Objective: To investigate the barrier membrane fixation performance and enhanced guided bone regeneration (GBR) capability of a thermosensitive adhesive containing bioactive glass nanoparticles in order to provide a novel solution for membrane fixation during GBR procedures. Methods: M2NP@BGN (methoxyethyl acrylate-co-N-isopropylacrylamide-co-protocatechuic acid@Bioactive glass nanoparticle), a thermosensitive adhesive, was synthesized via free radical polymerization by compositing methoxyethyl acrylate, N-isopropylacrylamide, and protocatechuic acid into a basic adhesive that was modified with bioactive glass nanoparticle (BGN). The successful fabrication of basic adhesive M2NP was characterized by attenuated total reflection-Fourier transform infrared spectroscopy and nuclear magnetic resonance spectroscopy. The thermosensitive adhesive M2NP@BGN (BGN concentration of 1 mg/mL) was characterized by scanning electron microscopy and a rheometer. By adjusting the BGN concentration (0.1 mg/mL, 0.5 mg/mL, 1 mg/mL, and 2 mg/mL), the adhesive and mechanical strengths were investigated with a universal testing machine. Biocompatibility was evaluated with a cell counting kit-8 assay and hemolysis test to identify the optimal formulation. The optimal material’s extract was co-cultured with mouse bone marrow mesenchymal stem cells, and its osteogenic activity was examined in vitro by quantitative real-time PCR, alkaline phosphatase, and alizarin red S staining. The rat mandibular defect model was established, filled with bone graft, and divided into 3 groups based on membrane fixation method: M2NP@BGN (BGN concentration of 1 mg/mL) fixation group (M2NP@BGN), titanium nail fixation group (Nail), and unfixed control group (Negative). Bone regeneration was analyzed after 8 weeks by micro computed tomography and histological staining. Results: M2NP@BGN (BGN concentration of 1 mg/mL) was successfully synthesized and demonstrated rapid gelation under warm, humid conditions. The adhesive with a BGN concentration of 1 mg/mL exhibited the highest adhesive strength (P < 0.001) and significantly enhanced mechanical strength (P < 0.001) under 37℃ wet conditions. All formulations showed excellent biocompatibility, with cell viability > 80% and hemolysis ratio < 5%. M2NP@BGN (BGN concentration of 1 mg/mL) significantly upregulated the expression of Runx2 and Col I (P < 0.001) and enhanced the activity of osteogenic differentiation markers (P < 0.05). In the animal model, the M2NP@BGN group (BGN concentration of 1 mg/mL) achieved significantly higher bone volume fraction and better bone maturity compared to the negative and nail groups (P < 0.05). Conclusion M2NP@BGN (BGN concentration of 1 mg/mL) combines excellent wet adhesion with potent osteogenic activity, enhances the bone augmentation efficacy of membranes, and presents a novel fixation strategy with significant clinical translation potential for GBR therapy.

Objective To explore the value of support vector machine(SVM)model based on gray matter volume(GMV)for identifying amyotrophic lateral sclerosis(ALS),also to analyze the relevant brain regions.Methods MR 3D T1WI data of 60 ALS patients(ALS group)and 60 healthy volunteers(control group)were retrospectively analyzed.Taken GMV of each brain region obtained by voxel-based morphometry as the input features.F-score analysis was used to select feature with the highest classification accuracy to construct SVM model.Receiver operating characteristic curve was drawn to evaluate the efficacy of SVM model for identifying ALS,and top 10％was used as the weight threshold to obtain gray matter brain regions contributed the most to this model.Results SVM model constructed based on the top 40％GMV features had the highest classification accuracy(82.50％),with sensitivity,specificity and area under the curve(AUG)of 85.05％,80.40％and 0.890,respectively.The left precentral gyrus,left anterior cingulate gyrus and paracingulate gyrus,right middle temporal gyrus,opercular part of left inferior frontal gyrus,right dorsolateral superior frontal gyrus,left temporal pole:middle temporal gyrus,right superior occipital gyrus,orbital part of right middle frontal gyrus,right calcarine fissure and surrounding cortex,right fusiform gyrus were the top 1-10 gray matter brain regions contributed to this model.Conclusion ALS had specific GMV change pattern.SVM model based on GMV could be used to effectively identify ALS,while the left precentral gyrus was the most contributive brain region to this model.

In the canonical model of gene transcription,transcription factors regulate the transcription of target genes by binding to double-stranded DNA(dsDNA)containing its specific consensus motifs.In contrast to dsDNA,G-quadruplexes are atypical nucleic acid secondary structures formed by guanine-rich sequences.G-quadruplex structures are involved in regulating various biological processes,including gene transcription,and have emerged as a significant research topic in the field of molecular biology.Re-cently,many research groups,including us,have revealed that G-quadruplex structures recruit transcrip-tion factors to bind to the promoters more efficiently than dsDNA,thereby enhancing target gene expres-sion.However,there is currently a lack of comprehensive summaries and discussions regarding this atypi-cal model of gene transcription regulation.In this review,we first elucidate the characteristics of G-quad-ruplex structures and the techniques used to identify these structures.G-quadruplexes can be classified into intramolecular and intermolecular types;intramolecular G-quadruplexes are further divided into par-allel,antiparallel and hybrid types.The G-quadruplex structures can be determined using techniques such as circular dichroism,nuclear magnetic resonance spectroscopy and gel migration,among others.Then,we discuss the regulatory role of G-quadruplex in gene transcription.G-quadruplexes are mainly highly enriched in gene promoter.Early studies revealed that G-quadruplexes can inhibit gene transcrip-tion.However,lots of studies have recently proven that this structure has a new function of recruiting transcription factors to activate gene transcription.Finally,we summarize the classification of transcrip-tion factors with G-quadruplex binding activity,including transcription factors with C2H2 zinc fingers,forked head/winged helices,and p53 domains.The DNA-binding domain determines the interaction be-tween transcription factors and G-quadruplex.Moreover,we propose future research directions in the field of G-quadruplex and transcription factors in regulating gene transcription.In conclusion,this review can provide important guidance for understanding the concept of G-quadruplex as a cis-acting element for transcriptional activation.

This study was aimed at investigating the effects and mechanisms of Toxoplasma gondii type Ⅰ(RH strain)ROP16 protein on proliferation and the cell cycle in mouse alveolar macrophage MH-S cells.We constructed a Toxoplasma gondii type Ⅰ(RH)ROP16 overexpression lentivirus,transduced MH-S cells,and then screened cells with puromycin to obtain a cell line stably overexpressing ROP16Ⅰ.RT-qPCR and western blotting were used to verify expression effects,CCK-8 assays were used to detect cell proliferation activity,and flow cytometry was used to detect cell cycle changes.Western blotting and RT-qPCR were used to detect the expression levels of p53,p21,CDK6,Cyclin D1,STAT3,p-STAT3(Y705),and JAK1 proteins or genes,and immunofluorescence was used to detect the expression levels of ROP16Ⅰ and p-STAT3(Y705)and their subcellular co-localization in MH-S cells.ROP16Ⅰ protein and gene expression were detected in MH-S cells transduced with lentivirus for ROP16Ⅰ overexpression.CCK-8 assays revealed that ROP16Ⅰ promoted the proliferation of MH-S cells(P＜0.01)and enhanced cell viability.Flow cytometry revealed that ROP16Ⅰ overexpression decreased the G0/G1 phase and elevated the G2 and S phases of the cell cycle in MH-S cells(P＜0.01 or P＜0.05).Compared with the MH-S cell group and MH-S-empty vector group,the MH-S-ROP16 cell group showed lower expression of p53 and p21 proteins;higher expression of CDK6,Cyclin D1,p-STAT3(Y705),and JAK1 proteins;lower expression of p53 and p21 mRNAs;and higher expression of CDK6 and Cyclin D1 mRNAs(all P＜0.01).Immunofluorescence revealed that ROP16Ⅰ co-localized with p-STAT3(Y705)in the nucleus and surrounding cytoplasm.Therefore,Toxoplasma gondii type Ⅰ(RH)ROP16Ⅰ protein activates the JAK-STAT3 pathway;shortens the G0/G1 phase and lengthens the G2/S phase of the cell cycle;and promotes cell proliferation.These findings provide a theoretical basis for revealing the mechanism of immune evasion of Toxoplasma gondii,and lay a foundation for research on the prevention and treatment of Toxoplasma gondii pneumonia.

In the canonical model of gene transcription,transcription factors regulate the transcription of target genes by binding to double-stranded DNA(dsDNA)containing its specific consensus motifs.In contrast to dsDNA,G-quadruplexes are atypical nucleic acid secondary structures formed by guanine-rich sequences.G-quadruplex structures are involved in regulating various biological processes,including gene transcription,and have emerged as a significant research topic in the field of molecular biology.Re-cently,many research groups,including us,have revealed that G-quadruplex structures recruit transcrip-tion factors to bind to the promoters more efficiently than dsDNA,thereby enhancing target gene expres-sion.However,there is currently a lack of comprehensive summaries and discussions regarding this atypi-cal model of gene transcription regulation.In this review,we first elucidate the characteristics of G-quad-ruplex structures and the techniques used to identify these structures.G-quadruplexes can be classified into intramolecular and intermolecular types;intramolecular G-quadruplexes are further divided into par-allel,antiparallel and hybrid types.The G-quadruplex structures can be determined using techniques such as circular dichroism,nuclear magnetic resonance spectroscopy and gel migration,among others.Then,we discuss the regulatory role of G-quadruplex in gene transcription.G-quadruplexes are mainly highly enriched in gene promoter.Early studies revealed that G-quadruplexes can inhibit gene transcrip-tion.However,lots of studies have recently proven that this structure has a new function of recruiting transcription factors to activate gene transcription.Finally,we summarize the classification of transcrip-tion factors with G-quadruplex binding activity,including transcription factors with C2H2 zinc fingers,forked head/winged helices,and p53 domains.The DNA-binding domain determines the interaction be-tween transcription factors and G-quadruplex.Moreover,we propose future research directions in the field of G-quadruplex and transcription factors in regulating gene transcription.In conclusion,this review can provide important guidance for understanding the concept of G-quadruplex as a cis-acting element for transcriptional activation.

This study was aimed at investigating the effects and mechanisms of Toxoplasma gondii type Ⅰ(RH strain)ROP16 protein on proliferation and the cell cycle in mouse alveolar macrophage MH-S cells.We constructed a Toxoplasma gondii type Ⅰ(RH)ROP16 overexpression lentivirus,transduced MH-S cells,and then screened cells with puromycin to obtain a cell line stably overexpressing ROP16Ⅰ.RT-qPCR and western blotting were used to verify expression effects,CCK-8 assays were used to detect cell proliferation activity,and flow cytometry was used to detect cell cycle changes.Western blotting and RT-qPCR were used to detect the expression levels of p53,p21,CDK6,Cyclin D1,STAT3,p-STAT3(Y705),and JAK1 proteins or genes,and immunofluorescence was used to detect the expression levels of ROP16Ⅰ and p-STAT3(Y705)and their subcellular co-localization in MH-S cells.ROP16Ⅰ protein and gene expression were detected in MH-S cells transduced with lentivirus for ROP16Ⅰ overexpression.CCK-8 assays revealed that ROP16Ⅰ promoted the proliferation of MH-S cells(P＜0.01)and enhanced cell viability.Flow cytometry revealed that ROP16Ⅰ overexpression decreased the G0/G1 phase and elevated the G2 and S phases of the cell cycle in MH-S cells(P＜0.01 or P＜0.05).Compared with the MH-S cell group and MH-S-empty vector group,the MH-S-ROP16 cell group showed lower expression of p53 and p21 proteins;higher expression of CDK6,Cyclin D1,p-STAT3(Y705),and JAK1 proteins;lower expression of p53 and p21 mRNAs;and higher expression of CDK6 and Cyclin D1 mRNAs(all P＜0.01).Immunofluorescence revealed that ROP16Ⅰ co-localized with p-STAT3(Y705)in the nucleus and surrounding cytoplasm.Therefore,Toxoplasma gondii type Ⅰ(RH)ROP16Ⅰ protein activates the JAK-STAT3 pathway;shortens the G0/G1 phase and lengthens the G2/S phase of the cell cycle;and promotes cell proliferation.These findings provide a theoretical basis for revealing the mechanism of immune evasion of Toxoplasma gondii,and lay a foundation for research on the prevention and treatment of Toxoplasma gondii pneumonia.

Objective To explore the value of support vector machine(SVM)model based on gray matter volume(GMV)for identifying amyotrophic lateral sclerosis(ALS),also to analyze the relevant brain regions.Methods MR 3D T1WI data of 60 ALS patients(ALS group)and 60 healthy volunteers(control group)were retrospectively analyzed.Taken GMV of each brain region obtained by voxel-based morphometry as the input features.F-score analysis was used to select feature with the highest classification accuracy to construct SVM model.Receiver operating characteristic curve was drawn to evaluate the efficacy of SVM model for identifying ALS,and top 10％was used as the weight threshold to obtain gray matter brain regions contributed the most to this model.Results SVM model constructed based on the top 40％GMV features had the highest classification accuracy(82.50％),with sensitivity,specificity and area under the curve(AUG)of 85.05％,80.40％and 0.890,respectively.The left precentral gyrus,left anterior cingulate gyrus and paracingulate gyrus,right middle temporal gyrus,opercular part of left inferior frontal gyrus,right dorsolateral superior frontal gyrus,left temporal pole:middle temporal gyrus,right superior occipital gyrus,orbital part of right middle frontal gyrus,right calcarine fissure and surrounding cortex,right fusiform gyrus were the top 1-10 gray matter brain regions contributed to this model.Conclusion ALS had specific GMV change pattern.SVM model based on GMV could be used to effectively identify ALS,while the left precentral gyrus was the most contributive brain region to this model.

Objective:To explore the diagnostic value of 18F-FDG PET/CT imaging in etiology of patients with hemophagocytic lymphohistiocytosis (HLH). Methods:Retrospective analysis was performed on 49 patients newly diagnosed as HLH (32 males, 17 females; age 19-61 years) who received 18F-FDG PET/CT imaging in Affiliated Hospital of Jining Medical University from January 2017 to January 2023. PET/CT images and clinical parameters were observed and recorded. Based on the pathological examination and clinical follow-up results, diagnostic efficacies for HLH etiology of PET/CT, PET and CT imaging were calculated. χ2 test, independent-sample t test and Mann-Whitney U test were used to compare the differences between hematologic tumors associated HLH and non-hematologic tumor associated HLH. Multivariate logistic regression was used to analyze the predictors of secondary HLH in hematologic tumors. ROC curve analysis was used to calculate AUCs and optimal threshold of lymph node SUV max and soluble CD25 (sCD25) to predict secondary HLH in patients with hematologic tumors. Results:The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of PET/CT, PET and CT in the etiological diagnosis of HLH were 85.7%(30/35), 8/10, 84.4%(38/45), 93.8%(30/32), 8/13; 77.1%(27/35), 6/10, 73.3%(33/45), 87.1%(27/31), 6/14; 62.9%(22/35), 5/10, 60.0%(27/45), 81.5%(22/27), 5/18, respectively. There were differences in lymph node distribution and boundary, liver and spleen and bone lesions, SUV max of lymph node and liver and spleen and bone, gender, age, WBC, neutrophil (ANC), PLT, lactate dehydrogenase (LDH), total bilirubin (TBIL), C-reactive protein (CRP) and sCD25 between different etiology groups ( χ2 values: 3.91-9.66, t values: 3.75-7.90, z values: 3.82-4.01, all P<0.05). SUV max of lymph nodes and sCD25 were predictive factors for secondary HLH of hematological tumors (odds ratio ( OR): 1.28 (95% CI: 1.09-1.72), 1.56 (95% CI: 1.17-2.49), P values: 0.004, 0.013). The optimal thresholds were 12.6 and 40 028 ng/L, with the AUC of 0.87 and 0.76, with the sensitivity and specificity of 88.6%(31/35) and 8/10, 65.7%(23/35) and 7/10, respectively. The combined AUC was 0.83 and the sensitivity and specificity were 74.3% (26/35) and 9/10. Conclusions:18F-FDG PET/CT imaging is of high value for the diagnosis of the cause of HLH. SUV max of lymph node and sCD25 are predictive factors for secondary HLH of hematologic tumors.

Objective To investigate the distribution and antimicrobial susceptibility of clinically isolated bacteria from intensive care units(ICUs)in hospitals of Hunan Province Antimicrobial Resistance Surveillance System from 2012 to 2021.Methods According to China Antimicrobial Resistance Surveillance System,data of clinically isolated bacterial strains and antimicrobial susceptibility testing results of bacteria from ICUs reported by all member units of Hunan Province Antimicrobial Resistance Surveillance System were analyzed with WHONET 2022 software.Results From 2012 to 2021,the total number of bacteria isolated from ICUs of member units of the Hunan Province Antimi-crobial Resistance Surveillance System was 5 777-22 369,with Gram-negative bacteria accounting for 76.1％-78.0％annually.Staphylococcus aureus ranked first among isolated Gram-positive bacteria each year.The top 5 bacteria among Gram-negative bacteria were Acinetobacter baumannii,Klebsiella pneumoniae,Escherichia coli,Pseudo-monas aeruginosa,and Stenotrophomonas maltophilia.Detection rate of methicillin-resistant Staphylococcus aureus showed a downward trend year by year.No Staphylococcus spp.were found to be resistant to vancomycin,teico-planin and linezolid.Detection rates of vancomycin-resistant Enterococcus faecalis and vancomycin-resistant Entero-coccus faecium were 0.6-1.1％and 0.6％-2.2％,respectively.Resistance rates of Escherichia coli and Kleb-siella pneumoniae to imipenem were 3.1％-5.7％and 7.7％-20.9％,respectively.Resistance rates of Pseudo-monasaeruginosa and Acinetobacter baumannii to imipenem were 24.6％-40.1％and 76.1％-80.9％,respective-ly.Detection rates of carbapenem-resistant Pseudomonas aeruginosa declined year by year.Acinetobacter baumannii maintained high susceptibility to polymyxin B,with resistance rate＜10％.Conclusion Antimicrobial resistance of bacteria from ICUs is serious.Carbapenem-resistant Enterobacteriales has an upward trend after 2019.It is nece-ssary to strengthen the surveillance of bacterial resistance and carry out multidisciplinary collaboration.

Deep vein thrombosis(DVT)is a common peripheral vascular disease in clinical practice.The lack of precise and efficient early diagnostic techniques renders it susceptible to being overlooked or misdiagnosed,and therefore,identifying trustworthy biomarkers is a major issue that has to be resolved.In this study,the endogenous metabolites in the urine of DVT rats were screened by metabolomics technology based on gas chromatograph-mass spectrometry(GC-MS)and the characteristic metabolites were identified by multiple feature selection algorithms and multivariate statistical analysis,for the development of a machine learning-based diagnostic model for DVT.The urine samples in metabolic cage in the thrombus development phase(between 48 and 72 h)of rats were collected,which was used as the models for inferior vena cava ligation.The metabolic profiles of the control group and DVT were obtained using the GC-MS method.A total of 176 kinds of endogenous metabolites were identified in rat urine through comparison with the FiehnLib database,26 kinds of differential metabolites associated with DVT were screened through a combination of the Mann-Whitney U test and orthogonal partial least squares discriminant analysis(OPLS-DA),and 13 kinds of significant metabolites strongly correlated with DVT were further evaluated in conjunction with various machine learning feature selection techniques.For DVT diagnosis,machine learning models such as Gaussian Naive Bayes(GNB),support vector machine(SVM),logistic regression(LR),and linear discriminant analysis(LDA)were developed.The diagnostic model constructed using 13 kinds of key metabolites demonstrated excellent accuracy and stability,and surpassed the predictive performance of the models utilizing 176 kinds of metabolites and 26 kinds of differential metabolites,as evidenced by examination and comparison of each model's efficacy.The study showed that the integration of multiple feature selection algorithms for analyzing metabolite information in DVT rat urine was capable of effectively identifying reliable potential markers of DVT.Furthermore,the developed machine learning model offered a novel technical approach for the automated diagnosis of DVT.