Purpose To evaluate the effectiveness of deep learning image reconstruction(DLIR)algorithms in reconstructing lumbar vertebrae images from abdominal CT scans,aiming to reduce radiation dose and eliminate the need for repeat lumbar CT examinations.Materials and Methods A retrospective collection was conducted from March to May 2024 in the First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine.Thirty-two patients who underwent both abdominal and lumbar CT scans in a supine head-first position were enrolled.The abdominal CT(DLIR group)utilized a tube voltage of 120 kVp and a current of 200 mA with high-intensity DLIR for lumbar reconstruction.The standard lumbar CT(lumbar group)used the same voltage with a tube current of 260 mA and was reconstructed using 60%weighted adaptive statistical iterative reconstruction.Objective assessments was used to measure the CT values,noise(standard deviation,SD value),signal-to-noise ratio and contrast-to-noise ratio(excluding adipose tissue)at the third lumbar vertebral pedicle level and the L2/L3 intervertebral disc level for muscle,adipose tissue,cancellous bone,intervertebral discs,dura mater and cortical bone.Subjective assessments employed a five-point scale to evaluate image contrast,noise and sharpness.Results The volume CT dose index in lumbar group and DLIR group were 15.25 mGy and 11.74 mGy,respectively.There was no statistical difference in CT values between the structures of both groups(all P>0.05).Compared with the lumbar group,the DLIR group showed significant reductions in SD values across the measured tissues by 31.09%,35.66%,13.48%,27.82%,24.93%and 15.09%(t=5.09-7.21,all P<0.05).The signal-to-noise ratio improved by 36.40%,52.31%,16.56%,34.13%,38.39%and 18.81%,and the contrast-to-noise ratio improved by 51.70%,51.32%,36.24%,34.47%and 53.56%(t=-9.58--4.23,all P<0.001).The DLIR group significantly outperformed the lumbar group in image contrast[4.45(4.00,5.00)points vs.4.75(4.00,5.00)points],image noise[4.06(4.00,4.00)points vs.4.39(4.00,5.00)points],and spatial resolution of fine structures[4.00(4.00,4.00)points vs.4.27(4.00,5.00)points](Z=-3.80,-4.38,-3.55,all P<0.001).Conclusion Using high-intensity DLIR for abdominal examinations can achieve high-quality lumbar CT images with a 25%reduction in radiation dose,enabling simultaneous abdominal and lumbar scanning in a single session.

Purpose To evaluate the effectiveness of deep learning image reconstruction(DLIR)algorithms in reconstructing lumbar vertebrae images from abdominal CT scans,aiming to reduce radiation dose and eliminate the need for repeat lumbar CT examinations.Materials and Methods A retrospective collection was conducted from March to May 2024 in the First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine.Thirty-two patients who underwent both abdominal and lumbar CT scans in a supine head-first position were enrolled.The abdominal CT(DLIR group)utilized a tube voltage of 120 kVp and a current of 200 mA with high-intensity DLIR for lumbar reconstruction.The standard lumbar CT(lumbar group)used the same voltage with a tube current of 260 mA and was reconstructed using 60%weighted adaptive statistical iterative reconstruction.Objective assessments was used to measure the CT values,noise(standard deviation,SD value),signal-to-noise ratio and contrast-to-noise ratio(excluding adipose tissue)at the third lumbar vertebral pedicle level and the L2/L3 intervertebral disc level for muscle,adipose tissue,cancellous bone,intervertebral discs,dura mater and cortical bone.Subjective assessments employed a five-point scale to evaluate image contrast,noise and sharpness.Results The volume CT dose index in lumbar group and DLIR group were 15.25 mGy and 11.74 mGy,respectively.There was no statistical difference in CT values between the structures of both groups(all P>0.05).Compared with the lumbar group,the DLIR group showed significant reductions in SD values across the measured tissues by 31.09%,35.66%,13.48%,27.82%,24.93%and 15.09%(t=5.09-7.21,all P<0.05).The signal-to-noise ratio improved by 36.40%,52.31%,16.56%,34.13%,38.39%and 18.81%,and the contrast-to-noise ratio improved by 51.70%,51.32%,36.24%,34.47%and 53.56%(t=-9.58--4.23,all P<0.001).The DLIR group significantly outperformed the lumbar group in image contrast[4.45(4.00,5.00)points vs.4.75(4.00,5.00)points],image noise[4.06(4.00,4.00)points vs.4.39(4.00,5.00)points],and spatial resolution of fine structures[4.00(4.00,4.00)points vs.4.27(4.00,5.00)points](Z=-3.80,-4.38,-3.55,all P<0.001).Conclusion Using high-intensity DLIR for abdominal examinations can achieve high-quality lumbar CT images with a 25%reduction in radiation dose,enabling simultaneous abdominal and lumbar scanning in a single session.

【Objective】 To compare the efficacy and safety between partial tubeless and standard percutaneous nephrolithotomy in the treatment of upper urinary tract calculi. 【Methods】 The clinical data of 802 patients with upper urinary tract calculi treated at our hospital during Jun.2018 and Dec.2021 were retrospectively analyzed. The patients were divided into the partial tubeless group and standard group, and 60 cases in either group were selected by a simple random method. Clinical data, complications and stone-free rate were compared between the two groups. 【Results】 All 120 patients completed the operation successfully. The postoperative hospital stay, pain score and postoperative recovery of the partial tubeless group were significantly superior to those of the standard group (P<0.05). There were no significant differences in operation time, blood loss, complications and stone clearance rate between the two groups (P>0.05). 【Conclusion】 In the treatment of upper urinary tract calculi, partial tubeless percutaneous nephrolithotomy can achieve satisfactory surgical results, and has obvious advantages in postoperative recovery and patients’ experience. It is worthy of clinical application.

OBJECTIVE: To investigate the effect of calmodulin (CaM) and its mutants on binding to voltage-gated Na channel isoleucine-glutamine domain (Na1.2 IQ). METHODS: The cDNA of Na1.2 IQ was constructed by PCR technique, CaM mutants CaM, CaM and CaM were constructed with Quickchange site-directed mutagenesis kit (QIAGEN). The binding of Na1.2 IQ to CaM and CaM mutants under calcium and calcium free conditions were detected by pull-down assay. RESULTS: Na1.2 IQ and CaM were bound to each other at different calcium concentrations, while GST alone did not bind to CaM. The binding affinity of CaM and Na1.2 IQ at [Ca]-free was greater than that at 100 nmol/L [Ca] ( < 0.05). In the absence of calcium, the binding amount of CaM wild-type to Na1.2 IQ was greater than that of its mutant, and the binding affinity of CaM to Na1.2 IQ was the weakest among the three mutants ( < 0.05). CONCLUSIONS The binding ability of CaM and CaM mutants to Na1.2 IQ is Ca-dependent. This study has revealed a new mechanism of Na1.2 regulated by CaM, which would be useful for the study of ion channel related diseases.
Calcium ; metabolism ; Calmodulin ; genetics ; metabolism ; Mutation ; NAV1.2 Voltage-Gated Sodium Channel ; metabolism ; Protein Binding ; genetics

ObjectiveTo explore the expression of N-cadherin and β-catenin mRNA in human brainstem and supratentorial gliomas. MethodsN-cadherin and β-catenin mRNA expression in 18 cases of brainstem gliomas and 18 cases of supratentorial gliomas tissues were detected with PT-PCR. Resultsβ-catenin mRNA expression was more in human brainstem gliomas than in supratentorial gliomas （t=2.255,P<0.05）, but was not significantly different of N-cadherin mRNA (P＞0.05). The expression of N-cadherin mRNA in human brainstem gliomas of grades Ⅰ～Ⅱ were less than those in human gliomas of grades Ⅲ～Ⅳ (t=2.711，P<0.05), but was not of β-catenin mRNA (P＞0.05). N-cadherin mRNA expression was positively correlated with the β-catenin mRNA expression in either brainstem gliomas or supratentorial gliomas （r=0.480，r=0.809 respectively, P<0.05）. ConclusionThe over expressions of N-cadherin and β-catenin may play an important role in the invasion and malignant progress of human brainstem gliomas.