Scale based entropy measures and deep learning methods for analyzing the dynamical characteristics of cardiorespiratory control system in COVID-19 subjects during and after recovery.

COVID-19, known as Coronavirus Disease 2019 primarily targets the respiratory system and can impact the cardiovascular system, leading to a range of cardiorespiratory complications. The current forefront in analyzing the dynamical characteristics of physiological systems and aiding clinical decision-making involves the integration of entropy-based complexity techniques with artificial intelligence. Entropy-based measures offer promising prospects for identifying disturbances in cardiorespiratory control system (CRCS) among COVID-19 patients by assessing the oxygen saturation variability (OSV) signals. In this investigation, we employ scale-based entropy (SBE) methods, including multiscale entropy (MSE), multiscale permutation entropy (MPE), and multiscale fuzzy entropy (MFE), to characterize the dynamical characteristics of OSV signals. These measurements serve as features for the application of traditional machine learning (ML) and deep learning (DL) approaches in the context of classifying OSV signals from COVID-19 patients during their illness and subsequent recovery. We use the Beurer PO-80 pulse oximeter which non-invasively acquired OSV and pulse rate data from COVID-19 infected patients during the active infection phase and after a two-month recovery period. The dataset comprises of 88 recordings collected from 44 subjects(26 men and 18 women), both during their COVID-19 illness and two months post-recovery. Prior to analysis, data preprocessing is performed to remove artifacts and outliers. The application of SBE measures to OSV signals unveils a reduction in signal complexity during the course of COVID-19. Leveraging these SBE measures as feature sets, we employ two DL techniques, namely the radial basis function network (RBFN) and RBFN with dynamic delay algorithm (RBFNDDA), for the classification of OSV data collected during and after COVID-19 recovery. To evaluate the classification performance, we employ standard metrics such as sensitivity, specificity, false positive rate (FPR), and the area under the receiver operator characteristic curve (AUC). Among the three scale-based entropy measures, MFE outperformed MSE and MPE by achieving the highest classification performance using RBFN with 13 best features having sensitivity (0.84), FPR (0.30), specificity (0.70) and AUC (0.77). The outcomes of our study demonstrate that SBE measures combined with DL methods offer a valuable approach for categorizing OSV signals obtained during and after COVID-19, ultimately aiding in the detection of CRCS dysfunction.

Complexity reduction of oxygen saturation variability signals in COVID-19 patients: Implications for cardiorespiratory control.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a respiratory illness that leads to severe acute respiratory syndrome and various cardiorespiratory complications, contributing to morbidity and mortality. Entropy analysis has demonstrated its ability to monitor physiological states and system dynamics during health and disease. The main objective of the study is to extract information about cardiorespiratory control by conducting a complexity analysis of OSV signals using scale-based entropy measures following a two-month timeframe after recovery. METHODS: This prospective study collected data from subjects meeting specific criteria, using a Beurer PO-80 pulse oximeter to measure oxygen saturation (SpO2) and pulse rate. Excluding individuals with a history of pulmonary/cardiovascular issues, the study analyzed 88 recordings from 44 subjects (26 men, 18 women, mean age 45.34 ± 14.40) during COVID-19 and two months post-recovery. Data preprocessing and scale-based entropy analysis were applied to assess OSV signals. RESULTS: The study found a significant difference in mean OSV during illness (95.08 ± 0.15) compared to post-recovery (95.59 ± 1.03), indicating reduced cardiorespiratory dynamism during COVID-19. Multiscale entropy analyses (MSE, MPE, MFE) confirmed lower entropy values during illness across all time scales, particularly at higher scales. Notably, the maximum distinction between illness and recovery phases was seen at specific time scales and similarity criteria for each entropy measure, showing statistically significant differences. CONCLUSIONS: The study demonstrates that the loss of complexity in OSV signals, quantified using scale-based entropy measures, has the potential to detect malfunctioning of cardiorespiratory control in COVID-19 patients. This finding suggests that OSV signals could serve as a valuable indicator for assessing the cardiorespiratory status of COVID-19 patients and monitoring their recovery progress.

Fractional order ATR-FTIR differential spectroscopy for detection of weak bands and assessing the radiation modifications in gamma sterilized UHMWPE.

This study presents a new method for identifying radiation modifications in UHMWPE polymer samples. The method involves using a mathematical technique called fractional order differential transformation on IR spectra obtained through ATR-FTIR spectroscopy. This new method was compared to existing techniques such as FTIR, XRD, and DSC, and it was found to be more sensitive and accurate in detecting radiation-induced changes in the polymer. The study focused on identifying changes in weak IR bands in the UHMWPE samples caused by gamma sterilization while simulating IR spectra using different orders of fractional derivatives and compared them to experimental spectra. It was found that applying a lower order of differentiation was more suitable for identifying radiation-induced changes in the UHMWPE samples. Using this method, they were able to identify specific changes in the gamma irradiated structure, such as the splitting of a single absorption peak into a doublet, which was only present in the 50 kGy irradiated sample. The study also used correlation index analysis, principal component analysis, and hierarchy cluster analysis to analyze the simulated and experimental spectra. These techniques allowed to confirm the effectiveness of the fractional order differential transformation method and to identify the specific regions of the IR spectra that were affected by radiation-induced changes in the UHMWPE samples. Overall, this study presents a new method for identifying radiation-induced changes in UHMWPE polymer samples that is more sensitive and accurate than existing techniques. By identifying these changes, researchers can better understand the effects of gamma sterilization on medical equipment and potentially develop new methods for sterilization that do not damage the equipment.

Simulation of Light Distribution in Gamma Irradiated UHMWPE Using Monte Carlo Model for Light (MCML) Transport in Turbid Media: Analysis for Industrial Scale Biomaterial Modifications.

(1) Background: This study investigated the miscibility of carbon-based fillers within industrial scale polymers for the preparation of superior quality polymer composites. It focuses on finding the light distribution in gamma irradiated ultra-high molecular weight polyethylene (UHMWPE). (2) Methods: The Kubleka-Munk model (KMM) was used to extract the optical properties, i.e., absorption coefficients (µa) and scattering coefficients (µs). Samples amounting to 30 kGy and 100 kGy of irradiated (in the open air) UHMWPE from 630 nm to 800 nm were used for this purpose. Moreover, theoretical validation of experimental results was performed while using extracted optical properties as inputs for the Monte Carlo model of light transport (MCML) code. (3) Conclusions: The investigations revealed that there was a significant decrease in absorption and scattering coefficient (µa & µs) values with irradiation, and 30 kGy irradiated samples suffered more compared to 100 kGy irradiated samples. Furthermore, the simulation of light transport for 800 nm showed an increase in penetration depth for UHMWPE after gamma irradiation. The decrease in dimensionless transport albedo  µs(µa+µs) from 0.95 to 0.93 was considered responsible for this increase in photon absorption per unit area with irradiation. The report results are of particular importance when considering the light radiation (from 600 nm to 899 nm) for polyethylene modification and/or stabilization via enhancing the polyethylene chain mobility.

A hybrid model for forecasting of particulate matter concentrations based on multiscale characterization and machine learning techniques.

Accurate prediction of particulate matter (PM) using time series data is a challenging task. The recent advancements in sensor technology, computing devices, nonlinear computational tools, and machine learning (ML) approaches provide new opportunities for robust prediction of PM concentrations. In this study, we develop a hybrid model for forecasting PM10 and PM2.5 based on the multiscale characterization and ML techniques. At first, we use the empirical mode decomposition (EMD) algorithm for multiscale characterization of PM10 and PM2.5 by decomposing the original time series into numerous intrinsic mode functions (IMFs). Different individual ML algorithms such as random forest (RF), support vector regressor (SVR), k-nearest neighbors (kNN), feed forward neural network (FFNN), and AdaBoost are then used to develop EMD-ML models. The air quality time series data from Masfalah air station Makkah, Saudi Arabia are utilized for validating the EMD-ML models, and results are compared with non-hybrid ML models. The PMs (PM10 and PM2.5) concentrations data of Dehli, India are also utilized for validating the EMD-ML models. The performance of each model is evaluated using root mean square error (RMSE) and mean absolute error (MAE). The average bias in the predictive model is estimated using mean bias error (MBE). Obtained results reveal that EMD-FFNN model provides the lowest error rate for both PM10 (RMSE = 12.25 and MAE = 7.43) and PM2.5 (RMSE = 4.81 and MAE = 3.02) using Misfalah, Makkah data whereas EMD-kNN model provides the lowest error rate for PM10 (RMSE = 20.56 and MAE = 12.87) and EMD-AdaBoost provides the lowest error rate for PM2.5 (RMSE = 15.29 and MAE = 9.45) using Dehli, India data. The findings also reveal that EMD-ML models can be effectively used in forecasting PM mass concentrations and to develop rapid air quality warning systems.

Structure and Performance Attributes Optimization and Ranking of Gamma Irradiated Polymer Hybrids for Industrial Application.

The selection of suitable composite material for high-strength industrial applications, from the list of available alternatives, is a tedious task as it requires an optimized structural performance-based solution. This study aimed to optimize the concentration of fillers, i.e., vinyl tri-ethoxy silane and absorbed gamma-dose, to enhance the properties of an industrial scale polymer, i.e., ultra-high molecular weight polyethylene (UHMWPE). The UHMWPE hybrids, in addition to silane, were treated with (30, 65, and 100 kGy) gamma dose and then tested for ten application-specific structural and performance attributes. The relative importance of attributes based on an 11-point fuzzy conversation was used for establishing the material assessment graph and corresponding adjacency matrix. Afterwards, the normalized values of attributes were used to establish the decision matrix for each alternative. The normalization was performed after the identification of high obligatory valued (HOV) and low obligatory valued (LOV) attributes. After this, suitability index values (SIVs) were calculated for ranking the hybrids that revealed hybrids 65 kGy irradiated the hybrid as the best choice and ranked as first among the existing alternatives. The major responsible factors were higher oxidation strength, a dense cross-linking network, and elongation at break. The values of the aforementioned factors for 65 kGy irradiated hybrids were 0.24, 91, and 360 MPa, respectively, as opposed to 0.54, 75, and 324 MPa for 100 kGy irradiated hybrids, thus placing the latter in second place regarding higher values of Yield Strength and Young Modulus. Finally, it is believed that the reported results and proposed model in this study will improve preoperative planning as far as considering these hybrids for high-strength industrial applications including total joint arthroplasty, textile-machinery pickers, dump trucks lining ships, and harbors bumpers and sliding, etc.

Bacteriological Profile And Antibiotic Susceptibility Patterns In Diabetic Foot Infections, At Lady Reading Hospital, Peshawar.

BACKGROUND: Diabetic foot ulcer is one of the common complications of diabetes and is also the major cause of hospitalization across the world. To treat it properly, bacteriological profile is important to institute appropriate treatment. This study is done with the objective to determine the microbiological profile and antibiotic susceptibility patterns of organisms isolated from diabetic foot ulcers in Lady Reading Hospital, Peshawar Pakistan. METHODS: This cross-sectional study was conducted from January to June 2019. Swab samples were collected from 114 patients with diabetic foot infections and inoculated on appropriate media. Antibiotic susceptibility tests were done by Kirby Bauer disk diffusion method. RESULTS: E. coli were predominately isolated in the study, with ESBL in 41.6% of the cases. Strains of Pseudomonas with MDR and XDR were isolated in 21.8% and 6.25% of the patients respectively. Majority of Gram-positive isolates were Staphylococcus aureus, those were MRSA in 76.6% of samples. The commonly involved sites of DFU were the toes and forefoot, and the main causes were blister formation or trauma. Most of the patients were identified to have risk factors such as peripheral neuropathy, peripheral arterial disease, over weight and poorly controlled diabetes. CONCLUSIONS: In our study, Gram negative aerobes were predominantly isolated in the diabetic foot infections. A significant number of MDR isolates were also observed. Lack of awareness about DFU and inappropriate use of broad-spectrum antibiotics may be the main cause of increase in the frequency of MDR isolates.

Detecting prostate cancer using deep learning convolution neural network with transfer learning approach.

Prostate Cancer in men has become one of the most diagnosed cancer and also one of the leading causes of death in United States of America. Radiologists cannot detect prostate cancer properly because of complexity in masses. In recent past, many prostate cancer detection techniques were developed but these could not diagnose cancer efficiently. In this research work, robust deep learning convolutional neural network (CNN) is employed, using transfer learning approach. Results are compared with various machine learning strategies (Decision Tree, SVM different kernels, Bayes). Cancer MRI database are used to train GoogleNet model and to train Machine Learning classifiers, various features such as Morphological, Entropy based, Texture, SIFT (Scale Invariant Feature Transform), and Elliptic Fourier Descriptors are extracted. For the purpose of performance evaluation, various performance measures such as specificity, sensitivity, Positive predictive value, negative predictive value, false positive rate and receive operating curve are calculated. The maximum performance was found with CNN model (GoogleNet), using Transfer learning approach. We have obtained reasonably good results with various Machine Learning Classifiers such as Decision Tree, Support Vector Machine RBF kernel and Bayes, however outstanding results were obtained by using deep learning technique.

Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data.

We propose a new method based on the idea of delegating regressors for predicting the soil radon gas concentration (SRGC) and anomalies in radon or any other time series data. The proposed method is compared to different traditional boosting e.g., Extreme Gradient Boosting (EGB) and simple regression methods e.g., support vector regressors with linear kernel and radial kernel in terms of accurate predictions. R language has been used for the statistical analysis of radon time series (RTS) data. The results obtained show that the proposed methodology predicts SRGC more accurately when compared to different traditional boosting and regression methods. The best correlation is found between the actual and predicted radon concentration for window size of 2 i.e., two days before and after the start of seismic activities. RTS data was collected from 05 February 2017 to 16 February 2018, including 7 seismic events recorded during the study period. Findings of study show that the proposed methodology predicts the SRGC with more precision, for all the window sizes, by overlapping predicted with the actual radon time series concentrations.

Descriptive analysis and earthquake prediction using boxplot interpretation of soil radon time series data.

Correlation of radon anomalies with meteorological parameters and earthquake occurrence has been reported in many studies. This paper reports descriptive statistical analysis and boxplot contingent earthquake prediction based upon soil radon time series data. Data has been collected over a fault line, passing beneath the Muzaffarabad, for the period of one year. Soil radon gas (SRG) was measured using RTM 1688-2 radiometric instrument (made by SARAD GmbH). The range of radon in soil was found to be 14349 Bqm-3, whereas the ranges of temperature, pressure and relative humidity were found as 38.50 C0, 29 mbar and 67% respectively. SRG data shows that time series follows normal distribution. Values of coefficient of variation (CV) indicate the consistency of the recorded values of radon in soil and metrological parameters. Variance inflation factor (VIF) and Durbin Watson test (d) indicate a moderate multicollinearity and autocorrelation between variables. The analysis of radon time series using boxplots and meteorological parameters show specific patterns in radon concentrations (outliers, variant IQRs, first quartile values, and median values) due to pre-earthquake underground seismic activities. On the basis of these patterns earthquake may be more early predicted without using complicated predictive systems. Boxplots also predicted that there is no significant pattern found in dispersion of meteorological factors measured in this study. To the best of our knowledge this is first ever attempt to predict earthquake using boxplot explanation.

Automated anomalous behaviour detection in soil radon gas prior to earthquakes using computational intelligence techniques.

In this article, three computational intelligence (CI) models were developed to automatically detect anomalous behaviour in soil radon gas (222Rn) time series data. Data were obtained at a fault line and analysed using three machine learning techniques with the aim at identifying anomalies in temporal radon data prompted by seismic events. Radon concentrations were modelled with corresponding meteorological and statistical parameters. This leads to the estimation of soil radon gas without and with meteorological parameters. The comparison between computed radon concentration and actual radon concentrations was used in finding radon anomaly based upon automated system. The anomaly in radon time series data could be considered due to noise or seismic activity. Findings of study show that under meticulously characterized environments, on exclusion of noise contribution, seismic activity is responsible for anomalous behaviour seen in radon time series data.

Detecting Brain Tumor using Machines Learning Techniques Based on Different Features Extracting Strategies.

BACKGROUND: Brain tumor is the leading cause of death worldwide. It is obvious that the chances of survival can be increased if the tumor is identified and properly classified at an initial stage. MRI (Magnetic Resonance Imaging) is one source of brain tumors detection tool and is extensively used in the diagnosis of brain to detect blood clots. In the past, many researchers developed Computer-Aided Diagnosis (CAD) systems that help the radiologist to detect the abnormalities in an efficient manner. OBJECTIVE: The aim of this research is to improve the brain tumor detection performance by proposing a multimodal feature extracting strategy and employing machine learning techniques. METHODS: In this study, we extracted multimodal features such as texture, morphological, entropybased, Scale Invariant Feature Transform (SIFT), and Elliptic Fourier Descriptors (EFDs) from brain tumor imaging database. The tumor was detected using robust machine learning techniques such as Support Vector Machine (SVM) with kernels: polynomial, Radial Base Function (RBF), Gaussian; Decision Tree (DT), and Naïve Bayes. Most commonly used Jack-knife 10-fold Cross- Validation (CV) was used for testing and validation of dataset. RESULTS: The performance was evaluated in terms of specificity, sensitivity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy (TA), Area under the receiver operating Curve (AUC), and P-value. The highest performance of 100% in terms of Specificity, Sensitivity, PPV, NPV, TA, AUC using Naïve Bayes classifiers based on entropy, morphological, SIFT and texture features followed by Decision Tree classifier with texture features (TA=97.81%, AUC=1.0) and SVM polynomial kernel with texture features (TA=94.63%). The highest significant p-value was obtained using SVM polynomial with texture features (P-value 2.65e-104) followed by SVM RB with texture features (P-value 1.96e-98). CONCLUSION: The results reveal that Naïve Bayes followed by Decision Tree gives highest detection accuracy based on entropy, morphological, SIFT and texture features.

Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis.

Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as interbeat interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features.

Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm.

In this paper, we have employed K-d tree algorithmic based multiscale entropy analysis (MSE) to distinguish alcoholic subjects from non-alcoholic ones. Traditional MSE techniques have been used in many applications to quantify the dynamics of physiological time series at multiple temporal scales. However, this algorithm requires O(N2), i.e. exponential time and space complexity which is inefficient for long-term correlations and online application purposes. In the current study, we have employed a recently developed K-d tree approach to compute the entropy at multiple temporal scales. The probability function in the entropy term was converted into an orthogonal range. This study aims to quantify the dynamics of the electroencephalogram (EEG) signals to distinguish the alcoholic subjects from control subjects, by inspecting various coarse grained sequences formed at different time scales, using traditional MSE and comparing the results with fast MSE (fMSE). The performance was also measured in terms of specificity, sensitivity, total accuracy and receiver operating characteristics (ROC). Our findings show that fMSE, with a K-d tree algorithmic approach, improves the reliability of the entropy estimation in comparison with the traditional MSE. Moreover, this new technique is more promising to characterize the physiological changes having an affect at multiple time scales.

Ex vivo assessment of carbon tetrachloride (CCl(4))-induced chronic injury using polarized light spectroscopy.

The liver performs various functions, such as the production and detoxification of chemicals; therefore, it is susceptible to hepatotoxins such as carbon tetrachloride (CCl4), which causes chronic injury. Thus, assessment of injury and its status of severity are of prime importance. Current work reports an ex vivo study for probing the severance of hepatic injury induced by CCl4 with polarized light over the spectral range 400-800 nm. Different concentrations of CCl4 were used to induce varying severity of hepatic injury in a rat model. Linear retardance, depolarization rates, and diagonal Mueller matrix elements (m22, m33, and m44), were successfully used as the distinguishing criterion for normal and different liver injuries. Our results show that linear retardance for injured liver samples with lower doses of CCl4 tends to increase when compared with normal liver samples, while samples injured at higher doses of CCl4 offer almost no retardance. Total, linear, and circular depolarizations follow decreasing trends with increased liver injury severity over the entire investigated wavelength range. Linear polarization states were observed to be better maintained as compared to circular polarization states for all samples. Furthermore, numerical values of diagonal elements of the experimentally measured Mueller matrix also increase with increasing doses of CCl4. Liver fibroses, change in transport albedo, and the relative refractive index of the extracellular matrix caused by CCl4 are responsible for the observed differences. These results will provide a pathway to gauge the severity of injury caused by toxic chemicals.

High altitude pulmonary edema among "Amarnath Yatris".

BACKGROUND: Annual pilgrimage (Yatra) to the cave shrine of Shri Amarnath Ji' is a holy ritual among the Hindu devotees of Lord Shiva. Located in the Himalayan Mountain Range (altitude 13,000 ft) in south Kashmir, the shrine is visited by thousands of devotees and altitude sickness is reportedly common. MATERIALS AND METHODS: More than 600,000 pilgrims visited the cave shrine in 2011 and 2012 with 239 recorded deaths. Thirty one patients with suspected altitude sickness were referred from medical centers en-route the cave to Sher-i-Kashmir Institute of Medical Sciences, a tertiary-care center in capital Srinagar (5,000 ft). The clinical features and the response to treatment were recorded. RESULTS: Thirty-one patients (all lowlanders, 19 male; age 18-60 years, median 41) had presented with acute onset breathlessness of 1-4 days (median 1.9 d) starting within 12-24 h of a rapid ascent; accompanied by cough (68%), headache (8%), dizziness and nausea (65%). Sixteen patients had associated encephalopathy. Clinical features on admission included tachypnea (n = 31), tachycardia (n = 23), bilateral chest rales (n = 29), cyanosis (n = 22) and grade 2-4 encephalopathy. Hypoxemia was demonstrable in 24 cases and bilateral infiltrates on radiologic imaging in 29. Ten patients had evidence of high-altitude cerebral edema. All patients were managed with oxygen, steroids, nifedipine, sildenafil and other supportive measures including invasive ventilation (n = 3). Three patients died due to multiorgan dysfunction. CONCLUSIONS: Altitude sickness is common among Amaranath Yatris from the plains and appropriate educational strategies should be invoked for prevention and prompt treatment.

Assessment of immunohistochemistry for p16INK4 and high-risk HPV DNA by in situ hybridization in esophageal squamous cell carcinoma.

The role of high-risk human papillomavirus (HPV) in the pathogenesis of esophageal squamous cell carcinoma (ESCC) remains unclear. p16(INK4) is used as a surrogate marker to detect HPV-related tumors but has had discrepant results in ESCC. In this study, 32 cases of ESCC were examined to determine the relationship between p16(INK4) expression and high-risk HPV. All the tumors were stained by immunohistochemistry for p16(INK4). Tumors having p16(INK4) nuclear and/or nuclear and cytoplasmic expression were considered positive. Tumors positive for p16(INK4) expression were tested for high-risk HPV by in situ hybridization (ISH). In all, 20 cases of ESCC (63%) showed only cytoplasmic staining for p16(INK4), and 11 cases (34%) showed both cytoplasmic and nuclear staining for p16(INK4); 4 cases (13%) showed no staining for p16(INK4). None of the p16(INK4) -positive cases were positive for high-risk HPV by ISH. These results indicate that p16(INK4) expression in ESCC does not correlate with the presence of high-risk HPV DNA by ISH. High-risk HPV does not seem to play a major role in the carcinogenesis of ESCC in low-risk areas.

Pilomatrix carcinoma of the thoracic spine: case report and review of the literature.

CONTEXT: Pilomatrixoma is a common head and neck neoplasm in children. Its malignant counterpart, pilomatrix carcinoma, is rare and found more often in men. METHOD: Case report of a 21-year-old man with pilomatrixoma of the thoracic spine that underwent malignant degeneration to pilomatrix carcinoma. FINDINGS: The appearance of a painless mobile axillary mass was followed by severe back pain 1 year later. Imaging revealed a compression fracture at the T5 level. The patient underwent resection of the axillary mass and spinal reconstruction of the fracture; the pathology was consistent with synchronous benign pilomatrixomas. Three months later he presented with a recurrence of the spinal lesion and underwent further surgical resection; the pathology was consistent with pilomatrix carcinoma. He received adjuvant radiotherapy and at his 1-year follow-up examination had no sign of recurrence. CONCLUSION/CLINICAL RELEVANCE: Pilomatrix carcinoma involving the spine is a rare occurrence. It has a high incidence of local recurrence, and wide excision may be necessary to reduce this risk. Radiotherapy may be a helpful adjuvant therapy. Clinicians should be aware of this entity because of its potential for distant metastasis.