Cervical spinal manipulation is considered to be a safe procedure for treating patients with neck pain and muscle-tension headache. Rarely has acute cervical spinal epidural hematoma after spinal manipulation been reported. Here, we report a 16-year-old healthy male adolescent who presented with progressive weakness in the right extremities following acute neck and shoulder pain after spinal manipulation from Acute cervical spinal epidural hematoma with compression of spinal cord. After emergency surgery the patient had full recovery from the profound neurological deficits.
Hematoma, Epidural, Spinal

The prevalence of asthma and allergic disease has increased worldwide over the last few decades. Many common environmental factors are associated with this increase. Several theories have been proposed to account for this trend, especially those concerning the impact of environmental toxicants. The development of the immune system, particularly in the prenatal period, has far-reaching consequences for health during early childhood, and throughout adult life. One underlying mechanism for the increased levels of allergic responses, secondary to exposure, appears to be an imbalance in the T-helper function caused by exposure to the toxicants. Exposure to environmental endocrine-disrupting chemicals can result in dramatic changes in cytokine production, the activity of the immune system, the overall Th1 and Th2 balance, and in mediators of type 1 hypersensitivity mediators, such as IgE. Passive exposure to tobacco smoke is a common risk factor for wheezing and asthma in children. People living in urban areas and close to roads with a high volume of traffic, and high levels of diesel exhaust fumes, have the highest exposure to environmental compounds, and these people are strongly linked with type 1 hypersensitivity disorders and enhanced Th2 responses. These data are consistent with epidemiological research that has consistently detected increased incidences of allergies and asthma in people living in these locations. During recent decades more than 100,000 new chemicals have been used in common consumer products and are released into the everyday environment. Therefore, in this review, we discuss the environmental effects on allergies of indoor and outside exposure.
Adult ; Asthma ; Child ; Humans ; Hypersensitivity ; Immune System ; Immunoglobulin E ; Incidence ; Inflammation* ; Prevalence ; Respiratory Sounds ; Risk Factors ; Smoke ; Smoking ; Tobacco ; Vehicle Emissions

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.