Prediction of Novel High-Pressure Structures of Magnesium Niobium Dihydride

On the basis of a combination of the particle-swarm optimization technique and density functional theory (DFT), we explore the crystal structures of MgH₂, NbH₂, and MgNbH₂ under high pressure. The enthalpy–pressure (<i>H</i>–<i>P</i>) diagrams indicate that the structural transition sequence of MgH₂ is α → γ → δ → ε → ζ and that NbH₂ transforms from the <i>Fm</i>3̅<i>m</i> phase to the <i>Pnma</i> phase at 47.80 GPa. However, MgNbH₂ is unstable when the pressure is too low or too high. Two novel MgNbH₂ structures, the hexagonal <i>P</i>6̅<i>m</i>2 phase and the orthorhombic <i>Cmcm</i> phase, are discovered, which are stable in the pressure ranges of 13.24–128.27 GPa and 128.27–186.77 GPa, respectively. The <i>P</i>6̅<i>m</i>2 phase of MgNbH₂ consists of alternate layers of polymetric NbH₆ and MgH₆ triangular prisms, while the <i>Cmcm</i> phase contains distorted MgH₆ trigonal prisms. The calculated elastic constants and phonon dispersions confirm that both phases are mechanically and dynamically stable. The analyses of density of states (DOS), electron localization function (ELF), and Bader charge demonstrate that a combination of ionic and metallic bonds exist in both <i>P</i>6̅<i>m</i>2 and <i>Cmcm</i> phases. We hope the newly predicted magnesium niobium dihydrides with desirable electronic properties will promote future experimental and theoretical studies on mixed main group-transition metal hydrides

Image_2_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.tif

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.</p

Prediction of Stable Ruthenium Silicides from First-Principles Calculations: Stoichiometries, Crystal Structures, and Physical Properties

We present results of an unbiased structure search for stable ruthenium silicide compounds with various stoichiometries, using a recently developed technique that combines particle swarm optimization algorithms with first-principles calculations. Two experimentally observed structures of ruthenium silicides, RuSi (space group <i>P</i>2₁3) and Ru₂Si₃ (space group <i>Pbcn</i>), are successfully reproduced under ambient pressure conditions. In addition, a stable RuSi₂ compound with β-FeSi₂ structure type (space group <i>Cmca</i>) was found. The calculations of the formation enthalpy, elastic constants, and phonon dispersions demonstrate the <i>Cmca</i>-RuSi₂ compound is energetically, mechanically, and dynamically stable. The analysis of electronic band structures and densities of state reveals that the <i>Cmca</i>-RuSi₂ phase is a semiconductor with a direct band gap of 0.480 eV and is stabilized by strong covalent bonding between Ru and neighboring Si atoms. On the basis of the Mulliken overlap population analysis, the Vickers hardness of the <i>Cmca</i> structure RuSi₂ is estimated to be 28.0 GPa, indicating its ultra-incompressible nature

Image_1_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.png

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.</p

Table_2_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.docx

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.</p

Table_1_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.docx

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.</p

Ab Initio Search for Global Minimum Structures of Pure and Boron Doped Silver Clusters

The global minimum structures of pure and boron doped silver clusters up to 16 atoms are determined through ab initio calculations and unbiased structure searching methods. The structural and electronic properties of neutral, anionic, and cationic Ag_<i>n</i>B (<i>n</i> ≤ 15) and Ag_<i>n</i>B₂ (<i>n</i> ≤ 14) clusters are much distinct from those of the corresponding pure silver. Considering that Ag and B possess one and three valence electrons, respectively, both the single and the double boron-atom doped silver clusters with even number of valence electrons are more stable than those with odd number of electrons, a feature also observed in the pure silver clusters. We demonstrate that the species with a valence count of 8 and 14 appear to be magic numbers with enhanced stability irrespective of component or the charged state. A new putative global minimum structure of Ag₁₃^– cluster, with high symmetry of <i>C</i>_2<i>v</i>, is unexpectedly observed as the ground state, which is lower in energy than the previous suggested bilayer structure

Therapeutic window of APN after reperfusion.

<p>Therapeutic window of APN after reperfusion.</p

Hepatocyte apoptosis was diminished by APN treatment.

<p>(A) Representative liver sections for TUNEL staining of apoptotic cells (green cells) from each experimental group were shown at 24h after reperfusion. (B) Quantitative analysis of TUNEL-positive hepatocyte nuclei per total nuclei in each experimental group is presented. (C) expression levels of cleaved caspase-3, in the liver in each experimental group at 24h after reperfusion are shown.</p

Exogenous APN increases circulatory APN level and improves liver dysfunction during I/R.

<p>(A) Serum APN concentration was increased after APN injection. (B) Serum ALT and (C) AST levels in the sham group, I/R control group I/R+APN group and I/R+APN+bAMP group. (D) Representative photomicrograph of liver histology in the sham group, I/R control group I/R+APN group and I/R+APN+bAMP group at 24h after reperfusion. (E) Suzuki scores were presented in the sham group, I/R control group, I/R+APN group and I/R+APN+bAMP group.</p