Objective:To explore and analyze the feasibility and efficacy of the 22G needle-guided suture for orbital septum fat flap fixation on the periosteum.Methods:The retrospective study was conducted. From January 2022 to November 2023, patients with tear trough deformity and eyelid bags underwent surgery of releasing the orbicularis retaining ligament (ORL) complex, orbital septum fat pad combined with 22G needle-guided suture in Department of Burn and Plastic Surgery, the First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology. Postoperative follow-up was conducted to observe the occurrence of complications. The lacrimal depression deformity was classified according to the Hirmand standard and the degree of lower eyelid bags was graded to evaluate the surgical outcome. Patients were evaluated their satisfaction using the visual analogue scale (very dissatisfied, dissatisfied, average, satisfied, very satisfied).Results:A total of 32 patients (30 females and 2 males) were included in this study. The age range was 31-62 years old with an average of 50.2 years. All patients were followed up for 6 months postoperatively. None of the patients had any severe complications, such as inferior eyelid ectropion, inferior eyelid retraction, scar hyperplasia, and diplopia. Four patients showed mild eyelid-eyeball separation, two patients had conjunctival edema, and all recovered in 1 month. The orbital fat protrusion, tear trough depression deformity, and lower eyelid skin laxity were significantly improved compared to before the operation. Postoperative satisfaction was 22 cases, very satisfied 10 cases, and patient satisfaction rate was 100% (32/32) after surgery.Conclusion:The method of 22G needle-guided suture to fix orbital septum fat pad represents a technically feasible, easy, and suitable for promotion.

Objective:To establish a rapid quantitative model of sodium chloride content in Halitum of traditional Chinese medicine by using X-ray diffraction technology and machine learning algorithm.Methods:The data of X-ray diffraction patterns of 90 batches of Halitum samples were collected,and the rapid prediction models of X-ray diffraction were constructed by using partial least squares(PLS),support vector regression(SVR)long short-term memory(LSTM)according to the reference values determined by the content determination method of Halitum in the Chinese Pharmacopoeia 2020 edition.Results:The data preprocessed by multivariate scatter correction(MSC)and selected by competitive adaptive reweighted sampling(CARS)were better in PLS model and LSTM model.The data pretreated by standard normal transformation(SNV)and selected by CARS feature variable perform well in SVR.Conclusion:The three models show good prediction potential,which shows that the combination of X-ray diffraction technology and machine learning algorithm is feasible for accurate prediction of the content of Halitum in traditional Chinese medicine.

Objective:To establish a rapid quantitative model for the determination of moisture,extractives,and content in Pollen Typhae.Methods:Near-infrared spectra of 91 batches of Pollen Typhae samples were collected.Spectral preprocessing was performed using S-G,MSC,SNV,and CWT methods.Variable selection was conducted using CARS,SPA,and VIP methods,and compared with full-spectrum modeling.Partial least squares(PLS)mod-els were established for the quantitative determination of moisture,total ash,extractives,and content.The model performance was evaluated by calculating the coefficient of determination for the calibration set and validation set(R2 c,R2v),root mean square error of calibration and validation(RMSEc,RMSEv),and residual prediction devia-tion(RPD).Results:The PLS models for moisture,extractives,and content in Pollen Typhae showed R2c and R2v values greater than 0.9,RMSEc and RMSEv values approaching 0,and RPD values greater than 3.Conclusion:In this study,near-infrared spectroscopy was used to construct quantitative prediction models for moisture,extractives,typhaneoside,and isorhamnetin-3-O-neohesperidoside content in Pollen Typhae.This method enables rapid detection of the main quality control indicators of Pollen Typhae,providing strong technical support for its quality supervision.

Objective To establish a rapid immunological detection method for MPT64 protein based on red microspheres and select highly-sensitive and highly-specific antibody pairs.Methods A His-tagged prokaryotic expression vector was constructed for expression of MPT64 protein that was used to immunize New Zealand white rabbits after purification and validation.Peripheral blood mononuclear cells(PBMCs)were isolated from the rabbits,and antigen-specific B cells expressing antibodies were sorted using single B-cell flow cytometry.mRNA in B cells was reverse-transcribed into cDNA,and paired antibody heavy-and light-chain sequences were amplified via nested PCR.Expression vectors were constructed,and recombinant antibodies were produced in Expi293F cells.Fluorescent immunochromatography was employed to screen for matched antibody pairs.The selected antibodies were used to establish a rapid detection method based on red microsphere immunochromatography.Results Ten high-affinity monoclonal antibodies against MPT64 were generated.Two antibody pairs were selected for MPT64 immunodetection that reached a sensitivity of 0.0125 ng/mL.Conclusion High-affinity rabbit monoclonal antibodies against MPT64 are obtained via single B-cell technology,and a rapid red microspheres-based immunodetection method is established,enabling highly sensitive detection of Mycobacterium tuberculosis MPT64 protein.

Objective To explore the effects of long non-coding RNA(LncRNA)metastasis associated in lung denocarcinoma transcript 1(MALAT1)on high glucose-induced cardiomyocyte injury by regulating miR-320-3p-protein kinase B3(Akt3)axis.Methods H9c2 cells were divided into normal control(NC)group and high glucose(HG)group.After transfection,the cells were cultured in HG group were further divdided into subgroups as follows:HG+MALAT1 knock-down(HG+sh-MALAT1)group,HG+sh-NC group,HG+miR-320-3p mimic group,HG+mimic-NC group,HG+sh-MALAT1+miR-320-3p inhibitor group and HG+sh-MALAT1+pcDNA3.1-Akt3 group.Cell activity,apoptosis rate,B-cell lymphoma 2-related protein X(Bax),B-cell lymphoma factor 2(Bcl-2),cleaved caspase-3,Akt3 protein,MALAT1,miR-320-3p,Akt3 mRNA expression,interleukin(IL-1β),IL-6,tumor necrosis factor-α(TNF-α)were compared among the groups.Results The apoptosis rate of cardiomyocytes,the expressions of IL-1β,IL-6,TNF-α,Bax,cleaved caspase-3/caspase-3 protein,MALAT1 and Akt3 mRNA increased(P<0.05),while the expression of Bcl-2 protein and miR-320-3p decreased in HG group(P<0.05).After knocking down LncRNA MALAT1,apoptosis rate of HG-induced cardiomyocytes and levels of inflammatory factors were decreased(P<0.05).MALAT1 could negatively regulate expression of miR-320-3p,and miR-320-3p could negatively regulate expression of Akt3 protein.The overexpression of miR-320-3p could decrease apoptosis rate and inflammation level,while overexpression of Akt3 or inhibition of miR-320-3p expression could increase apoptosis rate of cardiomyocytes and inflammation level(P<0.05).Conclusions LncRNA MALAT1 can promote the expression of Akt3 protein by inhibiting miR-320-3p expression,thereby reduce activity of HG-induced cardiomyocytes and aggravate cardiomyocytes injury.

Objective:To establish a rapid quantitative model of sodium chloride content in Halitum of traditional Chinese medicine by using X-ray diffraction technology and machine learning algorithm.Methods:The data of X-ray diffraction patterns of 90 batches of Halitum samples were collected,and the rapid prediction models of X-ray diffraction were constructed by using partial least squares(PLS),support vector regression(SVR)long short-term memory(LSTM)according to the reference values determined by the content determination method of Halitum in the Chinese Pharmacopoeia 2020 edition.Results:The data preprocessed by multivariate scatter correction(MSC)and selected by competitive adaptive reweighted sampling(CARS)were better in PLS model and LSTM model.The data pretreated by standard normal transformation(SNV)and selected by CARS feature variable perform well in SVR.Conclusion:The three models show good prediction potential,which shows that the combination of X-ray diffraction technology and machine learning algorithm is feasible for accurate prediction of the content of Halitum in traditional Chinese medicine.

Objective:To establish a rapid quantitative model for the determination of moisture,extractives,and content in Pollen Typhae.Methods:Near-infrared spectra of 91 batches of Pollen Typhae samples were collected.Spectral preprocessing was performed using S-G,MSC,SNV,and CWT methods.Variable selection was conducted using CARS,SPA,and VIP methods,and compared with full-spectrum modeling.Partial least squares(PLS)mod-els were established for the quantitative determination of moisture,total ash,extractives,and content.The model performance was evaluated by calculating the coefficient of determination for the calibration set and validation set(R2 c,R2v),root mean square error of calibration and validation(RMSEc,RMSEv),and residual prediction devia-tion(RPD).Results:The PLS models for moisture,extractives,and content in Pollen Typhae showed R2c and R2v values greater than 0.9,RMSEc and RMSEv values approaching 0,and RPD values greater than 3.Conclusion:In this study,near-infrared spectroscopy was used to construct quantitative prediction models for moisture,extractives,typhaneoside,and isorhamnetin-3-O-neohesperidoside content in Pollen Typhae.This method enables rapid detection of the main quality control indicators of Pollen Typhae,providing strong technical support for its quality supervision.

Objective To explore the mechanism underlying the role of disulfidptosis-related genes(DRGs)in the disease progression of metabolically associated fatty liver disease(MAFLD)based on bioinformatics.Methods In this study,the GEO database was utilized to screen for eligible MAFLD expression data,conduct differential gene analysis,and identify DRGs through consistent clustering to subtype MAFLD patients.The immune infiltration status among subtypes was further evaluated,and the infiltration of immune cells was analyzed using the CIBERSORT algorithm.The gene modules related to the disease were selected through weighted gene co-expression network analysis(WGCNA).Subsequently,a diagnostic model was constructed based on DRGs using machine learning models,and the performance of the model was verified.Finally,the stability of DRGs among different subtypes was evaluated using an external dataset,and the significance of the results was analyzed using statistical tests.Results Through the analysis of the dataset GSE31803,six disulfide death genes,namely,SLC3A2,NCKAP1,CYFIP1,FLNA,MYL6 and MYH10,which were closely related to the clinical characteristics of MAFLD,were screened out.MAFLD patients were classified into two subtypes,with subtype 1 having a higher level of immune cell infiltration.Key gene modules were identified through WGCNA.Through machine learning screening,the support vector machine(SVM)model was determined as the optimal classification model.External validation confirmed the stability and effectiveness of the key genes in different subtypes of MAFLD.Conclusion Based on DRGs,two highly heterogeneous subtypes of MAFLD were identified,which exhibited significant differences in clinical characteristics,biological processes and immune status,indicating that DRGs play a crucial role in the occurrence and development of MAFLD.

Objective To explore the effects of long non-coding RNA(LncRNA)metastasis associated in lung denocarcinoma transcript 1(MALAT1)on high glucose-induced cardiomyocyte injury by regulating miR-320-3p-protein kinase B3(Akt3)axis.Methods H9c2 cells were divided into normal control(NC)group and high glucose(HG)group.After transfection,the cells were cultured in HG group were further divdided into subgroups as follows:HG+MALAT1 knock-down(HG+sh-MALAT1)group,HG+sh-NC group,HG+miR-320-3p mimic group,HG+mimic-NC group,HG+sh-MALAT1+miR-320-3p inhibitor group and HG+sh-MALAT1+pcDNA3.1-Akt3 group.Cell activity,apoptosis rate,B-cell lymphoma 2-related protein X(Bax),B-cell lymphoma factor 2(Bcl-2),cleaved caspase-3,Akt3 protein,MALAT1,miR-320-3p,Akt3 mRNA expression,interleukin(IL-1β),IL-6,tumor necrosis factor-α(TNF-α)were compared among the groups.Results The apoptosis rate of cardiomyocytes,the expressions of IL-1β,IL-6,TNF-α,Bax,cleaved caspase-3/caspase-3 protein,MALAT1 and Akt3 mRNA increased(P<0.05),while the expression of Bcl-2 protein and miR-320-3p decreased in HG group(P<0.05).After knocking down LncRNA MALAT1,apoptosis rate of HG-induced cardiomyocytes and levels of inflammatory factors were decreased(P<0.05).MALAT1 could negatively regulate expression of miR-320-3p,and miR-320-3p could negatively regulate expression of Akt3 protein.The overexpression of miR-320-3p could decrease apoptosis rate and inflammation level,while overexpression of Akt3 or inhibition of miR-320-3p expression could increase apoptosis rate of cardiomyocytes and inflammation level(P<0.05).Conclusions LncRNA MALAT1 can promote the expression of Akt3 protein by inhibiting miR-320-3p expression,thereby reduce activity of HG-induced cardiomyocytes and aggravate cardiomyocytes injury.

Objective To explore the mechanism underlying the role of disulfidptosis-related genes(DRGs)in the disease progression of metabolically associated fatty liver disease(MAFLD)based on bioinformatics.Methods In this study,the GEO database was utilized to screen for eligible MAFLD expression data,conduct differential gene analysis,and identify DRGs through consistent clustering to subtype MAFLD patients.The immune infiltration status among subtypes was further evaluated,and the infiltration of immune cells was analyzed using the CIBERSORT algorithm.The gene modules related to the disease were selected through weighted gene co-expression network analysis(WGCNA).Subsequently,a diagnostic model was constructed based on DRGs using machine learning models,and the performance of the model was verified.Finally,the stability of DRGs among different subtypes was evaluated using an external dataset,and the significance of the results was analyzed using statistical tests.Results Through the analysis of the dataset GSE31803,six disulfide death genes,namely,SLC3A2,NCKAP1,CYFIP1,FLNA,MYL6 and MYH10,which were closely related to the clinical characteristics of MAFLD,were screened out.MAFLD patients were classified into two subtypes,with subtype 1 having a higher level of immune cell infiltration.Key gene modules were identified through WGCNA.Through machine learning screening,the support vector machine(SVM)model was determined as the optimal classification model.External validation confirmed the stability and effectiveness of the key genes in different subtypes of MAFLD.Conclusion Based on DRGs,two highly heterogeneous subtypes of MAFLD were identified,which exhibited significant differences in clinical characteristics,biological processes and immune status,indicating that DRGs play a crucial role in the occurrence and development of MAFLD.