Epidemiology and Characteristics of Ankle and Foot Injuries in Electric Scooter-Related Accidents.

Background: The use of electric scooters (e-scooters) continues to increase as a simple, inexpensive means of transport, resulting in a sharp increase in the incidence of scooter-related accidents. No study to date has closely examined the injury extent to the lower leg, joints, and extremities from e-scooter-related accidents. Here, we investigated the epidemiology and injury patterns of such accidents, focusing on injuries to the ankle and foot. Methods: Based on data from a single tertiary hospital's database, the demographics of 563 patients with scooter-associated injuries were analyzed retrospectively. Among the patients, 229 patients who were injured by e-scooter riding were further investigated. Based on the data, the general demographics of whole scooter-associated injuries and the injury characteristics and fracture cases of the lower leg, ankle, and foot were analyzed. Results: During the 4-year study period, the number of patients injured by e-scooters increased every year. Lower extremities were the most common injury site (67.2%) among riders, whereas injuries to the head and neck (64.3%) were more common in riders of non-electric scooters. Among the lower leg, ankle, and foot injuries of riders (52 cases), the ankle joint (53.8%) was the most commonly injured site, followed by the foot (40.4%) and lower leg (21.2%). The fracture group scored significantly higher on the Abbreviated Injury Scale than the non-fracture group (p < 0.001). Among the fracture group (20 cases), ankle fractures (9 cases) were most common, including pronation external rotation type 4 injuries (4 cases) and pilon fractures (2 cases). Five patients (25%) had open fractures, and 12 patients (60%) underwent surgical treatment. Conclusions: The ankle and foot are the most common injury sites in e-scooter-related accidents. Given the high frequency and severity of e-scooter-related ankle and foot injuries, we suggest that more attention be paid to preventing these types of injuries with greater public awareness of the dangers of using e-scooters.

Chiral Flipping in Viedma Deracemization.

Understanding deracemization is crucial for progress in chiral chemistry, especially for improving separation techniques. Here, we first report the phenomenon of chiral flipping (or reverse deracemization) in a chiral material (i.e., sodium chlorate crystals) during Viedma deracemization, employing a small-volume reactor system for precise analysis. We observe considerable chiral flipping, influenced by the initial imbalance in the numbers of L- and D-form particles. We developed a simple probabilistic model to further elucidate this behavior. We find that the fluctuation in the populations of chiral crystal particles resulting from their random dissolution and regeneration is the key factor behind chiral flipping. This study not only brings to light this intriguing observation of chiral flipping but also contributes to the enhancement of deracemization techniques.

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study.

BACKGROUND: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. METHODS: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO2/FIO2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine). The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley's additive explanations (SHAP). RESULTS: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756-0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626-0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. CONCLUSION: Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

A Deep Learning-Based Approach for Prediction of Vancomycin Treatment Monitoring: Retrospective Study Among Patients With Critical Illness.

BACKGROUND: Vancomycin pharmacokinetics are highly variable in patients with critical illnesses, and clinicians commonly use population pharmacokinetic (PPK) models based on a Bayesian approach to dose. However, these models are population-dependent, may only sometimes meet the needs of individual patients, and are only used by experienced clinicians as a reference for making treatment decisions. To assist real-world clinicians, we developed a deep learning-based decision-making system that predicts vancomycin therapeutic drug monitoring (TDM) levels in patients in intensive care unit. OBJECTIVE: This study aimed to establish joint multilayer perceptron (JointMLP), a new deep-learning model for predicting vancomycin TDM levels, and compare its performance with the PPK models, extreme gradient boosting (XGBoost), and TabNet. METHODS: We used a 977-case data set split into training and testing groups in a 9:1 ratio. We performed external validation of the model using 1429 cases from Kangwon National University Hospital and 2394 cases from the Medical Information Mart for Intensive Care-IV (MIMIC-IV). In addition, we performed 10-fold cross-validation on the internal training data set and calculated the 95% CIs using the metric. Finally, we evaluated the generalization ability of the JointMLP model using the MIMIC-IV data set. RESULTS: Our JointMLP model outperformed other models in predicting vancomycin TDM levels in internal and external data sets. Compared to PPK, the JointMLP model improved predictive power by up to 31% (mean absolute error [MAE] 6.68 vs 5.11) on the internal data set and 81% (MAE 11.87 vs 6.56) on the external data set. In addition, the JointMLP model significantly outperforms XGBoost and TabNet, with a 13% (MAE 5.75 vs 5.11) and 14% (MAE 5.85 vs 5.11) improvement in predictive accuracy on the inner data set, respectively. On both the internal and external data sets, our JointMLP model performed well compared to XGBoost and TabNet, achieving prediction accuracy improvements of 34% and 14%, respectively. Additionally, our JointMLP model showed higher robustness to outlier data than the other models, as evidenced by its higher root mean squared error performance across all data sets. The mean errors and variances of the JointMLP model were close to zero and smaller than those of the PPK model in internal and external data sets. CONCLUSIONS: Our JointMLP approach can help optimize treatment outcomes in patients with critical illnesses in an intensive care unit setting, reducing side effects associated with suboptimal vancomycin administration. These include increased risk of bacterial resistance, extended hospital stays, and increased health care costs. In addition, the superior performance of our model compared to existing models highlights its potential to help real-world clinicians.

Improving generalization performance of electrocardiogram classification models.

Objective.Recently, many electrocardiogram (ECG) classification algorithms using deep learning have been proposed. Because the ECG characteristics vary across datasets owing to variations in factors such as recorded hospitals and the race of participants, the model needs to have a consistently high generalization performance across datasets. In this study, as part of the PhysioNet/Computing in Cardiology Challenge (PhysioNet Challenge) 2021, we present a model to classify cardiac abnormalities from the 12- and the reduced-lead ECGs.Approach.To improve the generalization performance of our earlier proposed model, we adopted a practical suite of techniques, i.e. constant-weighted cross-entropy loss, additional features, mixup augmentation, squeeze/excitation block, and OneCycle learning rate scheduler. We evaluated its generalization performance using the leave-one-dataset-out cross-validation setting. Furthermore, we demonstrate that the knowledge distillation from the 12-lead and large-teacher models improved the performance of the reduced-lead and small-student models.Main results.With the proposed model, our DSAIL SNU team has received Challenge scores of 0.55, 0.58, 0.58, 0.57, and 0.57 (ranked 2nd, 1st, 1st, 2nd, and 2nd of 39 teams) for the 12-, 6-, 4-, 3-, and 2-lead versions of the hidden test set, respectively.Significance.The proposed model achieved a higher generalization performance over six different hidden test datasets than the one we submitted to the PhysioNet Challenge 2020.

In-Advance Prediction of Pressure Ulcers via Deep-Learning-Based Robust Missing Value Imputation on Real-Time Intensive Care Variables.

Pressure ulcers (PUs) are a prevalent skin disease affecting patients with impaired mobility and in high-risk groups. These ulcers increase patients' suffering, medical expenses, and burden on medical staff. This study introduces a clinical decision support system and verifies it for predicting real-time PU occurrences within the intensive care unit (ICU) by using MIMIC-IV and in-house ICU data. We develop various machine learning (ML) and deep learning (DL) models for predicting PU occurrences in real time using the MIMIC-IV and validate using the MIMIC-IV and Kangwon National University Hospital (KNUH) dataset. To address the challenge of missing values in time series, we propose a novel recurrent neural network model, GRU-D++. This model outperformed other experimental models by achieving the area under the receiver operating characteristic curve (AUROC) of 0.945 for the on-time prediction and AUROC of 0.912 for 48h in-advance prediction. Furthermore, in the external validation with the KNUH dataset, the fine-tuned GRU-D++ model demonstrated superior performances, achieving an AUROC of 0.898 for on-time prediction and an AUROC of 0.897 for 48h in-advance prediction. The proposed GRU-D++, designed to consider temporal information and missing values, stands out for its predictive accuracy. Our findings suggest that this model can significantly alleviate the workload of medical staff and prevent the worsening of patient conditions by enabling timely interventions for PUs in the ICU.

Predicting progression to dementia with "comprehensive visual rating scale" and machine learning algorithms.

Background and Objective: Identifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms. Methods: We included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated. Results: Of the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701-0.711) were used than when clinical data and cortical thickness (accuracy 0.650-0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression. Conclusions: Tree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.

How Many Private Data Are Needed for Deep Learning in Lung Nodule Detection on CT Scans? A Retrospective Multicenter Study.

Early detection of lung nodules is essential for preventing lung cancer. However, the number of radiologists who can diagnose lung nodules is limited, and considerable effort and time are required. To address this problem, researchers are investigating the automation of deep-learning-based lung nodule detection. However, deep learning requires large amounts of data, which can be difficult to collect. Therefore, data collection should be optimized to facilitate experiments at the beginning of lung nodule detection studies. We collected chest computed tomography scans from 515 patients with lung nodules from three hospitals and high-quality lung nodule annotations reviewed by radiologists. We conducted several experiments using the collected datasets and publicly available data from LUNA16. The object detection model, YOLOX was used in the lung nodule detection experiment. Similar or better performance was obtained when training the model with the collected data rather than LUNA16 with large amounts of data. We also show that weight transfer learning from pre-trained open data is very useful when it is difficult to collect large amounts of data. Good performance can otherwise be expected when reaching more than 100 patients. This study offers valuable insights for guiding data collection in lung nodules studies in the future.

Indirect Volume Estimation for Acute Ischemic Stroke from Diffusion Weighted Image Using Slice Image Segmentation.

The accurate estimation of acute ischemic stroke (AIS) using diffusion-weighted imaging (DWI) is crucial for assessing patients and guiding treatment options. This study aimed to propose a method that estimates AIS volume in DWI objectively, quickly, and accurately. We used a dataset of DWI with AIS, including 2159 participants (1179 for internal validation and 980 for external validation) with various types of AIS. We constructed algorithms using 3D segmentation (direct estimation) and 2D segmentation (indirect estimation) and compared their performances with those annotated by neurologists. The proposed pretrained indirect model demonstrated higher segmentation performance than the direct model, with a sensitivity, specificity, F1-score, and Jaccard index of 75.0%, 77.9%, 76.0, and 62.1%, respectively, for internal validation, and 72.8%, 84.3%, 77.2, and 63.8%, respectively, for external validation. Volume estimation was more reliable for the indirect model, with 93.3% volume similarity (VS), 0.797 mean absolute error (MAE) for internal validation, VS of 89.2% and a MAE of 2.5% for external validation. These results suggest that the indirect model using 2D segmentation developed in this study can provide an accurate estimation of volume from DWI of AIS and may serve as a supporting tool to help physicians make crucial clinical decisions.

DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural Networks.

Recent advances in next-generation sequencing technologies have led to the successful insertion of video information into DNA using synthesized oligonucleotides. Several attempts have been made to embed larger data into living organisms. This process of embedding messages is called steganography and it is used for hiding and watermarking data to protect intellectual property. In contrast, steganalysis is a group of algorithms that serves to detect hidden information from covert media. Various methods have been developed to detect messages embedded in conventional covert channels. However, conventional steganalysis algorithms are mostly limited to common covert media. Most common detection approaches, such as frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography and are easily bypassed by recently developed steganography techniques. To address the limitations of conventional approaches, a sequence-learning-based malicious DNA sequence analysis method based on neural networks has been proposed. The proposed method learns intrinsic distributions and identifies distribution variations using a classification score to predict whether a sequence is to be a coding or non-coding sequence. Based on our experiments and results, we have developed a framework to safeguard security against DNA steganography.

Imbalanced Data Classification via Cooperative Interaction Between Classifier and Generator.

Learning classifiers with imbalanced data can be strongly biased toward the majority class. To address this issue, several methods have been proposed using generative adversarial networks (GANs). Existing GAN-based methods, however, do not effectively utilize the relationship between a classifier and a generator. This article proposes a novel three-player structure consisting of a discriminator, a generator, and a classifier, along with decision boundary regularization. Our method is distinctive in which the generator is trained in cooperation with the classifier to provide minority samples that gradually expand the minority decision region, improving performance for imbalanced data classification. The proposed method outperforms the existing methods on real data sets as well as synthetic imbalanced data sets.

The message passing neural networks for chemical property prediction on SMILES.

Drug metabolism is determined by the biochemical and physiological properties of the drug molecule. To improve the performance of a drug property prediction model, it is important to extract complex molecular dynamics from limited data. Recent machine learning or deep learning based models have employed the atom- and bond-type information, as well as the structural information to predict drug properties. However, many of these methods can be used only for the graph representations. Message passing neural networks (MPNNs) (Gilmer et al., 2017) is a framework used to learn both local and global features from irregularly formed data, and is invariant to permutations. This network performs an iterative message passing (MP) operation on each object and its neighbors, and obtain the final output from all messages regardless of their order. In this study, we applied the MP-based attention network (Nikolentzos et al., 2019) originally developed for text learning to perform chemical classification tasks. Before training, we tokenized the characters, and obtained embeddings of each molecular sequence. We conducted various experiments to maximize the predictivity of the model. We trained and evaluated our model using various chemical classification benchmark tasks. Our results are comparable to previous state-of-the-art and baseline models or outperform. To the best of our knowledge, this is the first attempt to learn chemical strings using an MP-based algorithm. We will extend our work to more complex tasks such as regression or generation tasks in the future.

AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data.

Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can identify not only an individual, but also his or her relatives. Nonetheless, most countries and researchers agree on the necessity of collecting personal medical data. This stems from the fact that medical data, including genomic data, are an indispensable resource for further research and development regarding disease prevention and treatment. To prevent personal medical data from being misused, techniques to reliably preserve sensitive information should be developed for real world applications. In this paper, we propose a framework called anonymized generative adversarial networks (AnomiGAN), to preserve the privacy of personal medical data, while also maintaining high prediction performance. We compared our method to state-of-the-art techniques and observed that our method preserves the same level of privacy as differential privacy (DP) and provides better prediction results. We also observed that there is a trade-off between privacy and prediction results that depends on the degree of preservation of the original data. Here, we provide a mathematical overview of our proposed model and demonstrate its validation using UCI machine learning repository datasets in order to highlight its utility in practice. The code is available at https://github.com/hobae/AnomiGAN/.

Electroencephalographic Resting-State Functional Connectivity of Benign Epilepsy with Centrotemporal Spikes.

BACKGROUND AND PURPOSE: We aimed to reveal resting-state functional connectivity characteristics based on the spike-free waking electroencephalogram (EEG) of benign epilepsy with centrotemporal spikes (BECTS) patients, which usually appears normal in routine visual inspection. METHODS: Thirty BECTS patients and 30 disease-free and age- and sex-matched controls were included. Eight-second EEG epochs without artifacts were sampled and then bandpass filtered into the delta, theta, lower alpha, upper alpha, and beta bands to construct the association matrix. The weighted phase lag index (wPLI) was used as an association measure for EEG signals. The band-specific connectivity, which was represented as a matrix of wPLI values of all edges, was compared for analyzing the connectivity itself. The global wPLI, characteristic path length (CPL), and mean clustering coefficient were compared. RESULTS: The resting-state functional connectivity itself and the network topology differed in the BECTS patients. For the lower-alpha-band and beta-band connectivity, edges that showed significant differences had consistently lower wPLI values compared to the disease-free controls. The global wPLI value was significantly lower for BECTS patients than for the controls in lower-alpha-band connectivity (mean±SD; 0.241±0.034 vs. 0.276±0.054, p=0.024), while the CPL was significantly longer for BECTS in the same frequency band (mean±SD; 4.379±0.574 vs. 3.904±0.695, p=0.04). The resting-state functional connectivity of BECTS showed decreased connectivity, integration, and efficiency compared to controls. CONCLUSIONS: The connectivity differed significantly between BECTS patients and disease-free controls. In BECTS, global connectivity was significantly decreased and the resting-state functional connectivity showed lower efficiency in the lower alpha band.

Deep learning based low-cost high-accuracy diagnostic framework for dementia using comprehensive neuropsychological assessment profiles.

BACKGROUND: The conventional scores of the neuropsychological batteries are not fully optimized for diagnosing dementia despite their variety and abundance of information. To achieve low-cost high-accuracy diagnose performance for dementia using a neuropsychological battery, a novel framework is proposed using the response profiles of 2666 cognitively normal elderly individuals and 435 dementia patients who have participated in the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD). METHODS: The key idea of the proposed framework is to propose a cost-effective and precise two-stage classification procedure that employed Mini Mental Status Examination (MMSE) as a screening test and the KLOSCAD Neuropsychological Assessment Battery as a diagnostic test using deep learning. In addition, an evaluation procedure of redundant variables is introduced to prevent performance degradation. A missing data imputation method is also presented to increase the robustness by recovering information loss. The proposed deep neural networks (DNNs) architecture for the classification is validated through rigorous evaluation in comparison with various classifiers. RESULTS: The k-nearest-neighbor imputation has been induced according to the proposed framework, and the proposed DNNs for two stage classification show the best accuracy compared to the other classifiers. Also, 49 redundant variables were removed, which improved diagnostic performance and suggested the potential of simplifying the assessment. Using this two-stage framework, we could get 8.06% higher diagnostic accuracy of dementia than MMSE alone and 64.13% less cost than KLOSCAD-N alone. CONCLUSION: The proposed framework could be applied to general dementia early detection programs to improve robustness, preciseness, and cost-effectiveness.

hc-OTU: A Fast and Accurate Method for Clustering Operational Taxonomic Units Based on Homopolymer Compaction.

To assess the genetic diversity of an environmental sample in metagenomics studies, the amplicon sequences of 16s rRNA genes need to be clustered into operational taxonomic units (OTUs). Many existing tools for OTU clustering trade off between accuracy and computational efficiency. We propose a novel OTU clustering algorithm, hc-OTU, which achieves high accuracy and fast runtime by exploiting homopolymer compaction and k-mer profiling to significantly reduce the computing time for pairwise distances of amplicon sequences. We compare the proposed method with other widely used methods, including UCLUST, CD-HIT, MOTHUR, ESPRIT, ESPRIT-TREE, and CLUSTOM, comprehensively, using nine different experimental datasets and many evaluation metrics, such as normalized mutual information, adjusted Rand index, measure of concordance, and F-score. Our evaluation reveals that the proposed method achieves a level of accuracy comparable to the respective accuracy levels of MOTHUR and ESPRIT-TREE, two widely used OTU clustering methods, while delivering orders-of-magnitude speedups.

Comparison of Fat Mass Percentage and Body Mass Index in Koreans With Spinal Cord Injury According to the Severity and Duration of Motor Paralysis.

OBJECTIVE: To analyze the relationship of the change in fat mass percentage (FMP) and body mass index (BMI) with the change in obesity rate according to gender, extent of spinal cord injury (SCI) and the duration. METHODS: The retrospective study was conducted with medical records of 915 patients. FMP was calculated with BMI and bioelectrical impedance analysis (BIA). Statistical analysis of the relationship between FMP and gender, extent of SCI and the duration after SCI was done. RESULTS: FMP increased in relation to the duration. The mean FMP was higher in the motor complete tetraplegia group, as compared to the motor incomplete group. The rate of obesity was 69.8% with cutoff FMP values of over 22% and 35% for male and female patients, respectively. Rate of obesity was correlated with the duration after SCI and degree of paralysis. The rate of obesity was 17.1% with a cutoff value of BMI 25 kg/m(2) and 51.3% with a cutoff value of 22 kg/m(2). For evaluation of the diagnostic value of BMI to predict obesity according to FMP standards, a cutoff value of 25 kg/m(2) showed a sensitivity level of 22.3% and specificity level of 94.9%. When the cutoff level for BMI was set at 22 kg/m(2), the sensitivity and specificity were 59.3% and 67.0%, respectively. CONCLUSION: In Korean SCI patients, FMP showed good correlation with the duration of SCI and the extent of SCI, while BMI did not. Especially in the motor complete tetraplegia group, the diagnostic value of BMI decreased as the duration after SCI increased. This study suggested that FMP could be used complementarily when evaluating the obesity of SCI patients.

Pretreatment with paricalcitol attenuates inflammation in ischemia-reperfusion injury via the up-regulation of cyclooxygenase-2 and prostaglandin E2.

BACKGROUND: The effect of paricalcitol on renal ischemia-reperfusion injury (IRI) has not been investigated. We examined whether paricalcitol is effective in preventing inflammation in a mouse model of IRI, and evaluated the cyclooxygenase-2 (COX-2) and prostaglandin E2 (PGE2) pathways as a protective mechanism of paricalcitol. METHODS: Paricalcitol (0.3 µg/kg) was administered to male C57BL/6 mice 24 h before IRI. Bilateral kidneys were subjected to 23 min of ischemia, and mice were killed 72 h after IRI. The effects of paricalcitol on renal IRI were evaluated in terms of renal function, tubular necrosis, apoptotic cell death, inflammatory cell infiltration and inflammatory cytokines. The effects of paricalcitol on COX-2, PGE2 and its receptors were investigated. RESULTS: Paricalcitol pretreatment improved renal function (decreased blood urea nitrogen and serum creatinine levels), tubular necrosis and apoptotic cell death in IRI-mice kidneys. The infiltration of inflammatory cells (T cells and macrophages), and the production of proinflammatory cytokines (RANTES, tumor necrosis factor-α, interleukin-1ß and interferon-γ) were reduced in paricalcitol-treated mice with IRI. Paricalcitol up-regulated COX-2 expression, PGE2 synthesis and mRNA expression of receptor subtype EP4 in post-ischemic renal tissue. The cotreatment of a selective COX-2 inhibitor with paricalcitol restored functional injury and tubular necrosis in paricalcitol-treated mice with IRI. CONCLUSIONS: Our study demonstrates that paricalcitol pretreatment prevents renal IRI via the inhibition of renal inflammation, and the up-regulation of COX-2 and PGE2 is one of the protective mechanisms of paricalcitol in renal IRI.

Muscular polyarteritis nodosa.

We present an unusual case of a 26-year-old man with muscular polyarteritis nodosa (PAN) with severe calf pain and gait disturbance. Magnetic resonance imaging of the lower limbs demonstrated highly increased signal intensity in both soleus muscles and the lateral head of the left gastrocnemius muscle. Biopsies of the soleus muscle showed acute necrotizing arteritis. The calf pain and limited range of motion of ankle dorsiflexion subsided from day 1 on administration of oral corticosteroid at high dosage and were completely resolved by 4 months. After tapering corticosteroid to 10 mg, symptoms recurred. A combined regimen of immunosuppressants was found to maintain symptomatic relief.Muscular PAN should be included in the differential diagnosis of a patient presenting with symptoms of acute or subacute calf pain. Although this muscular PAN was so far been benign, complete remission of the underlying process may be difficult to achieve.