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Background: Daratumumab (Dara) is generally well tolerated, but is associated with increased risk of infection. Methods: We investigated hypogammaglobinemia occurrence in different Dara-based regimens. Multiple myeloma (MM) patients were treated with ⩾2 cycles of Dara-based therapy during 2016–2020, mainly for relapsed/refractory disease. Data on patient characteristics, treatment regimens, polyclonal IgG (poly-IgG) and uninvolved free light chain (Un-FLC) levels during treatment, as well as predictors for hypogammaglobinemia and predictors for infections, were evaluated retrospectively. Results: A total of 84 patients, median age 67.2 years, were included. Dara, mainly as ⩾2 line therapy (88.1%, n = 74), was combined with immunomodulating drugs (IMiDs) (53%), proteasome inhibitors (PIs) (15%), IMiDs-PIs (11%), or dexamethasone only (21%). Median treatment duration was 13 months. Median Poly-IgG levels at 0, 2, and 4 months were 7.1 g/l, 4.5 g/l, and 4 g/l, respectively, and remained low throughout treatment. Lower poly-IgG pre-Dara ( p = 0.001) and Dara-PIs (±IMiDs) regimen were associated with lower poly-IgG levels at 4 months ( p = 0.03). Only patients treated with Dara monotherapy had partial immune reconstitution, reflected by resumption of IgM levels. Most (85%) patients developed ⩾1 infections, mostly grade 1–2 respiratory (76%). A lower poly-IgG level post Dara (RR = 1.137 p = 0.026) predicted increased risk of any infection. Intravenous immunoglobulin (IVIG) was associated with a significant decrease in all infections. Conclusion: Relapsed MM patients treated with Dara, often experience persistent hypogammaglobinemia, irrespective of responsiveness to treatment. Infections, especially respiratory, are frequent and apparently related to low Poly-IgG levels. IVIG should be considered for reducing infections in these patients.

Plants transpire water through their tissues in order to move nutrients and water to the cells. Transpiration includes various mechanisms, primarily stomata movement, which controls the rate of CO2 and water vapor exchange between the tissues and the atmosphere. Assessment of stomatal conductance is available for gas exchange techniques at leaf level, yet these techniques are not scalable to the whole plant let alone a large vegetation area. Hyperspectral reflectance spectroscopy, which acquires hundreds of bands in a single scan, may capture a glimpse of the crop’s physiological activity and therefore meet the scalability challenge. In this study, classic chemometric analyses are used alongside advanced statistical learning algorithms in order to identify stomatal conductance cues in hyperspectral measurements of cotton plants experiencing a gradient of irrigation. Random forest of regression trees identified 23 wavelengths related to both structural properties of the plant as well as water content. Partial least squares regression succeeded in relating these wavelengths to stomatal conductance, but only partially (R2 < 0.2). An artificial neural network algorithm reported an R2 = 0.54 with an 89% error-free performance on the same data subset. This study discusses implementation of machine learning methodologies as a benchmark for deeper analysis of spectral information, such as required when searching for plant physiology-related attenuations embedded within reflectance spectra.