To analyze the disease burden of acute lymphoid leukemia(ALL) and its changing trends in China and globally from 1990 to 2021, aiming to provide a theoretical basis for disease prevention, treatment, and policy formulation.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

OBJECTIVE To establish a quality control method for Jinbei oral liquid based on multi-wavelength switching high performance liquid chromatography （HPLC） fingerprint， chemical pattern recognition and multi-index content determination. METHODS A total of 15 batches of Jinbei oral liquid were used as test samples. The Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （ Version 2012 ） was adopted to establish multi-wavelength switching HPLC fingerprints， followed by chromatographic peak identification and similarity evaluation. Cluster analysis， principal component analysis and orthogonal partial least squares-discriminant analysis were applied to conduct chemical pattern recognition on the 15 batches of samples. The multi-wavelength switching HPLC method was used to simultaneously determine the contents of neochlorogenic acid， chlorogenic acid， cryptochlorogenic acid， forsythoside A， baicalin， salvianoli c acid B and wogonoside in samples. RESULTS A total of 25 common peaks were calibrated in the fingerprints of 15 batches of Jinbei oral liquid， among which 7 common peaks were unambiguously identified. The similarity of all samples was higher than 0.960. Chemical pattern recognition results showed that samples S1-S8 were clustered into group 1， and samples S9-S15 were clustered into group 2. Baicalin， wogonoside， neochlorogenic acid， chlorogenic acid and salvianolic acid B were identified as the differential quality markers. The average contents of neochlorogenic acid， chlorogenic acid， cryptochlorogenic acid， forsythoside A， baicalin， salvianolic acid B and wogonoside in 15 batches of Jinbei oral liquid were 0.213 7， 0.085 3， 0.104 9， 0.287 3， 0.420 1， 0.062 1， 0.176 0 mg/mL， respectively. CONCLUSIONS The established quality control method for Jinbei oral liquid combining multi-wavelength switching HPLC fingerprint， chemical pattern recognition and multi-index content determination is stable and reliable， which can provide a reference for the formulation of quality standards of this preparation.

OBJECTIVE To establish a quality control method for Jinbei oral liquid based on multi-wavelength switching high performance liquid chromatography （HPLC） fingerprint， chemical pattern recognition and multi-index content determination. METHODS A total of 15 batches of Jinbei oral liquid were used as test samples. The Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （ Version 2012 ） was adopted to establish multi-wavelength switching HPLC fingerprints， followed by chromatographic peak identification and similarity evaluation. Cluster analysis， principal component analysis and orthogonal partial least squares-discriminant analysis were applied to conduct chemical pattern recognition on the 15 batches of samples. The multi-wavelength switching HPLC method was used to simultaneously determine the contents of neochlorogenic acid， chlorogenic acid， cryptochlorogenic acid， forsythoside A， baicalin， salvianoli c acid B and wogonoside in samples. RESULTS A total of 25 common peaks were calibrated in the fingerprints of 15 batches of Jinbei oral liquid， among which 7 common peaks were unambiguously identified. The similarity of all samples was higher than 0.960. Chemical pattern recognition results showed that samples S1-S8 were clustered into group 1， and samples S9-S15 were clustered into group 2. Baicalin， wogonoside， neochlorogenic acid， chlorogenic acid and salvianolic acid B were identified as the differential quality markers. The average contents of neochlorogenic acid， chlorogenic acid， cryptochlorogenic acid， forsythoside A， baicalin， salvianolic acid B and wogonoside in 15 batches of Jinbei oral liquid were 0.213 7， 0.085 3， 0.104 9， 0.287 3， 0.420 1， 0.062 1， 0.176 0 mg/mL， respectively. CONCLUSIONS The established quality control method for Jinbei oral liquid combining multi-wavelength switching HPLC fingerprint， chemical pattern recognition and multi-index content determination is stable and reliable， which can provide a reference for the formulation of quality standards of this preparation.

In order to further standardize the vaccination of kidney transplant candidates and recipients in China, the Branch of Organ Transplantation of Chinese Medical Association has organized experts in kidney transplantation and infectious diseases. Based on the "Vaccination of Solid Organ Transplant Candidates and Recipients: Guidelines from the American Society of Transplantation Infectious Diseases Community of Practice", and in combination with the clinical reality of infectious diseases and vaccination after organ transplantation in China, as well as referring to relevant recommendations from home and abroad in recent years, these guidelines are formulated from aspects such as epidemiology, types of vaccines, vaccination principles, target population, and specific vaccine administration. The "Guidelines for Vaccination of Kidney Transplant Candidates and Recipients in China" aims to provide theoretical reference for medical workers in the field of kidney transplantation in China, regarding the vaccination of kidney transplant candidates and recipients. It is expected to better guide the vaccination of kidney transplant candidates and recipients, reduce the risk of postoperative infection, and improve survival outcomes.

ObjectiveTo establish the quality standards for Sennae Folium（Cassia angustifolia） dispensing granules based on standard decoctions. MethodsHigh performance liquid chromatography（HPLC） specific chromatograms were established for 15 batches of Sennae Folium（C. angustifolia） standard decoctions and 10 of Sennae Folium（C. angustifolia） dispensing granules from different manufacturers， and the similarity evaluation， hierarchical cluster analysis（HCA） and principal component analysis（PCA） were performed. Linear calibration with two reference substances（LCTRS） and quantitative analysis of multi-components by single-marker（QAMS） were established for the common peaks in the specific chromatograms to determine the contents of main components in the decoction pieces， standard decoctions and dispensing granules， and to calculate their transfer rates from decoction pieces to standard decoctions and dispensing granules. ResultsThe similarities of specific chromatograms of 15 batches of Sennae Folium（C. angustifolia） standard decoctions and 10 batches of Sennae Folium（C. angustifolia） dispensing granules were all greater than 0.95， and a total of 8 characteristic peaks were calibrated， and five of them were identified， including kaempferol-3，7-O-diglucoside， apigenin-6，8-di-C-glucoside， quercetin-3-O-gentianoside， sennoside B and sennoside A. HCA and PCA results showed that there were certain differences in the composition of different batches of standard decoctions， but no clustering was observed in the production area. As the standard decoctions， the extract rate of 15 batches of samples was 26.54%-45.38%， the contents of kaempferol-3，7-O-diglucoside， apigenin-6，8-di-C-glucoside， quercetin-3-O-gentianoside， sennoside B and sennoside A were 12.16-19.26， 2.57-4.94， 3.27-5.11， 6.75-11.39， 4.69-7.79 mg·g^-1， and their transfer rates from decoction pieces to standard decoctions were 45.41%-79.02%， 29.12%-55.07%， 40.52%-67.90%， 24.72%-49.12%， 27.54%-49.34%， respectively. The extract rates of Sennae Folium（C. angustifolia） dispensing granules（C8-C10） were 38.10%-39.50%， the transfer rates of the above five components from decoction pieces to dispensing granules were 72.85%-73.58%， 53.43%-53.94%， 40.19%-40.74%， 24.62%-25.00%， 28.65%-29.11%， respectively， which were generally consistent with the transfer rates from decoction pieces to standard decoctions. ConclusionCompared with the relative retention time method， LCTRS has higher prediction accuracy and is more suitable for chromatographic columns. The established quality control standard of Sennae Folium（C. angustifolia） dispensing granules based on standard decoction is reasonable and reliable， and all indicators of samples from different manufacturers are within the range specified based on the standard decoction， which can provide reference for the quality control and process research of this dispensing granules.

ObjectiveTo establish the specific chromatogram of Chuanxiong Rhizoma dispensing granules（CRdg）， and to evaluate its quality by chemometrics and two reference substances for determination of multiple components（TRSDMC）. MethodsHigh performance liquid chromatography（HPLC） specific chromatograms were established using 13 batches of CRdg from 7 manufacturers， and preliminary quality evaluation was performed by similarity evaluation and chemometrics analysis. Eight characteristic peaks in the specific chromatogram of CRdg were measured on 22 different types of C₁₈ columns， and the actual retention times were recorded. Taking chlorogenic acid（peak 1） and senkyunolide A（peak 8） as double standard compounds， the retention times of the eight characteristic peaks were predicted by linear calibration using two reference substances（LCTRS）， and the method was validated on three other columns of different brands. Taking chlorogenic acid as reference peak， the relative correction factor method（RCFM） was used to quantify cryptochlorogenic acid， caffeic acid， ferulic acid， senkyunolide I and senkyunolide A， and the results were compared with the external standard method（ESM）. ResultsThe similarities of specific chromatograms of 13 batches of CRdg were all >0.90， and a total of 8 characteristic peaks were calibrated， and six of them were identified， including chlorogenic acid（peak 1）， cryptochlorogenic acid（peak 2）， caffeic acid（peak 3）， ferulic acid（peak 5）， senkyunolide I（peak 6） and senkyunolide A（peak 8）. Through chemometric analysis， it was found that ferulic acid， chlorogenic acid， senkyunolide I and cryptochlorogenic acid were the main components causing quality difference in CRdg， and the accuracy of LCTRS in predicting the retention time of 8 characteristic peaks was superior to that of the relative retention time method（RRT）. Further comparison of the results obtained from RCFM and ESM showed that there was no statistically significant difference between the two methods. ConclusionA quality evaluation method for CRdg based on HPLC specific chromatogram and TRSDMC is established， its qualitative accuracy is better than that of RRT， the quantitative accuracy is similar to that of ESM， and 4 quality-differentiated components among different manufacturers are found. This method is stable and reliable， and has reference value for the quality evaluation of other dispensing granules.

ObjectiveTo establish the specific chromatogram of Chuanxiong Rhizoma dispensing granules（CRdg）， and to evaluate its quality by chemometrics and two reference substances for determination of multiple components（TRSDMC）. MethodsHigh performance liquid chromatography（HPLC） specific chromatograms were established using 13 batches of CRdg from 7 manufacturers， and preliminary quality evaluation was performed by similarity evaluation and chemometrics analysis. Eight characteristic peaks in the specific chromatogram of CRdg were measured on 22 different types of C₁₈ columns， and the actual retention times were recorded. Taking chlorogenic acid（peak 1） and senkyunolide A（peak 8） as double standard compounds， the retention times of the eight characteristic peaks were predicted by linear calibration using two reference substances（LCTRS）， and the method was validated on three other columns of different brands. Taking chlorogenic acid as reference peak， the relative correction factor method（RCFM） was used to quantify cryptochlorogenic acid， caffeic acid， ferulic acid， senkyunolide I and senkyunolide A， and the results were compared with the external standard method（ESM）. ResultsThe similarities of specific chromatograms of 13 batches of CRdg were all >0.90， and a total of 8 characteristic peaks were calibrated， and six of them were identified， including chlorogenic acid（peak 1）， cryptochlorogenic acid（peak 2）， caffeic acid（peak 3）， ferulic acid（peak 5）， senkyunolide I（peak 6） and senkyunolide A（peak 8）. Through chemometric analysis， it was found that ferulic acid， chlorogenic acid， senkyunolide I and cryptochlorogenic acid were the main components causing quality difference in CRdg， and the accuracy of LCTRS in predicting the retention time of 8 characteristic peaks was superior to that of the relative retention time method（RRT）. Further comparison of the results obtained from RCFM and ESM showed that there was no statistically significant difference between the two methods. ConclusionA quality evaluation method for CRdg based on HPLC specific chromatogram and TRSDMC is established， its qualitative accuracy is better than that of RRT， the quantitative accuracy is similar to that of ESM， and 4 quality-differentiated components among different manufacturers are found. This method is stable and reliable， and has reference value for the quality evaluation of other dispensing granules.