Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To explore the value of improved ResNet18 lightweight deep learning(DL)models for automatically detecting gouty arthritis(GA)based on ultrasonogram of the first metatarsophalangeal joint(MTP1).Methods A total of 2 401 ultrasonograms obtained from 260 patients with suspected gout who underwent MTP1 ultrasound examination were included and divided into training set(1 910 ultrasonograms from 209 cases)and test set(491 ultrasonograms from 51 cases)at the ratio of 4∶1.GA lesions on ultrasonograms were manually labeled.After preprocessing,ResNet18 lightweight network was used to construct DL models for identifying the ultrasonogram category was normal or abnormal(with any manifestation of GA).Five-fold cross-validation method was adopted to evaluate the efficacy of the DL models constructed with 2,3,4 or 6 residual blocks,i.e.model 1,2,3 and 4,respectively,and the computational cost and the amount of parameters of each model were recorded.The efficacy of the models were verified using test set,and the best DL model was screened.Results The computational cost of model 1,2,3 and 4 was 7 558.27,2 963.73,4 012.33 and 6 093.39 M,respectively,while the amount of parameters was 4.61,4.91,4.91 and 5.28 M,respectively.Model 2 had the least computational cost with parameters only slightly more than model 1.In test set,no significant difference of accuracy nor the area under the curve was found among 4 models(all P＞0.05).The sensitivity of model 2 was higher than that of model 3,while its specificity was lower only than that of model 3(both P＜0.05),hence model 2 was the best DL model.Conclusion Improved ResNet18 lightweight DL models could be used for automatically detecting GA based on ultrasonogram of MTP1,among which model 2 was the best one.

AIM:To investigate the association between single nucleotide polymorphisms(SNPs)in the silent information regulator 1(SIRT1)gene and breast cancer risk in the Han Chinese population.METHODS:A total of 105 Han Chinese patients with breast cancer and 90 healthy controls were enrolled.Sequenom MassARRAY was used to detect the genotypes of SIRT1 gene loci,rs3740051,rs3758391,rs12778366 and rs2394443.The Hardy-Weinberg equilibrium(HWE)was analysed using the chi-square test.Multivariate logistic regression was employed to analyze the correlation be-tween each SNP and breast cancer susceptibility,as well as the relationship between the rs3758391 genotype and the clini-copathological characteristics of breast cancer in Han Chinese women.SHEsis software was used to assess linkage disequi-librium and haplotypes of the selected SNPs.The impact of genotypes at rs3758391 locus on SIRT1 protein expression was examined using Western blot.An additional 150 Han Chinese women with breast cancer and 150 healthy controls were en-rolled,and SIRT1 protein expression was assessed using immunohistochemistry.Logistic regression was performed to as-sess the relationship between high and low SIRT1 expression and the clinical characteristics of breast cancer.Kaplan-Mei-er website was used to determine the association between SIRT1 expression and patient prognosis.RESULTS:All four SNP loci conformed to the HWE test(P>0.05).The TC/TC+CC genotype of the SIRT1 rs3758391 locus significantly re-duced the risk of breast cancer compared with the TT genotype(TT vs TC:ORadj=0.348,95%CI:0.157～0.773,Padj=0.010;TT vs TC+CC:ORadj=0.381,95%CI:0.179～0.811,Padj=0.012),and correlated with earlier disease course(stage I/II),smaller tumor volume,and higher SIRT1 protein expression levels(P<0.05).SIRT1 expression was signifi-cantly lower in breast cancer tissues,and low SIRT1 expression was associated with larger tumor size,lymph node metasta-sis,and reduced recurrence-free survival(P<0.05).CONCLUSION:The TC/TC+CC genotype of the SIRT1 rs3758391 locus may be a protective factor for breast cancer in Han Chinese women,potentially reducing the risk of breast cancer and delaying disease progression by regulating SIRT1 expression.In addition,SIRT1 expression level is closely related to the clinical characteristics and prognosis of breast cancer.

Objective To explore the value of improved ResNet18 lightweight deep learning(DL)models for automatically detecting gouty arthritis(GA)based on ultrasonogram of the first metatarsophalangeal joint(MTP1).Methods A total of 2 401 ultrasonograms obtained from 260 patients with suspected gout who underwent MTP1 ultrasound examination were included and divided into training set(1 910 ultrasonograms from 209 cases)and test set(491 ultrasonograms from 51 cases)at the ratio of 4∶1.GA lesions on ultrasonograms were manually labeled.After preprocessing,ResNet18 lightweight network was used to construct DL models for identifying the ultrasonogram category was normal or abnormal(with any manifestation of GA).Five-fold cross-validation method was adopted to evaluate the efficacy of the DL models constructed with 2,3,4 or 6 residual blocks,i.e.model 1,2,3 and 4,respectively,and the computational cost and the amount of parameters of each model were recorded.The efficacy of the models were verified using test set,and the best DL model was screened.Results The computational cost of model 1,2,3 and 4 was 7 558.27,2 963.73,4 012.33 and 6 093.39 M,respectively,while the amount of parameters was 4.61,4.91,4.91 and 5.28 M,respectively.Model 2 had the least computational cost with parameters only slightly more than model 1.In test set,no significant difference of accuracy nor the area under the curve was found among 4 models(all P＞0.05).The sensitivity of model 2 was higher than that of model 3,while its specificity was lower only than that of model 3(both P＜0.05),hence model 2 was the best DL model.Conclusion Improved ResNet18 lightweight DL models could be used for automatically detecting GA based on ultrasonogram of MTP1,among which model 2 was the best one.

AIM:To investigate the association between single nucleotide polymorphisms(SNPs)in the silent information regulator 1(SIRT1)gene and breast cancer risk in the Han Chinese population.METHODS:A total of 105 Han Chinese patients with breast cancer and 90 healthy controls were enrolled.Sequenom MassARRAY was used to detect the genotypes of SIRT1 gene loci,rs3740051,rs3758391,rs12778366 and rs2394443.The Hardy-Weinberg equilibrium(HWE)was analysed using the chi-square test.Multivariate logistic regression was employed to analyze the correlation be-tween each SNP and breast cancer susceptibility,as well as the relationship between the rs3758391 genotype and the clini-copathological characteristics of breast cancer in Han Chinese women.SHEsis software was used to assess linkage disequi-librium and haplotypes of the selected SNPs.The impact of genotypes at rs3758391 locus on SIRT1 protein expression was examined using Western blot.An additional 150 Han Chinese women with breast cancer and 150 healthy controls were en-rolled,and SIRT1 protein expression was assessed using immunohistochemistry.Logistic regression was performed to as-sess the relationship between high and low SIRT1 expression and the clinical characteristics of breast cancer.Kaplan-Mei-er website was used to determine the association between SIRT1 expression and patient prognosis.RESULTS:All four SNP loci conformed to the HWE test(P>0.05).The TC/TC+CC genotype of the SIRT1 rs3758391 locus significantly re-duced the risk of breast cancer compared with the TT genotype(TT vs TC:ORadj=0.348,95%CI:0.157～0.773,Padj=0.010;TT vs TC+CC:ORadj=0.381,95%CI:0.179～0.811,Padj=0.012),and correlated with earlier disease course(stage I/II),smaller tumor volume,and higher SIRT1 protein expression levels(P<0.05).SIRT1 expression was signifi-cantly lower in breast cancer tissues,and low SIRT1 expression was associated with larger tumor size,lymph node metasta-sis,and reduced recurrence-free survival(P<0.05).CONCLUSION:The TC/TC+CC genotype of the SIRT1 rs3758391 locus may be a protective factor for breast cancer in Han Chinese women,potentially reducing the risk of breast cancer and delaying disease progression by regulating SIRT1 expression.In addition,SIRT1 expression level is closely related to the clinical characteristics and prognosis of breast cancer.

BACKGROUND:Due to the sudden release and the rapid removal by proteases,platelet-rich plasma hydrogel leads to shorter residence times of growth factors at the wound site.In recent years,researchers have focused on the use of hydrogels to encapsulate platelet-rich plasma in order to improve the deficiency of platelet-rich plasma hydrogels. OBJECTIVE:To prepare self-assembled polypeptide-platelet-rich plasma hydrogel and to explore its effects on the release of bioactive factors of platelet-rich plasma. METHODS:The self-assembled polypeptide was synthesized by the solid-phase synthesis method,and the solution was prepared by D-PBS.Hydrogels were prepared by mixing different volumes of polypeptide solutions with platelet-rich plasma and calcium chloride/thrombin solutions,so that the final mass fraction of polypeptides in the system was 0.1%,0.3%,and 0.5%,respectively.The hydrogel state was observed,and the release of growth factors in platelet-rich plasma was detected in vitro.The polypeptide self-assembly was stimulated by mixing 1%polypeptide solution with 1%human serum albumin solution,so that the final mass fraction of the polypeptide was 0.1%,0.3%,and 0.5%,respectively.The flow state of the liquid was observed,and the rheological mechanical properties of the self-assembled polypeptide were tested.The microstructure of polypeptide(mass fraction of 0.1%and 0.001%)-human serum albumin solution was observed by scanning electron microscope and transmission electron microscope. RESULTS AND CONCLUSION:(1)Hydrogels could be formed between different volumes of polypeptide solution and platelet-rich plasma.Compared with platelet-rich plasma hydrogels,0.1%and 0.3%polypeptide-platelet-rich plasma hydrogels could alleviate the sudden release of epidermal growth factor and vascular endothelial growth factor,and extend the release time to 48 hours.(2)After the addition of human serum albumin,the 0.1%polypeptide group still exhibited a flowing liquid,the 0.3%polypeptide group was semi-liquid,and the 0.5%polypeptide group stimulated self-assembly to form hydrogel.It was determined that human serum albumin in platelet-rich plasma could stimulate the self-assembly of polypeptides.With the increase of the mass fraction of the polypeptide,the higher the storage modulus of the self-assembled polypeptide,the easier it was to form glue.(3)Transmission electron microscopy exhibited that the polypeptide nanofibers were short and disordered before the addition of human serum albumin.After the addition of human serum albumin,the polypeptide nanofibers became significantly longer and cross-linked into bundles,forming a dense fiber network structure.Under a scanning electron microscope,the polypeptides displayed a disordered lamellar structure before adding human serum albumin.After the addition of human serum albumin,the polypeptides self-assembled into cross-linked and densely arranged porous structures.(4)In conclusion,the novel polypeptide can self-assemble triggered by platelet-rich plasma and the self-assembly effect can be accurately adjusted according to the ratio of human serum albumin to polypeptide.This polypeptide has a sustained release effect on the growth factors of platelet-rich plasma,which can be used as a new biomaterial for tissue repair.

Background::Hypothermia therapy has been suggested to attenuate myocardial necrosis; however, the clinical implementation as a valid therapeutic strategy has failed, and new approaches are needed to translate into clinical applications. This study aimed to assess the feasibility, safety, and efficacy of a novel selective intracoronary hypothermia (SICH) device in mitigating myocardial reperfusion injury.Methods::This study comprised two phases. The first phase of the SICH was performed in a normal porcine model for 30 minutes ( n = 5) to evaluate its feasibility. The second phase was conducted in a porcine myocardial infarction (MI) model of myocardial ischemia/reperfusion which was performed by balloon occlusion of the left anterior descending coronary artery for 60 minutes and maintained for 42 days. Pigs in the hypothermia group ( n = 8) received hypothermia intervention onset reperfusion for 30 minutes and controls ( n = 8) received no intervention. All animals were followed for 42 days. Cardiac magnetic resonance analysis (five and 42 days post-MI) and a series of biomarkers/histological studies were performed. Results::The average time to lower temperatures to a steady state was 4.8 ± 0.8 s. SICH had no impact on blood pressure or heart rate and was safely performed without complications by using a 3.9 F catheter. Interleukin-6 (IL-6), tumor necrosis factor-α, C-reactive protein (CRP), and brain natriuretic peptide (BNP) were lower at 60 min post perfusion in pigs that underwent SICH as compared with the control group. On day 5 post MI/R, edema, intramyocardial hemorrhage, and microvascular obstruction were reduced in the hypothermia group. On day 42 post MI/R, the infarct size, IL-6, CRP, BNP, and matrix metalloproteinase-9 were reduced, and the ejection fraction was improved in pigs that underwent SICH.Conclusions::The SICH device safely and effectively reduced the infarct size and improved heart function in a pig model of MI/R. These beneficial effects indicate the clinical potential of SICH for treatment of myocardial reperfusion injury.

Objective:To explore the anatomical parameters of the ideal trajectory for pedicle screw fixation through the lamina in the treatment of L 5 spondylolysis. Methods:CT data from 40 male patients with bilateral L 5 spondylolysis (age, 24.95±4.01 years; range, 20-36 years), treated at the 940th Hospital of PLA Joint Logistics Support Force between January 2021 and June 2024, were analyzed. Three-dimensional vertebral models were reconstructed using this data. Measurements included the lumbosacral angle, the thickness at the midpoint of the superior and inferior lamina edges, mid-lamina thickness, the distance from the lateral edge of the lamina to the spinous process midline, the thickness at the defect of the pars interarticularis, and the vertical diameter of the defect. The screws were inserted from the inferior edge of the lamina, passing through the pars interarticularis defect, and exiting at the superior edge of the pedicle. In the vertical direction of the lamina, the inferior and superior edges of the lamina were divided into three zones, named A, B, C (for the inferior lamina edge) and 1, 2, 3 (for the superior pedicle edge). Seven trajectories (A2, A3, B1, B2, B3, C2, and C3) were designed by combining these zones. Screws with diameters of 5.0, 4.5, 4.0, and 3.5 mm were sequentially inserted along each trajectory. Screw trajectories with an insertion success rate ≥95% were selected and evaluated for feasibility. Parameters such as screw length, medial inclination angle, caudal inclination angle, and entry point position were measured. The ideal trajectory and screw dimensions were determined by considering anatomical features, screw characteristics, and insertion safety. Results:The measurement results from the 3D model showed that the lumbosacral angle was 36.22°±5.23°, and the midpoint thickness of the superior lamina edge was 4.14±0.66 mm (left) and 4.18±0.65 mm (right), the mid-lamina thickness was 6.73±0.72 mm (left) and 6.72±0.70 mm (right), the midpoint thickness of the inferior lamina edge was 6.50±0.56 mm (left) and 6.50±0.66 mm (right), the distance from the lateral edge of the lamina to the spinous process midline was 25.95±2.86 mm (left) and 26.39±3.10 mm (right), the thickness at the pars defect was 9.67±0.57 mm (left) and 9.67±0.51 mm (right), and the vertical diameter of the pars defect was 18.76±2.16 mm (left) and 19.26±2.03 mm (right). No statistically significant differences were found between the left and right sides for these parameters ( P>0.05). The trajectories considered feasible and with an insertion success rate ≥95% were B2, B3, C2, and C3. Safe screw diameters were B2 (4.5 mm), B3 (4.0 mm), C2 (4.0 mm), and C3 (3.5 mm). Corresponding screw lengths were B2 (38.28±2.34 mm), B3 (37.03±2.99 mm), C2 (38.37±2.42 mm), and C3 (36.88±2.87 mm). The caudal inclination angles were B2 (52.73°±5.29°), B3 (55.06°± 4.46°), C2 (49.09°±3.92°), and C3 (50.18°±4.36°). The medial inclination angles were B2 (21.21°±3.01°), B3 (5.11°±1.58°), C2 (22.55°±2.46°), and C3 (12.59°±1.80°). The distances from the entry point to the spinous process midline were B2 (13.23±1.68 mm), B3 (13.15±1.46 mm), C2 (11.12±0.64 mm), and C3 (11.09±0.65 mm). The distances from the entry point to the root of the spinous process were B2 (8.23±1.46 mm), B3 (8.21±1.31 mm), C2 (6.65 ±0.76 mm), and C3 (6.67±0.72 mm). Differences in screw length, caudal inclination angle, medial inclination angle, and entry point position across trajectories were statistically significant ( P<0.05). Conclusion:The ideal screw trajectory for L 5 spondylolysis involves insertion through the midpoint of the entry zone, passing through the pars defect, and exiting at the midpoint of the superior edge of the pedicle. The optimal entry point is located on the inferior edge of the lamina, 8.23±1.46 mm from the root of the spinous process and 13.23±1.68 mm from the spinous process midline. The screw should be placed at a caudal inclination angle of 52.73°±5.29° and a medial inclination angle of 21.21°±3.01°. The recommended screw length is 38.28±2.34 mm, with a diameter of 4.5 mm (range, 4.5-5.0 mm).