Effect of Waiting Time from Pathological Diagnosis to Definitive Concurrent Chemoradiation for Cervical Cancer on Overall Survival.

PURPOSE: This study aimed to evaluate the effect of waiting time, from diagnosis to initiation of definitive concurrent chemoradiation (CCRT), on overall survival in cervical cancer patients. MATERIALS AND METHODS: Patients with cervical cancer who were treated with definitive CCRT between 2000 and 2017 were retrospectively reviewed. Time from initial pathological diagnosis to definitive CCRT was analyzed both as a continuous variable (per day) and as a categorical variable in two groups (group 1 ≤ median, group 2 > median). Patients with a waiting time of more than 60 days were excluded. RESULTS: The median waiting time was 14 days (0-60). There were differences between group 1 and group 2 in age and chemotherapy regimens. However, no significant difference was found in the International Federation of Gynecology and Obstetrics stage, cell type, or the number of cycles of chemotherapy received during CCRT. A longer waiting time was associated with poorer overall survival on the Kaplan-Meier curve (group 1 vs. group 2, p=0.042). On multivariate analysis, intervals as either a continuous variable (hazard ratio [HR], 1.023; 95% confidence interval [CI], 1.006 to 1.040; p=0.007) or a categorical variable (HR, 1.513; 95% CI, 1.073 to 2.134; p=0.018), FIGO stage, cell type, and the number of cycles of chemotherapy received during CCRT were significant independent prognostic factors for overall survival. CONCLUSION: A shorter waiting time from pathological diagnosis to definitive CCRT showed benefit on overall survival. Our findings suggest that an effort to minimize waiting times should be recommended in cervical cancer patients who are candidates for CCRT.

Accurate Segmentation of Nuclear Regions with Multi-Organ Histopathology Images Using Artificial Intelligence for Cancer Diagnosis in Personalized Medicine.

Accurate nuclear segmentation in histopathology images plays a key role in digital pathology. It is considered a prerequisite for the determination of cell phenotype, nuclear morphometrics, cell classification, and the grading and prognosis of cancer. However, it is a very challenging task because of the different types of nuclei, large intraclass variations, and diverse cell morphologies. Consequently, the manual inspection of such images under high-resolution microscopes is tedious and time-consuming. Alternatively, artificial intelligence (AI)-based automated techniques, which are fast and robust, and require less human effort, can be used. Recently, several AI-based nuclear segmentation techniques have been proposed. They have shown a significant performance improvement for this task, but there is room for further improvement. Thus, we propose an AI-based nuclear segmentation technique in which we adopt a new nuclear segmentation network empowered by residual skip connections to address this issue. Experiments were performed on two publicly available datasets: (1) The Cancer Genome Atlas (TCGA), and (2) Triple-Negative Breast Cancer (TNBC). The results show that our proposed technique achieves an aggregated Jaccard index (AJI) of 0.6794, Dice coefficient of 0.8084, and F1-measure of 0.8547 on TCGA dataset, and an AJI of 0.7332, Dice coefficient of 0.8441, precision of 0.8352, recall of 0.8306, and F1-measure of 0.8329 on the TNBC dataset. These values are higher than those of the state-of-the-art methods.

Differences in mandibular condyle and glenoid fossa morphology in relation to vertical and sagittal skeletal patterns: A cone-beam computed tomography study.

OBJECTIVE: This study aimed to evaluate the following null hypothesis: there are no differences in the morphology of the temporomandibular joint (TMJ) structures in relation to vertical and sagittal cephalometric patterns. METHODS: This retrospective study was performed with 131 participants showing no TMJ symptoms. The participants were divided into Class I, II, and III groups on the basis of their sagittal cephalometric relationships and into hyperdivergent, normodivergent, and hypodivergent groups on the basis of their vertical cephalometric relationships. The following measurements were performed using cone-beam computed tomography images and compared among the groups: condylar volume, condylar size (width, length, and height), fossa size (length and height), and condyle-to-fossa joint spaces at the anterior, superior, and posterior condylar poles. RESULTS: The null hypothesis was rejected. The Class III group showed larger values for condylar width, condylar height, and fossa height than the Class II group (p < 0.05). Condylar volume and superior joint space in the hyperdivergent group were significantly smaller than those in the other two vertical groups (p < 0.001), whereas fossa length and height were significantly larger in the hyperdivergent group than in the other groups (p < 0.01). The hypodivergent group showed a greater condylar width than the hyperdivergent group (p < 0.01). The sagittal and vertical cephalometric patterns showed statistically significant interactions for fossa length and height. CONCLUSIONS: TMJ morphology differed across diverse skeletal cephalometric patterns. The fossa length and height were affected by the interactions of the vertical and sagittal skeletal patterns.

Multi-Path and Group-Loss-Based Network for Speech Emotion Recognition in Multi-Domain Datasets.

Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IEMOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization.

Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network.

The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases-Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method.

An innovative strategy for standardized, structured, and interoperable results in ophthalmic examinations.

BACKGROUND: Although ophthalmic devices have made remarkable progress and are widely used, most lack standardization of both image review and results reporting systems, making interoperability unachievable. We developed and validated new software for extracting, transforming, and storing information from report images produced by ophthalmic examination devices to generate standardized, structured, and interoperable information to assist ophthalmologists in eye clinics. RESULTS: We selected report images derived from optical coherence tomography (OCT). The new software consists of three parts: (1) The Area Explorer, which determines whether the designated area in the configuration file contains numeric values or tomographic images; (2) The Value Reader, which converts images to text according to ophthalmic measurements; and (3) The Finding Classifier, which classifies pathologic findings from tomographic images included in the report. After assessment of Value Reader accuracy by human experts, all report images were converted and stored in a database. We applied the Value Reader, which achieved 99.67% accuracy, to a total of 433,175 OCT report images acquired in a single tertiary hospital from 07/04/2006 to 08/31/2019. The Finding Classifier provided pathologic findings (e.g., macular edema and subretinal fluid) and disease activity. Patient longitudinal data could be easily reviewed to document changes in measurements over time. The final results were loaded into a common data model (CDM), and the cropped tomographic images were loaded into the Picture Archive Communication System. CONCLUSIONS: The newly developed software extracts valuable information from OCT images and may be extended to other types of report image files produced by medical devices. Furthermore, powerful databases such as the CDM may be implemented or augmented by adding the information captured through our program.

Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network.

Although face-based biometric recognition systems have been widely used in many applications, this type of recognition method is still vulnerable to presentation attacks, which use fake samples to deceive the recognition system. To overcome this problem, presentation attack detection (PAD) methods for face recognition systems (face-PAD), which aim to classify real and presentation attack face images before performing a recognition task, have been developed. However, the performance of PAD systems is limited and biased due to the lack of presentation attack images for training PAD systems. In this paper, we propose a method for artificially generating presentation attack face images by learning the characteristics of real and presentation attack images using a few captured images. As a result, our proposed method helps save time in collecting presentation attack samples for training PAD systems and possibly enhance the performance of PAD systems. Our study is the first attempt to generate PA face images for PAD system based on CycleGAN network, a deep-learning-based framework for image generation. In addition, we propose a new measurement method to evaluate the quality of generated PA images based on a face-PAD system. Through experiments with two public datasets (CASIA and Replay-mobile), we show that the generated face images can capture the characteristics of presentation attack images, making them usable as captured presentation attack samples for PAD system training.

Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images.

Retinal fundus images are used to detect organ damage from vascular diseases (e.g. diabetes mellitus and hypertension) and screen ocular diseases. We aimed to assess convolutional neural network (CNN) models that predict age and sex from retinal fundus images in normal participants and in participants with underlying systemic vascular-altered status. In addition, we also tried to investigate clues regarding differences between normal ageing and vascular pathologic changes using the CNN models. In this study, we developed CNN age and sex prediction models using 219,302 fundus images from normal participants without hypertension, diabetes mellitus (DM), and any smoking history. The trained models were assessed in four test-sets with 24,366 images from normal participants, 40,659 images from hypertension participants, 14,189 images from DM participants, and 113,510 images from smokers. The CNN model accurately predicted age in normal participants; the correlation between predicted age and chronologic age was R2 = 0.92, and the mean absolute error (MAE) was 3.06 years. MAEs in test-sets with hypertension (3.46 years), DM (3.55 years), and smoking (2.65 years) were similar to that of normal participants; however, R2 values were relatively low (hypertension, R2 = 0.74; DM, R2 = 0.75; smoking, R2 = 0.86). In subgroups with participants over 60 years, the MAEs increased to above 4.0 years and the accuracies declined for all test-sets. Fundus-predicted sex demonstrated acceptable accuracy (area under curve > 0.96) in all test-sets. Retinal fundus images from participants with underlying vascular-altered conditions (hypertension, DM, or smoking) indicated similar MAEs and low coefficients of determination (R2) between the predicted age and chronologic age, thus suggesting that the ageing process and pathologic vascular changes exhibit different features. Our models demonstrate the most improved performance yet and provided clues to the relationship and difference between ageing and pathologic changes from underlying systemic vascular conditions. In the process of fundus change, systemic vascular diseases are thought to have a different effect from ageing. Research in context. Evidence before this study. The human retina and optic disc continuously change with ageing, and they share physiologic or pathologic characteristics with brain and systemic vascular status. As retinal fundus images provide high-resolution in-vivo images of retinal vessels and parenchyma without any invasive procedure, it has been used to screen ocular diseases and has attracted significant attention as a predictive biomarker for cerebral and systemic vascular diseases. Recently, deep neural networks have revolutionised the field of medical image analysis including retinal fundus images and shown reliable results in predicting age, sex, and presence of cardiovascular diseases. Added value of this study. This is the first study demonstrating how a convolutional neural network (CNN) trained using retinal fundus images from normal participants measures the age of participants with underlying vascular conditions such as hypertension, diabetes mellitus (DM), or history of smoking using a large database, SBRIA, which contains 412,026 retinal fundus images from 155,449 participants. Our results indicated that the model accurately predicted age in normal participants, while correlations (coefficient of determination, R2) in test-sets with hypertension, DM, and smoking were relatively low. Additionally, a subgroup analysis indicated that mean absolute errors (MAEs) increased and accuracies declined significantly in subgroups with participants over 60 years of age in both normal participants and participants with vascular-altered conditions. These results suggest that pathologic retinal vascular changes occurring in systemic vascular diseases are different form the changes in spontaneous ageing process, and the ageing process observed in retinal fundus images may saturate at age about 60 years. Implications of all available evidence. Based on this study and previous reports, the CNN could accurately and reliably predict age and sex using retinal fundus images. The fact that retinal changes caused by ageing and systemic vascular diseases occur differently motivates one to understand the retina deeper. Deep learning-based fundus image reading may be a more useful and beneficial tool for screening and diagnosing systemic and ocular diseases after further development.

Scale-space approximated convolutional neural networks for retinal vessel segmentation.

BACKGROUND AND OBJECTIVE: Retinal fundus images are widely used to diagnose retinal diseases and can potentially be used for early diagnosis and prevention of chronic vascular diseases and diabetes. While various automatic retinal vessel segmentation methods using deep learning have been proposed, they are mostly based on common CNN structures developed for other tasks such as classification. METHODS: We present a novel and simple multi-scale convolutional neural network (CNN) structure for retinal vessel segmentation. We first provide a theoretical analysis of existing multi-scale structures based on signal processing. In previous structures, multi-scale representations are achieved through downsampling by subsampling and decimation. By incorporating scale-space theory, we propose a simple yet effective multi-scale structure for CNNs using upsampling, which we term scale-space approximated CNN (SSANet). Based on further analysis of the effects of the SSA structure within a CNN, we also incorporate residual blocks, resulting in a multi-scale CNN that outperforms current state-of-the-art methods. RESULTS: Quantitative evaluations are presented as the area-under-curve (AUC) of the receiver operating characteristic (ROC) curve and the precision-recall curve, as well as accuracy, for four publicly available datasets, namely DRIVE, STARE, CHASE_DB1, and HRF. For the CHASE_DB1 set, the SSANet achieves state-of-the-art AUC value of 0.9916 for the ROC curve. An ablative analysis is presented to analyze the contribution of different components of the SSANet to the performance improvement. CONCLUSIONS: The proposed retinal SSANet achieves state-of-the-art or comparable accuracy across publicly available datasets, especially improving segmentation for thin vessels, vessel junctions, and central vessel reflexes.

Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning.

In this paper, we perform a systematic study about the on-body sensor positioning and data acquisition details for Human Activity Recognition (HAR) systems. We build a testbed that consists of eight body-worn Inertial Measurement Units (IMU) sensors and an Android mobile device for activity data collection. We develop a Long Short-Term Memory (LSTM) network framework to support training of a deep learning model on human activity data, which is acquired in both real-world and controlled environments. From the experiment results, we identify that activity data with sampling rate as low as 10 Hz from four sensors at both sides of wrists, right ankle, and waist is sufficient in recognizing Activities of Daily Living (ADLs) including eating and driving activity. We adopt a two-level ensemble model to combine class-probabilities of multiple sensor modalities, and demonstrate that a classifier-level sensor fusion technique can improve the classification performance. By analyzing the accuracy of each sensor on different types of activity, we elaborate custom weights for multimodal sensor fusion that reflect the characteristic of individual activities.

Preliminary study on contrast flow analysis of thoracic transforaminal epidural block.

BACKGROUND: The thoracic transforaminal epidural block (TTFEB) is usually performed to treat herpes zoster or postherpetic neuralgia (PHN). Especially, multiple segmental involvements and approximate contrast medium spread range, according to volume, help to choose the proper drug volume in the transforaminal epidural block. This study investigated the contrast medium spread patterns of 1-ml to 3-ml TTFEBs. METHODS: A total of 26 patients with herpes zoster or PHN were enrolled in this study. All participants received 1 ml, 2 ml, or 3 ml of contrast medium. Results were divided into Groups A, B and C based on the volume (1, 2, or 3 ml), with n = 26 for each group. After the injection of contrast medium, the spread levels were estimated in both the lateral and anteroposterior (AP) images using fluoroscopy. RESULTS: The cephalad spread of contrast medium in the lateral image as expressed by the median (interquartile range) was 2.00 levels (1.00-2.00) for Group A, 2.50 (2.00-3.00) for Group B, and 3.00 (2.00-4.00) for Group C. The caudal spread level of contrast medium was 1.00 (1.00-2.00) for Group A, 2.00 (2.00-3.00) for Group B, and 2.00 (2.00-3.00) for Group C. There was ventral and dorsal spread of the 3-ml contrast medium injection in 88% (23/26) of cases in the lateral image. CONCLUSIONS: Injection of 3 ml of contrast medium through the foramina spread 6 levels in a cephalocaudal direction. Spread patterns revealed a cephalad preference. TTFEB resulted in dorsal and ventral spread in a high percentage of cases. This procedure may be useful for transferring drugs to the dorsal and ventral roots.

A semi-automated volumetric software for segmentation and perfusion parameter quantification of brain tumors using 320-row multidetector computed tomography: a validation study.

PURPOSE: We developed a semi-automated volumetric software, NPerfusion, to segment brain tumors and quantify perfusion parameters on whole-brain CT perfusion (WBCTP) images. The purpose of this study was to assess the feasibility of the software and to validate its performance compared with manual segmentation. METHODS: Twenty-nine patients with pathologically proven brain tumors who underwent preoperative WBCTP between August 2012 and February 2015 were included. Three perfusion parameters, arterial flow (AF), equivalent blood volume (EBV), and Patlak flow (PF, which is a measure of permeability of capillaries), of brain tumors were generated by a commercial software and then quantified volumetrically by NPerfusion, which also semi-automatically segmented tumor boundaries. The quantification was validated by comparison with that of manual segmentation in terms of the concordance correlation coefficient and Bland-Altman analysis. RESULTS: With NPerfusion, we successfully performed segmentation and quantified whole volumetric perfusion parameters of all 29 brain tumors that showed consistent perfusion trends with previous studies. The validation of the perfusion parameter quantification exhibited almost perfect agreement with manual segmentation, with Lin concordance correlation coefficients (ρ c) for AF, EBV, and PF of 0.9988, 0.9994, and 0.9976, respectively. On Bland-Altman analysis, most differences between this software and manual segmentation on the commercial software were within the limit of agreement. CONCLUSIONS: NPerfusion successfully performs segmentation of brain tumors and calculates perfusion parameters of brain tumors. We validated this semi-automated segmentation software by comparing it with manual segmentation. NPerfusion can be used to calculate volumetric perfusion parameters of brain tumors from WBCTP.

Neurovascular Compression Caused by Popliteus Muscle Enlargement Without Discrete Trauma.

Popliteal entrapment syndrome caused by isolated popliteus muscle enlargement is very rare, although its occurrence has been reported after discrete trauma. However, popliteal artery stenosis with combined peroneal and proximal tibial neuropathy caused by popliteus muscle enlargement without preceding trauma has not been reported. A 57-year-old man presented with a tingling sensation and pain in his left calf. He had no previous history of an injury. The symptoms were similar to those of lumbosacral radiculopathy. Calf pain became worse despite treatment, and the inability to flex his toes progressed. Computed tomography angiography and magnetic resonance imaging of the lower extremity showed popliteal artery stenosis caused by popliteus muscle enlargement and surrounding edema. An electrodiagnostic study confirmed combined peroneal and proximal tibial neuropathy at the popliteal fossa. Urgent surgical decompression was performed because of the progressive neurologic deficit and increasing neuropathic pain. The calf pain disappeared immediately after surgery, and he was discharged after the neurologic functions improved.

Neurovascular Compression Caused by Popliteus Muscle Enlargement Without Discrete Trauma

Popliteal entrapment syndrome caused by isolated popliteus muscle enlargement is very rare, although its occurrence has been reported after discrete trauma. However, popliteal artery stenosis with combined peroneal and proximal tibial neuropathy caused by popliteus muscle enlargement without preceding trauma has not been reported. A 57-year-old man presented with a tingling sensation and pain in his left calf. He had no previous history of an injury. The symptoms were similar to those of lumbosacral radiculopathy. Calf pain became worse despite treatment, and the inability to flex his toes progressed. Computed tomography angiography and magnetic resonance imaging of the lower extremity showed popliteal artery stenosis caused by popliteus muscle enlargement and surrounding edema. An electrodiagnostic study confirmed combined peroneal and proximal tibial neuropathy at the popliteal fossa. Urgent surgical decompression was performed because of the progressive neurologic deficit and increasing neuropathic pain. The calf pain disappeared immediately after surgery, and he was discharged after the neurologic functions improved.

Neurovascular Compression Caused by Popliteus Muscle Enlargement Without Discrete Trauma

Popliteal entrapment syndrome caused by isolated popliteus muscle enlargement is very rare, although its occurrence has been reported after discrete trauma. However, popliteal artery stenosis with combined peroneal and proximal tibial neuropathy caused by popliteus muscle enlargement without preceding trauma has not been reported. A 57-year-old man presented with a tingling sensation and pain in his left calf. He had no previous history of an injury. The symptoms were similar to those of lumbosacral radiculopathy. Calf pain became worse despite treatment, and the inability to flex his toes progressed. Computed tomography angiography and magnetic resonance imaging of the lower extremity showed popliteal artery stenosis caused by popliteus muscle enlargement and surrounding edema. An electrodiagnostic study confirmed combined peroneal and proximal tibial neuropathy at the popliteal fossa. Urgent surgical decompression was performed because of the progressive neurologic deficit and increasing neuropathic pain. The calf pain disappeared immediately after surgery, and he was discharged after the neurologic functions improved.

Lower Extremity Radicular Pain Caused by Entrapped Sigmoid Colon Between L5 and S1 Vertebrae.

Intestinal entrapment between two vertebral bodies is very rare. In all previous cases, it occurred by major trauma. However, the bowel entrapment between two vertebral bodies without trauma has never been reported, not to mention as the cause of lower extremity radicular pain. We describe the case of an 82-year-old female patient with right lower extremity radicular pain without recent trauma history. The patient was diagnosed sigmoid colon entrapment between the L5 and S1 vertebrae by lumbar spinal computerized tomography and magnetic resonance imaging, and showed improvement in radicular pain after manual reduction of interpositioned colon during surgery. Intestinal entrapment between two vertebrae without trauma is caused by degenerative and vacuum changes of the intervertebral disc combined with the anterior longitudinal ligament injury.

Lower Extremity Radicular Pain Caused by Entrapped Sigmoid Colon Between L5 and S1 Vertebrae

Intestinal entrapment between two vertebral bodies is very rare. In all previous cases, it occurred by major trauma. However, the bowel entrapment between two vertebral bodies without trauma has never been reported, not to mention as the cause of lower extremity radicular pain. We describe the case of an 82-year-old female patient with right lower extremity radicular pain without recent trauma history. The patient was diagnosed sigmoid colon entrapment between the L5 and S1 vertebrae by lumbar spinal computerized tomography and magnetic resonance imaging, and showed improvement in radicular pain after manual reduction of interpositioned colon during surgery. Intestinal entrapment between two vertebrae without trauma is caused by degenerative and vacuum changes of the intervertebral disc combined with the anterior longitudinal ligament injury.

Analgesic effect of intrathecal gabapentin in a rat model of persistent muscle pain.

OBJECTIVE: To evaluate the analgesic effect of intrathecal gabapentin therapy on secondary hyperalgesia in a rat model of persistent muscle pain. METHODS: Intrathecal catheters were implanted into rats. Mechanical secondary hyperalgesia was induced by repeated intramuscular injections of acidic solution into the gastrocnemius muscle. Gabapentin was administrated intrathecally. Rats were allocated to control and experimental (gabapentin 30, 100, 300, and 1,000 µg) group. After gabapentin administration, mechanical withdrawal threshold was measured every 15 minutes and the motor function was measured 30 minutes later. RESULTS: Mechanical hyperalgesia was evoked after the second acidic buffer injection. There was a significant improvement on the mechanical threshold after administration of 100, 300, and 1,000 µg gabapentin compared to pre-injection and the control group. The analgesic effect continued for 105, 135, and 210 minutes, respectively. To discern side effects, motor function was measured. Motor function was preserved in both groups after gabapentin administration, except for rats who received 1,000 µg gabapentin. CONCLUSION: Intrathecal gabapentin administration produces dose-dependent improvements in mechanical hyperalgesia in a persistent muscle pain rat model. This implicates the central nervous system as having a strong influence on the development of persistent mechanical hyperalgesia. These results are helpful in understanding the pathophysiology of secondary hyperalgesia and in the treatment of patients with chronic muscle pain.

Prophylactic effect of erythropoietin injection to prevent acute mountain sickness: an open-label randomized controlled trial.

This study was performed to evaluate whether increasing hemoglobin before ascent by prophylactic erythropoietin injections prevents acute mountain sickness (AMS). This open-label, randomized, controlled trial involved 39 healthy volunteers with hemoglobin ≤ 15.5 g/dL who were divided randomly into erythropoietin (n=20) and control (n=19) groups. Epoetin alpha 10,000 IU injections were given weekly for four consecutive weeks. On day 1, and 7 days after the last injection (day 29), oxygen saturation (SaO2), and hemoglobin were measured. The subjects departed Seoul on day 30 and arrived at Annapurna base camp (ABC, 4,130 m) on day 34. AMS was diagnosed when headache and Lake Louise score (LLS) of ≥ 3 were present. Immediate descent criteria followed US Army recommendations. Two groups differ in hemoglobin levels on day 29 (15.4 ± 1.1 vs 14.2 ± 1.0 g/dL, P=0.001). At ABC, erythropoietin group had a significantly lower mean LLS, AMS incidence, and number of subjects who met immediate descent criteria. Multiple logistic regression analysis showed that SaO2<87% and control group, but not hemoglobin<15.0 g/dL, independently predicted satisfaction of immediate descent criteria. Erythropoietin-related adverse effects were not observed. In conclusion, erythropoietin may be an effective prophylaxis for AMS.(Clinical Trial Registry Number; NCT 01665781).

Analgesic Effect of Intrathecal Gabapentin in a Rat Model of Persistent Muscle Pain

OBJECTIVE: To evaluate the analgesic effect of intrathecal gabapentin therapy on secondary hyperalgesia in a rat model of persistent muscle pain. METHODS: Intrathecal catheters were implanted into rats. Mechanical secondary hyperalgesia was induced by repeated intramuscular injections of acidic solution into the gastrocnemius muscle. Gabapentin was administrated intrathecally. Rats were allocated to control and experimental (gabapentin 30, 100, 300, and 1,000 microg) group. After gabapentin administration, mechanical withdrawal threshold was measured every 15 minutes and the motor function was measured 30 minutes later. RESULTS: Mechanical hyperalgesia was evoked after the second acidic buffer injection. There was a significant improvement on the mechanical threshold after administration of 100, 300, and 1,000 microg gabapentin compared to pre-injection and the control group. The analgesic effect continued for 105, 135, and 210 minutes, respectively. To discern side effects, motor function was measured. Motor function was preserved in both groups after gabapentin administration, except for rats who received 1,000 microg gabapentin. CONCLUSION: Intrathecal gabapentin administration produces dose-dependent improvements in mechanical hyperalgesia in a persistent muscle pain rat model. This implicates the central nervous system as having a strong influence on the development of persistent mechanical hyperalgesia. These results are helpful in understanding the pathophysiology of secondary hyperalgesia and in the treatment of patients with chronic muscle pain.