Non-sicca is a common initial manifestation in pSS-ILD. Anti-SSA presence, elevated RF titer and hyperglobulinemia were less predominant, and pulmonary complications were more progressive and severe in non-sicca onset patients than sicca onset patients.
Background:Pulmonary embolism (PE) can be difficult to diagnose in elderly patients because of the coexistent diseases and the combination of drugs that they have taken. We aimed to compare the clinical diagnostic values of the Wells score, the revised Geneva score and each of them combined with D-dimer for suspected PE in elderly patients.Methods:Three hundred and thirty-six patients who were admitted for suspected PE were enrolled retrospectively and divided into two groups based on age (≥65 or <65 years old). The Wells and revised Geneva scores were applied to evaluate the clinical probability of PE, and the positive predictive values of both scores were calculated using computed tomography pulmonary arteriography as a gold standard; overall accuracy was evaluated by the area under the curve (AUC) of receiver operator characteristic curve; the negative predictive values of D-dimer, the Wells score combined with D-dimer, and the revised Geneva score combined with D-dimer were calculated.Results:Ninety-six cases (28.6%) were definitely diagnosed as PE among 336 cases, among them 56 cases (58.3%) were ≥65 years old. The positive predictive values of Wells and revised Geneva scores were 65.8% and 32.4%, respectively (P < 0.05) in the elderly patients; the AUC for the Wells score and the revised Geneva score in elderly was 0.682 (95% confidence interval [CI]: 0.612–0.746) and 0.655 (95% CI: 0.584–0.722), respectively (P = 0.389). The negative predictive values of D-dimer, the Wells score combined with D-dimer, and the revised Geneva score combined with D-dimer were 93.7%, 100%, and 100% in the elderly, respectively.Conclusions:The diagnostic value of the Wells score was higher than the revised Geneva score for the elderly cases with suspected PE. The combination of either the Wells score or the revised Geneva score with a normal D-dimer concentration is a safe strategy to rule out PE.
Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.
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