[Associations between serum GDF15 and glycolipid metabolism disorder in metabolic associated fatty liver patients]

Objective: To investigate relationships between serum growth differentiation factor 15 (GDF15) and glycolipid metabolism in patients with metabolic associated fatty liver disease (MAFLD). Methods: The current investigation was a cross-sectional study. A total of 333 patients from the Fengxian District Central Hospital were recruited into the study after physical examination from February 2020 to February 2021. There were 107 patients with MAFLD and type 2 diabetes mellitus (T2DM), including 54 males and 53 females with a mean age of (57±11) years. There were 65 patients with simple MAFLD only, including 32 men and 33 women with a mean age of (49±5) years. There were 105 patients with T2DM only, including 53 men and 52 women, with a mean age of (56±10) years. A control group of 56 people without MAFLD or diabetes,28 male, 28 female, mean age (48±6) years, was also included in the study. Serum GDF15 was measured via enzyme-linked immunosorbent assays. IBM SPSS 26.0 was used for statistical analysis. Logistic regression was used to evaluate relationships between GDF15 and metabolic abnormalities in MAFLD patients. Results: GDF15 progressively increased in the control [385 (296, 484) ng/L], nonobese MAFLD [388 (319, 435) ng/L], obese MAFLD [426 (354, 527) ng/L], T2DM [664 (483, 900) ng/L], and MAFLD+T2DM groups [770 (560, 1 074) ng/L](H=113.82, P=0.001). There was no significant difference in serum GDF15 between the simple MAFLD [406 (339, 524) ng/L] and control group (U=1 505.50, P=0.132). GDF15 was significantly higher in the MAFLD+T2DM group than in the T2DM-only group (U=4 573.50, P=0.019). In logistic regression analysis increased GDF15 was associated with increased risks of simple MAFLD [odds ratio (OR)=2.202], T2DM (OR=29.656), and MAFLD+T2DM(OR=58.197). In patients with MAFLD, serum GDF15 was higher in the FIB4 index>1.45 group [773 (534, 1 162) ng/L] than in the FIB4 index<1.45 group [527 (389, 787) ng/L] (U=1 709.50, P<0.001). Increased GDF15 was associated with an increased risk of advanced liver fibrosis (OR=2.388). Conclusion: In patients with simple MAFLD, GDF15 level was not significantly higher than in the control group. In the T2DM-only group and the MAFLD+T2DM group GDF15 was significantly higher than in the control group. Increased serum GDF15 was associated with increased risk and severity of MAFLD complicated with abnormal glucose and lipid metabolism. High GDF15 increased the risk of advanced fibrosis in MAFLD patients.

Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70-108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15 N- 1 H residual dipolar coupling data. For these nine small (70 - 108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

Highlights

AF2 models assessed against NMR data for 9 monomeric proteins not used in training.AF2 models fit NMR data almost as well as the experimentally-determined structures. RPF-DP, PSVS , and PDBStat software provide structure quality and RDC assessment. RPF-DP analysis using AF2 models suggests multiple conformational states.

Improved method for determination of optical constants of organic thin films from reflection and transmission measurements

A new technique for determining the optical properties of organic thin films is presented. A detailed evaluation of the accuracy of the determined optical constants has been performed, and the best combination of measured values yielding the smallest errors in the index of refraction for realistic experimental uncertainties has been found. The proposed method utilizes the fact that optical constants are smooth continuous functions, which reduces the possibility of encountering multiple solutions. The method consists of two steps. In the first step the optical constants at all wavelengths and the film thickness are determined. In the second step the thickness and the imaginary part of the index of refraction are kept fixed while we reevaluate the real part of the index of refraction by using a different objective function with improved sensitivity to the refractive index. After verifying that the proposed method is capable of an accurate estimation of optical constants, we determine the index of refraction data of vanadyl-phthalocyanine in the visible spectral range.

Modeling the Optical Constants of Diamonds from 0.06 to 30 eV

The optical properties of diamonds are modeled over a wide spectral range with the modified Adachi's model. Model parameters were estimated by use of the acceptance-probability-controlled simulated annealing algorithm. The employed model is quite flexible, as it uses an adjustable broadening function at each critical point. The broadening function can vary over a range of functions with similar kernels but different wings, so that extended absorption tails inherent to the conventional Lorentzian broadening can be eliminated. Good agreement with the experimental data is obtained in the entire investigated range. The obtained relative rms error for the real part of the index of refraction equals 4.7%, whereas for the imaginary part of the index-of-refraction relative rms error is 3.6%.

Modeling the index of refraction of insulating solids with a modified lorentz oscillator model

A modification of the Lorentz oscillator model for optical constants is proposed in an effort to achieve better agreement with experimental data while keeping the calculation simple. Improvement in agreement between theoretical and experimental data obtained with a variable line shape (frequency-dependent damping constant) over a wide spectral range is demonstrated through modeling the index of refraction of Si(3)N(4) (1-24 eV), SiO (0.15-25 eV) and amorphous and crystalline SiO(2) (0.15-25 eV). Model parameters are estimated by acceptance-probability-controlled simulated annealing. Excellent agreement between the modified model and the experimental data is obtained for both real and imaginary parts of the index of refraction.

Polarization-insensitive electroabsorption by use of quantum well interdiffusion

A paraboliclike quantum well structure can be used to produce polarization-insensitive waveguide type modulators. We propose the use of interdiffusion of AlGaAs/GaAs quantum wells to achieve the parabolic well shape. Criteria to achieve the paraboliclike quantum wells by interdiffusion are discussed. The theoretical results indicate that interdiffused quantum wells can produce equal eigenstate spacing, polarization insensitive Stark shifts, and modulation similar to an ideal parabolic quantum well. Three procedures are also proposed to develop polarization-insensitive on and off states in paraboliclike interdiffused quantum wells. The modulation depth is compatible with that of the measured parabolic quantum wells. For diffused quantum wells one can take advantage of using an as-grown rectangular quantum well with postgrowth thermal processing. These features demonstrate that an interdiffused quantum well structure can be used to produce a polarization-insensitive electroabsorptive modulator.