Objective:To develop a deep learning model based on 3D super-resolution reconstruction technology and to analyze its feasibility and effectiveness in predicting paraglottic space invasion in hypopharyngeal cancer.Methods:A retrospective study was conducted involving 382 patients with hypopharyngeal squamous cell carcinoma treated at Qilu Hospital of Shandong University between January 2014 and December 2020. The cohort included 364 males and 18 females, with a mean age of 62±7 years. Patients were divided into a training set ( n=300) and a test set ( n=82) based on enrollment time. A generative adversarial network was used to perform 3D super-resolution reconstruction on contrast-enhanced CT images, improving spatial resolution by 16 times. A 2.5D deep learning strategy was employed to construct Resnet-NR and Resnet-SR models based on conventional and super-resolution images, respectively, to predict whether the paraglottic space was invaded. Model performance was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). A multi-reader multi-case study was conducted to assess the impact of the artificial intelligence (AI) model on clinicians′ diagnostic capabilities. Results:The super-resolution model Resnet-SR achieved the highest accuracy in both the training set (AUC=0.87, 95% CI: 0.84-0.90) and the test set (AUC=0.88, 95% CI: 0.81-0.96), significantly outperforming traditional clinical indicators (T stage, N stage, tumor diameter, and pathological differentiation degree) (AUC range: 0.55-0.70, all P<0.05). In comparison, the conventional-resolution model Resnet-NR achieved AUCs of 0.81 (95% CI: 0.77-0.84, P=0.005) and 0.80 (95% CI: 0.71-0.89, P=0.184) in the training and test sets, respectively. Using Resnet-SR to assist clinical decision-making improved the diagnostic accuracy of junior physicians (AUC=0.793 without AI assistance vs. AUC=0.871 with AI assistance, P=0.012) and significantly reduced diagnosis time for clinicians of all experience levels (86.5 s without AI assistance vs. 82.5 s with AI assistance, t=2.01, P=0.032). Conclusion:This study successfully develops a deep learning model based on 3D super-resolution reconstruction technology, which can assist in preoperative prediction of paraglottic space invasion in hypopharyngeal cancer. The AI-assisted tool improves diagnostic accuracy for junior physicians and enhances diagnostic efficiency for clinicians across all experience levels.

Objective A scoping review of domestically and internationally published studies on finite element analysis(FEA)based on three-dimensional(3D)human body model in preventing pressure ulcer(PU)was conducted,aiming to provide new directions for improving the prevention strategies of pressure ulcer.Methods We conducted a systematic search in both Chinese and English medical databases,including PubMed,Embase,CINAHL,Web of Science,Cochrane Library,Sinomed,CNKI,Wanfang and VIP,and engineering database(Engineering Village Compen-dex).The search period was from the inception of each database to July 29,2024.The information was extracted,and the results were analyzed and standardized for reporting.Results A total of 30 studies were included.The general methods of FEA based on 3D human body model include establishing geometric model,meshing,defining material properties,loading and setting boundary conditions,and solving equations.According to the contents of studies,they could be categorized into 4 distinct application domains,including identification of risk groups(n=9),position management(n=9),preventive dressings(n=10)and supportive surfaces(n=7).Conclusion FEA based on 3D human body model provides a foundation to PU biomechanical mechanism research and a scientific basis to the supplement and optimization of clinical prevention.Future studies should integrate clinical problems with simulations and further optimize simulations techniques and protocols.

Objective A scoping review of domestically and internationally published studies on finite element analysis(FEA)based on three-dimensional(3D)human body model in preventing pressure ulcer(PU)was conducted,aiming to provide new directions for improving the prevention strategies of pressure ulcer.Methods We conducted a systematic search in both Chinese and English medical databases,including PubMed,Embase,CINAHL,Web of Science,Cochrane Library,Sinomed,CNKI,Wanfang and VIP,and engineering database(Engineering Village Compen-dex).The search period was from the inception of each database to July 29,2024.The information was extracted,and the results were analyzed and standardized for reporting.Results A total of 30 studies were included.The general methods of FEA based on 3D human body model include establishing geometric model,meshing,defining material properties,loading and setting boundary conditions,and solving equations.According to the contents of studies,they could be categorized into 4 distinct application domains,including identification of risk groups(n=9),position management(n=9),preventive dressings(n=10)and supportive surfaces(n=7).Conclusion FEA based on 3D human body model provides a foundation to PU biomechanical mechanism research and a scientific basis to the supplement and optimization of clinical prevention.Future studies should integrate clinical problems with simulations and further optimize simulations techniques and protocols.

Objective:To develop a deep learning model based on 3D super-resolution reconstruction technology and to analyze its feasibility and effectiveness in predicting paraglottic space invasion in hypopharyngeal cancer.Methods:A retrospective study was conducted involving 382 patients with hypopharyngeal squamous cell carcinoma treated at Qilu Hospital of Shandong University between January 2014 and December 2020. The cohort included 364 males and 18 females, with a mean age of 62±7 years. Patients were divided into a training set ( n=300) and a test set ( n=82) based on enrollment time. A generative adversarial network was used to perform 3D super-resolution reconstruction on contrast-enhanced CT images, improving spatial resolution by 16 times. A 2.5D deep learning strategy was employed to construct Resnet-NR and Resnet-SR models based on conventional and super-resolution images, respectively, to predict whether the paraglottic space was invaded. Model performance was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). A multi-reader multi-case study was conducted to assess the impact of the artificial intelligence (AI) model on clinicians′ diagnostic capabilities. Results:The super-resolution model Resnet-SR achieved the highest accuracy in both the training set (AUC=0.87, 95% CI: 0.84-0.90) and the test set (AUC=0.88, 95% CI: 0.81-0.96), significantly outperforming traditional clinical indicators (T stage, N stage, tumor diameter, and pathological differentiation degree) (AUC range: 0.55-0.70, all P<0.05). In comparison, the conventional-resolution model Resnet-NR achieved AUCs of 0.81 (95% CI: 0.77-0.84, P=0.005) and 0.80 (95% CI: 0.71-0.89, P=0.184) in the training and test sets, respectively. Using Resnet-SR to assist clinical decision-making improved the diagnostic accuracy of junior physicians (AUC=0.793 without AI assistance vs. AUC=0.871 with AI assistance, P=0.012) and significantly reduced diagnosis time for clinicians of all experience levels (86.5 s without AI assistance vs. 82.5 s with AI assistance, t=2.01, P=0.032). Conclusion:This study successfully develops a deep learning model based on 3D super-resolution reconstruction technology, which can assist in preoperative prediction of paraglottic space invasion in hypopharyngeal cancer. The AI-assisted tool improves diagnostic accuracy for junior physicians and enhances diagnostic efficiency for clinicians across all experience levels.

Diabetic foot ulcer is a serious and destructive complication of diabetes， with the rates of disability and mortality increasing year by year， which poses a serious threat to human physical and mental health. In the treatment of diabetic foot ulcer with traditional Chinese medicine （TCM）， the combination of syndrome differentiation and overall concept can not only alleviate TCM syndrome but also accelerate wound healing， reduce wound recurrence， delay the further deterioration of diabetic foot ulcer， and decrease the rates of disability and mortality. Modern studies have demonstrated that the difficult healing of diabetic foot ulcer is closely associated with the abnormal distribution of cytokines such as inflammatory cytokines， growth factors， and chemokines. With the deepening of modern medical research on TCM， the treatment of diabetic foot ulcer via regulation of cytokines by Chinese medicinal monomers and prescriptions has become a research focus. This paper summarizes the current research status at home and abroad and draws the following conclusions. ① Sesamol， geniposide， Danggui Buxuetang， and Zizhu ointment can regulate tumor necrosis factor-α （TNF-α）， interleukin （IL）-1， IL-6， IL-10 and other inflammatory cytokines to inhibit wound inflammation. ② Angelicae Dahuricae Radix， salvianolic acid B， Sixiao powder， Badu Shengji ointment （Zhuang medicine）， etc.， regulate vascular endothelial growth factor （VEGF）， platelet-derived growth factor （PDGF）， transforming growth factor （TGF）， epidermal growth factor （EGF） and other growth factors to promote collagen deposition and angiogenesis on wound surface. ③ Paeoniflorin， cryptanshinone， bee venom， and Huiyang Shengji decoction regulate CXC chemokine ligand （CXCL） 1， CXCL2， C-C chemokine ligand （CCL） 2， CCL3， stromal cell-derived factor-1α （SDF-1α）， monocyte chemoattract protein-1 （MCP-1） and other chemokines to reduce inflammatory response and promote neovascularization and wound granulation tissue formation. ④ In the treatment of diabetic foot ulcer， Chinese medicinal monomers and prescriptions have different and complicated mechanisms. The multi-target treatment manner determines that Chinese medicines can act on a variety of cytokines to participate in various stages of wound healing and thus play a therapeutic role. The conclusion above aims to provide ideas for the experimental research and clinical treatment of diabetic foot ulcer with TCM in the future.

Spatialomics is another research hotspot of biotechnology after single-cell sequencing technology, which can make up for the defect that single-cell sequencing technology can not obtain cell spatial distribution information. Spatialomics mainly studies the relative position of cells in tissue samples to reveal the effect of cell spatial distribution on diseases. In recent years, spatialomics has made new progress in the pathogenesis, target exploration, drug development and many other aspects of head and neck tumors. This paper summarizes the latest progress of spatialomics in the diagnosis and treatment of head and neck cancer.
Humans ; Head and Neck Neoplasms/therapy*

Objective:To evaluate the residual risk (RR) of transfusion transmitted HIV (TT-HIV) after the implementation of nucleic acid amplification test (NAT) in blood screening test among blood centers in China.Methods:The data of blood donors and HIV infection markers from 2017 to 2020 were collected from 28 blood centers via the Platform of Comparison of blood establishments Practice in Chinese Mainland. The new infection rate/window period mathematical model was used for two types of blood screening strategies, namely, two rounds ELISA plus individual NAT take turn with pooling NAT (2ELISA+ ID-NAT/MP-NAT) and two ELISA plus one round pooling NAT (2ELISA+ MP-NAT), and the RR of HIV infection was estimated also based on first donors (FDs) and repeated donors (RDs) in different blood donation years. T-test analyses were conducted for comparing TT HIV RR among FDs and RDs in different blood donation years with two blood screening strategies, and the variation trend of RR in HIV test was observed.Results:From 2017 to 2020, the RR of FDs in 2ELISA+ ID-NAT/MP-NAT blood screening strategy was 2.869/10 6 person-year, 3.795/10 6 persons-year, 3.879/10 6 person-year, and 2.890/10 6 person-year respectively. The RR of RDs was 1.797/10 6 person-year, 1.502/10 6 person-year, 1.857/10 6 person-year, and 1.483/10 6 person-year respectively. Significant difference exists between RR of FDs and RDs, with F=9.898 and p<0.05. In 2ELISA+ MP-NAT strategy, the RR of FDs was 3.508/10 6 person-year, 1.868/10 6 person-year, 2.204/10 6 person-year, and 1.765/10 6 person-year respectively. The RR of RDs was 0.948/10 6 person-year, 0.926/10 6 person-year, 0.748/10 6 person-year, and 0.682/10 6 person-year respectively. Statistical difference existed between RR of FDs and RDs, with F=17.126 and P<0.05. There was no significant difference between the RR of FDs in these two strategies with F=3.493 and P>0.05, while there was a difference between the RR of RDs in these two strategies with F=24.516 and P<0.05, and a difference between the RR of total donors (TDs) in these two strategies F=20.216 and P<0.05. Conclusions:The RR of TT HIV significantly decreased after the introduction of NAT into blood test among blood centers in China. There were some differences in the RR of HIV testing among different blood screening strategies. There could be significant differences in the RR of HIV testing among different groups of blood donors. Compared with FDs, RDs is the low risk group for HIV.

Small renal cell carcinoma refers to a renal malignant tumor with a maximum diameter of 4 cm.Due to the small size, its diagnosis and differential diagnosis have been difficult points in clinical work. CT texture analysis is an emerging technique, it determines the tumor heterogeneity by analyzing the distribution and relationship of pixel or voxel gray-scale levels in the CT images, it acts to more accurately predict the benign and malignant tumors and the classification of tumors.This paper reviews CT texture analysis on the diagnosis and differential diagnosis of small renal cell carcinoma, in order to guide the correct diagnosis of doctors and effectively clinical treatment.

Adventitia ; Atherosclerosis/genetics* ; Cell Communication ; Humans ; Hyperhomocysteinemia/genetics* ; Sequence Analysis, RNA

A fast,reliable,and cost-effective liquid chromatography-tandem mass spectrometry method was established to determine the effects of the traditional Chinese medicine employed to treat coronavirus disease 2019,namely,Lianhua Qingwen granules,Huoxiang Zhengqi capsules,Jinhua Qinggan granules,Shufeng Jiedu capsules,and Angong Niuhuang pills,on the pharmacokinetics of lopinavir/ritonavir in rats.Blood samples were prepared using the protein precipitation method and atazanavir was selected as the internal standard(IS).Separation was performed on an Agilent ZORBAX eclipse plus C18(2.1 mm x 50 mm,1.8 μm)column using acetonitrile and water containing 0.1％formic acid as the mobile phase for gradient elution.The flow rate was 0.4 mL/min and the injection volume was 2 μL Agilent Jet Stream electrospray ionization was used for mass spectrometry detection under positive ion multiple reaction monitoring mode at a transition of m/z 629.3→447.3 for lopinavir,m/z 721.3→296.1 for rito-navir,and m/z 705.4→168.1 for the IS.The method showed good linearity in the concentration range of 25-2500 ng/mL(r=0.9981)for lopinavir and 5-500 ng/mL(r=0.9984)for ritonavir.The intra-day and inter-day precision and accuracy were both within±15％.Items,such as dilution reliability and residual effect,were also within the acceptable limits.The method was used to determine the effects of five types of traditional Chinese medicines on the pharmacokinetics of lopinavir/ritonavir in rats.The pharmaco-kinetic results showed that the half-life of ritonavir in the groups administered Lianhua Qingwen granules and Huoxiang Zhengqi capsules combined with lopinavir/ritonavir was prolonged by approx-imately 1.5-to 2-fold relative to that in the control group.Similarly,the pharmacokinetic parameters of lopinavir were altered.Overall,the results of this study offer important theoretical parameters for the effective clinical use of five types of traditional Chinese medicines combined with lopinavir/ritonavir to reduce the occurrence of clinical adverse reactions.