A growing body of research points out that gut microbiota plays a key role in tumor immunotherapy. By optimizing the composition of intestinal microbiota, it is possible to effectively improve immunotherapy resistance and enhance its therapeutic effect. This article comprehensively analyzes the mechanism of intestinal microbiota influencing tumor immunotherapy resistance, expounds the current strategies for targeted regulation of intestinal microbiota, such as traditional Chinese medicine and plant components, fecal microbiota transplantation, probiotics, prebiotics and dietary therapy, and explores the potential mechanisms of these strategies to improve patients' resistance to tumor immunotherapy. At the same time, the article also briefly discusses the prospects and challenges of targeting intestinal microbiota to improve tumor immunotherapy resistance, which provides a reference for related research to help the strategy research of reversing tumor immunotherapy resistance.

Influenza virus causes serious threat to human life and health. Due to the inherent high variability of influenza virus, clinically resistant mutant strains of currently approved anti-influenza virus drugs have emerged. Therefore, it is urgent to develop antiviral drugs with new targets or mechanisms of action. RNA-dependent RNA polymerase is directly responsible for viral RNA transcription and replication, and plays key roles in the viral life cycle, which is considered an important target of anti-influenza drug design. From the point of view of medicinal chemistry, this review summarizes current advances in diverse small-molecule inhibitors targeting influenza virus RNA-dependent RNA polymerase, hoping to provide valuable reference for development of novel antiviral drugs.

With the advent of an aging society,Alzheimer's disease(AD)has gradually become a major ailment affecting the elderly.AD is a neurodegenerative disorder associated with cognitive impairments.In AD patients,brain network connections are disrupted,and their topological properties are also affected,leading to the disintegration of anatomical and functional connections.Anatomical connections can be tracked and evaluated using structural magnetic imaging(MRI)and diffusion tensor imaging(DTI),while functional connections are detected through functional MRI to assess their connectivity status.This review incorporates the findings of previous scholars and summarizes the current research of AD.It mainly discusses the imaging characteristics of large-scale brain network changes in AD patients,so as to provide researchers with scientific and objective imaging markers for AD prediction and early diagnosis,as well as future research.

By using thioflavin T(ThT)as displacement-based fluorescent probes,three kinds of aptasensors were constructed for rapid detection of three kinds of small molecules such as ochratoxin A(OTA),aflatoxin B1(AFB1)and adenosine.In the absence of target molecule,ThT bound with the aptamer to form an aptamer-ThT complex and exhibited a significant fluorescence response.Upon the addition of target molecule,because of the higher affinity between target and aptamer than that between ThT and the aptamer,ThT was displaced by the target molecule from the aptamer-ThT complex,resulting in weakened fluorescence signal.Based on this principle,the target molecule could be detected quantitatively.Further study through circular dichroism spectra showed that there was no significant change in the conformation of the aptamer after addition of ThT or target molecules.The stoichiometric ratios of ThT to OTAapt,AFB1apt and Adeapt measured through the method of equimolar continuous variation was 1∶1,1∶1 and 2∶1,respectively,and their dissociation constants were all larger than those between the target molecule and its aptamer.Therefore,the principle of this detection method was the displacement of fluorescent probe(ThT)in aptamer-ThT complex by target molecule,resulting in decrease of fluorescence intensity.Under optimal experimental conditions,the limits of detection(LODs)were 0.8 nmol/L for OTA,1.3 nmol/L for AFB1,and 0.10 μmol/L for adenosine,respectively.This method was label-free,simple to operate,with low cost,good selectivity and high sensitivity.The developed assay kit based on this method could be used for actual sample detection.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Colorectal cancer ranks among the malignancies with high incidence and mortality rates,po-sing significant challenges to its prevention and treatment.In recent years,accumulating evidence has highlighted the critical involvement of deubiquitinases in the development and progression of colorectal cancer.Deubiquitinases meticulously remove ubiquitin moieties from proteins,thereby regulating protein stability,cellular signaling cascades,and gene expression,which in turn impacts key processes in tumor cells such as proliferation,survival,and metastasis.Deubiquitinases can influence the stability of cell cycle proteins,promoting cell cycle progression and accelerating cellular proliferation.Within the Wnt/β-catenin signaling pathway,deubiquitinases contribute to pathway hyperactivation by enhancing nuclear localization of β-catenin,a pivotal event in colorectal cancer initiation.Deubiquitinases also play a role in modulating the stability of immune checkpoint regulators,affecting the function of immune cells within the tumor microenvironment and facilitating immune evasion.Through regulation of transcription factor ubiquitination status,deubiquitinases impact target gene expression,promoting epithelial-mesenchymal transition,thereby augmenting colorectal cancer's invasive and metastatic potential.Moreover,deubiq-uitinases mediate chemoresistance in tumor cells by controlling the stability of apoptosis inhibitors,DNA repair enzymes,or drug efflux pumps.Given the critical role of deubiquitinases in colorectal cancer pro-gression,the development of small molecule inhibitors targeting specific deubiquitinases has emerged as an attractive yet challenging field of research.Several inhibitors have demonstrated the capability to in-hibit colorectal cancer cell growth and induce apoptosis in vitro and animal models.This review delves in-to the advancements in understanding the roles of deubiquitinases in colorectal cancer and discusses the application of small molecule inhibitors in colorectal cancer,providing insights for therapeutic strategies against this disease.

Eleven compounds were isolated from the ethyl acetate fraction of the 95% aqueous ethanol extract of the roots of Sophora tonkinensis by silica gel, ODS, Sephadex LH-20 column chromatography, and semi-preparative RP-HPLC. Their structures were identified as 7-hydroxy-8-isopentenylchromone (1), furo[2,3-f]-1,3-benzodioxole-7-carboxylic acid (2), 6-[3-(2′,4′-dihydroxyphenyl)acryloyl]-7-hydroxy-2,2-dimethyl-8-(3-methyl-2-butenyl)-2H-benzopyran (3), flemichapparin B (4), tectorigenin (5), genistein (6), 6-hydroxy-1,3-benzodioxole-5-carboxylic acid (7), vanillic acid (8), protocatechuic acid (9), 2,4-dihydroxybenzoic acid (10), and p-hydroxybenzoic acid (11) through extensive spectroscopic data (IR, UV, HR-ESI-MS, and NMR spectra). Among them, compounds 1 and 2 were new compounds. In addition, compound 3 showed significant α-glucosidase and protein tyrosine phosphatase-1B (PTP1B) inhibitory activities with IC₅₀ values of 5.595 and 0.320 μmol·L^-1, respectively.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Abstract：Objective To prepare human monoclonal antibody against spike protein（S protein）of severe acute respiratory syndrome coronavirus 2（SARS⁃CoV⁃2）by using single B cell，and determine its neutralizing activity. Methods Venous blood with high antibody level was collected from people immunized with inactivated SARS⁃CoV⁃2 vaccine（Vero cells） twice，of which peripheral blood mononuclear cells（PBMCs）were isolated by lymphocyte stratified fluid and used to isolate single B cell expressing S protein antibody by magnetic beads coupled with S1 protein. Variable region genes of IgG heavy chain and light chain were amplified by nested PCR after reverse transcription of single B cell，which were connected with CMV promoter，IgG leader sequence，IgG constant region and polyA sequence by overlapping PCR to construct antibody linear expression cassette. Linear expression cassette of the heavy chain and light chain from the same B cell was transfected to HEK293T cells to express human monoclonal antibody of SARS⁃CoV⁃2 S protein. Immunoreactivity was detected by immuno⁃ fluorescence while neutralizing activity by pseudovirus neutralization test. Results A total of 26 monoclonal antibodies against SARS⁃CoV⁃2 S protein were expressed，which showed heavy chain and light chain protein bands of IgG antibody at

Sanhan Huashi formula(SHF) is the intermediate of a newly approved traditional Chinese medicine(TCM) Sanhan Huashi Granules for the treatment of COVID-19 infection. The chemical composition of SHF is complex since it contains 20 single herbal medicines. In this study, UHPLC-Orbitrap Exploris 240 was used to identify the chemical components in SHF and in rat plasma, lung and feces after oral administration of SHF, and heat map was plotted for characterizing the distribution of the chemical components. Chromatographic separation was conducted on a Waters ACQUITY UPLC BEH C_(18)(2.1 mm×100 mm, 1.7 μm) using 0.1% formic acid(A)-acetonitrile(B) as mobile phases in a gradient elution. Electrospray ionization(ESI) source was used to acquire data in positive and negative mode. By reference to quasi-molecular ions and MS/MS fragment ions and in combination with MS spectra of reference substances and compound information in literature reports, 80 components were identified in SHF, including 14 flavonoids, 13 coumarins, 5 lignans, 12 amino-compounds, 6 terpenes and 30 other compounds; 40 chemical components were identified in rat plasma, 27 in lung and 56 in feces. Component identification and characterization of SHF in vitro and in vivo lay foundations for disclosure of its pharmacodynamic substances and elucidation of the scientific connotation.
Rats ; Animals ; Tandem Mass Spectrometry ; Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; COVID-19 ; Lignans