Autopsy Findings of Brainstem in Head Trauma in Comparison with CT Scan Findings in Brain Trauma Ward in Tabriz, Iran

Computed tomography (CT) is now the primary diagnostic method for head trauma because of its ability to demonstrate the nature, extent, sites, and multiplicity of brain injuries. Although there have been numerous reports on the CT findings of most types of intracranial injury, the findings in brainstem injury have not been well described. This study aimed at comparing the autopsy findings of brainstem in head trauma in comparison with CT scan results. Two hundred patients with head trauma, who expired after a period of time of hospitalization, were assessed in a diagnostic value study. Brain stem involvement was determined by autopsy as well as CT scanning of the brain during their hospitalization. The results of the two methods were compared with each other, emphasizing on the type and location of probable lesions in the brain stem. Considering the autopsy as the method of the choice, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of CT scan in brain stem lesions of patients with head trauma were calculated. The effect of primary cause of head trauma, survival time and Glasgow Coma Scale (GCS) were evaluated, as well. Brain stem lesions were detected in 39 (19.5%) patients in autopsy. However, CT scan revealed brain stem lesions in 23(11.5%) cases. The sensitivity, specificity, PPV and NPV of CT scan was 59%, 100%, 100% and 91% respectively. The most common lesions of the brain stem region were as contusion of pons (8.5%), medulla (5%) and midbrain (4.5%). There were 6 (3%) cases of ponto-medullary junction tearing and 1 (0.5%) case of cervico-medullary junction tearing. CT scan is a specific method of evaluating patients with probable brain stem injuries after head trauma, but low sensitivity limits its efficacy. Our results are in conformity with the reports in the literature

An Evolutionary Neuro-Fuzzy-Based Approach to Estimate the Compressive Strength of Eco-Friendly Concrete Containing Recycled Construction Wastes

There has been a significant increase in construction and demolition (C&D) waste due to the growth of cities and the need for new construction, raising concerns about the impact on the environment of these wastes. By utilising recycled C&D waste, especially in concretes used in construction, further environmental damage can be prevented. By using these concretes, energy consumption and environmental impacts of concrete production can be reduced. The behaviour of these types of concrete in laboratories has been extensively studied, but reliable methods for estimating their behaviour based on the available data are required. Consequently, this research proposes a hybrid intelligent system, Fuzzy Group Method of Data Handling (GMDH)–Horse herd Optimisation Algorithm (HOA), for predicting one of the most important parameters in concrete structure design, compressive strength. In order to avoid uncertainty in the modelling process, crisp input values were converted to Fuzzy values (Fuzzification). Next, using Fuzzy input variables, the group method of data handling is used to predict the compressive strength of recycled aggregate concrete. The HOA algorithm is one of the newest metaheuristic algorithms being used to optimise the Fuzzy GMDH structure. Several databases containing experimental mix design records containing mixture components are gathered from published documents for compressive strength to assess the accuracy and reliability of the proposed hybrid Fuzzy-based model. Compared to other original approaches, the proposed Fuzzy GMDH model with the HOA optimiser outperformed them in terms of accuracy. A Monte Carlo simulation is also employed for uncertainty analysis of the empirical, standalone, and hybridised models in order to demonstrate that the evolutionary Fuzzy-based approach has less uncertainty than the standalone methods when simulating compressive strength

First Detection of 16S rRNA Methylase and blaCTX-M-15 Genes among Klebsiella pneumoniae Strains Isolated from Hospitalized Patients in Iran

Background: The increasing pattern of Multi-Drug Resistant (MDR) bacteria has limited therapeutic options especially for nosocomial isolates of Klebsiella pneumoniae. Therefore, the aim of this study was the molecular detection of 16S rRNA methylase and blaCTX-M-15 among K. pneumoniae strains isolated from hospitalized patients in Mofid, Imam Hossein and Taleghani hospitals. Materials and Methods:This study was done with 110 K.pneumoniae isolated of hospitals in Tehran, Iran. Antibiotic susceptibility tests were carried out by Kirby-Bauer disc diffusion and broth microdilution methods according to CLSI guidelines. ESBL, AmpC and KPC enzymes were detected by CDDT and MHT methods and the armA, rmtB, rmtC, rmtD and blaCTX-M-15 genes were detected by PCR and sequencing techniques. Typing of antibiotic resistance isolates was carried out by PFGE technique. Results: In this study, Fosfomycin, colistin and tigecycline were more active than other antibiotics. Among the 110 K. pneumoniae strains, 60(54.5%), 33(30%) and 5(4.5%) were ESBL, Amp-C and KPC positive, respectively. The existence of blaCTX-M-15, armA and rmtC was detected in 40(36.3%), 15 (13.6%) and 2 (1.8%) respectively. Of 15 representative armA-producing K. pneumoniae isolates analyzed by PFGE, 9 different pulsotypes (PF1–9) were identified with Dice coefficients of &ge90% similarity. Conclusions: High-level aminoglycoside resistance in human pathogens result of 16S rRNA methylases is one of the serious concerns in Iran

Porcine or human stentless valves for aortic valve replacement? Results of a 10-year comparative study.

BACKGROUND AND AIM OF THE STUDY: Stentless porcine valves in the aortic position exhibit similar excellent hemodynamic performance to homografts, but have the advantage of availability. Their performance was compared over a 10-year period in a single-surgeon and single-institution series. METHODS: Demographic, operative and mortality data were obtained retrospectively. Survivors were interviewed by telephone according to a defined protocol. Definitions and analyses were in accordance with joint STS/AATS guidelines. RESULTS: A total of 408 stentless porcine and homograft aortic valve replacements (AVR) was performed between 1991 and 2001. Five patients were excluded due to incomplete data, in addition to 82 patients who underwent AVR with a free-standing root replacement technique. Hence, 321 patients (217 males, 104 females; mean age 67 +/- 12 years) had a subcoronary implant. The median time to follow up was 4.9 years (range: 2.9-6.6 years). No differences were noted between homograft and stentless porcine valves in one- and five-year freedom from structural valve deterioration (99.1 versus 97.2% and 95.7 versus 93.1%; p = 0.10), reoperation (99.2 versus 99.4% and 97.8 versus 96.7%; p = 0.45) and endocarditis (98.3 versus 99.4% and 97.4 versus 99.4%; p = 0.14). Overall one- and five-year survival comparing homograft to stentless porcine valve was 90.4 versus 92.3% and 80.8 versus 73.7%, respectively; p = 0.23. Independent predictors of mortality on multivariate analysis were: ventricular function (p < 0.0001), increasing age (p < 0.001), increasing serum creatinine (p < 0.001) and concomitant coronary surgery (p = 0.05). Treated hypercholesterolemia was independently protective against mortality, with an odds ratio of 0.26 (CI 0.10 to 0.66; p = 0.005). CONCLUSION: The porcine stentless valve, when implanted in the subcoronary position, is an excellent alternative to the homograft and shows excellent clinical performance and durability at mid term

Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878–0.883) outperformed CEEMDAN-MARS (NSE = 0.819–0.818), CEEMDAN-GEP (NSE = 0.873–0.789), SaMARS (NSE = 0.846–0.769), MARS (NSE = 0.819–0.758), and GEP (NSE = 0.814–0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models