ObjectiveGastrodia elata has evolved ecological types with shortened rhizome internodes and diversified flower and fruit coloration in response to different altitudes. Studying the genetic mechanisms of different ecotype germplasm is significant for guiding variety breeding in different cultivation areas. MethodsThe bHLH gene family was identified based on the whole-genome datasets of G. elata f. elata and G. elata f. glauca. Subsequently， the gene family members were subject to analysis， including gene structure， chromosomal localization， cis-acting elements， gene synteny， and phylogeny. Combined with transcriptome data and quantitative Real-time PCR， the expression patterns of bHLH genes in the stems of the different G. elata ecotype germplasm were analyzed. Finally， correlation analysis was conducted between gene expression patterns and color to obtain the key bHLH genes regulating the color formation of stem. ResultsA total of 63 bHLH genes were identified in both G elata f. elata and G. elata f. glauca， unevenly distributed across 17 chromosomes and clustered into 16 subfamilies， with significant expansion in some family members. Obvious inversions of bHLH genes on the same chromosome and interchromosomal translocations were detected in the two ecotype germplasm. Among these genes， 12 bHLH genes （such as bHLH62-3 and bHLH74） were associated with the bright yellow color of G elata f. elata stem， while 9 bHLH genes （such as PIL13， UNE12， and bHLH130） were correlated with the red color of G. elata f. glauca stem. Compared to G. elata f. glauca， the bHLH48 expression level was significantly higher in flowers and scale leaves of G elata f. elata， and the bHLH62-3 expression level was significantly higher in all organs of G elata f. elata. ConclusionsFunctional pathway divergence of the bHLH family members has occurred across different chromosomes in G elata f. elata and G. elata f. glauca. Through synergism or antagonism with other genes， 21 bHLH genes participate in the coloration metabolic pathway regulation of stems， flowers， and fruits. Specifically， bHLH62-3 is involved in regulating stem color differentiation in the anthocyanin biosynthesis pathway of G. elata， thus relevant to the color formation of stem. Additionally， GebHLH48 positively regulates flowering-related pathways to promote the early-flowering phenotype of G. elata f. elata. These findings have laid the foundation for analyzing the genetic regulatory mechanisms underlying the color formation of the G. elata stem.

ObjectiveGastrodia elata has evolved ecological types with shortened rhizome internodes and diversified flower and fruit coloration in response to different altitudes. Studying the genetic mechanisms of different ecotype germplasm is significant for guiding variety breeding in different cultivation areas. MethodsThe bHLH gene family was identified based on the whole-genome datasets of G. elata f. elata and G. elata f. glauca. Subsequently， the gene family members were subject to analysis， including gene structure， chromosomal localization， cis-acting elements， gene synteny， and phylogeny. Combined with transcriptome data and quantitative Real-time PCR， the expression patterns of bHLH genes in the stems of the different G. elata ecotype germplasm were analyzed. Finally， correlation analysis was conducted between gene expression patterns and color to obtain the key bHLH genes regulating the color formation of stem. ResultsA total of 63 bHLH genes were identified in both G elata f. elata and G. elata f. glauca， unevenly distributed across 17 chromosomes and clustered into 16 subfamilies， with significant expansion in some family members. Obvious inversions of bHLH genes on the same chromosome and interchromosomal translocations were detected in the two ecotype germplasm. Among these genes， 12 bHLH genes （such as bHLH62-3 and bHLH74） were associated with the bright yellow color of G elata f. elata stem， while 9 bHLH genes （such as PIL13， UNE12， and bHLH130） were correlated with the red color of G. elata f. glauca stem. Compared to G. elata f. glauca， the bHLH48 expression level was significantly higher in flowers and scale leaves of G elata f. elata， and the bHLH62-3 expression level was significantly higher in all organs of G elata f. elata. ConclusionsFunctional pathway divergence of the bHLH family members has occurred across different chromosomes in G elata f. elata and G. elata f. glauca. Through synergism or antagonism with other genes， 21 bHLH genes participate in the coloration metabolic pathway regulation of stems， flowers， and fruits. Specifically， bHLH62-3 is involved in regulating stem color differentiation in the anthocyanin biosynthesis pathway of G. elata， thus relevant to the color formation of stem. Additionally， GebHLH48 positively regulates flowering-related pathways to promote the early-flowering phenotype of G. elata f. elata. These findings have laid the foundation for analyzing the genetic regulatory mechanisms underlying the color formation of the G. elata stem.

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.

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.

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 To analyze the efficacy, safety, and learning curve of the three-lobe holmium laser enucleation of the prostate(HoLEP)in the treatment of benign prostatic hyperplasia(BPH)in a county-level hospital.Methods A retrospective analysis was conducted on the clinical data of 65 BPH patients who underwent the three-lobe HoLEP performed by a single surgeon at the Department of Urology, Yining County People's Hospital, during Dec.2023 and Jun.2024.The enucleation efficiency was calculated by dividing the weight of the enucleated prostatic tissue by the enucleation time.A case scatter diagram of enucleation efficiency was plotted according to the chronological order of the operations, and the learning curve was analyzed after fitting.Based on the inflection points of the learning curve, the learning process was divided into the initial learning, mastery, and proficiency phases.The basic clinical data, perioperative indicators, postoperative complications, and follow-up indicators were compared among the different learning phases.Results All 65 procedures were successfully completed.Marked by the enucleation efficiency reaching a plateau, cases 1-20 were defined as the initial learning phase, cases 21-40 as the mastery phase, and cases 41 onwards as the proficiency phase.The prostate volume in the proficiency phase was significantly larger than that in the initial learning and mastery phases(both P＜0.05).There were no statistically significant differences in other baseline characteristics among the three groups(all P＞0.05).The operation time [(105.50±19.12)min vs.(85.25±26.92)min vs.(69.00±23.58)min] and enucleation time [(76.90±14.19)min vs.(63.70±22.24)min vs.(48.80±20.48)min] showed a decreasing trend across the three groups(all P＜0.05).The enucleation efficiency in both the mastery and proficiency phases was significantly higher than that in the initial learning phase [(1.16±0.44)g/min vs.(0.85 ±0.25)g/min, P＜0.05;(1.36±0.49)g/min vs.(0.85±0.25)g/min, P＜0.05].The enucleation efficiency in the proficiency phase was slightly higher than that in the mastery phase, but the difference was not statistically significant(P= 0.389).There was no significant difference in the incidence of perioperative complications among the three groups(all P＞0.05).At the 6-month follow-up, the international prostate symptom score(IPSS), post-void residual(PVR), and maximum urinary flow(Qmax)were significantly improved compared to preoperative values in all three groups(all P＜0.05);however, no significant differences were observed among the three groups(all P＞0.05).Conclusion The three-lobe HoLEP for the treatment of BPH is safe and effective in a county-level hospital setting.Surgeons with some experience in endoscopic surgery can preliminarily master this technique after a learning period of approximately 20 procedures.

Objective: To present the plotting of trend analysis charts and the application of multi-rule trend analysis method for monitoring the quality of whole blood, washed red blood cells, and leukocyte-depleted suspended red blood cells. Methods: Samples of whole blood, washed red blood cells, and leukocyte-depleted suspended red blood cells were collected in accordance with relevant protocols and requirements. The results of blood quality testing were compiled. The trend line charts were plotted for the deviation rates of volume, hemoglobin (Hb) content, supernatant protein content, and leukocyte residual count. In addition, the trend line chart were plotted for hematocrit and post-storage hemolysis rate. Results: The trend analysis rules for the deviation rate of volume from the standard were: 1 categorized as out-of-control, (3 of 4) as warning, R as warning, 8 as alert, and 12 as alert. For Hb content deviation rate, the rules were: <0% categorized as out-of-control; <20% but ≥0% (for whole blood) or <10% but ≥0% (for washed red blood cells and leukocyte-depleted suspended red blood cells) as a warning. For supernatant protein content deviation rate, the rules were: ≥0.0% as out-of-control, and >-10% but <0.0% as a warning. For hematocrit, the rules were: 1 as out-of-control and (3 of 4) as warning. For leukocyte residual count deviation rate, the rules were: >0.0% as out-of-control, and >-10% but ≤0.0% as a warning. For post-storage hemolysis rate, the rule was: ≥0.8% out-of-control. Conclusion: The application of multi-rule trend analysis facilitates the timely identification of trend abnormalities and factors causing deviations in the collection and preparation processes of whole blood, washed red blood cells, and leukocyte-depleted suspended red blood cells, thereby providing reliable numerical evidence for their correction and adjustment.

To investigate the positivity rates of specific antibodies, including parietal cell antibody (PCA) and anti-inner factor antibody (IFA), in a community population in Xinjiang for autoimmune gastritis (AIG), determine the prevalence of AIG, and analyze the correlations of PCA and IFA with clinical characteristics, pepsinogen (PG), gastrin 17 (G17), and Helicobacter pylori antibody (Hp-Ab).

ObjectiveTo explore the latent profiles of sleep patterns and their association with gastrointestinal heat accumulation syndrome in preschool children in Beijing. MethodsA cross-sectional study design was adopted， and preschool children were recruited from 57 kindergartens in Beijing using convenience sampling. Sleep characteristics and gastrointestinal heat accumulation syndrome was assessed using the Chinese version of the Sleep Disturbance Scale for Children （SDSC） and Self-rating Diagnostic Scale for Gastrointestinal Heat Retention Syndrome in children （GHRS）. Latent profile analysis （LPA） was applied to identify distinct sleep-related subgroups， and multivariate logistic regression was used to analyze the association between different sleep profiles and gastrointestinal heat accumulation syndrome. ResultsA total of 6878 preschool children were included in the study. Among them， 3.46% of the children （238/6878） had sleep disorders， while 54.42% （3743/6878） had gastrointestinal heat accumulation. Compared to children without gastrointestinal heat accumulation syndrome， those with this syndrome exhibited a higher prevalence of sleep disorders， higher SDSC scores， higher T-transformed total scores， and higher scores across all dimensions， including disorders of initiating sleep， disorders of maintaining sleep， sleep hyperhidrosis， sleep breathing disorders， parasomnias， nonrestorative sleep and excessive somnolence （P＜0.01）. Latent profile analysis identified four distinct sleep characteristic profiles， which were the mixed high-symptom group （32.39%， 2228/6878）， sleep initiation and maintenance difficulty symptom group （49.07%， 3375/6878）， low-symptom group （14.19%， 976/6878）， and the group with no significant symptoms （4.35%， 299/6878）. Multivariate logistic regression analysis demonstrated that the mixed high-symptom group， sleep initiation and maintenance difficulty symptom group， and low-symptom group were significantly associated with gastrointestinal heat accumulation syndrome. Among them， the mixed high-symptom group had the most significant association with gastrointestinal heat accumulation （OR=12.657， P＜0.001）， followed by the sleep initiation and maintenance difficulty symptom group （OR=4.964， P＜0.001）. ConclusionSleep characteristics in preschool children mainly present as composite patterns with different symptom combinations. Moreover， the sleep latent profiles of the mixed high-symptom group， the sleep initiation and maintenance difficulty symptom group， and the low-symptom group were significantly associated with gastrointestinal heat accumulation syndrome.