Colorectal cancer （CRC） is one of the most common cancers， with its incidence ranking high among cancers. It stands as the second leading cause of cancer-related death worldwide. In the early stages， CRC lacks specific symptoms， and most patients are diagnosed at advanced stages， making it a major research focus in the field of gastrointestinal tumors. Currently， clinical CRC treatments face several common challenges， including high surgical risks， frequent metastasis and recurrence， drug resistance， and significant side effects from chemotherapy and radiation therapy. With the development and application of traditional Chinese medicine （TCM）， it has been found that TCM and its active ingredients can effectively inhibit CRC cell proliferation， invasion， migration， and angiogenesis， and promote apoptosis and autophagy， thereby slowing the progression of CRC. This has become a key focus of CRC treatment research. Salvia Miltiorrhiza has multiple pharmacological effects， including activating blood circulation to dispel blood stasis， unlocking meridians to relieve pain， clearing heat to calm irritability， and cooling blood to reduce abscesses. It contains a variety of chemical components， including diterpenoids， phenolic acids， flavonoids， polysaccharides， nitrogen-containing compounds， steroids， and lactone compounds. This review summarized the molecular mechanisms of Salvia miltiorrhiza and its active ingredients in the treatment of CRC. It is found that these ingredients exert anti-CRC effects through various molecular mechanisms， including cell cycle arrest， promotion of apoptosis， inhibition of cell invasion and migration， induction of autophagy， suppression of tumor angiogenesis， and remodeling of the tumor microenvironment. The review aims to provide new insights for the drug development and clinical application of Salvia miltiorrhiza in CRC treatment.

Background/Aims: Acetyl coenzyme A (acetyl-CoA) is one of the most essential metabolites in cell metabolism but its function and concentration in hepatocellular carcinoma (HCC) remain elusive and controversial. Methods: A comprehensive analysis of acetyl-CoA levels and acetyl-CoA synthetase 2 (ACSS2) expression across a range of samples, including patient specimens from both hepatitis B virus (HBV) positive and HBV negative HCC individuals, HBV-transgenic mouse HCC models, and multiple cell lines. Furthermore, to evaluate the functional significance of ACSS2 in HBV-related HCC, we implemented both genetic and pharmacological inhibition strategies targeting ACSS2. Molecular mechanism and mitophagy assessment were revealed by cleavage under target and tagmentation sequencing, RNA sequencing, bioinformatic analyses, transmission electron microscopy and JC-1 staining. Results: Our study revealed a distinct metabolic signature of HBV-related HCC, marked by elevated acetyl-CoA, which was driven by ACSS2. ACSS2 was upregulated by the carbohydrate response element-binding protein in HBV-related HCC. Furthermore, ACSS2 improved tumor cell proliferation, an effect that was dependent on its enzymatic activity. Mechanistically, ACSS2-induced acetyl-CoA accumulation activated voltage-dependent anion channels 1 transcription through increased H3K27ac occupancy, which subsequently promoted mitophagy and HBV-related HCC tumorigenesis. Notably, targeting ACSS2 by depletion or inhibition with a catalytic inhibitor significantly suppressed tumor growth. Conclusions These findings not only illustrate the interplay between metabolic reprogramming, epigenetic modification, and tumorigenesis in the context of HBV infection, but also highlight ACSS2 as a novel metabolic vulnerability in HBV-related HCC. Therefore, targeting ACSS2 could be a novel strategy against HBV-related HCC.

Objective To investigate and compare the epidemiological characteristics of common respiratory viruses among influenza-like illness(ILI)and severe acute respiratory infection(SARI)cases in Huzhou,Zhejiang Province before and after the COVID-19 pandemic,so as to provide a basis for formulating and adjusting the prevention and control strategies for viral respiratory infectious diseases.Methods ILI and SARI cases at two influenza surveillance sentinel hospitals in Huzhou and had throat swab samples collected during Nov 2017 to Feb 2020(pre-COVID-19 pandemic period)and Dec 2022 to Apr 2024(post-COVID-19 mitigation phase)were selected as the participants.Seven common viral respiratory pathogens were tested,including influenza A virus(H1N1 and H3N2 subtypes),influenza B virus(Victoria lineage,FluB),respiratory syncytial virus(RSV),rhinovirus(HRV),adenovirus(ADV),and severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).The positive rates of respiratory pathogens before and after the COVID-19 pandemic were compared across different age groups and different time.Results A total of 7 948 ILI samples and 2 294 SARI samples were included.The overall positive rate of ILI samples increased from 33.6%to 47.1%,primarily due to the increase in influenza and COVID-19 infections;the overall positive rate of SARI samples decreased from 31.4%to 24.8%,mainly due to the reduction in HRV and ADV infections.During the post-COVID-19 mitigation phase,SARS-CoV-2(22.1%),H3N2(12.7%),and FluB(6.0%)were the primary pathogens in ILI samples,while RSV(7.1%),H3N2(5.3%),and HRV(4.5%)dominated in SARI samples.During the post-COVID-19 mitigation phase,the influenza virus circulation period was shortened.Before the COVID-19 pandemic,RSV was mainly detected in autumn and winter,while during the post-COVID-19 mitigation phase,out-of-season RSV epidemics were observed in spring and summer.Co-infection rate in ILI cases increased significantly in the post-COVID-19 mitigation phase,predominantly consisting of co-infections of COVID-19 and influenza A virus,while co-infection rate in SARI cases showed a decline.Conclusion We found important epidemiological changes in respiratory viruses in Huzhou during the post-COVID-19 mitigation phase compared to pre-COVID-19 period,including increased positive rates of influenza and COVID-19,and disruptions to the seasonal patterns of influenza and RSV.The prevention and control strategies should be adjusted in a timely manner based on the monitoring data.

Objective To explore the mechanism of malt alcohol extract improving depression-like behavior induced by CUMS in rats by regulating gut microbiota.Methods The depression model of rats was established using an 8-weeks CUMS procedure,and the administration group was given low(59.6 mg·kg-1)and high(178.8 mg·kg-1)doses of malt alcohol extract,respectively.The depression-like behavior of rats was evaluated by classic behavioral test.The composition of intestinal microbiota of rats was analyzed by 16S rRNA sequencing.The morphological changes of colon were observed by hematoxylin and eosin(HE),the expression of ZO-1 and Occludin in colon was detected by immunofluorescence(IF),and the expression of IL-10,IL-1βand 5-HT were detected by ELISA.Results The low dose of malt alcohol extract attenuated the depressive behavior and restored the expression of 5-HT in the brain of CUMS rats.16S rRNA sequencing results showed that the diversity and relative abundance of gut microbiota changed after treatment with the low dose of malt alcohol extract.ELISA results showed that the low dose of malt alcohol extract significantly reversed the CUMS-induced reduction of IL-10 and elevation of IL-1 β.HE results showed that the low dose of malt alcohol extract significantly ameliorated CUMS-induced structural damage in colon.IF results showed increased protain expression of intestinal epithelial barrier tight junction proteins ZO-1 and Occludin by the low dose of malt alcohol extract.Conclusion The low dose of malt alcohol extract can ameliorate CUMS-induced depressive-like behavior in rats by modulating intestinal flora,restoring 5-HT expression in the brain,inhibiting inflammation,and repairing the intestinal barrier.

Aim To explore the protective effects and underlying molecular mechanisms of periplogenin(Ppg)in preventing acetaminophen(APAP)-induced drug-induced liver injury(DILI).Methods An APAP-induced liver injury model was established in vi-vo and in vitro.Liver/spleen indices were recorded,and serum aspartate aminotransferase(AST)and ala-nine aminotransferase(ALT)levels were measured.The pathological changes in liver tissue were observed using hematoxylin-eosin(HE)staining.The influence of Ppg on inflammatory cytokines(IL-6,TNF-α,IL-1 β)and key proteins within the associated signaling pathways was examined using enzyme-linked immu-nosorbent assay(ELISA),quantitative polymerase chain reaction(qPCR),and Western blot.Additional-ly,glutathione(GSH),malondialdehyde(MDA),and superoxide dismutase(SOD)activity levels were meas-ured.The expression of markers related to the NF-κB signaling pathway was assessed using qPCR,Western blot,and immunofluorescence.Results In vivo exper-iments showed that Ppg improved the liver and spleen index of mice with drug-induced liver injury,and the levels of AST and ALT in the serum decreased signifi-cantly,reducing the pathological damage of liver tis-sue.In vitro experiments showed that Ppg significantly inhibited the expression of IL-6,TNF-α and IL-1 β,and decreased the levels of GSH and MDA and in-creased SOD activity in AML-12 cells.Additionally,the inhibition of the NF-κB signaling pathway using pyrrolidinedithiocarbamate ammonium(PDTC),an NF-κB signaling pathway inhibitor reduced inflammato-ry cytokine expression and significantly decreased p65 nuclear translocation,showing results comparable to those observed in the high-dose Ppg group.Conclu-sions Ppg alleviates APAP-induced liver injury,po-tentially by inhibiting the NF-κB signaling pathway in hepatocytes,thereby reducing the inflammatory re-sponse and protecting liver from APAP-induced dam-age.

During sudden outbreaks of major infectious diseases,traditional vaccine clinical trials often fail to deliver timely and meaningful outcomes.To address this,innovative trial designs are essential to accelerate or restructure the traditional three-phase clinical trial process while maintaining adherence to scientific principles of drug candidate safety and efficacy.This paper presents various innovative vaccine clinical trial designs and concepts,along with critical considerations for their application,to serve as a methodological reference for related research.Adaptive designs provide flexibility by dynamically adjusting trial parameters—such as dose selection,population stratification,and sample size reestimation—based on interim analysis results.Bayesian designs incorporate historical data and prior information,reducing sample size requirements.Master protocol designs enable the evaluation of multiple treatments or target populations within a unified framework,significantly improving efficiency.Additionally,real-world data(RWD),including electronic health records vaccination records and insurance claims,supports the creation of virtual control groups,addressing ethical concerns while enhancing trial feasibility.A hybrid design combining randomized controlled trials(RCTs)with RWD is also proposed to leverage the strengths of both methodologies.These innovative designs optimize the research process,accelerating vaccine development and regulatory approval.By integrating these approaches,robust evidence-based insights can be generated,advancing precision medicine goals and strengthening public health responses to emerging infectious diseases.

During sudden outbreaks of major infectious diseases,traditional vaccine clinical trials often fail to deliver timely and meaningful outcomes.To address this,innovative trial designs are essential to accelerate or restructure the traditional three-phase clinical trial process while maintaining adherence to scientific principles of drug candidate safety and efficacy.This paper presents various innovative vaccine clinical trial designs and concepts,along with critical considerations for their application,to serve as a methodological reference for related research.Adaptive designs provide flexibility by dynamically adjusting trial parameters—such as dose selection,population stratification,and sample size reestimation—based on interim analysis results.Bayesian designs incorporate historical data and prior information,reducing sample size requirements.Master protocol designs enable the evaluation of multiple treatments or target populations within a unified framework,significantly improving efficiency.Additionally,real-world data(RWD),including electronic health records vaccination records and insurance claims,supports the creation of virtual control groups,addressing ethical concerns while enhancing trial feasibility.A hybrid design combining randomized controlled trials(RCTs)with RWD is also proposed to leverage the strengths of both methodologies.These innovative designs optimize the research process,accelerating vaccine development and regulatory approval.By integrating these approaches,robust evidence-based insights can be generated,advancing precision medicine goals and strengthening public health responses to emerging infectious diseases.

Objective To establish a reliable prediction model for radiation pneumonitis(RP)based on multi-regional radiomics analysis of localizable CT images.Methods A retrospective analysis was conducted on 185 patients who received radiotherapy from January 2021 to June 2023 in the Department of Radiotherapy,Xuzhou Cancer Hospital.Patients were classified as having RP or not based on imaging combined with clinical diagnosis.Three regions of interest(ROI)were defined in the localizable CT images:Lung,Lung-PTV and PTV,and their radiomics features were extracted.After feature screening using methods such as Mann-Whitney Utest,recursive feature elimination,and Lasso,a prediction model was established using support vector machine classification algorithm.The model performance was validated using 6 evaluation metrics:the area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,positive predictive value,and negative predictive value.Results The prediction model consisted of 7 radiomics features.The clinical model of target-to-lung ratio,PTV model,Lung model,and Lung-PTV model achieved AUC values of 0.535,0.801,0.672,and 0.706 in the test set,respectively.The AUC value and accuracy of PTV model reached 0.843 and 0.775 in the training set,while 0.801 and 0.750 in the test set.PTV model was superior to Lung model,Lung-PTV model,and clinical model in predictive performance.The AUC values of the combined PTV+(Lung-PTV)model in the training and test sets were 0.867 and 0.806,respectively,higher than those of PTV model and Lung-PTV model.Conclusion The predictive ability of the prediction models constructed from radiomics features in different ROI for symptomatic RP varies.The radiomics prediction model using PTV as ROI exhibits superior predictive performance,and the combined multi-regional radiomics model can further improve the predictive ability for RP.

Objective To observe the impact of low-dose oxycodone-nalbuphine on pain mediator release in patient-controlled intravenous analgesia(PCIA)following laparoscopic radical resection of rectal cancer.Methods 120 patients who underwent laparoscopic radical surgery for colorectal cancer in our hospital from October 2022 to September 2023 were selected and randomly divided into the control group and the experimental group(60 cases in each).PCIA was administered within 48 h postoperatively,formulated as 10 mg hydromorphone and 40 mg nalbuphine diluted in saline to 100 ml.No background dose was administered in the control group,and a background dose of 1 ml/h was administered in the test group.Compare the number of effective PCIA compressions,the rate of remedial analgesia,the rate of remedial antiemesis,the dosage of analgesic pump,Visual Analogue Scale(VAS),Ramsay sedation score,the release of serum pain mediators and the occurrence of postoperative adverse reactions in patients within ≤24 hours and 24-48 hours.Results VAS Scores:The VAS scores during activity in the experimental group at 24 h and 48 h after surgery(2.21±0.25,1.78±0.44)were lower than those in the control group(2.58±0.23,2.12±0.27)(both P＜0.05).The VAS scores at rest in the experimental group at 12 h,24 h,and 48 h after surgery(2.52±0.28,2.14±0.26,1.75±0.32)were lower than those in the control group(3.17±0.39,2.68±0.33,2.04±0.21)(P＜0.05).Pump Usage:The amounts of analgesic pump used in the experimental group at 24 h and 48 h after surgery[(27.64±0.28)ml,(49.11±0.25)ml]were higher than those in the control group[(12.06±0.33)ml,(20.15±0.36)ml)](both P＜0.001).PCIA Pressing Times:The effective pressing times of PCIA in the experimental group within ≤24 h and 24-48 h(5.89±0.31,4.73±0.28)were lower than those in the control group(7.28±0.38,6.21±0.37)(both P＜0.001).Rescue Analgesia and Antiemesis Rates:The rescue analgesia rate(11.67％vs 28.33％,P=0.023)and rescue antiemesis rate(5.00％vs 18.33％,P=0.023)in the experimental group were both lower than those in the control group.Ramsay Sedation Scores:The Ramsay sedation scores in the experimental group at 12 h,24 h,and 48 h after surgery(2.18±0.31,1.74±0.42,1.46±0.14)were lower than those in the control group(2.43±0.48,2.12±0.32,2.03±0.38)(P＜0.001).In the experimental group,substance P[(35.26±5.27)pg/ml,(26.37±4.17)pg/ml],neuropeptide Y[(147.28±27.43)pg/ml,(127.26±18.49)pg/ml],and prostaglandin E2[(56.48±3.69)at 24 hours and 48 hours after the operation 9)The levels of pg/ml,(42.47±5.35)pg/ml and norepinephrine[(3.12±0.48)ng/ml,(1.57±0.19)ng/ml]were all lower than those of the control group[(41.27±6.48)pg/ml,(34.37±4.86)pg/ml,(35.26±5.27 pg/ml,(26.37±4.17)pg/ml,(62.47±4.26)pg/ml,(53.95±4.73)pg/ml,(3.64±0.64)ng/ml,(2.16±0.26)ng/ml,(P＜0.001)].Conclusion Low-dose oxycodone-nalbuphine in patient-controlled intravenous analgesia following laparoscopic radical resection of rectal cancer can improve analgesic effects and reduce serum pain mediator release.

Objective To establish a reliable prediction model for radiation pneumonitis(RP)based on multi-regional radiomics analysis of localizable CT images.Methods A retrospective analysis was conducted on 185 patients who received radiotherapy from January 2021 to June 2023 in the Department of Radiotherapy,Xuzhou Cancer Hospital.Patients were classified as having RP or not based on imaging combined with clinical diagnosis.Three regions of interest(ROI)were defined in the localizable CT images:Lung,Lung-PTV and PTV,and their radiomics features were extracted.After feature screening using methods such as Mann-Whitney Utest,recursive feature elimination,and Lasso,a prediction model was established using support vector machine classification algorithm.The model performance was validated using 6 evaluation metrics:the area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,positive predictive value,and negative predictive value.Results The prediction model consisted of 7 radiomics features.The clinical model of target-to-lung ratio,PTV model,Lung model,and Lung-PTV model achieved AUC values of 0.535,0.801,0.672,and 0.706 in the test set,respectively.The AUC value and accuracy of PTV model reached 0.843 and 0.775 in the training set,while 0.801 and 0.750 in the test set.PTV model was superior to Lung model,Lung-PTV model,and clinical model in predictive performance.The AUC values of the combined PTV+(Lung-PTV)model in the training and test sets were 0.867 and 0.806,respectively,higher than those of PTV model and Lung-PTV model.Conclusion The predictive ability of the prediction models constructed from radiomics features in different ROI for symptomatic RP varies.The radiomics prediction model using PTV as ROI exhibits superior predictive performance,and the combined multi-regional radiomics model can further improve the predictive ability for RP.