Effects of dry needling plus exercise therapy on post-stroke spasticity and motor function: A case report.

BACKGROUND AND PURPOSE: The use of dry needling (DN) with other treatments may be more beneficial in managing post-stroke spasticity. We report the effects of DN plus exercise therapy (ET) on wrist flexor spasticity. PATIENT PRESENTATION: The patient was a 45-year-old man with an 8-year history of stroke. The outcome measures included the Modified Modified Ashworth Scale (MMAS), Hmax/Mmax ratio, H-reflex latency, Action Research Arm Test (ARAT), Fugl-Meyer Assessment (FMA), and range of motion (ROM) which were assessed before (T1), after (T2), and after 3-week follow-up (T3). CONCLUSION: The MMAS was improved at T2 from "3" to "2". The Hmax/Mmax decreased from 0.77 to 0.53 at T3. The H-reflex latency increased from 15.4 ms to 18.5 ms at T3. The wrist active and passive ROM increased ∼30° and ∼20° at T2, respectively. A 4-session DN plus ET may improve spasticity and ROM. No meaningful improvement was observed in function.

Correction: The ß-cyclodextrin-modified nanosized ZSM-5 zeolite as a carrier for curcumin.

[This corrects the article DOI: 10.1039/C9RA04739E.].

The ß-cyclodextrin-modified nanosized ZSM-5 zeolite as a carrier for curcumin.

Herein, the nanosized ZSM-5 zeolite was synthesized based on a fractional factorial experimental design by a hydrothermal method to study the optimum conditions for the synthesis and formation of the ZSM-5 zeolite by employing different conditions. The samples were synthesized without any organic template, and different conditions, such as the molar composition of the synthesis gel and reaction time, were applied in a wide range. Then, the samples were analysed by X-ray diffraction to investigate the formation of the zeolite ZSM-5, and the results were compared to obtain the optimum conditions for its synthesis. The obtained samples were characterized by SEM, FTIR spectroscopy and TGA. Then, the functionalization of nano zeolite ZSM-5 crystals with ß-cyclodextrin (ß-CD) was investigated. The zeolite surface was first functionalized with amino groups using an amino alkoxysilane. Then, toluene diisocyanate was reacted with the amino-terminated ZSM-5 zeolite crystals and used for the incorporation of ß-CD via its remaining isocyanate groups. After this, a drug delivery system (DDS) was prepared based on the cyclodextrin-modified zeolite with the curcumin anticancer drug, and its formation was studied under experimental conditions. The results of in vitro studies show that this drug delivery system has better characteristics than free curcumin in terms of stability and anti-proliferative and anti-inflammatory effects.

Mesoporosity based classification of ZSM-5 nano catalysts using DRIFT spectroscopy and chemometrics.

DRIFT spectra were used for classification of ZSM-5 catalysts according to their mesopore volumes. The spectra were pretreated by Savitzky-Golay smoothing and standard normal variate (SNV) algorithms prior to outlier detection by Hotelling T2 statistic technique. Supervised classification was applied to the spectra using partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogies (SIMCA) algorithms. The samples were classified into three classes related to their mesopore volumes by the proposed method and the results were in accordance with N2 physisorption textural analysis using Brunauer-Emmett-Teller (BET) model. The confusion matrix and classification efficiency parameters including sensitivity, specificity, accuracy and precision were calculated. Classification accuracy of 96% and error rate of 2% was obtained using PLS-DA algorithm while SIMCA algorithm by providing 100% classification accuracy and zero error rate proved better performance in classification of ZSM-5 catalysts.

Infrared spectroscopic and chemometric approach for identifying binding medium in Sukias mansion's wall paintings.

This paper addresses the application of infrared spectroscopy in combination with chemometrics to identify wall painting's binding medium while employing pattern recognition techniques to process FTIR data-set of complex samples. In this regard, based on the historical documents and previous researches, firstly 56 standard samples were prepared to represent strata of Persian wall paintings in the Safavid period in addition to real historic samples from the case study; Sukias mansion. Then, each sample was analysed by the means of FTIR and chemometrics. Finally, SIMCA was applied to the whole region of studied IR spectra which predicted egg yolk as the binding medium of Sukias mansion samples.

Single item presenteeism question: Reliability and validity of Persian version in participants with low back pain.

BACKGROUND: Single item presenteeism question (SIPQ) is a rating scale to assess the impact of low back pain (LBP) on presenteeism. OBJECTIVE: To translate the SIPQ into Persian language (SIPQ-P) and evaluate the reliability and validity of the SIPQ-P in participants with LBP. METHODS: In the first stage, the English SIPQ was translated into Persian language in a cross-sectional design following standard forward-backward approach with expert panel review and pilot testing. In the second stage with a prospective cohort design, 100 participants with LBP (84 male and 16 female, mean age±SD: 33.9±11.2 years) participated. Participants were asked to answer the SIPQ-P and rate their LBP from 0 to 10 according to the numerical pain rating scale (NPRS) for concurrent criterion validity. To evaluate the convergent construct validity of SIPQ, participants completed Persian Functional Rating Index (PFRI). For the divergent validity, the Spearman's correlation test was used to evaluate the association between the SIPQ-P and education. For the test-retest reliability, 50 participants answered the SIPQ-P after 7 days. Fifty healthy participants (mean age±SD: 24.24±8.07 years) answered the SIPQ-P for evaluating discriminant validity. RESULTS: There was a significant difference between the SIPQ-P score of participants and the healthy participants (p <  0.001). The concurrent criterion validity was demonstrated by a significant correlation between the SIPQ-P and pain NPRS (Spearman's rho = 0.46, p <  0.001). The Spearman correlation coefficient showed a significant correlation between the SIPQ-P scores and the Persian FRI (r = 0.56, p <  0.001). There was no correlation between the SIPQ-P score and the education level (r = -0.001, p = 0.99). The ICCagreement was 0.77 indicating a very good test-retest reliability. CONCLUSIONS: This study showed that the Persian version of SIPQ is a reliable and valid scale to assess the effect of LBP on presenteeism in Persian speakers with LBP.

Malignancy probability map as a novel imaging biomarker to predict malignancy distribution: employing MRS in GBM patients.

The main aim of this study was to propose a new statistical method for evaluation of spatial malignancy distribution within Magnetic Resonance Spectroscopy (MRS) grid in Glioblastoma Multiforme patients. Voxels with different malignancy probabilities were presented as a novel MRS-based Malignancy Probability Map (MPM). For this purpose, a predictive probability-based clustering approach was developed, including the two following steps: (1) Gaussian Mixture Model, (2) Quadratic Discriminate Analysis coupled with Genetic Algorithm. Clustered probability values from two methods were then integrated to exploit the MPM. Results show that the suggested method is able to estimate the malignancy distribution with over 90% sensitivity and specificity. The proposed MRS-based MPM has an acceptable accuracy for providing useful complementary information about regional diffuse glioma malignancy, with the potential to lead to better detection of tumoral regions with high probability of malignancy. So, it also may encourage the use of additional information of this map as a tool for dose painting.

Application of diffuse reflectance near-infrared spectrometry and chemometrics in characterization of micro and mesoporous ZSM-5 zeolites.

Evaluation of porosity type of zeolites is one of the critical topics in catalysis science. The relationship between external surface area and diffuse reflectance (DR) spectra in the near-infrared spectral region has been employed to propose a method for estimation of micro or mesoporosity in ZSM-5 zeolite samples. Linear discriminant analysis (LDA) was utilized to estimate degree of porosity based on near-infrared diffuse reflectance spectra. The textural properties (surface area and pore volume) of micro and mesoporous ZSM-5 samples were measured using N2 adsorption/desorption technique at 77 K and external surface area was calculated by t-plot as a reference method in this work. Several porous ZSM-5 samples with only microporous channels or mesoporous besides them were classified in terms of external surface area and meso pore volume derived from t-plot as "Micro" or "Micro + Meso" type samples. It was concluded that LDA using the PCA for feature selection is capable of generalization and could precisely predict the type of porosity in ZSM-5 zeolites.

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.

Feature selection strategies for quality screening of diesel samples by infrared spectrometry and linear discriminant analysis.

A rapid approach has been developed for the characterization of diesel quality, based on attenuated total reflectance - Fourier transform infrared (ATR-FTIR) spectrometry, which could be useful for diagnosing the sample quality condition. As a supervised technique, linear discriminant analysis (LDA) was employed to process the spectrometric data. The role of variable selection methods was also evaluated. Successive projection algorithm (SPA) and genetic algorithm (GA) feature selection techniques were applied prior to the discriminative procedure. It was aimed to compare the effect of feature selection procedures on classification capability of IR spectrometry for the diesel samples according to their quality passed or quality failed situation. Predictive capability of LDA was compared with that obtained by GA-LDA and SPA-LDA. Results showed 91.1%, 93.3% and 95.6% of accuracy for LDA, GA-LDA and SPA-LDA respectively. Thus SPA-LDA together with ATR-FTIR spectrometry was proposed as a fast screening analytical test for the evaluation of quality passed/failed situation in diesel samples.

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).

Simultaneous determination of some components in detergent washing powder by mid-infrared spectrometry and artificial neural network.

A method has been established for simultaneous determination of sodium sulfate, sodium carbonate, and sodium tripolyphosphate in detergent washing powder samples based on attenuated total reflectance Fourier transform IR spectrometry in the mid-IR spectral region (800-1550 cm(-1)). Genetic algorithm (GA) wavelength selection followed by feed forward back-propagation artificial neural network (BP-ANN) was the chemometric approach. Root mean square error of prediction for BP-ANN and GA-BP-ANN was 0.0051 and 0.0048, respectively. The proposed method is simple, with no tedious pretreatment step, for simultaneous determination of the above-mentioned components in commercial washing powder samples.

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.

Simultaneous determination of levodopa and carbidopa in levodopa-carbidopa tablets by ATR-FTIR spectrometry.

A novel analytical procedure has been developed for quantitative determination of levodopa and carbidopa in aqueous binary solutions acidified by HCl and without any other sample pretreatment. The method is based partially on least squares treatment of data obtained by attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectrometry in 1211-1315 cm(-1) and 1488-1550 cm(-1) spectral regions. The simple, rapid, and accurate proposed method was applied to determine levodopa and carbidopa in Levodopa-C tablets. The statistical parameters, such as R2, RSD, SEE, SECV, LOD, and recovery were evaluated. Number of factors, scans, and resolution were optimized. In this method R2 and RSD for levodopa and carbidopa were (0.9965, 1.209) and (0.9537, 0.813), respectively.

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.