The Ubiquitin Ligase RBX2/SAG Regulates Mitochondrial Ubiquitination and Mitophagy.

BACKGROUND: Clearance of damaged mitochondria via mitophagy is crucial for cellular homeostasis. Apart from Parkin, little is known about additional Ub (ubiquitin) ligases that mediate mitochondrial ubiquitination and turnover, particularly in highly metabolically active organs such as the heart. METHODS: In this study, we have combined in silico analysis and biochemical assay to identify CRL (cullin-RING ligase) 5 as a mitochondrial Ub ligase. We generated cardiomyocytes and mice lacking RBX2 (RING-box protein 2; also known as SAG [sensitive to apoptosis gene]), a catalytic subunit of CRL5, to understand the effects of RBX2 depletion on mitochondrial ubiquitination, mitophagy, and cardiac function. We also performed proteomics analysis and RNA-sequencing analysis to define the impact of loss of RBX2 on the proteome and transcriptome. RESULTS: RBX2 and CUL (cullin) 5, 2 core components of CRL5, localize to mitochondria. Depletion of RBX2 inhibited mitochondrial ubiquitination and turnover, impaired mitochondrial membrane potential and respiration, increased cardiomyocyte cell death, and has a global impact on the mitochondrial proteome. In vivo, deletion of the Rbx2 gene in adult mouse hearts suppressed mitophagic activity, provoked accumulation of damaged mitochondria in the myocardium, and disrupted myocardial metabolism, leading to the rapid development of dilated cardiomyopathy and heart failure. Similarly, ablation of RBX2 in the developing heart resulted in dilated cardiomyopathy and heart failure. The action of RBX2 in mitochondria is not dependent on Parkin, and Parkin gene deletion had no impact on the onset and progression of cardiomyopathy in RBX2-deficient hearts. Furthermore, RBX2 controls the stability of PINK1 in mitochondria. CONCLUSIONS: These findings identify RBX2-CRL5 as a mitochondrial Ub ligase that regulates mitophagy and cardiac homeostasis in a Parkin-independent, PINK1-dependent manner.

Unraveling controversies over civic honesty measurement: An extended field replication in China.

Cohn et al. (2019) conducted a wallet drop experiment in 40 countries to measure "civic honesty around the globe," which has received worldwide attention but also sparked controversies over using the email response rate as the sole metric of civic honesty. Relying on the lone measurement may overlook cultural differences in behaviors that demonstrate civic honesty. To investigate this issue, we conducted an extended replication study in China, utilizing email response and wallet recovery to assess civic honesty. We found a significantly higher level of civic honesty in China, as measured by the wallet recovery rate, than reported in the original study, while email response rates remained similar. To resolve the divergent results, we introduce a cultural dimension, individualism versus collectivism, to study civic honesty across diverse cultures. We hypothesize that cultural differences in individualism and collectivism could influence how individuals prioritize actions when handling a lost wallet, such as contacting the wallet owner or safeguarding the wallet. In reanalyzing Cohn et al.'s data, we found that email response rates were inversely related to collectivism indices at the country level. However, our replication study in China demonstrated that the likelihood of wallet recovery was positively correlated with collectivism indicators at the provincial level. Consequently, relying solely on email response rates to gauge civic honesty in cross-country comparisons may neglect the vital individualism versus collectivism dimension. Our study not only helps reconcile the controversy surrounding Cohn et al.'s influential field experiment but also furnishes a fresh cultural perspective to evaluate civic honesty.

BESST: a novel LncRNA knockout strategy with less genome perturbance.

Long noncoding RNAs (lncRNAs) are >200 nt RNA transcripts without protein-coding potential. LncRNAs can be categorized into intergenic, intronic, bidirectional, sense, and antisense lncRNAs based on the genomic localization to nearby protein-coding genes. The current CRISPR-based lncRNA knockout strategy works efficiently for lncRNAs distant from the protein-coding gene, whereas it causes genomic perturbance inevitably due to technical limitations. In this study, we introduce a novel lncRNA knockout strategy, BESST, by deleting the genomic DNA fragment from the branch point to the 3' splicing site in the last intron of the target lncRNA. The BESST knockout exhibited comparable or superior repressive efficiency to RNA silencing or conventional promoter-exon1 deletion. Significantly, the BESST knockout strategy minimized the intervention of adjacent/overlap protein-coding genes by removing an average of ∼130 bp from genomic DNA. Our data also found that the BESST knockout strategy causes lncRNA nuclear retention, resulting in decapping and deadenylation of the lncRNA poly(A) tail. Further study revealed that PABPN1 is essential for the BESST-mediated decay and subsequent poly(A) deadenylation and decapping. Together, the BESST knockout strategy provides a versatile tool for investigating gene function by generating knockout cells or animals with high specificity and efficiency.

Common neural dysfunction of economic decision-making across psychiatric conditions.

Adaptive decision-making, which is often impaired in various psychiatric conditions, is essential for well-being. Recent evidence has indicated that decision-making capacity in multiple tasks could be accounted for by latent dimensions, enlightening the question of whether there is a common disruption of brain networks in economic decision-making across psychiatric conditions. Here, we addressed the issue by combining activation/lesion network mapping analyses with a transdiagnostic brain imaging meta-analysis. Our findings indicate that there were transdiagnostic alterations in the thalamus and ventral striatum during the decision or outcome stage of decision-making. The identified regions represent key nodes in a large-scale network, which is composed of multiple heterogeneous brain regions and plays a causal role in motivational functioning. The findings suggest that disturbances in the network associated with emotion- and reward-related processing play a key role in dysfunctions of decision-making observed in various psychiatric conditions. This study provides the first meta-analytic evidence of common neural alterations linked to deficits in economic decision-making.

DB-1310, an ADC comprised of a novel anti-HER3 antibody conjugated to a DNA topoisomerase I inhibitor, is highly effective for the treatment of HER3-positive solid tumors.

BACKGROUND: HER3 (ErbB3), a member of the human epidermal growth factor receptor family, is frequently overexpressed in various cancers. Multiple HER3-targeting antibodies and antibody-drug conjugates (ADCs) were developed for the solid tumor treatment, however none of HER3-targeting agent has been approved for tumor therapy yet. We developed DB-1310, a HER3 ADC composed of a novel humanized anti-HER3 monoclonal antibody covalently linked to a proprietary DNA topoisomerase I inhibitor payload (P1021), and evaluate the efficacy and safety of DB-1310 in preclinical models. METHODS: The binding of DB-1310 to Her3 and other HER families were measured by ELISA and SPR. The competition of binding epitope for DB-1310 and patritumab was tested by FACS. The sensitivity of breast, lung, prostate and colon cancer cell lines to DB-1310 was evaluated by in vitro cell killing assay. In vivo growth inhibition study evaluated the sensitivity of DB-1310 to Her3 + breast, lung, colon and prostate cancer xenograft models. The safety profile was also measured in cynomolgus monkey. RESULTS: DB-1310 binds HER3 via a novel epitope with high affinity and internalization capacity. In vitro, DB-1310 exhibited cytotoxicity in numerous HER3 + breast, lung, prostate and colon cancer cell lines. In vivo studies in HER3 + HCC1569 breast cancer, NCI-H441 lung cancer and Colo205 colon cancer xenograft models showed DB-1310 to have dose-dependent tumoricidal activity. Tumor suppression was also observed in HER3 + non-small cell lung cancer (NSCLC) and prostate cancer patient-derived xenograft (PDX) models. Moreover, DB-1310 showed stronger tumor growth-inhibitory activity than patritumab deruxtecan (HER3-DXd), which is another HER3 ADC in clinical development at the same dose. The tumor-suppressive activity of DB-1310 synergized with that of EGFR tyrosine kinase inhibitor, osimertinib, and exerted efficacy also in osimertinib-resistant PDX model. The preclinical assessment of safety in cynomolgus monkeys further revealed DB-1310 to have a good safety profile with a highest non severely toxic dose (HNSTD) of 45 mg/kg. CONCLUSIONS: These finding demonstrated that DB-1310 exerted potent antitumor activities against HER3 + tumors in in vitro and in vivo models, and showed acceptable safety profiles in nonclinical species. Therefore, DB-1310 may be effective for the clinical treatment of HER3 + solid tumors.

Ab Initio Potential Energy Surface for NaCl-H2 with Correct Long-Range Behavior.

We report a full dimensional ab initio potential energy surface for NaCl-H2 based on precise fitting of a large data set of CCSD(T)/aug-cc-pVTZ energies. A major goal of this fit is to describe the very long-range interaction accurately. This is done in this instance via the dipole-quadrupole interaction. The NaCl dipole and the H2 quadrupole are available through previous works over a large range of internuclear distances. We use these to obtain exact effect charges on each atom. Diffusion Monte Carlo calculations are done for the ground vibrational state using the new potential.

Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H3O2.

The singly hydrated hydroxide anion OH-(H2O) is of central importance to a detailed molecular understanding of water; therefore, there is strong motivation to develop a highly accurate potential to describe this anion. While this is a small molecule, it is necessary to have an extensive data set of energies and, if possible, forces to span several important stationary points. Here, we assess two machine-learned potentials, one using the symmetric gradient domain machine learning (sGDML) method and one based on permutationally invariant polynomials (PIPs). These are successors to a PIP potential energy surface (PES) reported in 2004. We describe the details of both fitting methods and then compare the two PESs with respect to precision, properties, and speed of evaluation. While the precision of the potentials is similar, the PIP PES is much faster to evaluate for energies and energies plus gradient than the sGDML one. Diffusion Monte Carlo calculations of the ground vibrational state, using both potentials, produce similar large anharmonic downshift of the zero-point energy compared to the harmonic approximation of the PIP and sGDML potentials. The computational time for these calculations using the sGDML PES is roughly 300 times greater than using the PIP one.

A New A Priori Method to Avoid Calculation of Negligible Hamiltonian Matrix Elements in CI Calculation.

Hamiltonian matrices typically contain many elements that are negligibly small compared to the diagonal elements, even with methods to prune the underlying basis. Because for general potentials the calculation of H-matrix elements is a major part of the computational effort to obtain eigenvalues and eigenfunctions of the Hamiltonian, there is strong motivation to investigate locating these negligible elements without calculating them or at least avoid calculating them. We recently demonstrated an effective means to "learn" negligible elements using machine learning classification (J. Chem. Phys. 2023, 159, 071101). Here we present a simple, new method to avoid calculating them by using a cut-off value for the absolute difference in the quantum numbers for the bra and ket. This method is demonstrated for many of the same case studies as were used in the paper above, namely for realistic H-matrices of H2O, the vinyl radical, C2H3, and glycine, C2H5NO2. The new method is compared to the recently reported machine learning approach. In addition, we point out an important synergy between the two methods.

MCM6 promotes intrahepatic cholangiocarcinoma progression by upregulating E2F1 and enhancing epithelial-mesenchymal transition.

Minichromosome maintenance complex component 6 (MCM6), a member of the MCM family, plays a pivotal role in DNA replication initiation and genome duplication of proliferating cells. MCM6 is upregulated in multiple malignancies and is considered a novel diagnostic biomarker. However, the functional contributions and prognostic value of MCM6 in intrahepatic cholangiocarcinoma (ICC) remain unexplored. In this study, we investigated the molecular function of MCM6 in ICC. Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO, GSE107943) indicated an upregulation of MCM6 in tumor tissues. Immunohistochemical analysis performed on 115 cases of ICC samples confirmed the upregulation of MCM6 and further suggested that a high level of MCM6 expression predicted shorter overall and disease-free survival in ICC patients. Functional studies suggested that MCM6 knockdown significantly suppressed cell viability, blocked cell cycle progression and inhibited metastasis, while the enhancement of MCM6 expression promoted the proliferation and migration of ICC cells both in vitro and in vivo. Mechanistically, Gene Set Enrichment Analysis (GSEA) suggested that the epithelial-mesenchymal transition (EMT) and E2F1-correlated genes were enriched in ICC tissues with high MCM6 expression. Further verification indicated that MCM6 promoted the EMT of ICC cells via upregulating E2F1. In addition, E2F1 knockdown partially blocked the pro-malignant effects of MCM6 overexpression. In summary, MCM6 was found to be a novel prognostic and predictive marker for ICC. MCM6 promoted ICC progression via activation of E2F1-mediated EMT.

Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands.

Tropolone, a 15-atom cyclic molecule, has received much interest both experimentally and theoretically due to its H-transfer tunneling dynamics. An accurate theoretical description is challenging owing to the need to develop a high-level potential energy surface (PES) and then to simulate quantum-mechanical tunneling on this PES in full dimensionality. Here, we tackle both aspects of this challenge and make detailed comparisons with experiments for numerous isotopomers. The PES, of near CCSD(T)-quality, is obtained using a Δ-machine learning approach starting from a pre-existing low-level DFT PES and corrected by a small number of approximate CCSD(T) energies obtained using the fragmentation-based molecular tailoring approach. The resulting PES is benchmarked against DF-FNO-CCSD(T) and CCSD(T)-F12 calculations. Ring-polymer instanton calculations of the splittings, obtained with the Δ-corrected PES are in good agreement with previously reported experiments and a significant improvement over those obtained using the low-level DFT PES. The instanton path includes heavy-atom tunneling effects and cuts the corner, thereby avoiding passing through the conventional saddle-point transition state. This is in contradistinction with typical approaches based on the minimum-energy reaction path. Finally, the subtle changes in the splittings for some of the heavy-atom isotopomers seen experimentally are reproduced and explained.

Linezolid dose adjustment according to therapeutic drug monitoring helps reach the goal concentration in severe patients, and the oldest seniors benefit more.

BACKGROUND: The elderly with severe infection increased dramatically in intensive care unit (ICU). Proper antimicrobial therapy help improve the prognosis. Linezolid, as an antimicrobial drug, is commonly utilized to treat patients infected with methicillin-resistant S. aureus and vancomycin-resistant enterococci. Clinical evidence suggests elderly patients prone to linezolid overexposure. Here, we describe the results of three years' linezolid adjustment experiences according to therapeutic drug monitoring (TDM), especially in the oldest old. METHODS: Linezolid therapeutic drug monitoring data were collected between January 2020 and November 2022 from patients who were admitted to ICU and treated with linezolid. All the patients started with a dosage of 600 mg, twice daily. The first TMD was carried out ten minutes before the seventh administration. The dosage adjustment was determined by the doctor according to the first TMD and patients' condition, and the repeated TDM was conducted as required. The dosage adjustment in different age group was recorded. Laboratory data were compared between the old and the oldest old. The high mortality risk of the oldest old was also explored. RESULTS: Data of 556 linezolid TDM from 330 patients were collected. Among which, 31.6%, 54.8%, and 75% of patients had supra-therapeutic linezolid trough concentrations at the first TDM assessment in different age group, leading to the dosage adjustment rate of 31.0%, 40.3%, 68.8% respectively. The linezolid dosage adjustments according to TDM help to reach therapeutic concentration. The oldest old was in high risk of linezolid overexposure with lowercreatinine clearance. The norepinephrine maximum dosage but not linezolid Cmin was associated with 28-day mortality in the oldest old. CONCLUSIONS: Elderly patients with linezolid conventional 600 mg twice-daily dose might be at a high risk of overexposure, especially in the oldest old. The linezolid dosage adjustments according to TDM help reach the therapeutic concentration. The high mortality of the oldest old was not related with initial linezolid overexposure.

PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials.

We wish to describe a potential energy surface by using a basis of permutationally invariant polynomials whose coefficients will be determined by numerical regression so as to smoothly fit a dataset of electronic energies as well as, perhaps, gradients. The polynomials will be powers of transformed internuclear distances, usually either Morse variables, exp(-ri,j/λ), where λ is a constant range hyperparameter, or reciprocals of the distances, 1/ri,j. The question we address is how to create the most efficient basis, including (a) which polynomials to keep or discard, (b) how many polynomials will be needed, (c) how to make sure the polynomials correctly reproduce the zero interaction at a large distance, (d) how to ensure special symmetries, and (e) how to calculate gradients efficiently. This article discusses how these questions can be answered by using a set of programs to choose and manipulate the polynomials as well as to write efficient Fortran programs for the calculation of energies and gradients. A user-friendly interface for access to monomial symmetrization approach results is also described. The software for these programs is now publicly available.

Machine learning classification can significantly reduce the cost of calculating the Hamiltonian matrix in CI calculations.

Hamiltonian matrices in electronic and nuclear contexts are highly computation intensive to calculate, mainly due to the cost for the potential matrix. Typically, these matrices contain many off-diagonal elements that are orders of magnitude smaller than diagonal elements. We illustrate that here for vibrational H-matrices of H2O, C2H3 (vinyl), and C2H5NO2 (glycine) using full-dimensional ab initio-based potential surfaces. We then show that many of these small elements can be replaced by zero with small errors of the resulting full set of eigenvalues, depending on the threshold value for this replacement. As a result of this empirical evidence, we investigate three machine learning approaches to predict the zero elements. This is shown to be successful for these H-matrices after training on a small set of calculated elements. For H-matrices of vinyl and glycine, of order 15 552 and 8828, respectively, training on a percent or so of elements is sufficient to obtain all eigenvalues with a mean absolute error of roughly 2 cm-1.

Efficient and Selective Adsorption of Cationic Dye Malachite Green by Kiwi-Peel-Based Biosorbents.

In this study, pristine kiwi peel (KP) and nitric acid modified kiwi peel (NA-KP) based adsorbents were prepared and evaluated for selective removal of cationic dye. The morphology and chemical structure of KP and NA-KP were fully characterized and compared, and results showed nitric acid modification introduced more functional groups. Moreover, the adsorption kinetics and isotherms of malachite green (MG) by KP and NA-KP were investigated and discussed. The results showed that the adsorption process of MG onto KP followed a pseudo-second-order kinetic model and the Langmuir isotherm model, while the adsorption process of MG onto NA-KP followed a pseudo-first-order kinetic model and the Freundlich isotherm model. Notably, the Langmuir maximum adsorption capacity of NA-KP was 580.61 mg g-1, which was superior to that of KP (297.15 mg g-1). Furthermore, thermodynamic studies demonstrated the feasible, spontaneous, and endothermic nature of the adsorption process of MG by NA-KP. Importantly, NA-KP showed superior selectivity to KP towards cationic dye MG against anionic dye methyl orange (MO). When the molar ratio of MG/MO was 1:1, the separation factor (αMG/MO) of NA-KP was 698.10, which was 5.93 times of KP. In addition, hydrogen bonding, π-π interactions, and electrostatic interaction played important roles during the MG adsorption process by NA-KP. This work provided a low-cost, eco-friendly, and efficient option for the selective removal of cationic dye from dyeing wastewater.

NUSAP1, a novel stemness-related protein, promotes early recurrence of hepatocellular carcinoma.

Early recurrence (within 2 years after resection) is the primary cause of poor outcomes among hepatocellular carcinoma (HCC) patients, and liver cancer stem cells are the main contributors to postsurgical HCC recurrence. Nucleolar and spindle-associated protein 1 (NUSAP1) has been reported to be involved in tumor progression. We investigated the function and clinical value of NUSAP1 in early recurrence of HCC. Data from public datasets and our cohort were used to assess the association between NUSAP1 expression and early HCC recurrence. Gain- and loss-of-function experiments were carried out in vivo and in vitro. The predictive effect of NUSAP1 on early HCC recurrence was further evaluated by a validation cohort. We found that elevated NUSAP1 expression in HCC specimens was correlated with poor outcome, especially in cases with postoperative early recurrence. Functional studies indicated that NUSAP1 significantly promotes HCC progression. A postsurgical recurrence murine model further revealed that upregulated NUSAP1 dramatically increased the likelihood of HCC early recurrence. RNA sequencing data revealed that the gene sets of cancer stemness and the signal transducer and activator of transcription 3 (STAT3) pathway were enriched by NUSAP1 overexpression. Mechanistically, NUSAP1 enhanced cancer stemness through stimulating STAT3 nuclear translocation and activation through receptor of activated protein C kinase 1 (RACK1). In a validation cohort with 112 HCC patients, NUSAP1 effectively predicted HCC early recurrence. Our results indicated that NUSAP1 promotes early recurrence of HCC by sustaining cancer stemness and could serve as a valuable predictive indicator for postsurgical intervention in HCC patients.

Myofibroblast-Specific Msi2 Knockout Inhibits HCC Progression in a Mouse Model.

BACKGROUND AND AIMS: Myofibroblasts play a pivotal role in the development and progression of HCC. Here, we aimed to explore the role and mechanism of myofibroblast Musashi RNA binding protein 2 (MSI2) in HCC progression. APPROACH AND RESULTS: Myofibroblast infiltration and collagen deposition were detected and assessed in the tissues from 117 patients with HCC. Transgenic mice (Msi2ΔCol1a1 ) with floxed Msi2 allele and collagen type I alpha 1 chain (Col1a1)-ligand inducible Cre recombinases (CreER) were constructed to generate a myofibroblast-specific Msi2 knockout model. Mouse HCC cells were orthotopically transplanted into the Msi2ΔCol1a1 or the control mice (Msi2F/F ). We found that the deposition of collagen fibers, the main product of myofibroblasts, predicted a poor prognosis for HCC; meanwhile, we detected high MSI2 expression in the peritumoral infiltrated myofibroblasts. Conditional deletion of Msi2 in myofibroblasts significantly inhibited the growth of orthotopically implanted HCC, reduced both intrahepatic and lung metastasis, and prolonged the overall survival of tumor-bearing mice (P = 0.002). In vitro analysis demonstrated that myofibroblasts promoted cell proliferation, invasion, and epithelial-mesenchymal transformation of HCC cells, whereas Msi2 deletion in myofibroblasts reversed these effects. Mechanically, Msi2 knockout decreased myofibroblast-derived IL-6 and IL-11 secretion by inhibiting the extracellular signal-regulated kinase 1/2 pathway, and thus attenuated the cancer stem cell-promoting effect of myofibroblasts. Interestingly, we found that the simultaneous knockout of Msi2 in myofibroblasts and knockdown of Msi2 in HCC cells could not further attenuate the implanted HCC progression. CONCLUSIONS: Myofibroblast-specific Msi2 knockout abrogated the tumor-promoting function of myofibroblasts and inhibited HCC progression in mouse models. Targeting myofibroblast MSI2 expression may therefore prove to be a therapeutic strategy for HCC treatment in the future.

Neurocomputational mechanisms at play when weighing concerns for extrinsic rewards, moral values, and social image.

Humans not only value extrinsic monetary rewards but also their own morality and their image in the eyes of others. Yet violating moral norms is frequent, especially when people know that they are not under scrutiny. When moral values and monetary payoffs are at odds, how does the brain weigh the benefits and costs of moral and monetary payoffs? Here, using a neurocomputational model of decision value (DV) and functional (f)MRI, we investigated whether different brain systems are engaged when deciding whether to earn money by contributing to a "bad cause" and when deciding whether to sacrifice money to contribute to a "good cause," both when such choices were made privately or in public. Although similar principles of DV computations were used to solve these dilemmas, they engaged 2 distinct valuation systems. When weighing monetary benefits and moral costs, people were willing to trade their moral values in exchange for money, an effect accompanied by DV computation engaging the anterior insula and the lateral prefrontal cortex (PFC). In contrast, weighing monetary costs against compliance with one's moral values engaged the ventral putamen. Moreover, regardless of the type of dilemma, a brain network including the anterior cingulate cortex (ACC), anterior insula, and the right temporoparietal junction (TJP) was more engaged in public than in private settings. Together, these findings identify how the brain processes three sources of motivation: extrinsic rewards, moral values, and concerns for image.

Electronic relaxation and dissociation dynamics in formaldehyde: pump wavelength dependence.

The effect of the incident UV pump wavelength on the subsequent excited state dynamics, electronic relaxation, and ultimate dissociation of formaldehyde is studied using first principles simulation and Coulomb explosion imaging (CEI) experiments. Transitions in a vibronic progression in the à ← X̃ absorption band are systematically prepared using a tunable UV source which generates pulses centered at 304, 314, 329, and 337 nm. We find, both via ab initio simulation and experimental results, that the rate of excited state decay and subsequent dissociation displays a prominent dependence on which vibronic transition in the absorption band is prepared by the pump. Our simulations predict that nonadiabatic transition rates and dissociation yields will increase by a factor of >100 as the pump wavelength is decreased from 337 to 304 nm. The experimental results and theoretical simulations are in broad agreement and both indicate that the dissociation rate plateaus rapidly after ≈2 ps following an ultrafast sub-ps rise.

Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China.

BACKGROUND: Dyslipidaemia is a risk factor for abnormal blood glucose. However, studies on the predictive values of lipid markers in prediabetes and diabetes simultaneously are limited. This study aimed to assess the associations and predictive abilities of lipid indices and abnormal blood glucose. METHODS: A sample of 7667 participants without diabetes were enrolled in this cross-sectional study conducted in 2016, and all of them were classified as having normal glucose tolerance (NGT), prediabetes or diabetes. Blood glucose, blood pressure and lipid parameters (triglycerides, TG; total cholesterol, TC; high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; non-high-density lipoprotein cholesterol, non-HDL-C; and triglyceride glucose index, TyG) were evaluated or calculated. Logistic regression models were used to analyse the association between lipids and abnormal blood glucose. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the discriminatory power of lipid parameters for detecting prediabetes or diabetes. RESULTS: After adjustment for potential confounding factors, the TyG was the strongest marker related to abnormal blood glucose compared to other lipid indices, with odds ratios of 2.111 for prediabetes and 5.423 for diabetes. For prediabetes, the AUCs of the TG, TC, HDL-C, LDL-C, TC/HDL-C, TG/HDL-C, non-HDL-C and TyG indices were 0.605, 0.617, 0.481, 0.615, 0.603, 0.590, 0.626 and 0.660, respectively, and the cut-off points were 1.34, 4.59, 1.42, 2.69, 3.39, 1.00, 3.19 and 8.52, respectively. For diabetes, the AUCs of the TG, TC, HDL-C, LDL-C, TC/HDL-C, TG/HDL-C, non-HDL-C and TyG indices were 0.712, 0.679, 0.440, 0.652, 0.686, 0.692, 0.705, and 0.827, respectively, and the cut-off points were 1.35, 4.68, 1.42, 2.61, 3.44, 0.98, 3.13 and 8.80, respectively. CONCLUSIONS: The TyG, TG and non-HDL-C, especially TyG, are accessible biomarkers for screening individuals with undiagnosed diabetes.

Semiclassical and VSCF/VCI Calculations of the Vibrational Energies of trans- and gauche-Ethanol Using a CCSD(T) Potential Energy Surface.

A recent full-dimensional Δ-Machine learning potential energy surface (PES) for ethanol is employed in semiclassical and vibrational self-consistent field (VSCF) and virtual-state configuration interaction (VCI) calculations, using MULTIMODE, to determine the anharmonic vibrational frequencies of vibration for both the trans and gauche conformers of ethanol. Both semiclassical and VSCF/VCI energies agree well with the experimental data. We find significant mixing between the VSCF basis states due to Fermi resonances between bending and stretching modes. The same effects are also accurately described by the full-dimensional semiclassical calculations. These are the first high-level anharmonic calculations using a PES, in particular a "gold-standard" CCSD(T) one.