Electrocardiography changes and different stages of heart failure in central Iran: A cross-sectional study from Yazd Health Study.

Background and Aims: Electrocardiography (ECG) is a widely accessible, noninvasive, and cost-effective diagnostic instrument used to evaluate patients with suspected heart failure (HF). The aim of this study is to investigate electrocardiographic changes in patients with different stages of HF in a random population of Yazd city. Methods: This prospective cross-sectional study included 319 individuals, randomly selected, aged 40 years and more, registered in the Yazd Health Study was conducted from March 2022 to May 2023 at Afshar Hospitals. In accordance with the AHA/ACC guidelines, HF was classified into four stages (A, B, C, and D). Results: The 159 individuals were classified in the stage 0 group, 77 were in Stage A, 65 were in Stage B, and 18 were in Stage C of HF. In the Stage 0, the PR interval (PRi) was 130.5 ± 18.1 ms, while in Stage C, it was 143.3 ± 21.9 ms, with a significant difference (p = 0.047). Similarly, the QRS interval (QRSi) increased with HF staging (p = 0.001). The frequency of diabetes mellitus (DM), hypertension (HTN), hyperlipidemia (HLP), chronic heart disease, alcoholism, and PRi, QRSi, QT interval levels were independent predictors of HF stage in multivariate regression analysis. Conclusion: The prevalence of HF stages, as classified by the AHA/ACC guidelines, was observed, with significant correlations between ECG parameters and HF progression. abnormal rhythms, left bundle branch block, ischemia, hypertrophy, and left atrial enlargement increased with higher HF stages. Major risk factors like DM and HTN exhibited a heightened prevalence in advanced HF stages, accentuating their pivotal role in the progression of HF.

Diagnosing Parkinson's disease by combining neuromelanin and iron imaging features using an automated midbrain template approach.

BACKGROUND AND PURPOSE: Early diagnosis of Parkinson's disease (PD) is still a clinical challenge. Most previous studies using manual or semi-automated methods for segmenting the substantia nigra (SN) are time-consuming and, despite raters being well-trained, individual variation can be significant. In this study, we used a template-based, automatic, SN subregion segmentation pipeline to detect the neuromelanin (NM) and iron features in the SN and SN pars compacta (SNpc) derived from a single 3D magnetization transfer contrast (MTC) gradient echo (GRE) sequence in an attempt to develop a comprehensive imaging biomarker that could be used to diagnose PD. MATERIALS AND METHODS: A total of 100 PD patients and 100 age- and sex-matched healthy controls (HCs) were imaged on a 3T scanner. NM-based SN (SNNM) boundaries and iron-based SN (SNQSM) boundaries and their overlap region (representing the SNpc) were delineated automatically using a template-based SN subregion segmentation approach based on quantitative susceptibility mapping (QSM) and NM images derived from the same MTC-GRE sequence. All PD and HC subjects were evaluated for the nigrosome-1 (N1) sign by two raters independently. Receiver Operating Characteristic (ROC) analyses were performed to evaluate the utility of SNNM volume, SNQSM volume, SNpc volume and iron content with a variety of thresholds as well as the N1 sign in diagnosing PD. Correlation analyses were performed to study the relationship between these imaging measures and the clinical scales in PD. RESULTS: In this study, we verified the value of the fully automatic template based midbrain deep gray matter mapping approach in differentiating PD patients from HCs. The automatic segmentation of the SN in PD patients led to satisfactory DICE similarity coefficients and volume ratio (VR) values of 0.81 and 1.17 for the SNNM, and 0.87 and 1.05 for the SNQSM, respectively. For the HC group, the average DICE similarity coefficients and VR values were 0.85 and 0.94 for the SNNM, and 0.87 and 0.96 for the SNQSM, respectively. The SNQSM volume tended to decrease with age for both the PD and HC groups but was more severe for the PD group. For diagnosing PD, the N1 sign performed reasonably well by itself (Area Under the Curve (AUC) = 0.783). However, combining the N1 sign with the other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an improved diagnosis of PD with an AUC as high as 0.947 (using an SN threshold of 50ppb and an NM threshold of 0.15). Finally, the SNQSM volume showed a negative correlation with the MDS-UPDRS III (R2 = 0.1, p = 0.036) and the Hoehn and Yahr scale (R2 = 0.04, p = 0.013) in PD patients. CONCLUSION: In summary, this fully automatic template based deep gray matter mapping approach performs well in the segmentation of the SN and its subregions for not only HCs but also PD patients with SN degeneration. The combination of the N1 sign with other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an AUC of 0.947 and provided a comprehensive set of imaging biomarkers that, potentially, could be used to diagnose PD clinically.

Automatic detection of neuromelanin and iron in the midbrain nuclei using a magnetic resonance imaging-based brain template.

Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in-vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC-GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC-GRE sequence was used to find the NM-rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45-81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine-tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM-rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template-based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei.

Pretreatment of lignocellulosic waste as a precursor for synthesis of high porous activated carbon and its application for Pb (II) and Cr (VI) adsorption from aqueous solutions.

Effects of Elm tree sawdust pretreatments using alkali and alkaline earth metals (NaCl, KCl, CaCl2, MgCl2 and Elm tree ash) and deashing solutions (water, HCl, HNO3 and aqua regia) before the carbonization process on the porosity of produced activated carbons and Pb (II) and Cr (VI) adsorption were studied. The activated carbons were characterized by pore size distribution, surface area, FTIR, and SEM-EDX analysies. Based on the results, HCl leaching pretreatment of the biomass increased the activated carbon adsorption capacity of Cr (VI) from 114 to 190 mg g-1. The treatment of biomass with alkali and alkali earth metal salts, especially MgCl2, remarkably increased the activated carbon adsorption capacity of Pb (II) from 233 to 1430 mg g-1. The results indicated that Pb (II) adsorption was attributed to both the mesoporous structure of activated carbon and the abundance of Mg on the activated carbon's surface. On the other hand, the micropores played a major role in Cr (VI) adsorption capacity. The development of the micro- or mesoporous structure of activated carbons through pretreatment of lignocellulosic precursor could be an approach for providing high performance activated carbons for Pb (II) and Cr (VI) removal from aqueous solutions.

Imaging iron and neuromelanin simultaneously using a single 3D gradient echo magnetization transfer sequence: Combining neuromelanin, iron and the nigrosome-1 sign as complementary imaging biomarkers in early stage Parkinson's disease.

Diagnosing early stage Parkinson's disease (PD) is still a clinical challenge. Previous studies using iron, neuromelanin (NM) or the Nigrosome-1 (N1) sign in the substantia nigra (SN) by themselves have been unable to provide sufficiently high diagnostic performance for these methods to be adopted clinically. Our goal in this study was to extract the NM complex volume, iron content and volume representing the entire SN, and the N1 sign as potential complementary imaging biomarkers using a single 3D magnetization transfer contrast (MTC) gradient echo sequence and to evaluate their diagnostic performance and clinical correlations in early stage PD. A total of 40 early stage idiopathic PD subjects and 40 age- and sex-matched healthy controls (HCs) were imaged at 3T. NM boundaries (representing the SN pars compacta (SNpc) and parabrachial pigmented nucleus) and iron boundaries representing the total SN (SNpc and SN pars reticulata) were determined semi-automatically using a dynamic programming (DP) boundary detection algorithm. Receiver operating characteristic analyses were performed to evaluate the utility of these imaging biomarkers in diagnosing early stage PD. A correlation analysis was used to study the relationship between these imaging measures and the clinical scales. We also introduced the concept of NM and total iron overlap volumes to demonstrate the loss of NM relative to the iron containing SN. Furthermore, all 80 cases were evaluated for the N1 sign independently. The NM and SN volumes were lower while the iron content was higher in the SN for PD subjects compared to HCs. Interestingly, the PD subjects with bilateral loss of the N1 sign had the highest iron content. The area under the curve (AUC) values for the average of both hemispheres for single measures were: .960 for NM complex volume; .788 for total SN volume; .740 for SN iron content and .891 for the N1 sign. Combining NM complex volume with each of the following measures through binary logistic regression led to AUC values for the averaged right and left sides of: .976 for total iron content; .969 for total SN volume, .965 for overlap volume and .983 for the N1 sign. We found a negative correlation between SN volume and UPDRS-III (R2 = .22, p = .002). While the N1 sign performed well, it does not contain any information about iron content or NM quantitatively, therefore, marrying this sign with the NM and iron measures provides a better physiological explanation of what is happening when the N1 sign disappears in PD subjects. In summary, the combination of NM complex volume, SN volume, iron content and the N1 sign as derived from a single MTC sequence provides complementary information for understanding and diagnosing early stage PD.

Molecular, chemical, and physiological analyses of sorghum leaf wax under post-flowering drought stress.

Wax accumulation on the sorghum surface plays an important role in drought tolerance by preventing non-stomatal water loss. Thereby, the effect of post-flowering drought stress (PFDS) on the epicuticular wax (EW) amount, relative water content (RWC), chlorophyll, and grain yield in sorghum drought contrasting genotypes were investigated. The experiment was conducted as a split-plot based on randomized complete block design (RCBD) with two water treatments (normal watering and water holding after 50% flowering stage), and three genotypes (Kimia and KGS23 as drought-tolerant and Sepideh as drought-susceptible). Scanning electron microscopy and GC-MS analyses were used to determine the wax crystals density and its compositions, respectively. In addition, based on literature reviews and publicly available datasets, six wax biosynthesis drought stress-responsive genes were chosen for expression analysis. The results showed that the amounts of EW and wax crystals density were increased in Kimia and Sepideh genotypes and no changed in KGS23 genotype under drought stress. Chemical compositions of wax were classified into six major groups including alkanes, fatty acids, aldehydes, esters, alcohols, and cyclic compounds. Alkanes increment in drought-tolerant genotypes led to make an effective barrier against the drought stress to control water losses. In addition, the drought-tolerant genotypes had higher levels of RWC compared to the drought-susceptible ones, resulted in higher yield produced under drought condition. According to the results, SbWINL1, FATB, and CER1 genes play important roles in drought-induced wax biosynthesis. The results of the present study revealed a comprehensive view of the wax and its compositions and some involved genes in sorghum under drought stress.

Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging.

PURPOSE: To develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging. METHODS: The proposed QSM reconstruction algorithm, referred to as "structurally constrained Susceptibility Weighted Imaging and Mapping" scSWIM, performs an ℓ 1 and ℓ 2 regularization-based reconstruction in a single step. The unique contrast of the T1 weighted enhanced (T1WE) image derived from STAGE imaging was used to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. The multi-echo multi-flip angle data were used for improving the contrast-to-noise ratio in QSM through a weighted averaging scheme. The measured susceptibility values from scSWIM for both simulated and in vivo data were compared to the: original susceptibility model (for simulated data only), the multi orientation COSMOS (for in vivo data only), truncated k-space division (TKD), iterative susceptibility weighted imaging and mapping (iSWIM), and morphology enabled dipole inversion (MEDI) algorithms. Goodness of fit was quantified by measuring the root mean squared error (RMSE) and structural similarity index (SSIM). Additionally, scSWIM was assessed in ten healthy subjects. RESULTS: The unique contrast and tissue boundaries from T1WE and iSWIM enable the accurate definition of edges of high susceptibility regions. For the simulated brain model without the addition of microbleeds and calcium, the RMSE was best at 5.21ppb for scSWIM and 8.74ppb for MEDI thanks to the reduced streaking artifacts. However, by adding the microbleeds and calcium, MEDI's performance dropped to 47.53ppb while scSWIM performance remained the same. The SSIM was highest for scSWIM (0.90) and then MEDI (0.80). The deviation from the expected susceptibility in deep gray matter structures for simulated data relative to the model (and for the in vivo data relative to COSMOS) as measured by the slope was lowest for scSWIM + 1%(-1%); MEDI + 2%(-11%) and then iSWIM -5%(-10%). Finally, scSWIM measurements in the basal ganglia of healthy subjects were in agreement with literature. CONCLUSION: This study shows that using a data fidelity term and structural constraints results in reduced noise and streaking artifacts while preserving structural details. Furthermore, the use of STAGE imaging with multi-echo and multi-flip data helps to improve the signal-to-noise ratio in QSM data and yields less artifacts.