A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration.

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott's index of agreement (WI), and Legates-McCabe's index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

Quantifying regional low flows under data scarce conditions.

Estimating low flow quantiles is essential for regulating minimum flow requirements and ensuring water availability, ecological health, and overall system sustainability. This study aims to quantify regional low flows by analyzing low-flow time series in data-limited environments. We utilized annual minimum 7-day instantaneous streamflow data collected from gaging stations. Discordancy measure assessments revealed that all sites were homogeneous, forming a single region. We employed Easy-Fit Statistical Software to select the best-fit probability distribution model and determine estimation parameter values. Through goodness-of-fit tests (GOFs), the Generalized Pareto model emerged as the most suitable, predicting low flow quantiles for 100-year returns. Rigorous application of GOFs ensures the statistical soundness of the model, capturing underlying data patterns. A correlation coefficient determination (R2) of 0.989 demonstrates the high satisfaction of the selected distribution model. The developed regression line for the region exhibited strong agreement between predicted low flows and catchment area. Thus, accurate estimation proves valuable in environmental and human-influenced decision-making processes, providing insights into low-flow behavior and mitigating drought effects on aquatic ecosystems.

Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran).

The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid-ML classes, was decreased to -25.3%, -29.6% and -57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet-ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has -204.9 value for AIC which has grown by 5.23% (-194.7) compared to the state without any pre-processing method (ANN_Relu_25).

Spatiotemporal changes in future precipitation of Afghanistan for shared socioeconomic pathways.

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020-2059) and the distant future (2060-2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

Influence of Optimism, Social Support, and Spirituality on COVID-19 Stress in Christian Church Community

Objective@#Optimism, social support, and spirituality can be important factors related to coronavirus disease-2019 (COVID-19) stress. However, studies investigating the influence and interplay of optimism, social support, and spirituality on COVID-19 simultaneously are still few. This study is aimed to explore the influence of optimism, social support, and spirituality on COVID-19 stress in the Christian church community. @*Methods@#A total 350 participants were included in this study. This study was cross-sectionally conducted by using an online survey on optimism, social support, spirituality, and COVID-19 stress that were measured by the Life Orientation Test-Revised (LOT-R), Multidimensional Scale of Perceived Social Support Scale (MSPSS), Spiritual Well-Being Scale (SWBS), and COVID-19 Stress Scale for Korean People (CSSK). The prediction models for COVID-19 stress were analyzed by using univariate and multiple linear regression. @*Results@#Based on the results of univariate linear regression, subjective feelings on income (p<0.001) and health status (p<0.001), LOTR (p<0.001), MSPSS (p=0.025), and SWBS (p<0.001) scores were significantly associated with COVID-19 stress. The multiple linear regression model with subjective feelings on income and health status and SWSB score was significant (p<0.001) and explained 17.7% of the variance (R2=0.177). @*Conclusion@#This study showed that subjective feeling on low income, those who had poor health status, lower optimism, lower perceived social support, and lower spirituality were significantly affected with COVID-19 stress. Especially, the model with subjective feelings on income and health status, and spirituality showed highly significant effects, despite the interaction with associated factors. To cope with unpredictable stressful situations like the COVID-19 pandemic, integrated interventions on psycho-socio-spiritual aspect are warranted.

Assessing the effect of urbanization on regional-scale surface water-groundwater interaction and nitrate transport.

Identifying regional-scale surface water-groundwater interactions (SGI) is vital for predicting anthropogenic effects on surface water bodies and underlying aquifers. However, large-scale water and nutrient flux studies rely on surface water or groundwater-focused models. This study aims to model the effect of urbanization, which is usually accompanied by high groundwater abstraction and surface water pollution, particularly in the developing world, on a regional-scale SGI and nitrate loading. In the study area, the urban expansion increased by over 3% in the last decade. The integrated SWAT-MODFLOW model, Soil and Water Assessment Tool (SWAT) and Modular Finite-Difference Groundwater Flow (MODFLOW) coupling code, was used to assess SGI. By coupling SWAT-MODFLOW with Reactive Transport in 3-Dimensions, the nutrient loading to the river from point and non-point sources was also modeled. Basin average annual results show that groundwater discharge declined with increasing groundwater abstraction and increased with Land use/Land cover (LULC) changes. Groundwater recharge decreased significantly in the Belge season (February to May), and the river seepage and groundwater discharge decreased correspondingly. High spatiotemporal changes in SGI and nitrate loading were found under the combined LULC and groundwater abstraction scenarios. The water yield decreased by 15%. In a large part of the region, the nitrate loading increased by 17-250%. Seasonally controlled groundwater abstraction and water quality monitoring are essential in this region.

Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction.

As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD5) concentration, which is a stream pollutant with an extreme circumstance of organic loading and controlling aquatic behavior in the eco-environment. Leading monitoring approaches including machine leaning and deep learning have been evolved for a correct, trustworthy, and low-cost prediction of BOD5 concentration. The addressed research investigated the efficiency of three standalone models including machine learning (extreme learning machine (ELM) and support vector regression (SVR)) and deep learning (deep echo state network (Deep ESN)). In addition, the novel double-stage synthesis models (wavelet-extreme learning machine (Wavelet-ELM), wavelet-support vector regression (Wavelet-SVR), and wavelet-deep echo state network (Wavelet-Deep ESN)) were developed by integrating wavelet transformation (WT) with the different standalone models. Five input associations were supplied for evaluating standalone and double-stage synthesis models by determining diverse water quantity and quality items. The proposed models were assessed using the coefficient of determination (R2), Nash-Sutcliffe (NS) efficiency, and root mean square error (RMSE). The significance of addressed research can be found from the overall outcomes that the predictive accuracy of double-stage synthesis models were not always superior to that of standalone models. Overall results showed that the SVR with 3th distribution (NS = 0.915) and the Wavelet-SVR with 4th distribution (NS = 0.915) demonstrated more correct outcomes for predicting BOD5 concentration compared to alternative models at Hwangji station, and the Wavelet-SVR with 4th distribution (NS = 0.917) was judged to be the most superior model at Toilchun station. In most cases for predicting BOD5 concentration, the novel double-stage synthesis models can be utilized for efficient and organized data administration and regulation of water pollutants on both stations, South Korea.

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.

Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.

Groundwater quality evaluation for different uses in the lower Ketar Watershed, Ethiopia.

Groundwater quality samples from 33 wells were collected in the lower Ketar watershed (Ethiopia) to study its suitability for domestic and irrigation purposes. Samples were evaluated for major ions and physicochemical properties. In 58% of the samples analyzed, Ca2+ is the dominant cation and Na+ dominates the remaining 42% of the samples. Among the anions found during analyzation, HCO3- is the solo dominant ion in all the wells sampled. The order of the concentration of the major ions was Ca2+ > Na+ > Mg2+ > K+ for the cations and HCO3- > SO42- > Cl > NO3- for the anions. AquaChem analysis shows that Ca-HCO3 and Na-HCO3 are the major water types in the area. The analyses indicated that the dissolution of fluorite or fluorapatite is the possible source of the high fluoride concentration in the area. And, the interactions between water and rock and cation exchanges mainly determine the water quality. The suitability of the groundwater for use in irrigation was evaluated based on the salinity (EC), SAR, %Na, RSC, PI, KR, and the USSL Salinity diagram. The groundwater from most of the wells can be used for irrigation without any significant restriction except for a few of the wells downstream. Its suitability for domestic use was evaluated by comparing with the WHO standard limits. The parameters limiting the use of this groundwater for drinking purposes are F- (94%), HCO3- (45%), and Ca2+ (33%). All the remaining major cations and anions complied with the WHO standard limits for drinking.

Feasibility Study of Synthetic Diffusion-Weighted MRIin Patients with Breast Cancer in Comparison withConventional Diffusion-Weighted MRI

Objective@#To investigate the clinical feasibility of synthetic diffusion-weighted imaging (sDWI) at different b-values in patientswith breast cancer by assessing the diagnostic image quality and the quantitative measurements compared with conventionaldiffusion-weighted imaging (cDWI). @*Materials and Methods@#Fifty patients with breast cancer were assessed using cDWI at b-values of 800 and 1500 s/mm2 (cDWI800and cDWI1500) and sDWI at b-values of 1000 and 1500 s/mm2 (sDWI1000 and sDWI1500). Qualitative analysis (normal glandulartissue suppression, overall image quality, and lesion conspicuity) was performed using a 4-point Likert-scale for all DWI setsand the cancer detection rate (CDR) was calculated. We also evaluated cancer-to-parenchyma contrast ratios for each DWI setin 45 patients with the lesion identified on any of the DWI sets. Statistical comparisons were performed using Friedman test,one-way analysis of variance, and Cochran’s Q test. @*Results@#All parameters of qualitative analysis, cancer-to-parenchyma contrast ratios, and CDR increased with increasingb-values, regardless of the type of imaging (synthetic or conventional) (p< 0.001). Additionally, sDWI1500 provided better lesionconspicuity than cDWI1500 (3.52 ± 0.92 vs. 3.39 ± 0.90, p< 0.05). Although cDWI1500 showed better normal glandular tissuesuppression and overall image quality than sDWI1500 (3.66 ± 0.78 and 3.73 ± 0.62 vs. 3.32 ± 0.90 and 3.35 ± 0.81, respectively;p< 0.05), there was no significant difference in their CDR (90.0%). Cancer-to-parenchyma contrast ratios were greater insDWI1500 than in cDWI1500 (0.63 ± 0.17 vs. 0.55 ± 0.18, p< 0.001). @*Conclusion@#sDWI1500 can be feasible for evaluating breast cancers in clinical practice. It provides higher tumor conspicuity,better cancer-to-parenchyma contrast ratio, and comparable CDR when compared with cDWI1500.

Analysis of Factors Affecting Corneal Deflection Amplitude in Normal Korean Eyes

Purpose@#To analyze the corneal deflection amplitude (DA), as measured by the Corvis ST tonometer (Oculus), and other ocular parameters that could affect the DA in normal Korean eyes. @*Methods@#A retrospective study was performed on 126 eyes of 126 normal Korean subjects. Intraocular pressure (IOP) was measured with Goldmann tonometry (IOP-G), and the Corvis ST was used to measure the corneal DA, central corneal thickness (CCT) and IOP; the biomechanically corrected IOP-B was measured with non-contact tonometry (IOP-N). Axial length (AxL), anterior chamber depth (ACD), and the mean keratometric value were obtained with the IOL Master instrument (Zeiss). The relationships between DA and other measurements were then evaluated. @*Results@#The DA was positively correlated with AxL (p = 0.002) and ACD (p = 0.010), and negatively correlated with IOP-G (p < 0.001), IOP-N (p < 0.001), IOP-B (p < 0.001), and CCT (p = 0.006). However, when AxL, ACD, CCT, and each type of IOP were analyzed together, AxL (p = 0.016) and IOP-G (p < 0.001) were the parameters most significantly associated with the DA. @*Conclusions@#A longer AxL and greater ACD were associated with a higher DA, while a higher IOP and thicker CCT were associated with a lower DA. DA, one of the biochemical parameters of the cornea, was affected by multiple ocular factors, i.e., AxL and IOP. These factors should therefore be considered when interpreting DA measurements obtained using the Corvis ST.

Predictors of a Favorable Outcome after Emergent Carotid Artery Stenting in Acute Anterior Circulation Stroke Patients

Purpose@#This study aimed to identify independent predictors of favorable outcomes associated with emergent carotid artery stenting (CAS) in patients with acute anterior circulation stroke. @*Materials and Methods@#This study included 93 patients with acute stroke who underwent emergent CAS to treat stenoocclusive lesions in the cervical internal carotid artery (ICA) within 6 hours of the onset of the associated symptoms. Data were compared between patients with and without favorable outcomes. The independent predictors of a favorable outcome were determined via logistic regression analysis (modified Rankin Scale 0–2 at 90 days). @*Results@#Intracranial tandem occlusion was noted in 81.7% of patients (76/93) among which (76/93), 55 of whom underwent intracranial recanalization therapy. Intracranial reperfusion was successful in 74.2% (69/93) and favorable outcomes were noted in 51.6% of patients (48/93). The mortality rate was 6.5% (6/93). In logistic regression analysis, diffusion-weighted imaging-Alberta Stroke Program Early CT Score [odds ratio (OR), 1.487; 95% confidence interval (CI), 1.018–2.173, p = 0.04], successful reperfusion (OR, 5.199; 95% CI, 1.566–17.265, p = 0.007), and parenchymal hemorrhage (OR, 0.042; 95% CI, 0.003–0.522, p = 0.014) were independently associated with a favorable outcome. @*Conclusion@#Baseline infarct size, reperfusion status, and parenchymal hemorrhage were independent predictors of favorable outcomes after emergent CAS to treat stenoocclusive lesions in the cervical ICA in patients with acute anterior circulation stroke.

Clinical Efficacy of Real-Time Sonoelastography for the Follow-Up of Congenital Sternocleidomastoid Muscle Torticollis

PURPOSE@#To evaluate the clinical efficacy of real-time sonoelastography (RTS) for the follow-up of congenital muscular torticollis, based on measurements of muscle elasticity.@*MATERIALS AND METHODS@#Thirty-four infants (23 male, 11 female) with congenital sternocleidomastoid (SCM) muscle torticollis underwent ultrasonography and elastography between November 2012 and December 2014. We evaluated the thickness, morphology (mass-like, fusiform, or overall thickened shape), and echogenicity of the SCM muscle on grayscale images and color patterns (homogeneous blue, mixed green < 50% and ≥ 50%, and green to red) on elastography. Strain ratios were measured using Q-lab software. A clinician classified the degree of neck rotation and side flexion deficits using a 5-point grade system based on angles of neck rotation and side flexion. Correlations between the ultrasonography and clinical findings were evaluated by statistical analysis.@*RESULTS@#Twenty-two infants had right and 12 had left SCM torticollis, respectively. Linear regression analysis showed that involved/contralateral SCM thickness differences, morphology, elasticity color scores, and strain ratios of the affected SCM muscles were significantly correlated with neck rotation and side flexion deficit scores (p < 0.05). The elasticity color score of the affected SCM muscle was the most significant factor.@*CONCLUSION@#RTS might provide a reliable means for evaluating and monitoring congenital muscular torticollis.

EEG Revealed That Fragrances Positively Affect Menopausal Symptoms in Mid-life Women

During mid-life, women experienced not only physical but also neurological transition. Because of this, many women suffer from physiological and/or psychological menopausal symptoms. Although hormone therapy (HT) was broadly used to alleviate menopausal symptoms, HT showed inconsistent effects in case of psychological symptoms. Moreover, mid-life women’s brains have distinct characteristics than in other periods of life, it is needed to study psychological symptoms in shifted brain network of mid-life women. As an alternative, inhalation of fragrances may alleviate psychological menopausal symptoms. To characterize the alleviation mechanism by fragrances, we tested the effect of fragrances on menopausal symptoms using electroencephalographic (EEG) methods. We hypothesized that fragrance could restore mid-life women’s brain response to stressful situations. We tested six fragrance conditions, including no-odor condition (solvent only) in twenty-eight mid-life women (49.75 years±3.49).Our results showed that fragrances increased alpha power and decreased β/α ratio depending on the severity of menopausal symptoms in a stressful situation. Our study would be helpful in psychological menopausal symptom alleviation as well as fragrance screening for well-being in mid-life.

Comparison of Intraocular Pressures Measured by the Corvis ST and Other Tonometers in Normal Eyes

PURPOSE@#We compared the intraocular pressures (IOPs) measured by the Corvis ST (CST), Goldmann applanation tonometer (GAT), and Rebound tonometer (RBT) and correlated the measured IOPs with the corneal center thickness (CCT).@*METHODS@#The IOPs were measured in 34 eyes of 34 normal subjects using the CST, GAT, and RBT by independent examiners. Comparisons between the IOPs measured by the CST and other tonometers were made using paired t-tests and Bland-Altman plots and Pearson's correlation coefficient was used to correlate the measured IOPs with the CCTs.@*RESULTS@#The IOP measured by the CST (C-IOP; 14.57 ± 2.37 mmHg) was significantly higher than that measured by the GAT(13.56 ± 2.25 mmHg) and RBT (13.57 ± 3.26 mmHg) (p 0.50). In analyzing the correlation with CCT, RBT-IOP (R =0.34; p = 0.705) showed a lower correlation than the GAT-IOP (R = 0.49; p < 0.01) and C-IOP (R = 0.48; p < 0.01). The C-bIOP showed no correlation with the CCT (p = 0.74).@*CONCLUSIONS@#The IOP measurements using the C-IOP were significantly higher than the RBT-IOP and GAT-IOP but the C-bIOP showed no difference from the RBT-IOP and GAT-IOP. C-IOP, RBT-IOP, and GAT-IOP were significantly correlated with corneal thickness, but there was no correlation between CCT and C-bIOP. C-bIOP may therefore be considered clinically useful.

Extraforaminal Extrusion of Intervertebral Disc Misdiagnosed as Neurogenic Tumor: a Case Report

A 55-year-old male presented with dysesthesia of the right anteromedial thigh. A magnetic resonance image revealed a globular mass at the right extraforaminal area of the L3/4 level. Based on the diagnosis of neurogenic tumor, surgical excision was performed. The surgical impression and pathology confirmed extrusion of intervertebral disc. In a retrospective review of the magnetic resonance image, we noticed a fibrillary pattern directed from the intervertebral disc space to the lesion, and disrupted annulus fibrosus and indentation caused by the ring apophysis. We suggest aforementioned features, indicative of intervertebral disc lesions, to be checked when interpreting mass lesions around the intervertebral foramen.

Extraforaminal Extrusion of Intervertebral Disc Misdiagnosed as Neurogenic Tumor: a Case Report

A 55-year-old male presented with dysesthesia of the right anteromedial thigh. A magnetic resonance image revealed a globular mass at the right extraforaminal area of the L3/4 level. Based on the diagnosis of neurogenic tumor, surgical excision was performed. The surgical impression and pathology confirmed extrusion of intervertebral disc. In a retrospective review of the magnetic resonance image, we noticed a fibrillary pattern directed from the intervertebral disc space to the lesion, and disrupted annulus fibrosus and indentation caused by the ring apophysis. We suggest aforementioned features, indicative of intervertebral disc lesions, to be checked when interpreting mass lesions around the intervertebral foramen.

Skull Base Dermoid Cyst in the Right Infratemporal Fossa Diagnosed Using the Dixon Technique: a Case Report and Review of Literature

Dermoid cysts are benign congenital tumors composed of keratinizing squamous epithelium and dermal derivatives. They account for less than 1% of all intracranial tumors and are rarely exhibited at the base of the skull. To the best of our knowledge, only one case report has presented computed tomography and conventional T1-weighted magnetic resonance (MR) findings that revealed an infratemporal dermoid cyst. In the present study, we report an unusual case of a dermoid cyst in the right infratemporal fossa, which was incidentally detected by MR imaging with the Dixon technique. This article also highlights the importance of meticulous radiological review and the usefulness of the Dixon technique in everyday clinical practice.

Skull Base Dermoid Cyst in the Right Infratemporal Fossa Diagnosed Using the Dixon Technique: a Case Report and Review of Literature

Dermoid cysts are benign congenital tumors composed of keratinizing squamous epithelium and dermal derivatives. They account for less than 1% of all intracranial tumors and are rarely exhibited at the base of the skull. To the best of our knowledge, only one case report has presented computed tomography and conventional T1-weighted magnetic resonance (MR) findings that revealed an infratemporal dermoid cyst. In the present study, we report an unusual case of a dermoid cyst in the right infratemporal fossa, which was incidentally detected by MR imaging with the Dixon technique. This article also highlights the importance of meticulous radiological review and the usefulness of the Dixon technique in everyday clinical practice.

External Snapping Hip Treated by Effective Designed N-plasty of the Iliotibial Band / 대한고관절학회지

PURPOSE: The purpose of this study is to present the effective design of N-plasty of the iliotibial band and surgical results of its use as a treatment for refractory external snapping hip. MATERIALS AND METHODS: We evaluated 17 patients (24 cases) with external snapping hip who underwent N-plasty between October 2013 and May 2016 and who were followed up for at least 12 months. All patients were male and the mean age was 20.8 years. The mean duration of symptoms prior to surgical intervention was 28.5 months with an average follow up of 24.5 months. Surgery was defined as being successful when patients could carry out their daily activities and exercise without a clicking sensation or pain 6 months after surgery until their last follow-up. Failure was defined when either a clicking sensation or pain was present. The visual analog scale (VAS) and modified Harris hip score (mHHS) were measured and compared preoperatively and at last follow-up. RESULTS: All patients had complete resolution of pain and snapping. The VAS decreased from 6.77 preoperatively to 0.09 postoperatively and mHHS improved from 69.5 to 97.8 after surgery. CONCLUSION: Modified designed N-plasty is considered to be an excellent treatment method facilitating operation reproducibility with maximum elongation effect of the iliotibial band.