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: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective To investigate the risk factors of hemodynamic instability after carotid artery stenting by meta-analysis.Methods Ten databases were searched:PubMed,ProQuest,ScienceDirect,Embase,Cochrane Library,Web of Science,China Knowledge Network,Wanfang Data,VIP Information Database,and China Biomedical Database.The search date was from inception until 2 February 2024,and meta-analysis was performed using Stata 16.0 statistical software.Results A total of 27 studies with 4199 subjects and 22 influencing factors were included.The studies showed a 37.4％(95％CI 30.3％-44.8％)incidence of haemodynamic instability after carotid stenting,Meta-analysis determined that age＞60 years(P＜0.001),hypertension(P＜0.001),calcified plaque(P＜0.001),stenosis＞70％(P=0.008),eccentric plaque(P=0.002),distance from the largest stenosis to the carotid bifurcation≤ 10 mm(P＜0.001),stenosis involvement of the balloon or bifurcation(P＜0.001),balloon post-dilation(P=0.003),open-loop stenting(P＜0.001),dilated balloon diameter≥5 mm(P=0.002),repeat balloon dilation(P=0.011)and balloon dilation pressure≥8 atm(P＜0.001)are risk factors for intraoperative and postoperative haemodynamic instability in patients undergoing carotid artery stenting surgery.Statin use was a protective factor(P＜0.001).Conclusions Medical staff working in the clinic should assess the patient's condition preoperatively,identify risk factors that may lead to haemodynamic instability,and avoid unnecessary intraoperative stimulation of patients who are already in a high-risk state.Reduce postoperative clinical complications in patients with carotid artery stenosis and improve patient recovery.

BACKGROUND:In recent studies,hydrogel loaded with exosomes has attracted wide attention as an emerging therapeutic strategy in the field of tissue engineering and regenerative medicine,and is considered as a promising means for the treatment of nerve regeneration and wound healing.OBJECTIVE:To review the application of hydrogel loaded with exosomes in nerve regeneration and wound healing and to provide reference and guidance for future research and clinical application.METHODS:The first author used a computer in May 2024 to retrieve the relevant literature published from January 2000 to May 2024 on PubMed and CNKI,searching for"exosome,hydrogel,nerve,nerve regeneration,wound,wound healing"in Chinese and English,eventually incorporating 66 papers for analysis.RESULTS AND CONCLUSION:(1)Hydrogel loaded with exosomes provides a promising path for nerve injury repair by exerting anti-inflammatory and anti-oxidation,stimulating axon growth and myelin regeneration.(2)Exosome-loaded hydrogel suppresses the level of inflammation and oxidative stress,accelerates the proliferation and migration of skin cells,collagen expression,and promotes blood vessel formation,significantly accelerates the wound healing process,and improves the healing quality.(3)The role of hydrogel loaded with exosomes in nerve regeneration and wound repair is still limited to cell and animal experiments,and does not involve clinical practice.In the future,more mechanistic studies,safety evaluation,and supplementary related clinical trials are still needed in the future.