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Patients with stable coronary artery disease (CAD) are at an increased risk of acute myocardial infarction (AMI), particularly among older individuals. Developing a reliable model to predict AMI occurrence in these patients holds the potential to expedite early diagnosis and intervention. This study is aimed at establishing a circulating amino acid‐assisted model, incorporating amino acid profiles alongside clinical variables, to predict AMI risk. A cohort of 874 CAD patients from two independent centers was analyzed. Plasma amino acid levels were quantified using liquid chromatography tandem mass spectrometry (LC‐MS/MS) employing a targeted metabolomics approach. This methodology incorporated 13C isotope‐labeled internal standards for precise quantification of 27 amino acids. Univariate logistic regression was applied to identify differentially expressed amino acids that distinguished between stable CAD and AMI patients. To assess prediction performance, receiver operating characteristic (ROC) curve and nomogram analyses were utilized. Five amino acids—lysine, methionine, tryptophan, tyrosine, and N6‐trimethyllysine—emerged as potential biomarkers (p < 0.05), exhibiting significant differences in their expression levels across the two centers when comparing stable CAD with AMI patients. For AMI risk prediction, the base model, utilizing 12 clinical variables, achieved areas under the curve (AUC) of 0.7387 in the discovery phase (n = 623) and 0.8205 in the external validation set (n = 251). Notably, the integration of these five amino acids into the prediction model significantly enhanced its performance, increasing the AUC to 0.7651 in the discovery phase (Delong's test, p = 1.43e‐02) and to 0.8958 in the validation set (Delong's test, p = 8.91e‐03). In conclusion, the circulating amino acid‐assisted model effectively enhances the prediction of AMI risk among CAD patients, indicating its potential clinical utility in facilitating early detection and intervention. [ABSTRACT FROM AUTHOR]

We formed planar waveguides in LiNb[O.sub.3] and LiTa[O.sub.3] crystals by megaelectron volt He-ion implantation. The dark modes of both waveguides are measured and their refractive-index profiles are described according to the parameterized index profile reconstruction method. The extraordinary indices of both ion-implanted waveguides exhibit quite different profiles. We compare the thermal stability of barriers in ion-implanted LiNb[O.sub.3] and LiTa[O.sub.3] waveguides by annealing at different temperatures. The results show that the barrier in a LiTa[O.sub.3] planar waveguide has higher thermal stability than that in a LiNb[O.sub.3] waveguide. The experiments also show that annealing at a temperature higher than 400 [degrees] C results in recrystallization of the barrier.

Abstract Retroviral transduction involves integrase-dependent linkage of viral and host DNA that leaves an intermediate that requires post-integration repair (PIR). We and others proposed that PIR hijacks the host cell double-strand DNA break (DSB) repair pathways. Nevertheless, the geometry of retroviral DNA integration differs considerably from that of DSB repair and so the precise role of host-cell mechanisms in PIR remains unclear. In the current study, we found that the Nijmegen breakage syndrome 1 protein (NBS1), an early sensor of DSBs, associates with HIV-1 DNA, recruits the ataxia telangiectasia-mutated (ATM) kinase, promotes stable retroviral transduction, mediates efficient integration of viral DNA and blocks integrase-dependent apoptosis that can arise from unrepaired viral-host DNA linkages. Moreover, we demonstrate that the ATM kinase, recruited by NBS1, is itself required for efficient retroviral transduction. Surprisingly, recruitment of the ATR kinase, which in the context of DSB requires both NBS1 and ATM, proceeds independently of these two proteins. A model is proposed emphasizing similarities and differences between PIR and DSB repair. Differences between the pathways may eventually allow strategies to block PIR while still allowing DSB repair.

Metabolomics offers a noninvasive methodology to identify metabolic markers for pathogenesis and diagnosis of diseases. This work aimed to characterize circulating metabolic signatures of benign thyroid nodule (BTN) and papillary thyroid carcinoma (PTC) via serum‐plasma matched metabolomics. A cohort of 1,540 serum‐plasma matched samples and 114 tissues were obtained from healthy volunteers, BTN and PTC patients enrolled from 6 independent centers. Untargeted metabolomics was determined by liquid chromatography‐quadrupole time‐of‐flight mass spectrometric and multivariate statistical analyses. The use of serum‐plasma matched samples afforded a broad‐scope detection of 1,570 metabolic features. Metabolic phenotypes revealed significant pattern differences for healthy versus BTN and healthy versus PTC. Perturbed metabolic pathways related mainly to amino acid and lipid metabolism. It is worth noting that, BTN and PTC showed no significant differences but rather overlap in circulating metabolic signatures, and this observation was replicated in all study centers. For differential diagnosis of healthy versus thyroid nodules (BTN + PTC), a panel of 6 metabolic markers, namely myo‐inositol, α‐N‐phenylacetyl‐L‐glutamine, proline betaine, L‐glutamic acid, LysoPC(18:0) and LysoPC(18:1) provided area under the curve of 97.68% in the discovery phase and predictive accuracies of 84.78–98.18% in the 4 validation centers. Taken together, serum‐plasma matched metabolomics showed significant differences in circulating metabolites for healthy versus nodules but not for BTN versus PTC. Our results highlight the true metabolic nature of thyroid nodules, and potentially decrease overtreatment that exposes patients to unnecessary risks. What's new? When thyroid nodules are classified "indeterminate," is it better to wait and see, or take out the thyroid? Usually, doctors remove the thyroid, resulting in a lifetime of levothyroxine replacement, yet most often the nodules are not cancerous. Here, the authors investigated whether metabolic profile could give a more accurate prediction of whether a thyroid nodule is cancerous. They tested healthy patients, those with benign nodules, and those with papillary thyroid carcinoma. Healthy patients showed distinct differences from those with benign nodules and those with carcinomas, while significant overlap was observed between circulating metabolites from BTN and PTC patients. [ABSTRACT FROM AUTHOR]