The 97th Annual Meeting of the Japanese Gastric Cancer Association was held from March 12 to March 14,2025,in Nagoya,Japan.The conference was chaired by Professor Kazuhiro Uyama from Fujita Medical University and attracted nearly 2 000 scholars from around the world,including Japan,China,the republic of Korea,the United States,and Europe.With the theme of"Digital Innovation in Gastric Tumors,"the conference focused on the application of artificial intelligence,robotic surgery,and other innovations in the treatment of gastric cancer.It explored how high-precision and highly reproducible robotic surgical techniques are transforming traditional approaches to gastric cancer surgery,along with topics such as digital innovation,future medical policies,and strategies that herald a new era in healthcare.The meeting featured one main venue and 60 sub-venues with different themes,ultimately accepting 1 003 submissions.A total of 158 oral presentations covering 80 topics and 203 poster presentations were delivered.Among them,approximately 145 lectures were related to robotic surgery for gastric cancer,and when including poster presentations,nearly 255 topics were associated with gastric cancer robotic surgery.Additionally,the 7th edition of the Japanese Gastric Cancer Treatment Guidelines was released during the meeting.Our team had the honor of participating in this prestigious event.Drawing from our experience at both this conference and the 17th Annual Meeting of the Japanese Society for Robotic Surgery held in Utsunomiya,Japan,from March 7 to March 8,2025,we provide a detailed report on the latest advancements in robotic surgery for gastric cancer,hoping to offer valuable insights and references for fellow surgeons both in China and abroad.

The 97th Annual Meeting of the Japanese Gastric Cancer Association was held from March 12 to March 14,2025,in Nagoya,Japan.The conference was chaired by Professor Kazuhiro Uyama from Fujita Medical University and attracted nearly 2 000 scholars from around the world,including Japan,China,the republic of Korea,the United States,and Europe.With the theme of"Digital Innovation in Gastric Tumors,"the conference focused on the application of artificial intelligence,robotic surgery,and other innovations in the treatment of gastric cancer.It explored how high-precision and highly reproducible robotic surgical techniques are transforming traditional approaches to gastric cancer surgery,along with topics such as digital innovation,future medical policies,and strategies that herald a new era in healthcare.The meeting featured one main venue and 60 sub-venues with different themes,ultimately accepting 1 003 submissions.A total of 158 oral presentations covering 80 topics and 203 poster presentations were delivered.Among them,approximately 145 lectures were related to robotic surgery for gastric cancer,and when including poster presentations,nearly 255 topics were associated with gastric cancer robotic surgery.Additionally,the 7th edition of the Japanese Gastric Cancer Treatment Guidelines was released during the meeting.Our team had the honor of participating in this prestigious event.Drawing from our experience at both this conference and the 17th Annual Meeting of the Japanese Society for Robotic Surgery held in Utsunomiya,Japan,from March 7 to March 8,2025,we provide a detailed report on the latest advancements in robotic surgery for gastric cancer,hoping to offer valuable insights and references for fellow surgeons both in China and abroad.

Objective:To construct an prediction model based on magnetic resonance imaging (MRI) machine learning algorithm and radiomics and investigate its application value in predicting lymphovascular invasion (LVI) status of rectal cancer without lymph node metastasis.Methods:The retrospective cohort study was conducted. The clinicopathological data of 204 rectal cancer patients without lymph node metastasis who were admitted to Gansu Provincial Hospital from February 2016 to January 2024 were collected. There were 123 males and 81 females, aged (61±7)years. All 204 patients were randomly divided into the training dataset of 163 cases and the testing dataset of 41 cases by a ratio of 8∶2 using the electronic computer randomization method. The training dataset was used to construct the prediction model, and the testing dataset was used to validate the prediction model. The clinical prediction model, radiomics model and joint prediction model were constructed based on the selected clinical and/or imaging features. Measurement data with normal distribution were represented as Mean± SD. Count data were described as absolute numbers, and the chi-square test or Fisher exact probability were used for comparison between the groups. Comparison of ordinal data was conducted using the nonparameter rank sum test. The inter-class correlation coefficient (ICC) was used to evaluate the consistency of the radiomics features of the two doctors, and ICC >0.80 was good consistency. Univariate analysis was conducted by corres-ponding statistic methods. Multivariate analysis was conducted by Logistic stepwise regression model. The receiver operating characteristic (ROC) curve was drawn, and the area under the curve (AUC), Delong test, decision curve and clinical impact curve were used to evaluate the diagnostic efficiency and clinical utility of the model. Result:(1) Analysis of factors affecting LVI status of patients. Of the 204 rectal cancer patients without lymph node metastasis, there were 71 cases with positive of LVI and 133 cases with negative of LVI. Results of multivariate analysis showed that gender, platelet (PLT) count and carcinoembryonic antigen (CEA) were independent factors affecting LVI status of rectal cancer without lymph node metastasis in training dataset [ odds ratio=2.405, 25.062, 2.528, 95% confidence interval ( CI) as 1.093-5.291, 2.748-228.604, 1.181-5.410, P<0.05]. (2) Construction of clinical prediction model. The clinical prediction model was conducted based on the results of multivariate analysis including gender, PLT count and CEA. Results of ROC curve showed that the AUC, accuracy, sensitivity and specificity of clinical prediction model were 0.721 (95% CI as 0.637-0.805), 0.675, 0.632 and 0.698 for the training dataset, and 0.795 (95% CI as 0.644-0.946), 0.805, 1.000 and 0.429 for the testing dataset. Results of Delong test showed that there was no significant difference in the AUC of clinical prediction model between the training dataset and the testing dataset ( Z=-0.836, P>0.05). (3) Construction of radiomics model. A total of 851 radiomics features were extracted from 204 patients, and seven machine learning algorithms, including logistic regression, support vector machine, Gaussian process, logistic regression-lasso algorithm, linear discriminant analysis, naive Bayes and automatic encoder, were used to construct the prediction model. Eight radiomics features were finally selected from the optimal Gaussian process learning algorithm to construct a radiomics prediction model. Results of ROC curve showed that the AUC, accuracy, sensitivity and specificity of radiomics prediction model were 0.857 (95% CI as 0.800-0.914), 0.748, 0.947 and 0.642 for the training dataset, and 0.725 (95% CI as 0.571-0.878), 0.634, 1.000 and 0.444 for the testing dataset. Results of Delong test showed that there was no significant difference in the AUC of radiomics prediction model between the training dataset and the testing dataset ( Z=1.578, P>0.05). (4) Construction of joint prediction model. The joint prediction model was constructed based on the results of multivariate analysis and the radiomics features. Results of ROC curve showed that the AUC, accuracy, sensitivity and specificity of radiomics prediction model were 0.885 (95% CI as 0.832-0.938), 0.791, 0.912 and 0.726 for the training dataset, and 0.857 (95% CI as 0.731-0.984), 0.854, 0.714 and 0.926 for the testing dataset. Results of Delong test showed that there was no significant difference in the AUC of joint prediction model between the training dataset and the testing dataset ( Z=0.395, P>0.05). (5) Performance comparison of three prediction models. Results of the Hosmer-Lemeshow goodness-of-fit test showed that all of the clinical prediction model, radiomics prodiction model and joint prediction model having good fitting degree ( χ2=1.464, 12.763, 10.828, P>0.05). Results of Delong test showed that there was no signifi-cant difference in the AUC between the clinical prediction model and the joint prediction model or the radiomics model ( Z=1.146, 0.658, P>0.05), and there was a significant difference in the AUC between the joint prediction model and the radiomics model ( Z=2.001, P<0.05). Results of calibra-tion curve showed a good performance in the joint prediction model. Results of decision curve and clinical impact curve showed that the performance of joint prediction model in predicting LVI status of rectal cancer without lymph node metastasis was superior to the clinical prediction model and the radiomics model. Conclusions:The clinical prediction model is constructed based on gender, PLT count and CEA. The radiomics predictive model is constructed based on 8 selected radiomics features. The joint prediction model is constructed based on the clinical prediction model and the radiomics predictive model. All of the three models can predict the LVI status of rectal cancer with-out lymph node metastasis, and the joint prediction model has a superior predictive performance.

To evaluate the efficacy,safety and acceptability of different drugs in the treatment of mixed states in bipolar disorder using the Cochrane systematic review method. The results showed that for the treatment of bipolar depression with mixed features,there is evidence to support the efficacy of lurasidone and quetiapine. Lurasidone has low metabolic and sedative side effects,and can be used in children and adolescents with bipolar depression with mixed features. Quetiapine has balanced anti-depressant and anti-manic effects,with low risk of phase transition. There is evidence to support the efficacy of olanzapine in the treatment of bipolar manic with mixed features. However,it is necessary to balance the efficacy and side effects of olanzapine,and pay attention to the risk of phase transition. For mixed episode in Bipolar disorder,aripiprazole had most research evidence. Aripiprazole has fewer side effects on metabolism and prolactin,and can be used in children with bipolar disorder. Valproate and lithium are recommended by experts for the treatment of mixed state in bipolar disorder. The inconsistency between current studies is still significant,and high-quality randomized controlled trials are urgently needed to provide more comprehensive evidence-based support for the treatment of mixed states in bipolar disorder.

To evaluate the efficacy,safety and acceptability of different drugs in the treatment of mixed states in bipolar disorder using the Cochrane systematic review method. The results showed that for the treatment of bipolar depression with mixed features,there is evidence to support the efficacy of lurasidone and quetiapine. Lurasidone has low metabolic and sedative side effects,and can be used in children and adolescents with bipolar depression with mixed features. Quetiapine has balanced anti-depressant and anti-manic effects,with low risk of phase transition. There is evidence to support the efficacy of olanzapine in the treatment of bipolar manic with mixed features. However,it is necessary to balance the efficacy and side effects of olanzapine,and pay attention to the risk of phase transition. For mixed episode in Bipolar disorder,aripiprazole had most research evidence. Aripiprazole has fewer side effects on metabolism and prolactin,and can be used in children with bipolar disorder. Valproate and lithium are recommended by experts for the treatment of mixed state in bipolar disorder. The inconsistency between current studies is still significant,and high-quality randomized controlled trials are urgently needed to provide more comprehensive evidence-based support for the treatment of mixed states in bipolar disorder.

Objective To investigate and predict the molecular targets and mechanism of Huanglian Jiedu Decoction (黄连解毒汤, HLJDD) in the treatment of Corona Virus Disease 2019 (COVID-19) through network pharmacology and molecular docking analysis. Methods The chemical constituents and action targets of HLJDD were retrieved on Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), SymMap v2, Encyclopedia of Traditional Chinese Medicine (ETCM), a High-throughput Experiment- and Reference-guided Database of Traditional Chinese Medicine (HERB), and Traditional Chinese Medicine Integrated Database (TCMID). UniProt and GeneCards were used to query the target genes that corresponding to the active compounds, and then a compound-target network was constructed using Cytoscape 3.7.2. Gene Ontology (GO) database was used to annotate GO functions. Kyoto Encyclopedia of Genes and Genomes (KEGG) was used to predict the possible mechanisms of active compounds. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analysis the tissue enrichment. The main active compounds in HLJDD are molecularly docked with their corresponding related targets. Results Seventy-six compounds were screened and 458 corresponding targets in the network were obtained. Gene annotation showed that the targets were involved mainly in 1 953 biological processes. 884 signaling pathways was enriched, involving signaling by interleukins, cytokine signaling in immune system, generic transcription pathway, and RNA polymerase II transcription. The targets mainly distributed in the lung, liver, and placenta, involving a variety of immune cells, such as T cells and B cells. The molecular docking results showed that core compounds such as wogonin, berberine, and baicalein had high affinity with tumor necrosis factor (TNF), insulin (INS), and tumor protein 53 (TP53). Conclusion The active compounds in HLJDD may have a therapeutic effect on COVID-19 through regulating multiple signal pathways by targeting genes such as vascular endothelial growth factor A (VEGFA), INS, interleukin-6 (IL-6), TNF, caspase-3 , TP53, and mitogen-activated protein kinase 3 (MAPK3).

Objective:To investigate drug resistance gene in Mycoplasma pneumoniae(MP) and the distribution of 13 respiratory pathogens in bronchoalveolar lavage fluid(BALF) of children with Mycoplasma pneumoniae pneumonia(MPP).Methods:A total of 100 BALF of children with MPP in Peking University Third Hospital and Peking University First Hospital from January 2018 to January 2019 were collected.Fluorogenic quantitative PCR was used to detect nucleic acid and it′s drug resistance gene of MP and multiple PCR method was adopted to detect influenza A virus, influenza A virus-H 1N 1, influenza A virus-H 3N 2, influenza B, human parainfluenza virus, adenovirus, human bocavirus, human rhinovirus, Chlamydia pneumoniae, human metapneumovirus, MP, human coronavirus, and respi-ratory syncytial virus gene, and the results were compared by using Chi square test. Results:In 100 BALF samples, MP and drug resistance gene were detected by fluorogenic quantitative PCR.Totally, 83 cases (83.00%) were MP positive and 78 cases (93.98%) were drug resistant.All of them had the point mutations A2063G in V region of 23S rRNA domain.A total of 13 kinds of respiratory pathogens were detected by multiplex PCR method, and 89 cases (89.00%) were positive.Totally, 79 cases (79.00%) were MP positive, of which 74 cases (74.00%) detected only MP, and 5 cases (5.00%) detected MP combined with other pathogens.Other pathogens were detected in 10 cases (10.00%). The virus detection rate of 0-4 years old group was higher than that of >4-6 years old group ( P=0.042) and >6 years old group ( P=0.002), and the differences were statistically significant. Conclusions:MP can be detected in most BALF samples of MPP children, the drug resistance phenomenon is serious, and the main point mutation is A2063G.There were other respiratory pathogens and 2 or 3 pathogens were detected in a small number of BALF samples.

AIM: To investigate the effect of hypercapnia on hypoxia-induced pulmonary hypertension and the changes of lysyl oxidase (LOX) and extracellular matrix collagen cross-links in the rat.METHODS: Sprague-Dawley rats were randomly divided into 4 groups: normoxia group, hypoxia group, hypercapnia group and hypoxia+hypercapnia group.LOX activity was detected by fluorescence spectrophotometry.LOX protein expression was detected by immunohistochemistry and Western blot.The mRNA expression of LOX in the pulmonary artery was detected by real-time PCR.RESULTS: The levels of mean pulmonary artery pressure (mPAP), RV/(LV+S) and WA/TA in hypoxia group were significantly higher than those in normoxia group (P<0.01).Moreover, the levels of mPAP and RV/(LV+S) in hypoxia+hypercapnia group were significantly lower than those in hypoxia group (P<0.01).However, no significant difference of mPAP and RV/(LV+S) between hypercapnia group and normoxia group was observed.In hypoxia group, the collagen cross-links in the lung tissue was significantly higher than that in normoxia group and hypercapnia group (P<0.01).Importantly, collagen cross-links in the lung tissue of hypoxia+hypercapnia group was significantly lower than that in hypoxia group (P<0.01).There was no significant difference in collagen cross-links between hypercapnia group and normoxia group.The expression of LOX at mRNA and protein levels and its activity in the pulmonary arteries of hypoxia group were significantly increased as compared with normoxia group (P<0.01).Furthermore, the expression of LOX at mRNA and protein levels and its activity in the pulmonary arteries in hypoxia+hypercapnia group were lower than those in hypoxia group (P<0.01).CONCLUSION: Hypoxia not only up-regulates LOX but also promotes collagen cross-linking in the rat lung, which contributes to the development of pulmonary hypertension.Hypercapnia inhibits hypoxia-induced LOX expression and collagen cross-linking, therefore impairing the progress in hypoxia-induced pulmonary hypertension.

OBJECTIVETo study the effects of Zhuhong ointment on accumulation in the body of mercury and the pathological morphology changes of kidney, via the measurement of related indicators of the skin-impaired model rat.

METHODEighty-eight SD rats were randomly divided into the impairment control group, and high-, middle-, low-dose Zhuhong ointment groups. Each group was treated by corresponding methods for 4 weeks, and recovering for 4 weeks. Urinary potein (PRO), pH, Beta N-acetyl aminoglycosidase enzymes (NAG) and beta2-microglobulin (beta2-MG) contents in urine were taken as monitoring indexes, blood urea nitrogen (BUN) and serum creatinine (SCr) in blood and the levels of mercury in urine, blood and kidney were tested, and the pathological morphology changes of kidney were observed.

RESULTAfter treatment for 4 weeks, compared with impairment control group, the levels of mercury in urine, blood and kidney in every dose group increased significantly (P < 0.01). And the relation exists between toxicity and dose on Zhuhong ointment. After recovery for 4 weeks, the levels of mercury in urine and blood in every dose group restore normal, while the level of mercury in kidney in high- dose group still increased (P < 0.01). The level of NAG increased only in high-dose group. There was no significant difference in NAG contents between Zhuhong ointment groups and the impairment control group (P < 0.05).

CONCLUSIONExcess using Zhuhong ointment repeatedly may lead to accumulation of mercury and pathological morphology changes of kidney. So the levels of mercury in the body and related indicators of renal functions should be tested in clinical when long-term using Zhuhong ointment.

Acetylglucosaminidase ; drug effects ; urine ; Animals ; Blood Urea Nitrogen ; Creatinine ; blood ; Disease Models, Animal ; Drugs, Chinese Herbal ; pharmacology ; toxicity ; Female ; Hydrogen-Ion Concentration ; drug effects ; Kidney ; drug effects ; enzymology ; metabolism ; pathology ; Male ; Mercury ; blood ; metabolism ; urine ; Ointments ; Random Allocation ; Rats ; Rats, Sprague-Dawley ; Retinol-Binding Proteins ; drug effects ; urine ; Skin ; drug effects ; injuries ; Time Factors ; beta 2-Microglobulin ; urine

OBJECTIVETo study the effect of repeated administration of Zhuhong ointment on renal antioxidant capability of ulcerous skin in rats, in order to further discuss the mechanism of mercury contained in Zhuhong ointment on the antioxidant capability of kidney in skin ulcer rats.

METHODEighty SD rats were randomly divided into eight groups: Zhuhong ointment A, B, C, D, E (1.219, 0.609, 0.305, 0.152, 0.76 g x kg(-1)) groups, the vaseline group, the ulcer model group and the impairment control group. The levels of NAG and RBP of toxicity for early kidney tubular injury and T-AOC, SOD, GSH-PX and GSH in kidney were determined after consecutive administration for 14 days.

RESULTCompared with ulcer model group, the levels of RBP in groups A, B, C and D increased, while the levels of NAG increased only in the group A. The level of T-AOC increased in groups A, B and C. The level of T-SOD increased in the group E, while it dropped down greatly in the group A. The level of GSH-PX increased in groups A, B and C. The content of GSH increased in every dose groups.

CONCLUSIONAntioxidant capacity in rats can be increased in a reasonable dose of Zhuhong ointment, but some antioxidant activity can be notably inhibited by with the increase of dose.

Acetylglucosaminidase ; drug effects ; urine ; Animals ; Antioxidants ; analysis ; metabolism ; Disease Models, Animal ; Dose-Response Relationship, Drug ; Drugs, Chinese Herbal ; administration & dosage ; pharmacology ; toxicity ; Glutathione ; drug effects ; metabolism ; Glutathione Peroxidase ; drug effects ; metabolism ; Kidney Tubules ; drug effects ; injuries ; metabolism ; Male ; Mercury ; metabolism ; Ointments ; Random Allocation ; Rats ; Rats, Sprague-Dawley ; Retinol-Binding Proteins ; drug effects ; urine ; Skin Ulcer ; metabolism ; microbiology ; Specific Pathogen-Free Organisms ; Staphylococcal Skin Infections ; metabolism ; Superoxide Dismutase ; drug effects ; metabolism ; Time Factors