Objective:To establish a digital quality evaluation system for Salvia miltiorrhiza-Monascus fermentation products,screen critical quality markers,and provide methodological support for their intelligent quality control.Methods:Ultra-performance liquid chromatography coupled with quadrupole-orbitrap mass spectrometry(UPLC-Q-Orbitrap-MS)was employed to quantitatively analyze 34 bioactive components(e.g.,tanshinone Ⅱ A and alvianol-ic acid B)in 20 batches of fermentation products.The key markers were screened through hierarchical cluster anal-ysis and partial least squares discriminant analysis,and an intelligent discriminant model was constructed with sup-port vector machine machine learning algorithm to digitally analyze the characteristics of quality differences between batches.Results:Thirty-four common peaks were calibrated across all batches.Combined with partial least squares analysis,six key difference markers were further screened,including terpenoids such as isotanshinone Ⅱ A and tan-shinone Ⅱ A and phenolic acids such as salvianolic acid G.The support vector machine model can achieve 100％accuracy of origin discrimination by optimizing parameters jointly with genetic algorithm and grid search.Conclusion:This study developed a digital quality evaluation system for Salvia miltiorrhiza-Monascus fermentation products through a three-step analytical strategy("chemical feature exploration-marker screening-model valida-tion"),providing a transferable technical pathway for the intelligent transformation of traditional Chinese medicine quality control.

With the advancements in artificial intelligence (AI), computational power, and surgical robotics, the analysis of surgical performance at the granular level of individual surgical gestures has become feasible. Surgical gestures, defined as the smallest independent units of inter-action between surgical instruments and tissues, offer a quantifiable framework for surgical skill assessment. Evidence suggests that the selection and execution of surgical gestures are strongly correlated with the expertise of the surgeon and patient outcomes, underscoring their significance in both surgical training and clinical practice. Moreover, the establishment of a standardized classifi-cation system for surgical gestures and the adoption of uniform terminology have the potential to improve communication efficiency during surgical education and training. The authors synthesize existing classification systems for surgical gestures, with a focus on their applications in diverse tasks such as suturing, exposure and dissection. By examining the latest advancements in AI models applied to surgical gesture, as well as the current research landscape of surgical gesture recognition in digestive surgery, the authors explore the potential applications of such technologies in assisting surgeons during operations in the future.

With the rapid development of minimally invasive techniques in surgery, arti-ficial intelligence (AI), particularly deep learning, is playing an increasingly important role in mini-mally invasive surgery. By automated analysis of surgical videos, AI can efficiently perform key tasks such as instrument recognition, surgical phase identification, action analysis, anatomical structure recognition, intraoperative diagnosis, adverse event monitoring and smart desmoking. These appli-cations provide essential support for real-time monitoring, surgical navigation and skill assessment during surgery. The authors summarize the current research progress of AI in minimally invasive surgery, including its applications in the fields of hepatobiliary and pancreatic surgery, as well as gastrointestinal surgery. It also explores the potential of AI in enhancing surgical safety, efficiency and skill assessment. By synthesizing the latest research achievements of AI technology in the field of surgery, as well as analyzing its technical challenges and risks, it aims to provide guidance for future innovations and clinical applications, promoting the advancement and implementation of AI in minimally invasive surgery.

Objective:To establish a digital quality evaluation system for Salvia miltiorrhiza-Monascus fermentation products,screen critical quality markers,and provide methodological support for their intelligent quality control.Methods:Ultra-performance liquid chromatography coupled with quadrupole-orbitrap mass spectrometry(UPLC-Q-Orbitrap-MS)was employed to quantitatively analyze 34 bioactive components(e.g.,tanshinone Ⅱ A and alvianol-ic acid B)in 20 batches of fermentation products.The key markers were screened through hierarchical cluster anal-ysis and partial least squares discriminant analysis,and an intelligent discriminant model was constructed with sup-port vector machine machine learning algorithm to digitally analyze the characteristics of quality differences between batches.Results:Thirty-four common peaks were calibrated across all batches.Combined with partial least squares analysis,six key difference markers were further screened,including terpenoids such as isotanshinone Ⅱ A and tan-shinone Ⅱ A and phenolic acids such as salvianolic acid G.The support vector machine model can achieve 100％accuracy of origin discrimination by optimizing parameters jointly with genetic algorithm and grid search.Conclusion:This study developed a digital quality evaluation system for Salvia miltiorrhiza-Monascus fermentation products through a three-step analytical strategy("chemical feature exploration-marker screening-model valida-tion"),providing a transferable technical pathway for the intelligent transformation of traditional Chinese medicine quality control.

With the advancements in artificial intelligence (AI), computational power, and surgical robotics, the analysis of surgical performance at the granular level of individual surgical gestures has become feasible. Surgical gestures, defined as the smallest independent units of inter-action between surgical instruments and tissues, offer a quantifiable framework for surgical skill assessment. Evidence suggests that the selection and execution of surgical gestures are strongly correlated with the expertise of the surgeon and patient outcomes, underscoring their significance in both surgical training and clinical practice. Moreover, the establishment of a standardized classifi-cation system for surgical gestures and the adoption of uniform terminology have the potential to improve communication efficiency during surgical education and training. The authors synthesize existing classification systems for surgical gestures, with a focus on their applications in diverse tasks such as suturing, exposure and dissection. By examining the latest advancements in AI models applied to surgical gesture, as well as the current research landscape of surgical gesture recognition in digestive surgery, the authors explore the potential applications of such technologies in assisting surgeons during operations in the future.

With the rapid development of minimally invasive techniques in surgery, arti-ficial intelligence (AI), particularly deep learning, is playing an increasingly important role in mini-mally invasive surgery. By automated analysis of surgical videos, AI can efficiently perform key tasks such as instrument recognition, surgical phase identification, action analysis, anatomical structure recognition, intraoperative diagnosis, adverse event monitoring and smart desmoking. These appli-cations provide essential support for real-time monitoring, surgical navigation and skill assessment during surgery. The authors summarize the current research progress of AI in minimally invasive surgery, including its applications in the fields of hepatobiliary and pancreatic surgery, as well as gastrointestinal surgery. It also explores the potential of AI in enhancing surgical safety, efficiency and skill assessment. By synthesizing the latest research achievements of AI technology in the field of surgery, as well as analyzing its technical challenges and risks, it aims to provide guidance for future innovations and clinical applications, promoting the advancement and implementation of AI in minimally invasive surgery.

From September 26 to 28,2024,the 11th Korean International Gastric Cancer Week(KINGCA WEEK 2024),a prestigious academic conference in the field of gastric cancer,was held in Seoul.Organized by the Korean Gastric Cancer Association,the conference featured one main venue,18 sub-venues,and 16 thematic symposiums,including 100 invited presentations and four keynote speeches,attracting 788 experts and scholars from around the world.Additionally,the conference set 16 themes and received 425 submissions from 24 countries,including Republic of Korea,China,Japan,and the United States.After a review process,365 submissions were accepted,which included eight plenary oral presentations,78 oral reports,and 279 poster presentations.The conference covered many hot topics in gastric cancer diagnosis and treatment,with a particular focus on surgical-related areas such as treatment strategies for metastatic gastric cancer,an international consensus meeting on the conversion therapy for stage Ⅳgastric cancer,future research directions of the Korean Laparoendoscopic Gastrointestinal Surgery Study Group,the development of new surgical instruments and equipment,and key issues in lymph node dissection,resection,and reconstruction during minimally invasive gastric cancer surgeries.Furthermore,our team was invited to present two oral reports on"the application of artificial intelligence in minimally invasive gastric cancer surgery".This report aims to detail the dynamics and hotspots related to surgical treatment for gastric cancer,providing valuable references and insights for domestic surgical peers.

Objective: To investigate the effects of acupotomy on skeletal muscle fibrosis and collagen deposition in a rabbit knee osteoarthritis (KOA) model. Methods: Rabbits (n = 18) were randomly divided into control, KOA, and KOA + acupotomy (Apo) groups (n = 6). The rabbits in the KOA and Apo groups were modeled using the modified Videman's method for 6 weeks. After modeling, the Apo group was subjected to acupotomy once a week for 3 weeks on the vastus medialis, vastus lateralis, rectus femoris, biceps femoris, and anserine bursa tendons around the knee. The behavior of all animals was recorded, rectus femoris tissue was obtained, and histomorphological changes were observed using Masson staining and transmission electron microscopy. The expression of transforming growth factor-β1 (TGF-β1), Smad 3, Smad 7, fibrillar collagen types I (Col-I) and III (Col-III) was detected using Western blot and real-time polymerase chain reaction (RT-PCR). Results: Histological analysis revealed that acupotomy improved the microstructure and reduced the collagen volume fraction of rectus femoris, compared with the KOA group (P = .034). Acupotomy inhibited abnormal collagen deposition by modulating the expression of fibrosis-related proteins and mRNA, thus preventing skeletal muscle fibrosis. Western blot and RT-PCR analysis revealed that in the Apo group, Col-I, and Col-III protein levels were significantly lower than those in the KOA group (both P < .01), same as Col-I and Col-III mRNA levels (P = .0031; P = .0046). Compared with the KOA group, the protein levels of TGF-β1 and Smad 3 were significantly reduced (both P < .01), as were the mRNA levels of TGF-β1 and Smad 3 (P = .0007; P = .0011). Conversely, the levels of protein and mRNA of Smad 7 were significantly higher than that in the KOA group (P < .01; P = .0271). Conclusion Acupotomy could alleviate skeletal muscle fibrosis and delay KOA progress by inhibiting collagen deposition through the TGF-β/Smad pathway in the skeletal muscle of KOA rabbits.

Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.
Cluster Analysis ; Transcriptome

Objective:To investigate the effects of laparoscopic surgery versus open surgery on acute appendicitis in children. Methods:We retrospectively analyzed the data of 50 children with acute appendicitis who underwent surgery in Zhoushan Women and Children Hospital from December 2016 to December 2019. They were randomly divided into observation ( n = 27) and control ( n = 23) groups. The observation group underwent three-port laparoscopy. The control group underwent open surgery. Operation-related indicators, postoperative recovery, parental satisfaction, and complications were compared between the two groups. Results:Operative time was significantly longer in the observation group than in the control group [(56.57 ± 5.35) minutes vs. (40.23 ± 6.31) minutes, t = 9.78, P < 0.001). Intraoperative blood loss was significantly less in the observation group than in the control group [(10.11 ± 2.36) mL vs. (18.36 ± 3.21) mL, t = 10.45, P < 0.001]. Duration of pain was significantly shorter in the observation group than in the control group [(1.23 ± 0.23) days vs. (2.98 ± 0.87) days, t = 10.06, P < 0.001). Time to postoperative exhaust was significantly shorter in the observation group than in the control group [(21.39 ± 4.35) minutes vs. (39.88 ±5.39) minutes, t = 13.35, P < 0.001]. Time to defection was significantly shorter in the observation group than in the control group [(50.12 ± 3.35) minutes vs. (61.23 ± 4.21) minutes, t = 10.33, P < 0.001]. Time to first diet was significantly shorter in the observation group than in the control group [(13.25 ± 2.56) hours vs. (19.96 ± 2.67) hours, t = 9.07, P < 0.001]. Length of hospital stay in the observation group was significantly shorter in the observation group than in the control group [(4.13 ± 1.12) days vs. (7.98 ± 1.96) days, t = 8.53, P < 0.001). Parental satisfaction was significantly higher in the observation group than in the control group [96.30% vs. 47.83%, χ2 = 13.360, P < 0.001]. The incidence of complications was significantly lower in the observation group than in the control group [7.41% vs. 52.17%, χ2 = 10.58, P < 0.001]. Conclusion:Laparoscopic surgery is superior and safer to open surgery in the treatment of acute appendicitis in children.