Utjecaj prirodne smole divljeg drva pistacije (same i u kombinaciji s bornom kiselinom) na fizička svojstva i trajnost bukovine

This study was carried out to investigate the physical properties and decay resistance of beech wood treated with natural pistachio resin (PR) from Iranian wild pistachio trees (Pistacia atlantica), alone and in combination with boric acid (BA). Wood samples were impregnated with different concentration of PR dissolved in ethanol (3 to 20 %) with vacuum-pressure technology. The combination of PR (20 %) and BA (2 %) was also conducted to evaluate any interaction or synergistic effects. The water absorption, volumetric swelling, and decay resistance against Trametes versicolor fungi, before and after a leaching test (EN 84), were measured on treated and untreated samples. The chemical compositions of PR were also identified by gas chromatography–mass spectrometry (GC-MS) techniques. The chemicals analysis identified more than 20 different compounds in the PR, monoterpenoids being the predominant fraction and α-pinene the major component. The samples treated with a higher concentration of PR showed much higher weight gain percentage (WG%). The results showed that the increase in WG% reduced the average values of water absorption and volumetric swelling of treated samples even after long terms of soaking in water. The decay resistance of the treated samples increased against white rotting fungi as the values of WG% increased. Efficient protection was seen when a combined treatment of PR and BA was used. Even after the leaching process, the weight loss of the treated samples was less than 3 percent. The samples treated with BA alone largely lost their effectiveness against fungal attack after the leaching. The use of PR along with an environmental friendly co-biocide can also be recommended for wood preservation in places that require minimal toxicity.Cilj ovog rada bio je istražiti fizička svojstva i otpornost na propadanje bukovine tretirane prirodnom smolom pistacije (PR) dobivene iz divljeg drva pistacije koja uspijeva u Iranu (Pistacia atlantica) te kombinacijom smole pistacije i borne kiseline (BA). Uzorci drva vakuumsko-tlačnim su postupkom impregnirani različitim koncentracijama PR-a otopljenoga u etanolu (3 do 20 %). Provedena je i impregnacija uzorka kombinacijom PR-a (20 %) i BA-a (2 %) kako bi se procijenili svi interakcijski i sinergijski učinci. Za tretirane i netretirane uzorke mjerena je upojnost vode, volumetrijsko bubrenje i otpornost na djelovanje gljive Trametes versicolor prije i nakon ispiranja (EN 84). Ujedno je uz pomoć plinske kromatografije s masenom spektrometrijom (GC-MS) utvrđen i kemijski sastav smole (PR). Kemijskom analizom u PR-u je identificirano više od 20 različitih spojeva; momoterpeni su bili dominantna frakcija, a alfa-pinen glavna komponenta. Uzorci tretirani većom koncentracijom PR-a rezultirali su većim dobitkom mase (WG%). Rezultati su pokazali da se s porastom WG% smanjuje prosječna vrijednost upojnosti vode i volumetrijsko bubrenje tretiranih uzoraka, čak i nakon dugotrajnog potapanja u vodi. Otpornost tretiranih uzoraka na djelovanje gljiva bijele truleži povećala se s povećanjem WG%. Učinkovita zaštita primijećena je pri primjeni kombiniranog tretmana PR-om i BA-om; čak je i nakon postupka ispiranja gubitak mase tretiranih uzoraka bio manji od 3 %. Uzorci tretirani samo BA-om nakon ispiranja su uglavnom izgubili otpornost na napad gljiva. Moguće je zaključiti da se za zaštitu drva na mjestima koja zahtijevaju minimalnu otrovnost može preporučiti upotreba PR-a zajedno s ekološki prihvatljivim biocidom

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Application of Bland-Altman Method in Comparing Transrectal and Transabdominal Ultrasonography for Estimating Prostate Volume

Background: Estimating prostate volume using less invasive transabdominal ultrasonography (TAUS) instead of transrectal ultrasonography (TRUS) is of interest in terms of identifying their agreement level. Previous reports on this subject, applied general correlation coefficient as the level of agreement. This study uses Bland-Altman method to quantify TAUS and TRUS agreement on estimating prostate volume. Methods: Total prostate gland volume of 40 patients with signs and symptoms of benign prostatic hyperplasia were measured using TAUS and TRUS. The study was carried out at the Urology Research Center, Razi Hospital, Guilan University of Medical Sciences (Rasht, Iran) from March to October 2010. Both methods were performed in one session by the same experienced radiologist. Data were analyzed using Pearson correlation coefficient and Bland-Altman method. Results: Total prostate volume estimated by TAUS and TRUS were 50.30±23 and 50.73±24.6 mL, respectively. The limits of agreement for the total prostate volume were -6.86/9.84 that was larger than predefined clinical acceptable margin of 5 mL. Conclusion: There is a lack of agreement between TAUS and TRUS for estimating the total prostate volume. It is not recommended to apply TAUS instead of TRUS for estimating prostate volume

Atmospheric pollution by potentially toxic elements: measurement and risk assessment using lichen transplants

The lichen Usnea articulata collected from an unpolluted area was exposed for 6 months at 26 sites for the sample chosenusing a stratified random design, and the content of potentially toxic elements (PTEs) including As, Cd, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Sn, V, and Zn, was assessed using ICP-MS. The health risk for both adults and children was then calculated using the PTEs concentrations. The results showed that despite the hostile urban conditions, transplanted lichens depicted clear deposition patterns of airborne PTEs, mostly associated with industrial sites, where As and other elements showed remarkably high values. The cumulative hazard index was below the risk threshold, both for adults and children. For the entire population (particularly children) residing in areas surrounding industrial sites, As and Cr appeared to be potentially carcinogenic elements

Assessment of incremental lifetime cancer risks of ambient air PM10-bound PAHs in oil-rich cities of Iran

This study investigates the concentrations of PM10-bound PAHs and their seasonal variations in three cities of Ahvaz, Abadan, and Asaluyeh in Iran. The mean concentrations of PM10 in two warm and cold seasons in Ahvaz were higher and in Abadan and Assaluyeh were lower than the national standard of Iran and the guidelines of the World Health Organization. The Σ16 PAHs concentration in ambient air PM10 during the cold season in Ahvaz, Abadan and Asaluyeh was 244.6, 633, and 909 ng m− 3, respectively, and during the warm season in Ahvaz, Abadan, and Asaluyeh was 242.1, 1570 and 251 ng m− 3, respectively. The high molecular weight PAHs were the most predominant components. The most abundant PAHs species were Pyr, Chr, B [ghi] P, and Flt. The results showed that the total PAHs concentration in the cold and warm seasons was dependent on industrial activities, particularly the neighboring petrochemical units of the city, vehicular exhausts, traffic and use of oil, gas, and coal in energy production. The total cancer risk values as a result of exposure to PAHs in ambient air PM10 in all three cities for children and adults and in both cold and warm seasons were between 1 × 10− 6 and 1 × 10− 4, and this indicates a potential carcinogenic risk. Therefore, considering the various sources of air pollutants and its role on people’s health, decision makers should adopt appropriate policies on air quality to reduce the ambient air PAHs and to mitigate human exposure

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning : A large multicentric cohort study

To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.</p