxybenzothiazole,and furosemide as key pollutants.Conclusion This direct injection coupled with HRMS workflow enables efficient non-targeted screening and prioritization of emerging EPs in sewage samples,highlighting five ecotoxicologically critical EPs.The methodology enhances environmental monitoring capabilities and provide critical technical support for interdisciplinary research such as environmental forensics and health risk assessment.

Objective:To construct a key item checklist for the Mini-HTA report specification,providing scientific guidance for drafting each section of Mini-HTA research reports,enhancing their standardization,scientific rigor,and completeness,thereby improving the efficiency and quality of health decision-making.Methods:Based on preliminary literature review and qualitative systematic review,a pool of problem items for the Mini-HTA report specification was formed.Delphi questionnaires were distributed,and the Delphi technique was employed through two rounds of expert consultation to optimize and select key items.Results:Through two rounds of Delphi expert consultation,the initial Mini-HTA report specification item checklist was screened,integrated,and supplemented.A finalized key item checklist was constructed,comprising 8 first-level items(Title,Abstract,Introduction,Methods,Results,Discussion,Conclusion,and Other Relevant Information)and 48 second-level items.Conclusion:The constructed key item checklist for the Mini-HTA report specification provides scientific guidance for drafting Mini-HTA research reports.It helps enhance the standardization and transparency of the assessment process and the reliability of results,thereby optimizing the efficiency and quality of health decision-making.

ObjectiveTo establish a model that can quickly identify the aroma components in different parts of Nardostachys jatamansi， so as to provide a quality control basis for the market circulation and clinical use of N. jatamansi. MethodsHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） combined with Smart aroma database and National Institute of Standards and Technology（NIST） database were used to characterize the aroma components in different parts of N. jatamansi， and the aroma components were quantified according to relative response factor（RRF） and three internal standards， and the markers of aroma differences in different parts of N. jatamansi were identified by orthogonal partial least squares-discriminant analysis（OPLS-DA） and cluster thermal analysis based on variable importance in the projection（VIP） value >1 and P<0.01. The odor data of different parts of N. jatamansi were collected by Heracles Ⅱ Neo ultra-fast gas phase electronic nose， and the correlation between compound types of aroma components collected by the ultra-fast gas phase electronic nose and the detection results of HS-SPME-GC-MS was investigated by drawing odor fingerprints and odor response radargrams. Chromatographic peak information with distinguishing ability≥0.700 and peak area≥200 was selected as sensor data， and the rapid identification model of different parts of N. jatamansi was established by principal component analysis（PCA）， discriminant factor alysis（DFA）， soft independent modeling of class analogies（SIMCA） and statistical quality control analysis（SQCA）. ResultsThe HS-SPME-GC-MS results showed that there were 28 common components in the underground and aboveground parts of N. jatamansi， of which 22 could be quantified and 12 significantly different components were screened out. Among these 12 components， the contents of five components（ethyl isovalerate， 2-pentylfuran， benzyl alcohol， nonanal and glacial acetic acid，） in the aboveground part of N. jatamansi were significantly higher than those in the underground part（P<0.01）， the contents of β-ionone， patchouli alcohol， α-caryophyllene， linalyl butyrate， valencene， 1，8-cineole and p-cymene in the underground part of N. jatamansi were significantly higher than those in the aboveground part（P<0.01）. Heracles Ⅱ Neo electronic nose results showed that the PCA discrimination index of the underground and aboveground parts of N. jatamansi was 82， and the contribution rates of the principal component factors were 99.94% and 99.89% when 2 and 3 principal components were extracted， respectively. The contribution rate of the discriminant factor 1 of the DFA model constructed on the basis of PCA was 100%， the validation score of the SIMCA model for discrimination of the two parts was 99， and SQCA could clearly distinguish different parts of N. jatamansi. ConclusionHS-SPME-GC-MS can clarify the differential markers of underground and aboveground parts of N. jatamansi. The four analytical models provided by Heracles Ⅱ Neo electronic nose（PCA， DFA， SIMCA and SQCA） can realize the rapid identification of different parts of N. jatamansi. Combining the two results， it is speculated that terpenes and carboxylic acids may be the main factors contributing to the difference in aroma between the underground and aboveground parts of N. jatamansi.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.