With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Aim To establish a stable hepatic stellate cell ( HSC ) -specific G protein-coupled receptor kinase 2 ( GRK2 ) knockout mice and provide the important animal model for further studying the biological function of GRK2 in HSC. Methods The loxP-labeled Grk2 gene mouse (Grk2

To explore the mechanism of spleen- were obtained for the treatment of acute-on-chronic livstrengthening and moisture-nourishing liver prescription er failure, and 244 intersecting target genes and 7 core (JPLSYGF) in the treatment of acute-on-chronic liver target genes were screened. Molecular docking showed failure using network pharmacology and the molecular that the core target genes AKT1, SRC, VEGFA, docking. Methods Relying on TCMSP and Gene- STAT3 , EGFR, MAPK3 , HRAS had good affinity with Cards and other databases, the relevant targets of JPL- quercetin, the main active component in the JPLSYGF in the treatment of acute-on-chronic liver failure SYGF, and had strong binding activity. In addition, in were obtained. String and Cytoscape were used to con- vivo tests verified that the JPLSYGF could reduce the struct PPI networks of targets, core targets were expression of HRAS, EGFR, STAT3 , SRC, and VEGscreened out, and DAVID was used for GO function FA, to delay the progression of acute-on-chronic liver annotation and KEGG pathway enrichment analysis. failure. Conclusions JPLSYGF may act on core tar- The main active ingredients of the traditional Chinese gets such as HRAS, EGFR, STAT3, SRC, VEGFA medicine compound formula for JPLSYGF were select- and so on, to achieve the effect of treating acute-oned with a bioavailability OB value of =Э 30% and a chronic liver failure. drug-like DL^O. 18 as the screening conditions, and.

Objective To investigate the diagnostic efficiency of artificial intelligence(AI)in rib frac-ture under the computed tomography(CT)images with different reconstruction slice thickness.Methods The first CT images of 100 patients with rib fractures were selected,and the interval-free recon-struction was carried out with the thickness of 0.625 mm,1.250 mm,2.500 mm and 5.000 mm,respectively.The rib fracture screening function of AI was used to automatically detect the CT images of four groups,and the diagnostic efficiency of AI for rib fracture under different reconstruction thickness conditions was com-pared.Results The sensitivity of AI in the diagnosis of rib fracture at 0.625 mm,1.250 mm,2.500 mm and 5.000 mm thickness was 99.32%(436/439),98.41%(432/439),89.52%(393/439)and 83.60%(367/439),respectively.The false positive rate was 4.80%(22/458),0.92%(4/436),0.76%(3/396)and 0.27%(1/368).The diagnostic sensitivity of AI in 0.625 mm and 1.250 mm thickness was higher than that in 2.500 mm and 5.000 mm,and the difference was statistically significant(P<0.05),while there was no significant difference in the thickness of 0.625 mm and 1.250 mm.The false positive rate of AI in the diagnosis of 0.625 mm slice thickness was higher than that of 1.250 mm,2.500 mm and 5.000 mm,and the difference was sta-tistically significant(P<0.05),while there was no significant difference in the thickness of 1.250 mm,2.500 mm and 5.000 mm(P>0.05).Conclusion The diagnostic efficiency of AI in 1.250 mm CT images is better than that in 0.625 mm,2.500 mm and 5.000 mm CT images.

A new method for simultaneous determination of 23 kinds of per-and polyfluoroalkyl substances(PFASs)(13 kinds of perfluoro carboxylic acids,4 kinds of perfluoro sulfonic acids,and 6 kinds of new substitutes)in plant leaf tissue by ultra-high performance liquid chromatography-tandem mass spectrometry(UHPLC-MS/MS)using automatic online solid phase extraction(SPE)to remove the matrix interference components in plant crude extracts was developed.The plant leaf samples were extracted twice with 1%formic acid-methanol solution,then evaporated to dry,redissolved with 70%methanol solution,and directly injected for analysis.After 23 kinds of target PFASs were purified automatically by online SPE with a WAX column,the six-way valve was switched to rinse PFASs onto an alkaline mobile phase system-compatible C18 analytical column.Then,the 23 kinds of target PFASs were separated within 16 min by gradient elution using a binary mobile phase system of methanol/water(Containing 0.4%ammonium hydroxide).Tandem mass spectrometry was performed in multiple reaction monitoring(MRM)mode for online detection of various PFASs,and quantification was carried out by internal standard method.The results of the method validation showed that satisfactory average recoveries of 23 kinds of PFASs in plant leaf samples(64.2%-125.5%),precision(relative standard deviations(RSDs)of 0.7%-12.8%),linearity(R2＞0.990),and sensitivity(the detection limits(S/N=3)were in the range of 0.02-0.50 μg/kg)were achieved.Finally,this method was used to detect PFASs in the marine green tide algae(Enteromorpha prolifera)and several tree leaves,and a total of 6 kinds of PFASs were detected,in which PFBA was the main contaminant.Compared with the reported offline SPE methods,the proposed online SPE technique significantly simplified the sample pretreatment process and provided an automatic,simple,and environment-friendly method for the routine monitoring of legacy and emerging PFASs in plant tissues.