Background: High-mobility group box protein 1 (HMGB1) is a chromatin-binding protein involved in arthritis, ischemia, sepsis, atherosclerosis, neurodegenerative disorders, meningitis, and cancer. HMGB1 exhibits dual roles in cancer, acting as either a tumor suppressor or oncoprotein depending on context. Objective: This research aimed to elucidate HMGB1’s functional significance in cutaneous squamous cell carcinoma (cSCC). Methods: We overexpressed HMGB1 in cSCC cell lines using recombinant adenovirus and examined its effects on cell proliferation, colony formation, and cell migration. Results: Immunohistochemical analysis revealed elevated HMGB1 expression levels in cSCC tissue relative to normal epidermis. To assess the influence of HMGB1, we employed recombinant adenoviruses expressing HMGB1 to transduce SCC cell lines (SCC12 and SCC13). Enhanced HMGB1 expression significantly promoted cellular proliferation and colony formation capacity.Notably, HMGB1 overexpression elevated the levels of proliferation regulators, including P63, SOX2, CDK4 and CDK6. Furthermore, HMGB1 overexpression substantially enhanced tumor invasiveness, accompanied by upregulation of epithelial-mesenchymal transition (EMT) biomarkers. Mechanistically, overexpression of HMGB1 enhanced transforming growth factor-β signaling by increasing phosphorylation of SMAD2/3, the key mediators of EMT. Conclusion These data imply that HMGB1 acts as a tumor-promoting factor in cSCC.

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans

Aim To explore the mechanism by which the compound Chaijin Jieyu formula(CCJJY)regulates the TLR4/NLRP3 signaling pathway to inhibit central oxidative stress and improve hippocampal synaptic plasticity damage in depression.Methods SD rats were randomly divided into the control group,chronic unpredictable mild stress group,sleep deprivation group,chronic unpredictable mild stress combined with sleep deprivation group,positive drug group(venlafax-ine+melatonin),low-dose group of CCJJY,medium dose group of CCJJY,and high-dose group of CCJJY,with nine rats in each group.Except for the control group,a rat model of depression complicated with in-somnia was established using chronic unpredictable mild stress combined with sleep deprivation.Depres-sion-like and sleep behaviors in rats were evaluated through weight,food intake,water maze,and pento-barbital sodium tests.ELisa was used to detect ROS,AANAT,and HPLC-EC was used to detect 5-HT con-tent,while Western blot/RT-PCR was used to detect the expression of IL-1β,TLR4,NLRP3,PSD-95,and SYN related proteins and mRNA.HE and Golgic stai-ning were used to observe the pathological changes in the third ventricle,hippocampus,and neuronal synap-ses.Results Compared with the control group,the depression-like behaviors of the model group rats were significant.The expression of IL-1β,TLR4,and NL-RP3 in the hippocampus increased,while the expres-sion of PSD-95 and SYN decreased.Activation of NL-RP3 inflammasomes led to "sleeve like" pathological changes in the third ventricle,with hippocampal neu-rons undergoing apoptosis and significant damage to neuronal synaptic plasticity.Compared with the model group,after intervention with CCJJY,the expression of ROS,IL-1β,TLR4,and NLRP3 decreased,while the expression of AANAT,5-HT,PSD-95,and SYN in-creased.Pathological damage to the third ventricle and hippocampal neurons was repaired.Conclusion The CCJJY improves hippocampal synaptic plasticity dam-age in depression by regulating the TLR4/NLRP3 sig-naling pathway to inhibit central oxidative stress.

Aim To explore the mechanism by which the compound Chaijin Jieyu formula(CCJJY)regulates the TLR4/NLRP3 signaling pathway to inhibit central oxidative stress and improve hippocampal synaptic plasticity damage in depression.Methods SD rats were randomly divided into the control group,chronic unpredictable mild stress group,sleep deprivation group,chronic unpredictable mild stress combined with sleep deprivation group,positive drug group(venlafax-ine+melatonin),low-dose group of CCJJY,medium dose group of CCJJY,and high-dose group of CCJJY,with nine rats in each group.Except for the control group,a rat model of depression complicated with in-somnia was established using chronic unpredictable mild stress combined with sleep deprivation.Depres-sion-like and sleep behaviors in rats were evaluated through weight,food intake,water maze,and pento-barbital sodium tests.ELisa was used to detect ROS,AANAT,and HPLC-EC was used to detect 5-HT con-tent,while Western blot/RT-PCR was used to detect the expression of IL-1β,TLR4,NLRP3,PSD-95,and SYN related proteins and mRNA.HE and Golgic stai-ning were used to observe the pathological changes in the third ventricle,hippocampus,and neuronal synap-ses.Results Compared with the control group,the depression-like behaviors of the model group rats were significant.The expression of IL-1β,TLR4,and NL-RP3 in the hippocampus increased,while the expres-sion of PSD-95 and SYN decreased.Activation of NL-RP3 inflammasomes led to "sleeve like" pathological changes in the third ventricle,with hippocampal neu-rons undergoing apoptosis and significant damage to neuronal synaptic plasticity.Compared with the model group,after intervention with CCJJY,the expression of ROS,IL-1β,TLR4,and NLRP3 decreased,while the expression of AANAT,5-HT,PSD-95,and SYN in-creased.Pathological damage to the third ventricle and hippocampal neurons was repaired.Conclusion The CCJJY improves hippocampal synaptic plasticity dam-age in depression by regulating the TLR4/NLRP3 sig-naling pathway to inhibit central oxidative stress.

The multiple systemic symptoms appearing during the convalescence of novel coronavirus infection(COVID-19)are called long-term symptoms after COVID-19,also named as long COVID.Traditional Chinese medicine(TCM)has unique advantages in the treatment of long COVID.In the view of Professor ZHANG Zhong-De,the novel coronavirus infection belongs to the category of damp-toxin pestilence in TCM.During the convalescence of novel coronavirus infection,the damp-toxin pathogen causes the dysfunction of qi,blood,yin and yang of zang-fu organs,and then results in the consumption of yang qi or the depletion of fluid and blood.Damp-turbidity obstructing qi movement and entering collaterals and then developing into blood stasis is the key pathogenesis of long COVID.Therefore,the therapeutic principle of harmonizing yin,yang,qi and blood of the five zang-organs is proposed,and the methods for transporting and transforming damp-toxin and phlegm-turbidity,regulating qi,activating blood and removing stasis should be adopted.According to the exuberance or decline of qi,blood and body fluid,and yin and yang of the five zang-organs,the Fuzheng Series Formulas(series formulas for supporting healthy qi)were recommended for the specific syndrome types:Fuzheng Shengbai Formula can be used for the syndrome of deficiency of original qi;Fuzheng Yifei Formula can be used for lung-spleen-kidney deficiency syndrome;Fuzheng Anshen Formula can be used for the syndrome of liver qi stagnation and heart-spleen deficiency.During the treatment,Professor ZHANG Zhong-De makes the modification of drugs indicated for the retention sites of damp-toxin and turbid qi,and addresses the importance of spleen and stomach in the whole treatment process by protecting spleen and stomach,so as to promote the rehabilitation of patients after novel coronavirus infection and improve their quality of life.

Objective To explore the risk factors for bile leakage after laparoscopic common bile duct exploration(LCBDE)with primary closure.Methods The clinical data of 560 patients with choledocholithiasis who underwent LCBDE with primary closure in Tianjin Hospital of Integrated Traditional Chinese and Western Medicine from September 2021 to September 2023 were retrospectively analyzed,and the patients were divided into the bile leak group and the non-bile leak group according to the occurrence of postoperative bile leakage.The risk factors affecting the occurrence of postoperative bile leakage were analyzed by multivariate analysis.Results A total of 64 cases(11.4% )experienced varying degrees of bile leakage,including 55 cases of grade A bile leakage,7 cases of grade B,and 2 cases of grade C.The thin common bile duct(OR=0.07,P<0.001),history of hypertension(OR=4.56,P<0.001),and high BMI(OR=1.17,P=0.002)were the risk factors for postoperative bile leakage in patients with choledocholithiasis.Conclusion Patients with thin common bile duct,hypertension and obesity are more likely to occur postoperative bile leakage.Patients with choledocholithiasis who have the above high-risk factors should be cautious in choosing LCBDE with primary closure.

This study was aimed at understanding the serotypes,virulence,drug resistance,and genetics of the pathogenic genes from 29 strains of foodborne Listeria monocytogenes in Guizhou Province in 2022.The minimum inhibitory concentration(MIC)values against eight antibiotics were determined with the microbroth dilution method,and whole genome sequencing was performed on 29 L.monocytogenes strains isolated from food microbiology and foodborne disease surveillance efforts in the province in 2022.The genome sequences were assembled,and bioinformatics analyses were conducted to examine genealogy,serogroups,sequence analysis(ST type),clonal groups(CC type),resistance genes,and virulence genes.A total of 29 strains of L.monocytogenes had resistance to zero to eight antibiotics,and carried six resistance genes.All strains of L.monocytogenes carried fosX,mprF,Lin,and norB.The 29 strains of L.monocytogenes belonged to 2 lineages(Ⅰ and Ⅱ):17 strains belonged to lineage Ⅱ,which was the dominant strain,and 12 strains belonged to lineage Ⅰ.The strains were classified into 13 ST types,among which ST8 was dominant,accounting for 31.03％(9/29 strains),and was followed by ST619 and ST121,accounting for three strains each.The strains were di-vided into four serogroups,with 15 strains in serogroups 1/2a and 3a;11 strains in serogroups 1/2b,3b,and 7;2 strains in serogroups 1/2c and 3c;and 1 strain in serogroups 4b,4d,and 4e.The strains were divided into 12 CC types,and one unsub-divided CC type,ST2348,among which CC8 was dominant,accounting for 27.59％(nine strains).A total of 25 virulence genes were found,which belonged to three virulence islands(LIPI-1,LIPI-2,and LIPI-3)and 23 CL types,including one or two strains each,and three CL types including two strains each.Foodborne L.monocytogenes in Guizhou Province has a low level of drug resistance,carries a high number of virulence genes,and shows genetic diversity in serogroups and molecular phe-notypes.These findings should strengthen the continuous surveillance efforts for Listeria monocytogenes.

OBJECTIVE: To explore the clinical significance of extended pelvic lymph node dissection (EPLND) under the robot-assisted laparoscope in the treatment of high-risk PCa. METHODS: This study included 29 cases of high-risk PCa treated by robot-assisted laparoscopic radical prostatectomy and EPLND from April 2020 to January 2023. We collected the general data on the patients, recorded the status of dissection of the lymph nodes and postoperative complications, and analyzed the significance of EPLND. RESULTS: The patients were aged (69.3±6.6) years old, with the preoperative PSA level of 8.43－434 μg/L, Gleason score (GS) 6 in 1, GS 7 in 9, and GS ≥8 in 19 cases. The operation time averaged (97.2±15.7) min, with the mean blood loss of (30.5±11.2) ml, and 3－42 (median = 13) lymph nodes dissected, less than 10 in 10 cases, 11－19 in 12, and more than 20 in 7. Positive pelvic lymph nodes (median = 4) were found in 13 cases, with a positive rate of 44.8%. Positive incisal margin was observed in 11 cases (37.9%), lymphovascular invasion (LVI) in 4 (13.8%), and perineural invasion (PNI) in another 4 (13.8%). Lymph node metastasis was significantly correlated with positive incisal margin (P<0.05), but not with LVI, PNI or age (P>0.05). No significant vascular or nerve injuries occurred during the operation. GS 6 was detected in 1, GS 7 in 7, and GS ≥8 in 21 cases postoperatively. CONCLUSION Robot-assisted laparoscopic EPLND is an important strategy for the treatment of high-risk PCa, which contributes to accurate pathological staging of the malignancy. However, evidence is lacking for its benefit to the survival of high-risk PCa patients, and more follow-up studies are needed to confirm its treatment effect.
Humans ; Male ; Lymph Node Excision/methods* ; Prostatic Neoplasms/pathology* ; Laparoscopy/methods* ; Robotic Surgical Procedures ; Aged ; Prostatectomy/methods* ; Pelvis ; Lymphatic Metastasis ; Lymph Nodes/surgery* ; Middle Aged