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Despite concerns about an increased risk of adverse outcomes following coronavirus disease (COVID‐19) in multiple myeloma patients treated with anti‐CD38 Abs, the impact of COVID‐19 on this group of patients is unclear. We tried to evaluate the clinical outcomes of these patients. We collected data from 1036 patients with multiple myeloma and enrolled 509 cases with COVID‐19. We divided enrolled patients into daratumumab or nondaratumumab cohorts based on whether they had received daratumumab‐based treatment within 6 months of COVID‐19 infection. We applied a propensity score matching method to reduce the bias of baseline characteristics, and then compared the incidence of adverse outcomes between these two cohorts. A total of 117 patients were enrolled in the daratumumab cohort, and 392 patients in the nondaratumumab cohort. After propensity score matching, 204 patients were matched. The proportions of patients who developed COVID‐19 pneumonia (59.8% vs. 34.3%, p < 0.001), were hospitalized (33.3% vs. 11.8%, p < 0.001) and developed severe disease (23.5% vs. 6.9%, p = 0.001) were higher in the matched daratumumab cohort. By multivariate analysis, daratumumab exposure was an independent risk factor for severe disease. An ECOG performance status >2 and history of chronic kidney disease were independent risk factors for COVID‐19‐related mortality among patients who received daratumumab‐based therapy. This study suggested that multiple myeloma patients exposed to daratumumab were at a higher risk of adverse outcomes from COVID‐19. [ABSTRACT FROM AUTHOR]

Fuzzy c-means (FCM) clustering is an unsupervised method derived from fuzzy logic that is suitable for solving multiclass and ambiguous clustering problems. In this study, FCM clustering is applied to cluster metabolomics data. FCM is performed directly on the data matrix to generate a membership matrix which represents the degree of association the samples have with each cluster. The method is parametrized with the number of clusters (C) and the fuzziness coefficient (m), which denotes the degree of fuzziness in the algorithm. Both have been optimized by combining FCM with partial least-squares (PLS) using the membership matrix as the Y matrix in the PLS model. The quality parameters [R.sup.2]Y and [Q.sup.2] of the PLS model have been used to monitor and optimize C and m. Data of metabolic profiles from three gene types of Escherichia coli were used to demonstrate the method above. Different multivariable analysis methods have been compared. Principal component analysis failed to model the metabolite data, while partial least-squares discriminant analysis yielded results with overfitting. On the basis of the optimized parameters, the FCM was able to reveal main phenotype changes and individual characters of three gene types of E. coli. Coupled with PLS, FCM provides a powerful research tool for metabolomics with improved visualization, accurate classification, and outlier estimation.

Liquid chromatography/mass spectrometry (LC/MS) followed by multivariate statistical analysis has been successfully applied to the plasma phospholipids metabolic profiling in type 2 diabetes mellitus (DM-2). Principal components analysis and partial least-squares discriminant analysis (PLS-DA) models were tested and compared in class separation between the DM2 and control. The application of an orthogonal signal correction filtered model highly improved the class distinction and predictive power of PLS-DA models. Additionally, unit variance scaling was also tested. With this methodology, it was possible not only to differentiate the DM2 from the control but also to discover and identify the potential biomarkers with LC/MS/MS. The proposed method shows that LC/MS combining with multivariate statistical analysis is a complement or an alternative to NMR for metabonomics applications.

Cigarette smoke condensate is a complex chemical matrix, and analysis of its components is very difficult because of the limitation of the peak capacity and sensitivity of conventional chromatography and the extensive and laborious sample preparation that is frequently required. In this study, the acidic fraction of mainstream cigarette smoke condensate has been investigated by using comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry (GCxGC/TOFMS). Different column systems were tested and compared under proper GCxGC/TOFMS conditions. Auto data processing by TOFMS software combined with manual identification was used to assign the components. Over 1000 compounds, with S/N [greater than or equal to] 100, including 139 organic acids and over 150 phenols were tentatively identified by the developed method.

A useful model for general time-varying channels is a finite state Markov chain. In this paper, maximum likelihood sequence estimation (MLSE) for signals over finite state Markov channels (FSMC's) is studied. Also studied is the maximum a posteriori (MAP) channel state estimation. When coded signals with interleaving are transmitted, the channel estimates can be used to make soft-decision decoding. The error performance of the proposed sequence and channel state estimation schemes are evaluated through computer simulations. The effect of channel modeling error is also discussed.

Due to the receiver complexity introduced by interleaving, the implementation of maximum likelihood (ML) decoding of interleaved coded signals transmitted over frequency nonselective Rayleigh fading channels has been shown to be practically impossible. As an alternative, a two-stage receiver structure has been proposed, where the channel estimation and sequence decoding are done separately. The channel estimation issue in a two-stage receiver is examined in detail in this paper. It is shown that although an optimum [maximum a posteriori (MAP)] channel estimation is not possible in practice, it can be approached asymptotically by joint MAP estimation of the channel and the coded data sequence. The implementation of the joint MAP estimation is shown to be an ML sequence estimator followed by an minimum mean-square error (MMSE) channel estimator. Approximate ML sequence estimation using pilot symbol interpolation is also studied, and through computer simulations, its performance is compared to receivers using ML sequence estimation. The effect of decision delay (DD), prediction order, and pilot insertion rate (PIR) on the reduced complexity ML sequence estimation is investigated as well. Finally, a practical receiver is proposed that makes the best compromise among the error performance, receiver complexity, DD, and power (or bandwidth) expansion due to pilot insertion. Index Terms - Frequency nonselective, interleaved coding, Rayleigh fading, two-stage receiver.