Use of Thread-hanging Microwave Antenna to Assist in Precise Puncture of Lung Nodule and Influencing Factors

Objective To explore a precise method with a microwave antenna for puncture of pulmonary nodules and analyze phenomena that affect the puncture results. Methods Clinical data of 107 cases with solitary malignant pulmonary nodules were collected, and the mean length of pulmonary nodules was 13.6±0.6 mm in CT axial position. A thread-hanging method was used to assist the puncture of pulmonary nodules. The procedure was successful when the needle was not withdrawn and inserted into the central region of the nodule. The success rate and complications of the pulmonary procedure were recorded. The incidence of the following phenomena were also documented: needle coercing, needle slipping, needle tip pushing, pulmonary nodule prolapsing, radial nodule deformation, nodular masking, and radial movement distance of needle tip. Results In all of 107 cases evaluated, the antenna puncture was successful in 101 cases (94.4%) but failed in 6 cases (5.6%). Pneumothorax and pulmonary hemorrhage occurred in 23 (21.5%) and 19 cases (17.8%), respectively. The following phenomena occurred: needle coercing in 9 cases (8.4%), needle slipping in 6 cases (5.6%), needle tip pushing in 19 cases (17.8%), pulmonary nodule prolapsing in 15 cases (14%), radial nodule deformation in 14 cases (13.1%), and nodular masking in 5 cases (4.7%). The mean radial adjusting distance of needle tip was 0.7±0.4 cm. Conclusion The thread-hanging method can assist in the accurate puncture of microwave antenna for pulmonary nodules. We should focus and deal with phenomena that may occur and affect the result of puncture

Fusobacterium nucleatum upregulates MMP7 to promote metastasis-related characteristics of colorectal cancer cell via activating MAPK(JNK)-AP1 axis

Abstract Background Colorectal cancer (CRC) is the third most common malignant tumor. Fusobacterium nucleatum (F. nucleatum) is overabundant in CRC and associated with metastasis, but the role of F. nucleatum in CRC cell migration and metastasis has not been fully elucidated. Methods Differential gene analysis, protein−protein interaction, robust rank aggregation analysis, functional enrichment analysis, and gene set variation analysis were used to figure out the potential vital genes and biological functions affected by F. nucleatum infection. The 16S rDNA sequencing and q-PCR were used to detect the abundance of F. nucleatum in tissues and stools. Then, we assessed the effect of F. nucleatum on CRC cell migration by wound healing and transwell assays, and confirmed the role of Matrix metalloproteinase 7 (MMP7) induced by F. nucleatum in cell migration. Furthermore, we dissected the mechanisms involved in F. nucleatum induced MMP7 expression. We also investigated the MMP7 expression in clinical samples and its correlation with prognosis in CRC patients. Finally, we screened out potential small molecular drugs that targeted MMP7 using the HERB database and molecular docking. Results F. nucleatum infection altered the gene expression profile and affected immune response, inflammation, biosynthesis, metabolism, adhesion and motility related biological functions in CRC. F. nucleatum was enriched in CRC and promoted the migration of CRC cell by upregulating MMP7 in vitro. MMP7 expression induced by F. nucleatum infection was mediated by the MAPK(JNK)-AP1 axis. MMP7 was highly expressed in CRC and correlated with CMS4 and poor clinical prognosis. Small molecular drugs such as δ-tocotrienol, 3,4-benzopyrene, tea polyphenols, and gallic catechin served as potential targeted therapeutic drugs for F. nucleatum induced MMP7 in CRC. Conclusions Our study showed that F. nucleatum promoted metastasis-related characteristics of CRC cell by upregulating MMP7 via MAPK(JNK)-AP1 axis. F. nucleatum and MMP7 may serve as potential therapeutic targets for repressing CRC advance and metastasis

Minimally Invasive Concepts in Treating Synchronous Liver Metastases Rectal Cancer Patients: Report of Six Cases

Background Rectal cancer patients with synchronous liver metastases (SLM) is common in clinical practice. However, the application of conventional natural orifice specimen extraction surgery (NOSES) and NOSES with specimen extraction via stoma/hepatectomy incision in the special population is rarely explored. Case report Six SLM rectal cancer patients were treated with simultaneous surgical resection and the specimens were extracted via anal/stoma/hepatectomy incision. Respectively, intraoperative and postoperative data, anal function 3 months after surgery and long-term prognosis were reviewed. Results Intraoperative and postoperative data and anal function were reliable for the six cases. Only one patient died of brain and bone metastases at 84 months after surgery and the other five patients were alive at their last follow-up. Conclusions Simultaneous surgical resection with the concept of conventional NOSES and NOSES with specimen extraction via stoma/hepatectomy incision is safety for SLM rectal patients

Table3_Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning.docx

The precise alignment of tibiofemoral components in total knee arthroplasty is a crucial factor in enhancing the longevity and functionality of the knee. However, it is a substantial challenge to quickly predict the biomechanical response to malrotation of tibiofemoral components after total knee arthroplasty using musculoskeletal multibody dynamics models. The objective of the present study was to conduct a comparative analysis between a deep learning method and four conventional machine learning methods for predicting knee biomechanics with different tibial component malrotation during a walking gait after total knee arthroplasty. First, the knee contact forces and kinematics with different tibial component malrotation in the range of ±5° in the three directions of anterior/posterior slope, internal/external rotation, and varus/valgus rotation during a walking gait after total knee arthroplasty were calculated based on the developed musculoskeletal multibody dynamics model. Subsequently, deep learning and four conventional machine learning methods were developed using the above 343 sets of biomechanical data as the dataset. Finally, the results predicted by the deep learning method were compared to the results predicted by four conventional machine learning methods. The findings indicated that the deep learning method was more accurate than four conventional machine learning methods in predicting knee contact forces and kinematics with different tibial component malrotation during a walking gait after total knee arthroplasty. The deep learning method developed in this study enabled quickly determine the biomechanical response with different tibial component malrotation during a walking gait after total knee arthroplasty. The proposed method offered surgeons and surgical robots the ability to establish a calibration safety zone, which was essential for achieving precise alignment in both preoperative surgical planning and intraoperative robotic-assisted surgical navigation.</p

Table2_Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning.docx

The precise alignment of tibiofemoral components in total knee arthroplasty is a crucial factor in enhancing the longevity and functionality of the knee. However, it is a substantial challenge to quickly predict the biomechanical response to malrotation of tibiofemoral components after total knee arthroplasty using musculoskeletal multibody dynamics models. The objective of the present study was to conduct a comparative analysis between a deep learning method and four conventional machine learning methods for predicting knee biomechanics with different tibial component malrotation during a walking gait after total knee arthroplasty. First, the knee contact forces and kinematics with different tibial component malrotation in the range of ±5° in the three directions of anterior/posterior slope, internal/external rotation, and varus/valgus rotation during a walking gait after total knee arthroplasty were calculated based on the developed musculoskeletal multibody dynamics model. Subsequently, deep learning and four conventional machine learning methods were developed using the above 343 sets of biomechanical data as the dataset. Finally, the results predicted by the deep learning method were compared to the results predicted by four conventional machine learning methods. The findings indicated that the deep learning method was more accurate than four conventional machine learning methods in predicting knee contact forces and kinematics with different tibial component malrotation during a walking gait after total knee arthroplasty. The deep learning method developed in this study enabled quickly determine the biomechanical response with different tibial component malrotation during a walking gait after total knee arthroplasty. The proposed method offered surgeons and surgical robots the ability to establish a calibration safety zone, which was essential for achieving precise alignment in both preoperative surgical planning and intraoperative robotic-assisted surgical navigation.</p

Table1_Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning.docx

The precise alignment of tibiofemoral components in total knee arthroplasty is a crucial factor in enhancing the longevity and functionality of the knee. However, it is a substantial challenge to quickly predict the biomechanical response to malrotation of tibiofemoral components after total knee arthroplasty using musculoskeletal multibody dynamics models. The objective of the present study was to conduct a comparative analysis between a deep learning method and four conventional machine learning methods for predicting knee biomechanics with different tibial component malrotation during a walking gait after total knee arthroplasty. First, the knee contact forces and kinematics with different tibial component malrotation in the range of ±5° in the three directions of anterior/posterior slope, internal/external rotation, and varus/valgus rotation during a walking gait after total knee arthroplasty were calculated based on the developed musculoskeletal multibody dynamics model. Subsequently, deep learning and four conventional machine learning methods were developed using the above 343 sets of biomechanical data as the dataset. Finally, the results predicted by the deep learning method were compared to the results predicted by four conventional machine learning methods. The findings indicated that the deep learning method was more accurate than four conventional machine learning methods in predicting knee contact forces and kinematics with different tibial component malrotation during a walking gait after total knee arthroplasty. The deep learning method developed in this study enabled quickly determine the biomechanical response with different tibial component malrotation during a walking gait after total knee arthroplasty. The proposed method offered surgeons and surgical robots the ability to establish a calibration safety zone, which was essential for achieving precise alignment in both preoperative surgical planning and intraoperative robotic-assisted surgical navigation.</p

Structural and photoluminescent properties of ZnO films deposited by radio frequency reactive sputtering

Zinc oxide films with c-axis preferred orientation were deposited on silicon (100) substrates by radio frequency (RF) reactive sputtering. The properties of the samples were characterized by X-ray diffractometer, X-ray photoelectron spectroscopy and fluorescent-spectrophotometer. The effect of sputtering power and substrate temperature on the structural and photoluminescent (PL) properties of the ZnO films was investigated. The results indicated that when the sputtering power is 100 W and the substrate temperature is 300-400 degrees C, it is suitable for the growth of high c-axis orientation and small strain ZnO films. A violet peak at about 380 nm and a blue band at about 430 nm were observed in the room temperature photoluminescence spectra, and the origin of blue emission was investigated

The Incidence Characteristics of Second Primary Malignancy after Diagnosis of Primary Colon and Rectal Cancer: A Population Based Study

<div><p>Background</p><p>With the expanding population of colorectal cancer (CRC) survivors in the United States, one concerning issue is the risk of developing second primary malignancies (SPMs) for these CRC survivors. The present study attempts to identify the incidence characteristics of SPMs after diagnosis of first primary colon cancer (CC) and rectal cancer (RC).</p><p>Methods</p><p>189,890 CC and 83,802 RC cases were identified from Surveillance, Epidemiology and End Results Program (SEER) database. We performed rate analysis on incidence trend of SPMs in both CC and RC. Expected incidence rates were stratified by age, race and stage, calendar year of first CRC diagnosis and latency period since first CRC diagnosis. The standardized incidence ratios (SIRs), measure for estimating risk of SPMs, were calculated for CC and RC respectively.</p><p>Results</p><p>The trends of incidence of SPMs in both CC and RC were decreasing from 1992 to 2012. Both CC and RC survivors had higher risk of developing SPMs (SIRCC = 1.13; SIRRC = 1.05). For CC patients, the highest risks of SPM were cancers of small intestine (SIR = 4.03), colon (SIR = 1.87) and rectum (SIR = 1.80). For RC patients, the highest risks of SPMs were cancers of rectum (SIR = 2.88), small intestine (SIR = 2.16) and thyroid (SIR = 1.46). According to stratified analyses, we also identified incidence characteristics which were contributed to higher risk of developing SPMs, including the age between 20 and 40, American Indian/Alaska Native, localized stage, diagnosed at calendar year from 2002 to 2012 and the latency between 12 and 59 months.</p><p>Conclusions</p><p>Both CC and RC survivors remain at higher risk of developing SPMs. The identification of incidence characteristics of SPMs is extremely essential for continuous cancer surveillance among CRC survivors.</p></div