The genomic profiling of high-risk smoldering myeloma patients treated with an intensive strategy unveils potential markers of resistance and progression.

Smoldering multiple myeloma (SMM) precedes multiple myeloma (MM). The risk of progression of SMM patients is not uniform, thus different progression-risk models have been developed, although they are mainly based on clinical parameters. Recently, genomic predictors of progression have been defined for untreated SMM. However, the usefulness of such markers in the context of clinical trials evaluating upfront treatment in high-risk SMM (HR SMM) has not been explored yet, precluding the identification of baseline genomic alterations leading to drug resistance. For this reason, we carried out next-generation sequencing and fluorescent in-situ hybridization studies on 57 HR and ultra-high risk (UHR) SMM patients treated in the phase II GEM-CESAR clinical trial (NCT02415413). DIS3, FAM46C, and FGFR3 mutations, as well as t(4;14) and 1q alterations, were enriched in HR SMM. TRAF3 mutations were specifically associated with UHR SMM but identified cases with improved outcomes. Importantly, novel potential predictors of treatment resistance were identified: NRAS mutations and the co-occurrence of t(4;14) plus FGFR3 mutations were associated with an increased risk of biological progression. In conclusion, we have carried out for the first time a molecular characterization of HR SMM patients treated with an intensive regimen, identifying genomic predictors of poor outcomes in this setting.

Particle Tracking and Micromixing Performance Characterization with a Mobile Device.

Strategies to stir and mix reagents in microfluid devices have evolved concomitantly with advancements in manufacturing techniques and sensing. While there is a large array of reported designs to combine and homogenize liquids, most of the characterization has been focused on setups with two inlets and one outlet. While this configuration is helpful to directly evaluate the effects of features and parameters on the mixing degree, it does not portray the conditions for experiments that involve more than two substances required to be subsequently combined. In this work, we present a mixing characterization methodology based on particle tracking as an alternative to the most common approach to measure homogeneity using the standard deviation of pixel intensities from a grayscale image. The proposed algorithm is implemented on a free and open-source mobile application (MIQUOD) for Android devices, numerically tested on COMSOL Multiphysics, and experimentally tested on a bidimensional split and recombine micromixer and a three-dimensional micromixer with sinusoidal grooves for different Reynolds numbers and geometrical features for samples with fluids seeded with red, blue, and green microparticles. The application uses concentration field data and particle track data to evaluate up to eleven performance metrics. Furthermore, with the insights from the experimental and numerical data, a mixing index for particles (mp) is proposed to characterize mixing performance for scenarios with multiple input reagents.

Impaired FADD/BID signaling mediates cross-resistance to immunotherapy in Multiple Myeloma.

The treatment landscape in multiple myeloma (MM) is shifting from genotoxic drugs to immunotherapies. Monoclonal antibodies, immunoconjugates, T-cell engaging antibodies and CART cells have been incorporated into routine treatment algorithms, resulting in improved response rates. Nevertheless, patients continue to relapse and the underlying mechanisms of resistance remain poorly understood. While Impaired death receptor signaling has been reported to mediate resistance to CART in acute lymphoblastic leukemia, this mechanism yet remains to be elucidated in context of novel immunotherapies for MM. Here, we describe impaired death receptor signaling as a novel mechanism of resistance to T-cell mediated immunotherapies in MM. This resistance seems exclusive to novel immunotherapies while sensitivity to conventional anti-tumor therapies being preserved in vitro. As a proof of concept, we present a confirmatory clinical case indicating that the FADD/BID axis is required for meaningful responses to novel immunotherapies thus we report impaired death receptor signaling as a novel resistance mechanism to T-cell mediated immunotherapy in MM.

A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters.

Monocytes and neutrophils play key roles in the cytokine storm triggered by SARS-CoV-2 infection, which changes their conformation and function. These changes are detectable at the cellular and molecular level and may be different to what is observed in other respiratory infections. Here, we applied machine learning (ML) to develop and validate an algorithm to diagnose COVID-19 using blood parameters. In this retrospective single-center study, 49 hemogram parameters from 12,321 patients with clinical suspicion of COVID-19 and tested by RT-PCR (4239 positive and 8082 negative) were analysed. The dataset was randomly divided into training and validation sets. Blood cell parameters and patient age were used to construct the predictive model with the support vector machine (SVM) tool. The model constructed from the training set (5936 patients) achieved an accuracy for diagnosis of SARS-CoV-2 infection of 0.952 (95% CI: 0.875-0.892). Test sensitivity and specificity was 0.868 and 0.899, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.896 and 0.872, respectively (prevalence 0.50). The validation set model (4964 patients) achieved an accuracy of 0.894 (95% CI: 0.883-0.903). Test sensitivity and specificity was 0.8922 and 0.8951, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.817 and 0.94, respectively (prevalence 0.34). The area under the receiver operating characteristic curve was 0.952 for the algorithm performance. This algorithm may allow to rule out COVID-19 diagnosis with 94% of probability. This represents a great advance for early diagnostic orientation and guiding clinical decisions.

Large-scale real-life analysis of survival and usage of therapies in multiple myeloma.

Survival in multiple myeloma has improved significantly in recent years, especially in young patients. We reviewed the evolution of the survival of patients with MM in three groups based on age at MM diagnosis over three time periods between 1999 and 2020 at our 12 de Octubre Hospital institution (H12O). Then, to confirm our results, we used data from TriNetx, a global health research platform that includes patients from Europe to US. Finally, we analysed differences in the patterns of treatment between networks across the world. Kaplan‒Meier analysis was used to estimate survival probabilities, and between-group differences were tested using the log-rank test and hazard ratio. For patients from H12O, the median OS was 35.61, 55.59 and 68.67 months for the 1999-2009, 2010-2014 and 2015-2020 cohorts, respectively (p = 0.0001). Among all patients included in the EMEA network, the median OS was 20.32 months versus 34.75 months from 1999-2009 versus 2010-2014. The median OS from the 2010-2014 versus 2015-2020 time cohorts was 34.75 months versus 54.43 months, respectively. In relation to the US cohort, the median OS from before 2010 versus 2010-2014 was not reached in either time cohort and neither when comparing the 2010-2014 versus 2015-2019 time cohorts. Bortezomib is the most commonly used drug in the EMEA cohort, while lenalidomide is the most commonly used drug in the US cohort. This large-scale study based on real-world data confirms the previous finding that MM patients have increased their survival in the last two decades.

Predictive Modeling of Soft Stretchable Nanocomposites Using Recurrent Neural Networks.

Human skin is characterized by rough, elastic, and uneven features that are difficult to recreate using conventional manufacturing technologies and rigid materials. The use of soft materials is a promising alternative to produce devices that mimic the tactile capabilities of biological tissues. Although previous studies have revealed the potential of fillers to modify the properties of composite materials, there is still a gap in modeling the conductivity and mechanical properties of these types of materials. While traditional Finite Element approximations can be used, these methodologies tend to be highly demanding of time and processing power. Instead of this approach, a data-driven learning-based approximation strategy can be used to generate prediction models via neural networks. This paper explores the fabrication of flexible nanocomposites using polydimethylsiloxane (PDMS) with different single-walled carbon nanotubes (SWCNTs) loadings (0.5, 1, and 1.5 wt.%). Simple Recurrent Neural Networks (SRNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) models were formulated, trained, and tested to obtain the predictive sequence data of out-of-plane quasistatic mechanical tests. Finally, the model learned is applied to a dynamic system using the Kelvin-Voight model and the phenomenon known as the bouncing ball. The best predictive results were achieved using a nonlinear activation function in the SRNN model implementing two units and 4000 epochs. These results suggest the feasibility of a hybrid approach of analogy-based learning and data-driven learning for the design and computational analysis of soft and stretchable nanocomposite materials.

A strategic reflection for the management and implementation of CAR-T therapy in Spain: an expert consensus paper.

CAR-T cell therapy represents a therapeutic revolution in the prognosis and treatment of patients with certain types of hematological cancer. However, they also pose new challenges in the healthcare, regulatory and financial fields. The aim of the RET-A project was to undertake a strategic reflection on the management of CAR-T therapies within the Spanish National Health System, to agree on recommendations that will help to better deal with the new context introduced by these cell therapies in the present and in the future. This think tank involved 40 key agents and opinion leaders. The experts identified three great challenges for implementing advanced therapies in Spain: therapeutic individualisation, with a multidisciplinary approach; acceleration of access times, by minimizing bureaucracy; and increase in the number of centers qualified to manage the CAR-T therapies in the NHS. The experts agreed on the ideal criteria for designating those qualified centers. They also agreed on a comprehensive CAR-T care pathway with the timings and roles which would ideally be involved in each part of the process.

Psychiatric beds and prison populations in 17 Latin American countries between 1991 and 2017: rates, trends and an inverse relationship between the two indicators.

BACKGROUND: In 1990, Latin American countries committed to psychiatric reforms including psychiatric bed removals. Aim of the study was to quantify changes in psychiatric bed numbers and prison population rates after the initiation of psychiatric reforms in Latin America. METHODS: We searched primary sources to collect numbers of psychiatric beds and prison population rates across Latin America between the years 1991 and 2017. Changes of psychiatric bed numbers were compared against trends of incarceration rates and tested for associations using fixed-effects regression of panel data. Economic variables were used as covariates. Reliable data were obtained from 17 Latin American countries: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, Honduras, Guatemala, Mexico, Nicaragua, Panama, Paraguay, Peru, El Salvador, Uruguay and Venezuela. RESULTS: The number of psychiatric beds decreased in 15 out of 17 Latin American countries (median -35%) since 1991. Our findings indicate the total removal of 69 415 psychiatric beds. The prison population increased in all countries (median +181%). Panel data regression analyses showed a significant inverse relationship -2.70 (95% CI -4.28 to -1.11; p = 0.002) indicating that prison populations increased more when and where more psychiatric beds were removed. This relationship held up when introducing per capita income and income inequality as covariates -2.37 (95% CI -3.95 to -0.8; p = 0.006). CONCLUSIONS: Important numbers of psychiatric beds have been removed in Latin America. Removals of psychiatric beds were related to increasing incarceration rates. Minimum numbers of psychiatric beds need to be defined and addressed in national policies.

Low-Cost Light-Based GelMA 3D Bioprinting via Retrofitting: Manufacturability Test and Cell Culture Assessment.

Light-based bioprinter manufacturing technology is still prohibitively expensive for organizations that rely on accessing three-dimensional biological constructs for research and tissue engineering endeavors. Currently, most of the bioprinting systems are based on commercial-grade-based systems or modified DIY (do it yourself) extrusion apparatuses. However, to date, few examples of the adoption of low-cost equipment have been found for light-based bioprinters. The requirement of large volumes of bioinks, their associated cost, and the lack of information regarding the parameter selection have undermined the adoption of this technology. This paper showcases the retrofitting and assessing of a low-cost Light-Based 3D printing system for tissue engineering. To evaluate the potential of a proposed design, a manufacturability test for different features, machine parameters, and Gelatin Methacryloyl (GelMA) concentrations for 7.5% and 10% was performed. Furthermore, a case study of a previously seeded hydrogel with C2C12 cells was successfully implemented as a proof of concept. On the manufacturability test, deviational errors were found between 0.7% to 13.3% for layer exposure times of 15 and 20 s. Live/Dead and Actin-Dapi fluorescence assays after 5 days of culture showed promising results in the cell viability, elongation, and alignment of 3D bioprinted structures. The retrofitting of low-cost equipment has the potential to enable researchers to create high-resolution structures and three-dimensional in vitro models.

Impact of COVID-19 in patients with multiple myeloma based on a global data network.

The COVID-19 pandemic has represented a major cause of morbidity/mortality worldwide, overstressing health systems. Multiple myeloma (MM) patients show an increased risk for infections and they are expected to be particularly vulnerable to SARS-CoV-2 infection. Here we have obtained a comprehensive picture of the impact of COVID-19 in MM patients on a local and a global scale using a federated data research network (TriNetX) that provided access to Electronic Medical Records (EMR) from Health Care Organizations (HCO) all over the world. Through propensity score matched analyses we found that the number of new diagnoses of MM was reduced in 2020 compared to 2019 (RR 0.86, 95%CI 0.76-0.96) and the survival of newly diagnosed MM cases decreased similarly (HR 0.61, 0.38-0.81). MM patients showed higher risk of SARS-CoV-2 infection (RR 2.09, 1.58-2.76) and a higher excess mortality in 2020 (difference in excess mortality 9%, 4.4-13.2) than non-MM patients. By interrogating large EMR datasets from HCO in Europe and globally, we confirmed that MM patients have been more severely impacted by COVID-19 pandemic than non-MM patients. This study highlights the necessity of extending preventive measures worlwide to protect vulnerable patients from SARS-CoV-2 infection by promoting social distancing and an intensive vaccination strategies.

Trends in allogeneic haematopoietic cell transplantation for myelofibrosis in Europe between 1995 and 2018: a CMWP of EBMT retrospective analysis.

We performed a retrospective assessment of patient- and transplant-specific characteristics and outcomes for 4142 patients undergoing allogeneic haematopoietic cell transplant for myelofibrosis between 1995 and 2018 across 278 centres. Activity increased steadily across the four analysed eras (<2006, 2006-2010, 2011-2014 and 2015-2018). Median recipient age increased over time between the earliest and most recent cohort (49.4 years (range, 20.1-68) versus 59.3 years (range, 18.1-78.1). Increasing number of patients with a Karnofsky performance status <90 underwent transplant over time. Increased utilisation of matched unrelated donors was apparent (<2006, 22.5% versus 2015-18, 45.2%; p < 0.001). Decreased use of myeloablative conditioning, increased use of busulphan-based platforms and anti-thymocyte globulin was evident. Of note, rates of acute (a)GVHD grade II-IV by day +100 decreased over time (p = 0.027) as did rates of chronic (c) GVHD, predominantly extensive cGVHD (<2006, 36% (31-41%) versus 2015-18, 23% (21-25%); p = 0.001). Overall, significant factors associated with worse overall survival and non-relapse mortality (NRM) remained older age, use of donors other than matched sibling, recipient CMV seropositivity and a lower Karnofsky performance status (<90). Multivariable analysis demonstrated improvements in overall survival and reductions in relapse risk over time with stable NRM rates despite increasing numbers of older, less fit patients and use of unrelated donors.

Vertebral fractures are increased in rheumatoid arthritis despite recent therapeutic advances: a case-control study.

Prevalence and risk factors of vertebral fractures in postmenopausal RA women were assessed in 323 patients and compared with 660 age-matched women. Of patients, 24.15% had at least one vertebral fracture vs.16.06% of controls. Age, glucocorticoids and falls were the main fracture risks. Vertebral fractures were associated with disease severity. INTRODUCTION: There is little quality data on the updated prevalence of fractures in rheumatoid arthritis (RA) that may have changed due to advances in the therapeutic strategy in recent years. This study was aimed at analysing the prevalence and risk factors of vertebral fractures in postmenopausal women with RA and comparing it with that of the general population. METHODS: We included 323 postmenopausal women diagnosed with RA from 19 Spanish Rheumatology Departments, randomly selected and recruited in 2018. Lateral radiographs of the thoracic and lumbar spine were obtained to evaluate morphometric vertebral fractures and the spinal deformity index. We analysed subject characteristics, factors related to RA, and fracture risk factors. The control group consisted of 660 age-matched Spanish postmenopausal women from the population-based Camargo cohort. RESULTS: Seventy-eight (24.15%) RA patients had at least one vertebral fracture. RA patients had increased fracture risk compared with controls (106 of 660, 16.06%) (p = 0.02). Logistic regression analysis showed that age (OR 2.17; 95% CI 1.27-4.00), glucocorticoids (OR 3.83; 95% CI 1.32-14.09) and falls (OR 3.57; 95% CI 1.91-6.86) were the independent predictors of vertebral fractures in RA patients. The subgroup with vertebral fractures had higher disease activity (DAS28: 3.15 vs. 2.78, p = 0.038) and disability (HAQ: 0.96 vs. 0.63, p = 0.049), as compared with those without vertebral fractures. CONCLUSION: The risk of vertebral fracture in RA is still high in recent years, when compared with the general population. The key determinants of fracture risk are age, glucocorticoids and falls. Patients with vertebral fractures have a more severe RA.

E-Skin Development and Prototyping via Soft Tooling and Composites with Silicone Rubber and Carbon Nanotubes.

The strategy of embedding conductive materials on polymeric matrices has produced functional and wearable artificial electronic skin prototypes capable of transduction signals, such as pressure, force, humidity, or temperature. However, these prototypes are expensive and cover small areas. This study proposes a more affordable manufacturing strategy for manufacturing conductive layers with 6 × 6 matrix micropatterns of RTV-2 silicone rubber and Single-Walled Carbon Nanotubes (SWCNT). A novel mold with two cavities and two different micropatterns was designed and tested as a proof-of-concept using Low-Force Stereolithography-based additive manufacturing (AM). The effect SWCNT concentrations (3 wt.%, 4 wt.%, and 5 wt.%) on the mechanical properties were characterized by quasi-static axial deformation tests, which allowed them to stretch up to ~160%. The elastomeric soft material's hysteresis energy (Mullin's effect) was fitted using the Ogden-Roxburgh model and the Nelder-Mead algorithm. The assessment showed that the resulting multilayer material exhibits high flexibility and high conductivity (surface resistivity ~7.97 × 104 Ω/sq) and that robust soft tooling can be used for other devices.

SCRT1 is a novel beta cell transcription factor with insulin regulatory properties.

Here we show that scratch family transcriptional repressor 1 (SCRT1), a zinc finger transcriptional regulator, is a novel regulator of beta cell function. SCRT1 was found to be expressed in beta cells in rodent and human islets. In human islets, expression of SCRT1 correlated with insulin secretion capacity and the expression of the insulin (INS) gene. Furthermore, SCRT1 mRNA expression was lower in beta cells from T2D patients. siRNA-mediated Scrt1 silencing in INS-1832/13 cells, mouse- and human islets resulted in impaired glucose-stimulated insulin secretion and decreased expression of the insulin gene. This is most likely due to binding of SCRT1 to E-boxes of the Ins1 gene as shown with ChIP. Scrt1 silencing also reduced the expression of several key beta cell transcription factors. Moreover, Scrt1 mRNA expression was reduced by glucose and SCRT1 protein was found to translocate between the nucleus and the cytosol in a glucose-dependent fashion in INS-1832/13 cells as well as in a rodent model of T2D. SCRT1 was also regulated by a GSK3ß-dependent SCRT1-serine phosphorylation. Taken together, SCRT1 is a novel beta cell transcription factor that regulates insulin secretion and is affected in T2D.

Management of Psoriasis During Preconception, Pregnancy, Postpartum, and Breastfeeding: A Consensus Statement. / Consenso sobre las actuaciones a seguir durante la edad fértil, el embarazo, el posparto y la lactancia en pacientes con psoriasis.

OBJECTIVE: To develop evidence- and experience-based recommendations for the management of psoriasis during preconception, pregnancy, postpartum, and breastfeeding. METHODS: The nominal group technique and the Delphi method were used. Fifteen experts (12 dermatologists, 2 of whom were appointed coordinators; 1 rheumatologist; and 2 gynecologists) were selected to form an expert panel. Following a systematic review of the literature on fertility, pregnancy, postpartum, and breastfeeding in women with psoriasis, the coordinators drew up a series of preliminary recommendations for discussion by the panel at a nominal group meeting. The experts defined the scope, sections, and intended users of the statement and prepared a final list of recommendations. Consensus was obtained using a Delphi process in which an additional 51 dermatologists rated their level of agreement with each recommendation on a scale of 1 (total disagreement) to 10 (total agreement). Consensus was defined by a score of 7 or higher assigned by at least 70% of participants. Level of evidence and strength of recommendation were reported using the Oxford Center for Evidence-Based Medicine categories. The final statement was approved by the expert panel. RESULTS: The resulting consensus statement includes 23 recommendations on preconception (fertility and contraception), pregnancy (planning, pharmacological management, and follow-up), and breastfeeding (management and follow-up). Consensus was achieved for all recommendations generated except one. CONCLUSIONS: These recommendations for the better management of psoriasis in women of childbearing age could improve outcomes and prognosis.

Characterization of Soft Tooling Photopolymers and Processes for Micromixing Devices with Variable Cross-Section.

In this paper, we characterized an assortment of photopolymers and stereolithography processes to produce 3D-printed molds and polydimethylsiloxane (PDMS) castings of micromixing devices. Once materials and processes were screened, the validation of the soft tooling approach in microfluidic devices was carried out through a case study. An asymmetric split-and-recombine device with different cross-sections was manufactured and tested under different regime conditions (10 < Re < 70). Mixing performances between 3% and 96% were obtained depending on the flow regime and the pitch-to-depth ratio. The study shows that 3D-printed soft tooling can provide other benefits such as multiple cross-sections and other potential layouts on a single mold.