The need for permanent pacemaker after restoration of conduction following atrioventricular block: a retrospective cohort study.

OBJECTIVE: A permanent pacemaker (PPM) is necessary for patients with a symptomatic third-degree or advanced second-degree atrioventricular (AV) block. An AV block due to medication use can often be reversed; however, subsequent relapse can occur and necessitate subsequent PPM implantation. The aim of this study was to explore the course and prognosis of patients with an AV block. METHODS: This historical cohort study was conducted between January 2013 and June 2018. A total of 1900 patient records were analyzed and 1123 subjects with an AV block on admission were enrolled. The patients were categorized into 2 groups: Group 1 comprised patients with an AV block due to medication use (n=316, 28%) and Group 2 included patients with an AV block caused by other etiologies (n=807, 72%). Data of the cause of AV block, recurrence, and PPM implantation were analyzed. Patients in both groups who did not require a PPM during the index admission were followed up regarding subsequent implantation of a PPM. RESULTS: AV conduction was recovered in 38 (12%) patients in Group 1 and 48 (6%) patients in Group 2 during the index hospitalization. However, recurrence of the AV block was observed in 18% of Group 1 patients and 40% of Group 2 patients. Only 25 patients in each group (4.5% of the whole study population) remained PPM-free during a median 3-year follow-up period. CONCLUSION: The study findings suggest that drug-induced AV blocks may not be as benign as previously thought. The high relapse rate indicates that watchful follow-up may be required despite discontinuation of the responsible medication and that consideration of earlier PPM implantation in cases of early recurrence may be warranted.

Eosinophilic Mucin Rhinosinusitis in Iranian Patients Undergoing Endoscopic Sinus Surgery.

INTRODUCTION: Eosinophilic mucin rhinosinusitis is a type of chronic rhinosinusitis (CRS). Diagnosis and treatment of this condition play a significant role in reducing the patients' clinical symptoms. This type of rhinosinusitis has a higher relapse rate, compared to the other types. This disease is more resistant to treatment and more dependent on corticosteroid therapy, compared to the other types of rhinosinusitis. Regarding this, the present study was designed to evaluate the frequency of eosinophilic mucin rhinosinusitis in patients undergoing sinus surgery in a tertiary referral center and examine some clinical and laboratory characteristics regarding this type of rhinosinusitis. MATERIALS AND METHODS: This cross-sectional observational study was performed on patients over the age of 16 years, who were diagnosed with CRS in the otolaryngology clinic of a referral tertiary-level hospital, and were candidates for endoscopic sinus surgery. Based on the detection of eosinophilic mucin, the subjects were divided into two groups of eosinophilic mucin and non-eosinophilic mucin rhinosinusitis (controls). The groups were compared in terms of sino-nasal outcome test (SNOT-22) scores, Lund-Mackay staging scores, osteitis status, immunoglobulin E (IgE) level, and eosinophilia. RESULTS: In this study, 46 subjects participated, 29 (63%) cases of whom had eosinophilic mucin. The SNOT-22 score and serum IgE level were significantly higher in the eosinophilic mucin group, compared to those in the control group. Osteitis and Lund-Mackay scores were also higher in the eosinophilic mucin group than those in the control group; however, this difference was not statistically significant. CONCLUSION: Patients with eosinophilic mucin rhinosinusitis showed a more severe clinical involvement. Seemingly, the Iranian patients have a lower and higher frequency of eosinophilic mucin rhinosinusitis, compared to the patients from the Western countries and East Asia, respectively.

Biphasic effect of sumatriptan on PTZ-induced seizures in mice: Modulation by 5-HT1B/D receptors and NOS/NO pathway.

Sumatriptan has been among the top choices in the management of migraine headaches. The association between migraine and epilepsy highlights the possible effect of sumatriptan on seizures. In this regard, we investigated sumatriptan effects on PTZ-induced seizures thresholds and delineated the modulatory role of 5-HT1B/D receptors and NOS/NO pathway. Our data revealed the anti-convulsant effects of lower doses of sumatriptan, and pro-convulsant effects of higher doses of sumatriptan. GR 127935, a selective 5-HT1B/D antagonist, could abolish the sumatriptan anti-convulsant effects, but it was ineffective against the sumatriptan pro-convulsant effects. Serotonin depletion by consecutive administration of p-CPA, a selective irreversible inhibitor of tryptophan hydroxylase, could not affect the anti-convulsant effects of sumatriptan. The anti-convulsant effects of sumatriptan was potentiated by L-NAME, a non-selective NOS inhibitor, 7-NI, a selective nNOS inhibitor, but not AG, an iNOS inhibitor. It was also neutralized by L-ARG, a NO precursor. The pro-convulsant effects of sumatriptan were blocked by L-NAME and AG, but not 7-NI. It was also potentiated by L-ARG. Our data revealed that anti-convulsive effects of sumatriptan is mediated by interaction between non-serotonergic 5-HT1B/D receptors and nNOS/NO pathway. Besides, the pro-convulsive effect of sumatriptan is mediated by iNOS/NO pathway independent of 5-HT1B/D receptors. For the first time, this study reported the biphasic effect of sumatriptan on an animal model of GCS and its modulatory pathways.

Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.

Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd.

Levosimendan exerts anticonvulsant properties against PTZ-induced seizures in mice through activation of nNOS/NO pathway: Role for KATP channel.

AIMS: Although approving new anticonvulsants was a major breakthrough in the field of epilepsy control, so far we have met limited success in almost one third of patients suffering from epilepsy and a definite and reliable method is yet to be found. Levosimendan demonstrated neuroprotective effects and reduced mortality in conditions in which seizure can be an etiology of death; however, the underlying neuroprotective mechanisms of levosimendan still eludes us. In the light of evidence suggesting levosimendan can be a KATP channel opener and nitrergic pathway activator, levosimendan may exert antiseizure effects through KATP channels and nitrergic pathway. MAIN METHODS: In this study, the effects of levosimendan on seizure susceptibility was studied by PTZ-induced seizures model in mice. KEY FINDINGS: Administration of a single effective dose of levosimendan significantly increased seizures threshold and the nitrite level in the hippocampus and temporal cortex. Pretreatment with noneffective doses of glibenclamide (a KATP channel blocker) and L-NAME (a non-selective NOS inhibitor) neutralize the anticonvulsant and nitrite elevating effects of levosimendan. While 7-NI (a neural NOS inhibitor) blocked the anticonvulsant effect of levosimendan, Aminoguanidine (an inducible NOS inhibitor) failed to affect the anticonvulsant effects of levosimendan. Cromakalim (a KATP channel opener) or l-arginine (an NO precursor) augmented the anticonvulsant effects of a subeffective dose of levosimendan. Moreover, co-administration of noneffective doses of Glibenclamide and L-NAME demonstrated a synergistic effect in blocking the anticonvulsant effects of levosimendan. SIGNIFICANCE: Levosimendan has anticonvulsant effects possibly via KATP/nNOS/NO pathway activation in the hippocampus and temporal cortex.

Investigation of Size and Morphology of Chitosan Nanoparticles Used in Drug Delivery System Employing Chemometric Technique.

The polymeric nanoparticles are prepared from biocompatible polymers in size between 10-1000 nm. Chitosan is a biocompatible polymer that - can be utilized as drug delivery systems. In this study, chitosan nanoparticles were synthesized using an optimized spontaneous emulsification method. Determining particle size and morphology are two critical parameters in nanotechnology. The aim of this study is to introduce methodology based on relation between particle size and diffuse reflectance infrared fourier transform (DRIFT) spectroscopy technique. Partial least squares (PLS) technique was used to estimate the average particle size based on DRIFT spectra. Forty two different chitosan nanoparticle samples with different particle sizes were analyzed using DRIFT spectrometry and the obtained data were processed by PLS. Results obtained from the real samples were compared to those obtained using field emission scanning electron microscope(FE-SEM) as a reference method. It was observed that PLS could correctly predict the average particle size of synthesized sample. Nanoparticles and their morphological state were determined by FE-SEM. Based on morphological characteristics analyzing with proposed method the samples were separated into two groups of "appropriate" and "inappropriate". Chemometrics methods such as principal component analysis, cluster analysis (CA) and linear discriminate analysis (LDA) were used to classify chitosan nanoparticles in terms of morphology. The percent of correctly classified samples using LDA were 100 %and 90% for training and test sets, respectively.

Diagnostic prediction of renal failure from blood serum analysis by FTIR spectrometry and chemometrics.

A new diagnostic approach based on Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectrometry and classification algorithm has been introduced which provides a rapid, reliable, and easy way to perform blood test for the diagnosis of renal failure. Blood serum samples from 35 renal failure patients and 40 healthy persons were analyzed by ATR-FTIR spectrometry. The resulting data was processed by Quadratic Discriminant Analysis (QDA) and QDA combined with simple filtered method. Spectroscopic studies were performed in 900-2000cm(-)(1) spectral region with 3.85cm(-1) data space. Results showed 93.33% and 100% of accuracy for QDA and filter-QDA models, respectively. In the first step, 30 samples were applied to construct the model. In order to modify the capability of QDA in prediction of test samples, filter-based feature selection methods were applied. It was found that the filtered spectra coupled with QDA could correctly predict the test samples in most of the cases.

Preparation and optimization of chitosan nanoparticles and magnetic chitosan nanoparticles as delivery systems using Box-Behnken statistical design.

Chitosan nanoparticles and magnetic chitosan nanoparticles can be applied as delivery systems for the anti-Alzheimer drug tacrine. Investigation was carried out to elucidate the influence of process parameters on the mean particle size of chitosan nanoparticles produced by spontaneous emulsification. The method was optimized using design of experiments (DOE) by employing a 3-factor, 3-level Box-Behnken statistical design. This statistical design is used in order to achieve the minimum size and suitable morphology of nanoparticles. Also, magnetic chitosan nanoparticles were synthesized according to optimal method. The designed nanoparticles have average particle size from 33.64 to 74.87nm, which were determined by field emission scanning electron microscopy (FE-SEM). Drug loading in the nanoparticles as drug delivery systems has been done according to the presented optimal method and appropriate capacity of drug loading was shown by ultraviolet spectrophotometry. Chitosan and magnetic chitosan nanoparticles as drug delivery systems were characterized by Diffuse Reflectance Fourier Transform Mid Infrared spectroscopy (DR-FTMIR).

Application of linear discriminant analysis and Attenuated Total Reflectance Fourier Transform Infrared microspectroscopy for diagnosis of colon cancer.

Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) microspectroscopy was applied for detection of colon cancer according to the spectral features of colon tissues. Supervised classification models can be trained to identify the tissue type based on the spectroscopic fingerprint. A total of 78 colon tissues were used in spectroscopy studies. Major spectral differences were observed in 1,740-900 cm(-1) spectral region. Several chemometric methods such as analysis of variance (ANOVA), cluster analysis (CA) and linear discriminate analysis (LDA) were applied for classification of IR spectra. Utilizing the chemometric techniques, clear and reproducible differences were observed between the spectra of normal and cancer cases, suggesting that infrared microspectroscopy in conjunction with spectral data processing would be useful for diagnostic classification. Using LDA technique, the spectra were classified into cancer and normal tissue classes with an accuracy of 95.8%. The sensitivity and specificity was 100 and 93.1%, respectively.

Diagnosis of colon cancer by attenuated total reflectance-Fourier transform infrared microspectroscopy and soft independent modeling of class analogy.

This study tries to demonstrate that attenuated total reflectance-fourier transform infrared (ATR-FTIR) microspectroscopy in combination with chemometric methods can reliably distinguish malignant colon tissues from healthy ones. It is important to explore a noninvasive and rapid method for detection of colon cancer biopsies. Initially, principal component analysis was applied to examine the degree of separation between tissue samples. Soft independent modeling of class analogy (SIMCA) was also employed to evaluate the prediction accuracy of ATR-FTIR microspectroscopy for the diagnosis of colon cancer. There were significant differences in the fourier transform infrared spectra of normal and cancerous colon biopsies in the 1,800-900 cm(-1) spectral region. The SIMCA results demonstrated that the accuracy, specificity, and sensitivity of the proposed diagnostic method were 93.3, 100, and 88.2%, respectively, which could help satisfy clinical diagnostic requirements.

Diagnosis of basal cell carcinoma by infrared spectroscopy of whole blood samples applying soft independent modeling class analogy.

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy was applied to discriminate the blood samples obtained from healthy people and those with basal cell carcinoma, demonstrating high accuracy while soft independent modeling class analogy (SIMCA) chemometric technique is benefited. It was aimed to classify the normal case and cancer case blood samples through the use of ATR-FTIR spectroscopy as a rapid method while the sample preparation is so easy in comparison with the common pathologic methods. A total of 72 blood samples, including 32 cancer and 40 normal cases, were analyzed in 1,800-900 cm(-1) spectral region. Results showed 97.6% of accuracy being compared with the current clinical methods. Research results were exemplified with comparable data of other classification methods such as principal component analysis (PCA) and Cluster analysis. The residual errors in prediction (REP) of calibration model for normal and cancerous groups in SIMCA method were 0.00362 and 0.00343, respectively.