BACKGROUND:Gradient artificial bone repair scaffolds can mimic unique anatomical features in musculoskeletal tissues,showing great potential for repairing injured musculoskeletal tissues. OBJECTIVE:To review the latest research advances in gradient artificial bone repair scaffolds for tissue engineering in the musculoskeletal system and describe their advantages and fabrication strategies. METHODS:The first author of the article searched the Web of Science and PubMed databases for articles published from 2000 to 2023 with search terms"gradient,bone regeneration,scaffold".Finally,76 papers were analyzed and summarized after the screening. RESULTS AND CONCLUSION:(1)As an important means of efficient and high-quality repair of skeletal system tissues,gradient artificial bone repair scaffolds are currently designed bionically for the natural gradient characteristics of bone tissue,bone-cartilage,and tendon-bone tissue.These scaffolds can mimic the extracellular matrix of native tissues to a certain extent in terms of structure and composition,thus promoting cell adhesion,migration,proliferation,differentiation,and regenerative recovery of damaged tissues to their native state.(2)Advanced manufacturing technology provides more possibilities for gradient artificial bone repair scaffold preparation:Gradient electrospun fiber scaffolds constructed by spatially differentiated fiber arrangement and loading of biologically active substances have been developed;gradient 3D printed scaffolds fabricated by layered stacking,graded porosity,and bio-3D printing technology;gradient hydrogel scaffolds fabricated by in-situ layered injections,simple layer-by-layer stacking,and freeze-drying method;and in addition,there are also scaffolds made by other modalities or multi-method coupling.These scaffolds have demonstrated good biocompatibility in vitro experiments,were able to accelerate tissue regeneration in small animal tests,and were observed to have significantly improved histological structure.(3)The currently developed gradient artificial bone repair scaffolds have problems such as mismatch of gradient scales,unclear material-tissue interactions,and side effects caused by degradation products,which need to be further optimized by combining the strengths of related disciplines and clinical needs in the future.

Objective:To evaluate the image quality and radiation dose in contrast-enhanced thoracic-abdominal-pelvic CT using automatic tube voltage modulation (ATVM) coupled with artificial intelligence iterative reconstruction (AIIR) versus routine tube voltage combined with Karl-3D iterative reconstruction (Karl-3D IR), and to determine the optimal noise level for AIIR in contrast-enhanced thoracic-abdominal-pelvic CT.Methods:A total of 100 patients who underwent contrast-enhanced thoracic-abdominal-pelvic CT examination in the Affiliated Hospital of Hebei University from April to October, 2023 were randomly divided into group A and group B using a random number table, with 50 patients in each group. Group A was scanned using ATVM, and images were reconstructed using AIIR with 1-5 noise levels. Group B was scanned using tube voltage 120 kVp and images were reconstructed with Karl-3D IR and noise level 5. The single-to-noise ratio (SNR), contrast-to-noise ratio (CNR), effective dose (E), and size-specific dose estimate (SSDE) were recorded or calculated for all patients or images. Subjective evaluations of all images were performed. The quality of the reconstructed images using AIIR with 1-5 noise levels were compared and the optimal noise level of AIIR for image reconstruction was determined. Image quality and radiation dose were statistically analyzed for Group A (image reconstruction with optimal AIIR noise level) and Group B.Results:The mean SNR and mean CNR of the reconstructed images using AIIR with noise levels 1, 2, and 3 in group A were higher than those using AIIR with noise levels 4 and 5. The images reconstructed using AIIR with noise levels 3 and 4 scored higher in subjective assessment than those reconstructed using AIIR with noise levels 1, 2, and 5. Therefore, noise level 3 was optimal for AIIR in reconstruction of contrast-enhanced thoracic-abdominal-pelvic CT images. The mean SNR, mean CNR, and subjective evaluation score of group A using AIIR with noise level 3 were higher than those of group B using Karl-3D IR with noise level 5 ( P<0.001). The mean SSDE and the mean E of group A were reduced by 46% and 41%, respectively, compared with those of group B. Conclusions:ATVM technology combined with the AIIR algorithm can improve image quality and reduced patient radiation dose in contrast-enhanced thoracic-abdominal-pelvic CT. Noise level 3 is optimal for AIIR in the reconstruction of arterial-phase and venous-phase contrast-enhanced thoracic-abdominal-pelvic CT images.

Purpose To explore deep transfer learning(DTL)based on multi-parameter MRI for joint prediction of uterine endometrial cancer microsatellite expression with clinical parameters.Materials and Methods Retrospective analysis was conducted on 262 patients with uterine endometrial cancer in the Affiliated Huaian NO.1 People's Hospital of Nanjing Medical University from January 2020 to December 2022,who were randomly divided into a training cohort(n=183)and a validation cohort(n=79)in a 7∶3 ratio.DTL features were extracted from T2WI and diffusion-weighted imaging(DWI)using a 50-layer residual neural network(ResNet50).Subsequently,T2-net,DWI-net,multi-sequence fusion and a combined model were constructed.A combined model was constructed via incorporating multiparametric fusion DTL features and clinically independent prognostic factors identified through both univariate and multivariate Logistic regression analysis.Model performance was assessed using the area under the curve,calibration curve and decision curve.Grad-CAM was used for model visualization analysis.Results Compared with single-sequence models,the multi-sequence fusion models exhibited superior performance,with area under the curve value of 0.898 for the validation cohort,accuracy of 0.823,sensitivity of 0.812,specificity of 0.825,respectively,and F1 score was 0.650.Univariate and multivariate Logistic regression analyses revealed that serum human epididymis protein 4 levels and the presence of uterine fibroids were clinically independent risk factors.Ultimately,the combined model demonstrated the best predictive performance in the validation cohort,with area under the curve value of 0.924,accuracy of 0.835,sensitivity of 0.875,specificity of 0.825,respectively,and F1 score was 0.683.Conclusion The multi-parameter fusion model based on DTL features can effectively and non-invasively predict the microsatellite status of endometrial cancer patients.

Objective:To evaluate the image quality and radiation dose in contrast-enhanced thoracic-abdominal-pelvic CT using automatic tube voltage modulation (ATVM) coupled with artificial intelligence iterative reconstruction (AIIR) versus routine tube voltage combined with Karl-3D iterative reconstruction (Karl-3D IR), and to determine the optimal noise level for AIIR in contrast-enhanced thoracic-abdominal-pelvic CT.Methods:A total of 100 patients who underwent contrast-enhanced thoracic-abdominal-pelvic CT examination in the Affiliated Hospital of Hebei University from April to October, 2023 were randomly divided into group A and group B using a random number table, with 50 patients in each group. Group A was scanned using ATVM, and images were reconstructed using AIIR with 1-5 noise levels. Group B was scanned using tube voltage 120 kVp and images were reconstructed with Karl-3D IR and noise level 5. The single-to-noise ratio (SNR), contrast-to-noise ratio (CNR), effective dose (E), and size-specific dose estimate (SSDE) were recorded or calculated for all patients or images. Subjective evaluations of all images were performed. The quality of the reconstructed images using AIIR with 1-5 noise levels were compared and the optimal noise level of AIIR for image reconstruction was determined. Image quality and radiation dose were statistically analyzed for Group A (image reconstruction with optimal AIIR noise level) and Group B.Results:The mean SNR and mean CNR of the reconstructed images using AIIR with noise levels 1, 2, and 3 in group A were higher than those using AIIR with noise levels 4 and 5. The images reconstructed using AIIR with noise levels 3 and 4 scored higher in subjective assessment than those reconstructed using AIIR with noise levels 1, 2, and 5. Therefore, noise level 3 was optimal for AIIR in reconstruction of contrast-enhanced thoracic-abdominal-pelvic CT images. The mean SNR, mean CNR, and subjective evaluation score of group A using AIIR with noise level 3 were higher than those of group B using Karl-3D IR with noise level 5 ( P<0.001). The mean SSDE and the mean E of group A were reduced by 46% and 41%, respectively, compared with those of group B. Conclusions:ATVM technology combined with the AIIR algorithm can improve image quality and reduced patient radiation dose in contrast-enhanced thoracic-abdominal-pelvic CT. Noise level 3 is optimal for AIIR in the reconstruction of arterial-phase and venous-phase contrast-enhanced thoracic-abdominal-pelvic CT images.

OBJECTIVE Hospital-acquired infections have emerged as an increasingly prominent and hard-to-com-pletely-avoid problem,with their complexity and challenges continuing to intensify.As a major public health con-cern,these hospital-acquired infections pose a serious threat to the safety of individuals within hospitals.Among the various routes of transmission,airborne transmission is one of the most important pathways leading to hospi-tal-acquired infections.A variety pathogenic viruses can attach to infectious respiratory particles produced by hu-man body and spread through these particles.Patients in hospitals,as the primary group releasing respiratory in-fectious particles,have behaviors(such as breathing,coughing,talking,etc.)that are closely related to the trans-mission,dissemination and spread of pathogenic microorganisms.It is generally believed that pathogens re-leased into the air by patients will propagate and spread with air currents,thereby elevating the risk of infection.In order to comprehensively safeguard the safety of healthcare workers and patients,and effectively curb the occur-rence of hospital-acquired infections,it is essential to investigate the impact of pathogen-releasing behaviors on the transmission of pathogenic microorganisms.This paper aims to review the research progress on the impact of pathogen-releasing behaviors of patients on the transmission of respiratory pathogenic microorganisms within healthcare buildings,as well as to provide an outlook for further research directions.

Purpose To explore deep transfer learning(DTL)based on multi-parameter MRI for joint prediction of uterine endometrial cancer microsatellite expression with clinical parameters.Materials and Methods Retrospective analysis was conducted on 262 patients with uterine endometrial cancer in the Affiliated Huaian NO.1 People's Hospital of Nanjing Medical University from January 2020 to December 2022,who were randomly divided into a training cohort(n=183)and a validation cohort(n=79)in a 7∶3 ratio.DTL features were extracted from T2WI and diffusion-weighted imaging(DWI)using a 50-layer residual neural network(ResNet50).Subsequently,T2-net,DWI-net,multi-sequence fusion and a combined model were constructed.A combined model was constructed via incorporating multiparametric fusion DTL features and clinically independent prognostic factors identified through both univariate and multivariate Logistic regression analysis.Model performance was assessed using the area under the curve,calibration curve and decision curve.Grad-CAM was used for model visualization analysis.Results Compared with single-sequence models,the multi-sequence fusion models exhibited superior performance,with area under the curve value of 0.898 for the validation cohort,accuracy of 0.823,sensitivity of 0.812,specificity of 0.825,respectively,and F1 score was 0.650.Univariate and multivariate Logistic regression analyses revealed that serum human epididymis protein 4 levels and the presence of uterine fibroids were clinically independent risk factors.Ultimately,the combined model demonstrated the best predictive performance in the validation cohort,with area under the curve value of 0.924,accuracy of 0.835,sensitivity of 0.875,specificity of 0.825,respectively,and F1 score was 0.683.Conclusion The multi-parameter fusion model based on DTL features can effectively and non-invasively predict the microsatellite status of endometrial cancer patients.

OBJECTIVE Hospital-acquired infections have emerged as an increasingly prominent and hard-to-com-pletely-avoid problem,with their complexity and challenges continuing to intensify.As a major public health con-cern,these hospital-acquired infections pose a serious threat to the safety of individuals within hospitals.Among the various routes of transmission,airborne transmission is one of the most important pathways leading to hospi-tal-acquired infections.A variety pathogenic viruses can attach to infectious respiratory particles produced by hu-man body and spread through these particles.Patients in hospitals,as the primary group releasing respiratory in-fectious particles,have behaviors(such as breathing,coughing,talking,etc.)that are closely related to the trans-mission,dissemination and spread of pathogenic microorganisms.It is generally believed that pathogens re-leased into the air by patients will propagate and spread with air currents,thereby elevating the risk of infection.In order to comprehensively safeguard the safety of healthcare workers and patients,and effectively curb the occur-rence of hospital-acquired infections,it is essential to investigate the impact of pathogen-releasing behaviors on the transmission of pathogenic microorganisms.This paper aims to review the research progress on the impact of pathogen-releasing behaviors of patients on the transmission of respiratory pathogenic microorganisms within healthcare buildings,as well as to provide an outlook for further research directions.

Objective:To compare the image quality, radiation dose, and total iodine content of abdominal computed tomography (CT) enhancement scanning of overweight and obese patients with different scanning protocols, and to explore the optimal keV image serial for abdominal CT.Methods:A total of 90 overweight or obese patients [24 kg/m 2 ≤ body mass index (BMI) < 28 kg/m 2 or BMI ≥ 28 kg/m 2] were divied into groups A, B and C, with 30 patients in each group. Group A used Gemstone spectral imaging (GSI) mode and contrast medium with 320 mg I/ml, group B used low tube voltage mode (100 kVp) and contrast medium with 370 mg I/ml, and group C used conventional tube voltage mode (120 kVp) and contrast medium with 370 mg I/ml. Monochromatic energy images at 50-70 keV (5 keV interval) were reconstructed for the arterial and portal vein phases of group A. Radiation dose and total iodine content were recorded and calculated for the 3 groups. The region of interest was placed on the organ, blood vessel, and erector spinae muscle at same level. The CT values and image noise values were measured, and the contrast-to-noise ratio (CNR) was calculated. All images were scored subjectively in double-blinded by two radiologists. One-way analysis of variance or Kruskal-Wallis H test were used to compare The CT values, CNRs, and subjective scores of each subgroup image in group A, group B and group C, and the radiation doses and total iodine contents in 3 groups were compared. The optimal keV value for group A was selected. Results:At 50-60 keV, the CT values and CNRs of arterial and portal vein phases in group A were higher ( P < 0.05) than or similar to those in groups B and C ( P > 0.05), and the subjective scores were lower than those of groups B and C at 50 and 55 keV ( H = 34.47, 41.27, P < 0.05), whereas there was no statistically significant difference at 60 keV ( P > 0.05). At 65 and 70 keV, only the CT value and CNR of the renal cortex in group A at the 65 keV of arterial phase were higher than those in groups B and C ( F = 102.38, 29.47, P < 0.001). The subjective scores were not significantly between groups B and C ( P > 0.05). There were no statistically significant difference between CT values, CNRs, or subjective scores in group B and group C ( P > 0.05). The effective doses in groups A and B were 24.72% and 25.78% lower than those in group C, respectively. Compared to groups B and C, the total iodine content in group A decreased by 12.50% and 13.34%, respectively. Conclusions:GSI model combined with a low-concentration contrast medium in abdominal CT for overweight and obese patients can meet the image quality requirements while reducing patient total iodine content and radiation dose. The optimal keV value of enhanced abdominal CT for double phases was 60 keV.

The male patient of middle-age was admitted to Zhujiang Hospital, Southern Medical University on September 5, 2020, due to wear and rupture of the left foot hallux for more than 10 days, gradually developed swelling and blackening of the left foot and accompanied by drowsiness for 1day. Results from etiological examination of his wound secretions suggested the mixed infection of Wohlfahrtiimonas chitiniclastica and Enterococcus faecalis. The disease progressed rapidly and the patient still died of septic shock after anti-infective treatment. The infection of W. chitiniclastica is rare and has a great relationship with poor hygiene and chronic open wounds. It is necessary to use matrix-assisted laser desorption/ionization-time of flight mass spectrometry or 16S ribosomal RNA gene sequencing for strain identification. Moreover, it is recommended to use cephalosporins or carbapenems as first-line drugs due to the poor prognosis of patients with bloodstream infections.

OBJECTIVES: With the increasing detection rate of lung nodules, the qualitative problem of lung nodules has become one of the key clinical issues. This study aims to evaluate the value of combining dynamic contrast-enhanced (DCE) MRI based on time-resolved imaging with interleaved stochastic trajectories-volume interpolated breath hold examination (TWIST-VIBE) with T₁ weighted free-breathing star-volumetric interpolated breath hold examination (T₁WI star-VIBE) in identifying benign and malignant lung nodules. METHODS: We retrospectively analyzed 79 adults with undetermined lung nodules before the operation. All nodules of patients included were classified into malignant nodules (n=58) and benign nodules (n=26) based on final diagnosis. The unenhanced T₁WI-VIBE, the contrast-enhanced T₁WI star-VIBE, and the DCE curve based on TWIST-VIBE were performed. The corresponding qualitative [wash-in time, wash-out time, time to peak (TTP), arrival time (AT), positive enhancement integral (PEI)] and quantitative parameters [volume transfer constant (Ktrans), interstitium-to-plasma rate constant (Kep), and fractional extracellular space volume (Ve)] were evaluated. Besides, the diagnostic efficacy (sensitivity and specificity) of enhanced CT and MRI were compared. RESULTS: There were significant differences in unenhanced T₁WI-VIBE hypo-intensity, and type of A, B, C DCE curve type between benign and malignant lung nodules (all P<0.001). Pulmonary malignant nodules had a shorter wash-out time than benign nodules (P=0.001), and the differences of the remaining parameters were not statistically significant (all P>0.05). After T₁WI star-VIBE contrast-enhanced MRI, the image quality was further improved. Compared with enhanced CT scan, the sensitivity (82.76% vs 80.50%) and the specificity (69.23% vs 57.10%) based on MRI were higher than that of CT (both P<0.001). CONCLUSIONS T₁WI star-VIBE and dynamic contrast-enhanced MRI based on TWIST-VIBE were helpful to improve the image resolution and provide more information for clinical differentiation between benign and malignant lung nodules.
Adult ; Humans ; Retrospective Studies ; Magnetic Resonance Imaging ; Plasma ; Tomography, X-Ray Computed ; Lung