Fibroblast growth factor 3 contributes to neuropathic pain through Akt/mTOR signaling in mouse primary sensory neurons.

Neuropathic pain (NP), a severe chronic pain condition, remains a substantial clinical challenge due to its complex pathophysiology and limited effective treatments. An association between the members of the Fibroblast Growth Factors (FGFs), particularly Fgf3, and the development of NP has become evident. In this study, utilizing a mouse model of NP, we observed a time-dependent increase in Fgf3 expression at both mRNA and protein levels within the dorsal root ganglia (DRG). Functional studies revealed that blocking Fgf3 expression mitigated nerve injury induced nociceptive hypersensitivity, suggesting its pivotal role in pain modulation. Moreover, our findings elucidate that Fgf3 contributes to pain hypersensitivity through the activation of the Akt/mTOR signaling in injured DRG neurons. These results not only shed light on the involvement of Fgf3 in nerve injury-induced NP but also highlight its potential as a promising therapeutic target for pain management. This study thereby advances our understanding of the molecular mechanisms underlying NP and opens new avenues for the development of effective treatment strategies.

[The use of bronchial occlusion test in a preterm infant with severe bronchopulmonary dysplasia complicated by severe lobar emphysema]. / æ”¯æ°”ç®¡å°å µè¯•éªŒåœ¨æ—©äº§å„¿é‡åº¦æ”¯æ°”ç®¡è‚ºå‘è‚²ä¸è‰¯ä¼´ä¸¥é‡è‚ºå¶æ°”è‚¿ä¸­çš„åº”ç”¨1例.

In infants with severe bronchopulmonary dysplasia (sBPD), severe pulmonary lobar emphysema may occur as a complication, contributing to significant impairment in ventilation. Clinical management of these infants is extremely challenging and some may require lobectomy to improve ventilation. However, prior to the lobectomy, it is very difficult to assess whether the remaining lung parenchyma would be able to sustain adequate ventilation postoperatively. In addition, preoperative planning and perioperative management are also quite challenging in these patients. This paper reports the utility of selective bronchial occlusion in assessing the safety and efficacy of lobectomy in a case of sBPD complicated by severe right upper lobar emphysema. Since infants with sBPD already have poor lung development and significant lung injury, lobectomy should be viewed as a non-traditional therapy and be carried out with extreme caution. Selective bronchial occlusion test can be an effective tool in assessing the risks and benefits of lobectomy in cases with sBPD and lobar emphysema. However, given the technical difficulty, successful application of this technique requires close collaboration of an experienced interdisciplinary team.

An indirect estimation of x-ray spectrum via convolutional neural network and transmission measurement.

Objective.In this work, we aim to propose an accurate and robust spectrum estimation method by synergistically combining x-ray imaging physics with a convolutional neural network (CNN).Approach.The approach relies on transmission measurements, and the estimated spectrum is formulated as a convolutional summation of a few model spectra generated using Monte Carlo simulation. The difference between the actual and estimated projections is utilized as the loss function to train the network. We contrasted this approach with the weighted sums of model spectra approach previously proposed. Comprehensive studies were performed to demonstrate the robustness and accuracy of the proposed approach in various scenarios.Main results.The results show the desirable accuracy of the CNN-based method for spectrum estimation. The ME and NRMSE were -0.021 keV and 3.04% for 80 kVp, and 0.006 keV and 4.44% for 100 kVp, superior to the previous approach. The robustness test and experimental study also demonstrated superior performances. The CNN-based approach yielded remarkably consistent results in phantoms with various material combinations, and the CNN-based approach was robust concerning spectrum generators and calibration phantoms.Significance. We proposed a method for estimating the real spectrum by integrating a deep learning model with real imaging physics. The results demonstrated that this method was accurate and robust in estimating the spectrum, and it is potentially helpful for broad x-ray imaging tasks.

A qualitative study of improving megavoltage computed tomography image quality and maintaining dose accuracy using cycleGAN-based image synthesis.

BACKGROUND: Due to inconsistent positioning, tumor shrinking, and weight loss during fractionated treatment, the initial plan was no longer appropriate after a few fractional treatments, and the patient will require adaptive helical tomotherapy (HT) to overcome the issue. Patients are scanned with megavoltage computed tomography (MVCT) before each fractional treatment, which is utilized for patient setup and provides information for dose reconstruction. However, the low contrast and high noise of MVCT make it challenging to delineate treatment targets and organs at risk (OAR). PURPOSE: This study developed a deep-learning-based approach to generate high-quality synthetic kilovoltage computed tomography (skVCT) from MVCT and meet clinical dose requirements. METHODS: Data from 41 head and neck cancer patients were collected; 25 (2995 slices) were used for training, and 16 (1898 slices) for testing. A cycle generative adversarial network (cycleGAN) based on attention gate and residual blocks was used to generate MVCT-based skVCT. For the 16 patients, kVCT-based plans were transferred to skVCT images and electron density profile-corrected MVCT images to recalculate the dose. The quantitative indices and clinically relevant dosimetric metrics, including the mean absolute error (MAE), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), gamma passing rates, and dose-volume-histogram (DVH) parameters (Dmax , Dmean , Dmin ), were used to assess the skVCT images. RESULTS: The MAE, PSNR, and SSIM of MVCT were 109.6 ± 12.3 HU, 27.5 ± 1.1 dB, and 91.9% ± 1.7%, respectively, while those of skVCT were 60.6 ± 9.0 HU, 34.0 ± 1.9 dB, and 96.5% ± 1.1%. The image quality and contrast were enhanced, and the noise was reduced. The gamma passing rates improved from 98.31% ± 1.11% to 99.71% ± 0.20% (2 mm/2%) and 99.77% ± 0.18% to 99.98% ± 0.02% (3 mm/3%). No significant differences (p > 0.05) were observed in DVH parameters between kVCT and skVCT. CONCLUSION: With training on a small data set (2995 slices), the model successfully generated skVCT with improved image quality, and the dose calculation accuracy was similar to that of MVCT. MVCT-based skVCT can increase treatment accuracy and offer the possibility of implementing adaptive radiotherapy.

Commissioning dose computation model for proton source in pencil beam scanning therapy by convolution neural networks.

Objective. Proton source model commissioning (PSMC) is critical for ensuring accurate dose calculation in pencil beam scanning (PBS) proton therapy using Monte Carlo (MC) simulations. PSMC aims to match the calculated dose to the delivered dose. However, commissioning the 'nominal energy' and 'energy spread' parameters in PSMC can be challenging, as these parameters cannot be directly obtained from solving equations. To efficiently and accurately commission the nominal energy and energy spread in a proton source model, we developed a convolution neural network (CNN) named 'PSMC-Net.'Methods. The PSMC-Net was trained separately for 33 energies (E, 70-225 MeV with a step of 5 MeV plus 226.09 MeV). For eachE, a dataset was generated consisting of 150 source model parameters (15 nominal energies ∈ [E,E+ 1.5 MeV], ten spreads ∈ [0, 1]) and the corresponding 150 MC integrated depth doses (IDDs). Of these 150 data pairs, 130 were used for training the network, 10 for validation, and 10 for testing.Results. The source model, built by 33 measured IDDs and 33 PSMC-Nets (cost 0.01 s), was used to compute the MC IDDs. The gamma passing rate (GPRs, 1 mm/1%) between MC and measured IDDs was 99.91 ± 0.12%. However, when no commissioning was made, the corresponding GPR was reduced to 54.11 ± 22.36%, highlighting the tremendous significance of our CNN commissioning method. Furthermore, the MC doses of a spread-out Bragg peak and 20 patient PBS plans were also calculated, and average 3D GPRs (2 mm/2% with a 10% threshold) were 99.89% and 99.96 ± 0.06%, respectively.Significance. We proposed a nova commissioning method of the proton source model using CNNs, which made the PSMC process easy, efficient, and accurate.

Evaluation of Dose Calculation Based on Cone-Beam CT Using Different Measuring Correction Methods for Head and Neck Cancer Patients.

Purpose: To investigate and compare 2 cone-beam computed tomography (CBCT) correction methods for CBCT-based dose calculation. Materials and Methods: Routine CBCT image sets of 12 head and neck cancer patients who received volumetric modulated arc therapy (VMAT) treatment were retrospectively analyzed. The CBCT images obtained using an on-board imager (OBI) at the first treatment fraction were firstly deformable registered and padded with the kVCT images to provide enough anatomical information about the tissues for dose calculation. Then, 2 CBCT correction methods were developed and applied to correct CBCT Hounsfield unit (HU) values. One method (HD method) is based on protocol-specific CBCT HU to physical density (HD) curve, and the other method (HM method) is based on histogram matching (HM) of HU value. The corrected CBCT images (CBCTHD and CBCTHM for HD and HM methods) were imported into the original planning system for dose calculation based on the HD curve of kVCT (the planning CT). The dose computation result was analyzed and discussed to compare these 2 CBCT-correction methods. Results: Dosimetric parameters, such as the Dmean, Dmax and D5% of the target volume in CBCT plan doses, were higher than those in the kVCT plan doses; however, the deviations were less than 2%. The D2%, in parallel organs such as the parotid glands, the deviations from the CBCTHM plan dose were less than those of the CBCTHD plan dose. The differences were statistically significant (P < .05). Meanwhile, the V30 value based on the HM method was better than that based on the HD method in the oral cavity region (P = .016). In addition, we also compared the γ passing rates of kVCT plan doses with the 2 CBCT plan doses, and negligible differences were found. Conclusion: The HM method was more suitable for head and neck cancer patients than the HD one. Furthermore, with the CBCTHM-based method, the dose calculation result better matches the kVCT-based dose calculation.

Microstructure and mechanical properties of the Mg-Gd-Zn alloy prepared by sintering of rapidly-solidified ribbons.

Mg-15Gd-1Zn (wt.%) alloy was successfully prepared via the spark plasma sintering rapid solidification ribbons process. Microstructure investigation showed that the sintered alloys consisted of fine grains, the ß1 phase, and long-perioded stacking ordered phase (LPSO). The sintering temperature and time have a significant effect on the microstructural evolution. A lower sintering temperature (430 °C ) was beneficial for obtaining finer grain sizes with less than 5 µm and a higher content of ß1 phase with a content of 3-15 vol.% and a size-distribution of (10-600) nm. A higher temperature for a longer sintering time, 450-470 °C and 5-10 min, helpfully promoted precipitating the abundantly lamellar LPSO phase, and its content was 2-10 vol.% for LPSO phase with the width of (10-100) nm. The mechanical properties indicated that the fine grain size and supersaturated solid solution contributed at least 50% of the yield stress, and the residual contribution was related to the ß1 phase and LPSO phase strengthening, which were based on their contents and the sizes.

Accuracy Improvement Method Based on Characteristic Database Classification for IMRT Dose Prediction in Cervical Cancer: Scientifically Training Data Selection.

Purpose: Consistent training and testing datasets can lead to good performance for deep learning (DL) models. However, a large high-quality training dataset for unusual clinical scenarios is usually not easy to collect. The work aims to find optimal training data collection strategies for DL-based dose prediction models. Materials and Methods: A total of 325 clinically approved cervical IMRT plans were utilized. We designed comparison experiments to investigate the impact of (1) beam angles, (2) the number of beams, and (3) patient position for DL dose prediction models. In addition, a novel geometry-based beam mask generation method was proposed to provide beam setting information in the model training process. What is more, we proposed a new training strategy named "full-database pre-trained strategy". Results: The model trained with a homogeneous dataset with the same beam settings achieved the best performance [mean prediction errors of planning target volume (PTV), bladder, and rectum: 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%] compared with that trained with large mixed beam setting plans (mean errors of PTV, bladder, and rectum: 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4%). A homogeneous dataset is more accessible to train an accurate dose prediction model (mean errors of PTV, bladder and rectum: 2.2 ± 0.15%, 5 ± 2.1%, and 3.23 ± 1.53%) than a non-homogeneous one (mean errors of PTV, bladder and rectum: 2.55 ± 0.12%, 6.33 ± 2.46%, and 4.76 ± 2.91%) without other processing approaches. The added beam mask can constantly improve the model performance, especially for datasets with different beam settings (mean errors of PTV, bladder, and rectum improved from 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4% to 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%). Conclusions: A consistent dataset is recommended to form a patient-specific IMRT dose prediction model. When a consistent dataset is not accessible to collect, a large dataset with different beam angles and a training model with beam information can also get a relatively good model. The full-database pre-trained strategies can rapidly form an accuracy model from a pre-trained model. The proposed beam mask can effectively improve the model performance. Our study may be helpful for further dose prediction studies in terms of training strategies or database establishment.

[Study on Automatic Plan Method for Radiotherapy after Breast-conserving Surgery Based on TiGRT System].

To study an automatic plan(AP) method for radiotherapy after breast-conserving surgery based on TiGRT system and and compare with manual plan (MP). The dosimetry parameters of 10 patients and the evaluation of scoring table were analyzed, it was found that the targets dose of AP were better than that of MP, but there was no statistical difference except for CI, The V5, V20 and V30 of affected lungs and whole lungs in AP were lower than all that in MP, the Dmean of hearts was slightly higher than that of MP, but the difference was not statistically significant, the MU of AP was increase by 16.1% compared with MP, the score of AP evaluation was increase by 6.1% compared with MP. So the AP could be programmed and automated while ensuring the quality of the plan, and can be used to design the plans for radiotherapy after breast-conserving surgery.

Long non-coding RNA rhabdomyosarcoma 2-associated transcript contributes to neuropathic pain by recruiting HuR to stabilize DNA methyltransferase 3 alpha mRNA expression in dorsal root ganglion neuron.

Introduction: Long non-coding RNAs (lncRNAs) act as key regulators in multiple human diseases. In particular, the dysfunction of lncRNAs in dorsal root ganglion (DRG) contributes to the pathogenesis of neuropathic pain (NP). Nevertheless, the role and mechanism of most lncRNAs in NP remain unclear. Methods: Two classic chronic NP models, including L4 spinal nerve ligation (SNL) model and chronic constriction injury (CCI) of the sciatic nerve, were performed. Mechanical allodynia and heat hyperalgesia were used to evaluate neuropathic pain. DRG microinjection was used to deliver agents into DRG. qRT-PCR, immunofluorescence, immunoprecipitation, western blotting, siRNA transfection, AAV transduction were performed to investigate the phenotypes and molecular basis. Results: Here, we discovered that Rmst as a lncRNA was specifically expressed in Atf3 + injured DRG neurons and significantly upregulated following peripheral nerve damage. Rmst overexpression by direct DRG injection of AAV5-Rmst causes neuropathic symptoms in the absence of nerve damage. Conversely, blocking Rmst expression in injured DRGs alleviated nerve injury-induced pain hypersensitivities and downregulated Dnmt3a expression. Furthermore, we found peripheral nerve damage induced Rmst increase could interact with RNA-binding protein HuR to stabilize the Dnmt3a mRNA. Conclusion: Our findings reveal a crucial role of Rmst in damaged DRG neurons under NP condition and provide a novel target for drug development against NP.

Artificial intelligence in image-guided radiotherapy: a review of treatment target localization.

Modern conformal beam delivery techniques require image-guidance to ensure the prescribed dose to be delivered as planned. Recent advances in artificial intelligence (AI) have greatly augmented our ability to accurately localize the treatment target while sparing the normal tissues. In this paper, we review the applications of AI-based algorithms in image-guided radiotherapy (IGRT), and discuss the indications of these applications to the future of clinical practice of radiotherapy. The benefits, limitations and some important trends in research and development of the AI-based IGRT techniques are also discussed. AI-based IGRT techniques have the potential to monitor tumor motion, reduce treatment uncertainty and improve treatment precision. Particularly, these techniques also allow more healthy tissue to be spared while keeping tumor coverage the same or even better.

The Antagonism of Corticotropin-Releasing Factor Receptor-1 in Brain Suppress Stress-Induced Propofol Self-Administration in Rats.

Propofol addiction has been detected in humans and rats, which may be facilitated by stress. Corticotropin-releasing factor acts through the corticotropin-releasing factor (CRF) receptor-1 (CRF1R) and CRF2 receptor-2 (CRF2R) and is a crucial candidate target for the interaction between stress and drug abuse, but its role on propofol addiction remains unknown. Tail clip stressful stimulation was performed in rats to test the stress on the establishment of the propofol self-administration behavioral model. Thereafter, the rats were pretreated before the testing session at the bilateral lateral ventricle with one of the doses of antalarmin (CRF1R antagonist, 100-500 ng/site), antisauvagine 30 (CRF2R antagonist, 100-500 ng/site), and RU486 (glucocorticoid receptor antagonist, 100-500 ng/site) or vehicle. The dopamine D1 receptor (D1R) in the nucleus accumbens (NAc) was detected to explore the underlying molecular mechanism. The sucrose self-administration establishment and maintenance, and locomotor activities were also examined to determine the specificity. We found that the establishment of propofol self-administration was promoted in the tail clip treated group (the stress group), which was inhibited by antalarmin at the dose of 100-500 ng/site but was not by antisauvagine 30 or RU486. Accordingly, the expression of D1R in the NAc was attenuated by antalarmin, dose-dependently. Moreover, pretreatments fail to change sucrose self-administration behavior or locomotor activities. This study supports the role of CRF1R in the brain in mediating the central reward processing through D1R in the NAc and provided a possibility that CRF1R antagonist may be a new therapeutic approach for the treatment of propofol addiction.

Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy.

PURPOSE: This study focused on predicting 3D dose distribution at high precision and generated the prediction methods for nasopharyngeal carcinoma patients (NPC) treated with Tomotherapy based on the patient-specific gap between organs at risk (OARs) and planning target volumes (PTVs). METHODS: A convolutional neural network (CNN) is trained using the CT and contour masks as the input and dose distributions as output. The CNN is based on the "3D Dense-U-Net", which combines the U-Net and the Dense-Net. To evaluate the model, we retrospectively used 124 NPC patients treated with Tomotherapy, in which 96 and 28 patients were randomly split and used for model training and test, respectively. We performed comparison studies using different training matrix shapes and dimensions for the CNN models, i.e., 128 ×128 ×48 (for Model I), 128 ×128 ×16 (for Model II), and 2D Dense U-Net (for Model III). The performance of these models was quantitatively evaluated using clinically relevant metrics and statistical analysis. RESULTS: We found a more considerable height of the training patch size yields a better model outcome. The study calculated the corresponding errors by comparing the predicted dose with the ground truth. The mean deviations from the mean and maximum doses of PTVs and OARs were 2.42 and 2.93%. Error for the maximum dose of right optic nerves in Model I was 4.87 ± 6.88%, compared with 7.9 ± 6.8% in Model II (p=0.08) and 13.85 ± 10.97% in Model III (p<0.01); the Model I performed the best. The gamma passing rates of PTV60 for 3%/3 mm criteria was 83.6 ± 5.2% in Model I, compared with 75.9 ± 5.5% in Model II (p<0.001) and 77.2 ± 7.3% in Model III (p<0.01); the Model I also gave the best outcome. The prediction error of D95 for PTV60 was 0.64 ± 0.68% in Model I, compared with 2.04 ± 1.38% in Model II (p<0.01) and 1.05 ± 0.96% in Model III (p=0.01); the Model I was also the best one. CONCLUSIONS: It is significant to train the dose prediction model by exploiting deep-learning techniques with various clinical logic concepts. Increasing the height (Y direction) of training patch size can improve the dose prediction accuracy of tiny OARs and the whole body. Our dose prediction network model provides a clinically acceptable result and a training strategy for a dose prediction model. It should be helpful to build automatic Tomotherapy planning.

A systematic study of independently-tuned room-specific PBS beam model in a beam-matched multiroom proton therapy system.

BACKGROUND: In the existing application of beam-matched multiroom proton therapy system, the model based on the commissioning data from the leading treatment room was used as the shared model. The purpose of this study is to investigate the ability of independently-tuned room-specific beam models of beam-matched gantries to reproduce the agreement between gantries' performance when considering the errors introduced by the modeling process. METHODS: Raw measurements of two gantries' dosimetric characteristics were quantitatively compared to ensure their agreement after initially beam-matched. Two gantries' beam model parameters, as well as the model-based computed dosimetric characteristics, were analyzed to study the introduced errors and gantries' post-modeling consistency. We forced two gantries to share the same beam model. The model-sharing patient-specific quality assurance (QA) tasks were retrospectively performed with 36 cancer patients to study the clinical impact of beam model discrepancies. RESULTS: Intra-gantry comparisons demonstrate that the modeling process introduced the errors to a certain extent indeed, which made the model-based reproduced results deviate from the raw measurements. Among them, the deviation introduced to the IDD curves was generally larger than that to the beam spots during modeling. Cross-gantry comparisons show that, from the beam model perspective, the introduced deviations deteriorated the high agreement of the dosimetric characteristics originally shown between two beam-matched gantries, but the cross-gantry discrepancy was still within the clinically acceptable tolerance. In model-sharing patient-specific QA, for the particular gantry, the beam model usage for intensity-modulated proton therapy (IMPT) QA plan generation had no significant effect on the actual delivering performance. All reached a high level of 95.0% passing rate with a 3 mm/3% criterion. CONCLUSIONS: It was preliminary recognized that among beam-matched gantries, the independently-tuned room-specific beam model from any gantry is reasonable to be chosen as the shared beam model without affecting the treatment efficacy.

[Advances in magnetic resonance imaging guided radiation therapy].

Image-guided radiation therapy using magnetic resonance imaging (MRI) is a new technology that has been widely studied and developed in recent years. The technology combines the advantages of MRI imaging, and can offer online real-time tracking of tumor and adjacent organs at risk, as well as real-time optimization of radiotherapy plan. In order to provide a comprehensive understanding of this technology, and to grasp the international development and trends in this field, this paper reviews and summarizes related researches, so as to make the researchers and clinical personnel in this field to understand recent status of this technology, and carry out corresponding researches. This paper summarizes the advantages of MRI and the research progress of MRI linear accelerator (MR-Linac), online guidance, adaptive optimization, and dosimetry-related research. Possible development direction of these technologies in the future is also discussed. It is expected that this review can provide a certain reference value for clinician and related researchers to understand the research progress in the field.

[A New Generation of Radiotherapy Technology-Flash Radiotherapy].

Flash radiotherapy is a kind of radiotherapy method using ultra-high dose rate radiation. Compared with the traditional dose rate radiotherapy, it has unique radiobiological advantages. In this paper, the principle of flash radiotherapy, the process and results of biological experiments are summarized. At the same time, the advantages and challenges of flash radiotherapy are analyzed, and the future clinical application is prospected.

Fretting Wear Behavior and Damage Mechanisms of Inconel X-750 Alloy in Dry Contact Condition.

Frictional and fretting wear behaviors of Inconel X-750 alloy against GCr15 steel ball were investigated in dry contact condition with ∼60% air humidity. Fretting tests were run at the high frequency tribosystem SRV 4 in room temperature and ball-on-flat contact configuration were adopted with the relative oscillatory motion of small displacement amplitude (40 µm). Sliding regimes, wear volumes, frictional properties, and material damage mechanisms were studied with regard to different normal loading and test durations. After the tests, the worn surface morphologies were analyzed by three-dimensional (3D) optical surface profiler, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) to distinguish fretting running conditions and material responses for different test cases. It was found that the material removals by abrasive and adhesive wear, debris formation and oxidization, and wear delamination were the main damage mechanisms under the lower normal load where the full slide or gross slip regime (GSR) was dominant between the contact surfaces. On the other hand, fretting regime was found to be a stick-slip or a partial slip at greater loads where damage mechanisms were correlated with deformed asperities, fatigue cracks, and thick layer removal due to highly concentrated cyclic stresses. Time dependence was crucial during GSR where the wear volume increased substantially; however, the wear volumes and scars sizes were consistent over time because of stick-slip effects under the higher normal load.

Biomechanical Analysis of Two-level Cervical Disc Replacement With a Stand-alone U-shaped Disc Implant.

STUDY DESIGN: Biomechanical study using a three-dimensional nonlinear finite element model. OBJECTIVE: To analyze biomechanical changes with three prostheses based on two-level arthroplasty and to verify the biomechanical efficiency of dynamic cervical implants (DCIs) with a stand-alone U-shaped structure. SUMMARY OF BACKGROUND DATA: Few studies have compared biomechanical behavior of various prostheses as they relate with clinical results after two-level total disc replacement. METHODS: Three arthroplasty devices Mobi-C, porous coated motion (PCM), and DCI were inserted at the C4-C6 disc space and analyzed. Displacement loading was applied to the center of the endplate at the C3 level to simulate flexion and extension motions. RESULTS: The motion distributions in extension with DCI and in flexion with DCI and Mobi-C were relatively close to that in the intact model. Mobi-C and PCM obviously increased the combined extension range of motion at the index levels, but both resulted in about 45% decrease in extension moment. DCI showed a trend in strain energy similar to that of healthy discs. PCM exhibited a facet joint stress distribution almost similar to that of the intact model. DCI did not generate significant overloading at cartilage between the index levels, whereas the maximum facet joint stress increased with Mobi-C was about 39%. The maximum stress on a ultrahigh molecular-weight-polyethylene core was above the yield stress (42.43 MPa for Mobi-C and 30.94 MPa for PCM). CONCLUSION: Each prosthesis shows its biomechanical advantages and disadvantages. However, DCI has the capacity to preserve motion and store energy under external loading, similar to the behavior of normal discs. Compared with Mobi-C, both DCI and PCM showed a lower stress at cartilage between index levels, which may avoid facet joint degeneration to some extent. Such a well-controlled arthroplasty device with a stand-alone structure may be a potential candidate and needs to be investigated in future studies. LEVEL OF EVIDENCE: 5.

Median Effective Dose of Intranasal Dexmedetomidine for Rescue Sedation in Pediatric Patients Undergoing Magnetic Resonance Imaging.

BACKGROUND: The median effective dose (ED50) of intranasal dexmedetomidine after failed chloral hydrate sedation has not been described for children. This study aims to determine the ED50 of intranasal dexmedetomidine for rescue sedation in children aged 1 to 36 months, who were inadequately sedated by chloral hydrate administration during magnetic resonance imaging (MRI). METHODS: This study was performed on 120 children, who were 1 to 36 months old and underwent MRI scanning. Intranasal dexmedetomidine was administered as a rescue sedative to children not adequately sedated after the initial oral dose of chloral hydrate (50 mg/kg). Children were stratified into four age groups. ED50 values were estimated from the up-and-down method of Dixon and Massey and probit regression. Other variables included induction time, time to wake up, vital signs, oxygen saturation, MRI scanning time, and recovery characteristics. RESULTS: ED50 of intranasal dexmedetomidine for rescue sedation was 0.4 µg/kg (95% CI, 0.34 to 0.50) in children aged 1 to 6 months, 0.5 µg/kg (95% CI, 0.48 to 0.56) in children aged 7 to 12 months, 0.9 µg/kg (95% CI, 0.83 to 0.89) in children aged 13 to 24 months, and 1.0 µg/kg (95% CI, 0.94 to 1.07) in children aged 25 to 36 months. There were no significant differences in sedation induction time or time to wake up between the different age groups. Additionally, no significant adverse hemodynamic or hypoxemic effects were noted. CONCLUSIONS: The authors determined the ED50 for rescue sedation using intranasal dexmedetomidine after failed chloral hydrate sedation in children. It was found that ED50 increases with advancing age during the first 3 yr of life.

Glucocorticoid receptor mediated the propofol self-administration by dopamine D1 receptor in nucleus accumbens.

Propofol, a widely used anesthetic, can cause addictive behaviors in both human and experimental animals. In the present study, we examined the involvement of glucocorticoid receptor (GR) signaling in the molecular process by which propofol may cause addiction. The propofol self-administration model was established by a fixed ratio 1 (FR1) schedule of reinforced dosing over successive 14days in rats. On day 15, the rats were treated with dexamethasone, a GR agonist (10-100µg/kg), or RU486, a GR antagonist (10-100µg/kg) at 1h prior to the last training. The animal behaviors were recorded automatically by the computer. The expression of dopamine D1 receptor in the nucleus accumbens (NAc) was examined by Western blot and the concentrations of plasma corticosterone were measured by enzyme-linked immunosorbent assay (ELISA). To further examine the specificity of GR in the process, mineralocorticoid receptor (MR) antagonist, spironolactone, and dexamethasone plus MR agonist, aldosterone, were also tested. Administration of dexamethasone (100µg/kg) or RU486 (⩾10mg/kg) significantly attenuated the rate of propofol maintained active nose-poke responses and infusions, which were accompanied by reductions in both plasma corticosterone level and the expression of D1 receptor in the NAc. Neither spironolactone alone nor dexamethasone combined with aldosterone affected the propofol-maintaining self-administrative behavior, indicating GR, but not MR, modulates the propofol reward in rats. In addition, neither the food-maintaining sucrose responses under FR1 schedule nor the locomotor activity was affected by any doses of dexamethasone or RU486 tested. These findings provide evidence that GR signaling may play an important role in propofol reward.