Phase 1a/b study of monoclonal antibody CAEL-101 (11-1F4) in patients with AL amyloidosis.

Systemic immunoglobulin light-chain amyloidosis is characterized by pathologic deposition of immunoglobulin light chains as amyloid fibrils in vital organs, leading to organ impairment and eventual death. That the process is reversible was evidenced in an in vivo experimental model in which fibril-reactive chimeric monoclonal antibody (mAb) 11-1F4 directly targeted human light-chain amyloid deposits and affected their removal via a phagocyte-mediated response. To determine the tolerability and potential amyloidolytic effect of this agent (now designated mAb CAEL-101), we conducted a phase 1a/b study involving 27 patients, most of whom had manifestations of organ involvement. This was an open-label study in which phase 1a patients received mAb CAEL-101 as a single intravenous infusion with escalating dose levels from 0.5 mg/m2 to 500 mg/m2 to establish the maximum tolerated dose (MTD). In phase 1b, the antibody was administered as a graded series of 4 weekly infusions. For both phases, there were no drug-related serious adverse events or dose-limiting toxicities among recipients, and the MTD was not reached. The majority of patients had deep hematologic responses but persistent organ disease prior to treatment. Fifteen of 24 patients (63%) who manifested cardiac, renal, hepatic, gastrointestinal, or soft tissue involvement had a therapeutic response to mAb CAEL-101 as evidenced by serum biomarkers or objective imaging modalities with a median time to response of 3 weeks. Infusions of mAb CAEL-101 were well tolerated and, for the majority, resulted in improved organ function, notably for those with cardiac impairment. This trial was registered at www.clinicaltrials.gov as #NCT02245867.

Conditional cross-map-based technique: From pairwise dynamical causality to causal network reconstruction.

Causality detection methods based on mutual cross mapping have been fruitfully developed and applied to data originating from nonlinear dynamical systems, where the causes and effects are non-separable. However, these pairwise methods still have shortcomings in discriminating typical network structures, including common drivers, indirect dependencies, and facing the curse of dimensionality, when they are stepping to causal network reconstruction. A few endeavors have been devoted to conquer these shortcomings. Here, we propose a novel method that could be regarded as one of these endeavors. Our method, named conditional cross-map-based technique, can eliminate third-party information and successfully detect direct dynamical causality, where the detection results can exactly be categorized into four standard normal forms by the designed criterion. To demonstrate the practical usefulness of our model-free, data-driven method, data generated from different representative models covering all kinds of network motifs and measured from real-world systems are investigated. Because correct identification of the direct causal links is essential to successful modeling, predicting, and controlling the underlying complex systems, our method does shed light on uncovering the inner working mechanisms of real-world systems only using the data experimentally obtained in a variety of disciplines.

Reservoir computing decoupling memory-nonlinearity trade-off.

Reservoir computing (RC), a variant recurrent neural network, has very compact architecture and ability to efficiently reconstruct nonlinear dynamics by combining both memory capacity and nonlinear transformations. However, in the standard RC framework, there is a trade-off between memory capacity and nonlinear mapping, which limits its ability to handle complex tasks with long-term dependencies. To overcome this limitation, this paper proposes a new RC framework called neural delayed reservoir computing (ND-RC) with a chain structure reservoir that can decouple the memory capacity and nonlinearity, allowing for independent tuning of them, respectively. The proposed ND-RC model offers a promising solution to the memory-nonlinearity trade-off problem in RC and provides a more flexible and effective approach for modeling complex nonlinear systems with long-term dependencies. The proposed ND-RC framework is validated with typical benchmark nonlinear systems and is particularly successful in reconstructing and predicting the Mackey-Glass system with high time delays. The memory-nonlinearity decoupling ability is further confirmed by several standard tests.

Reviving the Dynamics of Attacked Reservoir Computers.

Physically implemented neural networks are subject to external perturbations and internal variations. Existing works focus on the adversarial attacks but seldom consider attack on the network structure and the corresponding recovery method. Inspired by the biological neural compensation mechanism and the neuromodulation technique in clinical practice, we propose a novel framework of reviving attacked reservoir computers, consisting of several strategies direct at different types of attacks on structure by adjusting only a minor fraction of edges in the reservoir. Numerical experiments demonstrate the efficacy and broad applicability of the framework and reveal inspiring insights into the mechanisms. This work provides a vehicle to improve the robustness of reservoir computers and can be generalized to broader types of neural networks.

Distributed Consensus Algorithms in Sensor Networks with Higher-Order Topology.

Information aggregation in distributed sensor networks has received significant attention from researchers in various disciplines. Distributed consensus algorithms are broadly developed to accelerate the convergence to consensus under different communication and/or energy limitations. Non-Bayesian social learning strategies are representative algorithms for distributed agents to learn progressively an underlying state of nature by information communications and evolutions. This work designs a new non-Bayesian social learning strategy named the hypergraph social learning by introducing the higher-order topology as the underlying communication network structure, with its convergence as well as the convergence rate theoretically analyzed. Extensive numerical examples are provided to demonstrate the effectiveness of the framework and reveal its superior performance when applying to sensor networks in tasks such as cooperative positioning. The designed framework can assist sensor network designers to develop more efficient communication topology, which can better resist environmental obstructions, and also has theoretical and applied values in broad areas such as distributed parameter estimation, dispersed information aggregation and social networks.

Randomly distributed embedding making short-term high-dimensional data predictable.

Future state prediction for nonlinear dynamical systems is a challenging task, particularly when only a few time series samples for high-dimensional variables are available from real-world systems. In this work, we propose a model-free framework, named randomly distributed embedding (RDE), to achieve accurate future state prediction based on short-term high-dimensional data. Specifically, from the observed data of high-dimensional variables, the RDE framework randomly generates a sufficient number of low-dimensional "nondelay embeddings" and maps each of them to a "delay embedding," which is constructed from the data of a to be predicted target variable. Any of these mappings can perform as a low-dimensional weak predictor for future state prediction, and all of such mappings generate a distribution of predicted future states. This distribution actually patches all pieces of association information from various embeddings unbiasedly or biasedly into the whole dynamics of the target variable, which after operated by appropriate estimation strategies, creates a stronger predictor for achieving prediction in a more reliable and robust form. Through applying the RDE framework to data from both representative models and real-world systems, we reveal that a high-dimension feature is no longer an obstacle but a source of information crucial to accurate prediction for short-term data, even under noise deterioration.

Use of Bisphosphonates in Elderly Patients With Newly Diagnosed Multiple Myeloma.

Background: Bisphosphonates reduce skeletal-related events (SREs) in patients with multiple myeloma (MM) and, in some studies, improved survival. Since 2011, bisphosphonate use has been recommended by NCCN for all patients with newly diagnosed MM receiving antineoplastic therapy independent of the presence of bone disease. This study investigated their use after these guidelines were established. Methods: We identified patients aged ≥65 years in the SEER-Medicare database with newly diagnosed MM between January 1, 2012, and December 31, 2013, who received antineoplastic therapy, had ≥6 months of follow-up, and did not receive prior bisphosphonates. Presence of SREs at diagnosis was identified, including pathologic fracture, spinal cord compression, radiation to bone, or surgery to bone. Use of bisphosphonates was defined as having ≥1 claim for an intravenous or oral bisphosphonate within 6 months after the start of antineoplastic therapy. We used multivariable modeling to compare users with nonusers, controlling for demographic and clinical covariates. We compared overall survival between users and nonusers using proportional hazards analysis. Results: Of 1,309 patients identified, 720 (55%) used a bisphosphonate. Factors associated with use included SRE at diagnosis (adjusted odds ratio [AOR], 2.60; 95% CI, 1.98-3.40), hypercalcemia (AOR, 1.74; 95% CI, 1.26-2.41), and use of proteasome inhibitor + immunomodulatory imide therapy (AOR, 1.70; 95% CI, 1.21-2.39). Chronic kidney disease (AOR, 0.48; 95% CI, 0.35-0.66) was associated with decreased use. Bisphosphonate use was associated with reduced mortality (hazard ratio, 0.70; 95% CI, 0.56-0.88). Conclusions: Although bisphosphonate use is recommended for all patients with newly diagnosed MM receiving antineoplastic therapy, 45% of patients in the United States did not receive this guideline-recommended care.

Management and outcomes of proteasome inhibitor associated chalazia and blepharitis: a case series.

BACKGROUND: The purpose of this case series was to further characterize proteasome inhibitor associated chalazia and blepharitis, to investigate outcomes of different management strategies, and to propose a treatment algorithm for eyelid complications in this patient population. METHODS: This retrospective case series included sixteen patients found to have chalazia and/or blepharitis while receiving proteasome inhibitors for plasma cell disorders at Mount Sinai Hospital in New York, NY from January 2010 through January 2017. Main outcomes were complete resolution of eyelid complications and time to resolution. Student's t-test was used to compare average values and Fisher's exact test was used to compare proportions. RESULTS: Fourteen patients had chalazia and 10 had blepharitis. Chalazia averaged 5.4 mm, and 11 patients with chalazia experienced two or more lesions. Median follow-up time was 17 months. Average time from bortezomib exposure to onset of first eyelid complication was 3.4 months. Chalazia episodes were more likely to completely resolve than blepharitis episodes (p = 0.03). Ocular therapy alone was trialed for an average of 1.8 months before proceeding to bortezomib omission. Average time to eyelid complication resolution using ocular therapy alone was 1.8 months versus 3.1 months after bortezomib omission. In this series, the combination of ocular therapy and bortezomib omission led to complete resolution of eyelid complications more often than ocular therapy alone. CONCLUSION: Proteasome inhibitor associated eyelid complications were identified in sixteen patients with plasma cell disorders. Eyelid complications may be treated with a 2-month trial of conservative ocular therapies alone, followed by continuation of ocular therapy in combination with bortezomib omission if eyelid signs persist.

Reconstructing directional causal networks with random forest: Causality meeting machine learning.

Inspired by the decision tree algorithm in machine learning, a novel causal network reconstruction framework is proposed with the name Importance Causal Analysis (ICA). The ICA framework is designed in a network level and fills the gap between traditional mutual causality detection methods and the reconstruction of causal networks. The potential of the method to identify the true causal relations in complex networks is validated by both benchmark systems and real-world data sets.

Closed-Loop Control of Complex Networks: A Trade-Off between Time and Energy.

Controlling complex nonlinear networks is largely an unsolved problem at the present. Existing works focus either on open-loop control strategies and their energy consumptions or on closed-loop control schemes with an infinite-time duration. We articulate a finite-time, closed-loop controller with an eye toward the physical and mathematical underpinnings of the trade-off between the control time and energy as well as their dependence on the network parameters and structure. The closed-loop controller is tested on a large number of real systems including stem cell differentiation, food webs, random ecosystems, and spiking neuronal networks. Our results represent a step forward in developing a rigorous and general framework to control nonlinear dynamical networks with a complex topology.

Reinforcement learning-based pinning control for synchronization suppression in complex networks.

Synchronization in complex networks is a ubiquitous and important phenomenon with implications in various fields. Excessive synchronization may lead to undesired consequences, making desynchronization techniques essential. Exploiting the Proximal Policy Optimization algorithm, this work studies reinforcement learning-based pinning control strategies for synchronization suppression in global coupling networks and two types of irregular coupling networks: the Watts-Strogatz small-world networks and the Barabási-Albert scale-free networks. We investigate the impact of the ratio of controlled nodes and the role of key nodes selected by the LeaderRank algorithm on the performance of synchronization suppression. Numerical results demonstrate the effectiveness of the reinforcement learning-based pinning control strategy in different coupling schemes of the complex networks, revealing a critical ratio of the pinned nodes and the superior performance of a newly proposed hybrid pinning strategy. The results provide valuable insights for suppressing and optimizing network synchronization behavior efficiently.

Real-world analysis of adverse event rates after initiation of ibrutinib among Medicare beneficiaries with chronic lymphocytic leukemia.

BACKGROUND: The first-generation BTK inhibitor ibrutinib is a standard-of-care therapy in the treatment of chronic lymphocytic leukemia (CLL) despite potential side effects that often lead to discontinuation. METHODS: This study used 2013-2019 claims data to describe the incidence rate of adverse events (AEs) among elderly Medicare beneficiaries newly initiating ibrutinib for CLL. RESULTS: The final sample contained 11,870 Medicare beneficiaries with CLL (mean age 77.2) newly initiating ibrutinib, of whom 65.2% discontinued over mean follow-up of 2.3 years. The overall incidence rate of AEs was 62.5 per 1000 patient-months for all discontinuers and 32.9 per 1000 patient-months for non-discontinuers. Discontinuers had a higher incidence rate of AEs per 1000 patient-months compared with non-discontinuers for all AEs examined, including infection (22.8 vs. 14.5), atrial fibrillation (15.1 vs. 7.0), anemia (21.9 vs. 14.5), and arthralgia/myalgia (19.5 vs. 13.6). CONCLUSION: In this first real-world study of a national sample of elderly US patients treated with ibrutinib, we found a clear unmet need for improved management of ibrutinib-related AEs and/or new treatments to improve real-world outcomes in patients with CLL.

Real-world patterns and sequences of targeted therapy use in chronic lymphocytic leukemia and small lymphocytic lymphoma in the United States: a longitudinal study.

With increasing focus on novel targeted therapies for chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL), this longitudinal claims-based study evaluated real-world CLL/SLL treatment sequences, particularly sequential targeted therapy. Among patients with first-line (1 L) treatment in 2014-2017 (N = 2,612; median follow-up = 3 years), the most common 1 L treatment was chemoimmunotherapy (CIT; 44.6%), followed by CD20 (25.2%) and Bruton's tyrosine kinase inhibitors (BTKi; 21.7%). Among those with 1 L in 2018-2021 (N = 4,534; median follow-up = 1 year), these were BTKi (45.5%), CD20 (20.4%), CIT (17.5%), and B-cell lymphoma 2 inhibitor (8.3%). In 2014-2017, the proportion of patients receiving sequential targeted therapy in the first 2 LOTs was 11.2% (80.2% was BTKi→BTKi); in 2018-2021, this proportion was 34.3% (66.4% was BTKi→BTKi). Over time, there was a substantial increase in targeted therapy use in 1 L and sequential targeted therapy, particularly with BTKi→BTKi. Future studies should assess clinical outcomes to determine optimal sequences for CLL/SLL and reasons for restarting BTKi.

Ibrutinib discontinuation and associated factors in a real-world national sample of elderly Medicare beneficiaries with chronic lymphocytic leukemia.

Prior studies evaluating ibrutinib discontinuation are limited to clinical trials and selected medical centers and hence may not reflect real-world practice. This study used Medicare claims (2013-2019) to examine ibrutinib discontinuation and associated factors among elderly patients with chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL). Over a median follow-up of 2.1 years, two-thirds (65.2%) of the 11,870 new ibrutinib initiators were discontinued, with half (45.1%) of patients discontinuing within 12 months of initiation. Factors such as advanced age, lack of Part D low-income subsidy, evidence of prior CLL/SLL treatment, and cardiovascular comorbidities (e.g. atrial fibrillation) were associated with higher risk of discontinuation. Over a median of 1.2 years from discontinuation, 40% of discontinuers initiated another CLL/SLL treatment after ibrutinib discontinuation; 25% of patients restarted ibrutinib treatment at some point over follow-up. Our findings point to a large unmet need with the widely used BTKi ibrutinib and underscore the importance of ongoing development of efficacious and well-tolerated CLL/SLL therapies.

Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately.

Data-based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering, and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross-map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.

Prevalence of Monoclonal Gammopathy of Undetermined Significance in Black South African Men.

BACKGROUND: Both multiple myeloma and its precursor, monoclonal gammopathy of undetermined significance (MGUS), occur twice as often within Black compared with White populations, suggesting that racial disparities lie within the development of MGUS. Nonetheless, MGUS has been studied mainly in White cohorts; the study that first described the natural history of MGUS was conducted in 97.3% White Olmsted County, Minnesota. METHODS: We determined the prevalence of MGUS among 386 Black South African (SA) men >30 years at Chris Hani Baragwanath Hospital in Johannesburg. We conducted serum protein electrophoresis and free light chain quantification to define MGUS by the same criteria as the Olmsted County studies. We also investigated the association between MGUS and various clinical factors, including human immunodeficiency virus (HIV) infection and smoking. RESULTS: We found the prevalence of MGUS to be 8.03% [95% confidence interval (CI), 5.32-10.74], nearly 1.6-fold higher than in the White Olmsted County male population. In a univariable logistic regression model, MGUS was associated with HIV status (OR, 2.39; 95% CI, 0.95-5.49), but in an adjusted model that included body mass index and cigarette use, the association was not statistically significant. Those who were current (vs. never) cigarette smokers were more likely to have MGUS in both univariable (OR, 5.60; 95% CI, 2.16-17.42) and multivariable models (OR, 4.49; 95% CI, 1.63-14.56). CONCLUSIONS: The prevalence of MGUS in Black SA men is substantially higher than in White populations and may be associated with HIV status and cigarette use. IMPACT: Racial disparities in MGUS exist and may be associated with potentially modifiable risk factors.

Common stochastic inputs induce neuronal transient synchronization with partial reset.

Neuronal synchronization plays important roles in information encoding and transmission in the brain. Mathematical models of neurons have been widely used to simulate synchronization behavior and analyze its mechanisms. Common stochastic inputs are considered to be effective in facilitating synchronization. However, the mechanisms of how partial reset affects neuronal synchronization are still not well understood. In this paper, the synchronization of Stein's model neurons with partial reset is studied. The differences in synchronization mechanisms between neurons with full reset and those with partial reset are analyzed, and the findings lead to the novel concept of transient synchronization. Furthermore, it is proven analytically that due to common stochastic inputs, Stein's model neurons with different initial membrane potentials and partial reset achieve transient synchronization with probability 1. Additionally, a systematic numerical analysis is performed to explore the similarities and differences between full reset and partial reset regarding model parameters, synchronization time, and desynchronization behavior. Thus, partial reset is a powerful and flexible tool that facilitates neuronal synchronization while reserving the possibility of desynchronization. Our analysis also provides an alternative approach to analyze neurons of the integrate-and-fire family and a theoretical complement implying possible information encoding mechanisms in the brain.

Partial cross mapping eliminates indirect causal influences.

Causality detection likely misidentifies indirect causations as direct ones, due to the effect of causation transitivity. Although several methods in traditional frameworks have been proposed to avoid such misinterpretations, there still is a lack of feasible methods for identifying direct causations from indirect ones in the challenging situation where the variables of the underlying dynamical system are non-separable and weakly or moderately interacting. Here, we solve this problem by developing a data-based, model-independent method of partial cross mapping based on an articulated integration of three tools from nonlinear dynamics and statistics: phase-space reconstruction, mutual cross mapping, and partial correlation. We demonstrate our method by using data from different representative models and real-world systems. As direct causations are keys to the fundamental underpinnings of a variety of complex dynamics, we anticipate our method to be indispensable in unlocking and deciphering the inner mechanisms of real systems in diverse disciplines from data.

Timing of Autologous Stem Cell Transplantation for Multiple Myeloma in the Era of Current Therapies.

BACKGROUND: Autologous stem cell transplantation (SCT) during the initial treatment of multiple myeloma has been shown to improve progression-free survival (PFS) but not overall survival (OS). While awaiting further prospective data, we retrospectively analyzed the outcomes of patients at our program. PATIENTS AND METHODS: We included consecutive patients with newly diagnosed myeloma who had undergone stem cell harvest (SCH) from 2005 to 2014 and separated them into early (SCT within 12 months of diagnosis) and delayed (all others, including SCT not yet) groups. The outcomes were OS, PFS to first relapse, and PFS to second relapse. RESULTS: Of the 514 patients who had undergone SCH, 227 were in the early and 287 in the delayed groups. Patients in the delayed group who had undergone SCT had received more therapy before SCT (55% had received ≥ 2 lines vs. 6% in the early group; P < .001), had had more progressive disease at SCT (34% vs. 4%; P < .001), had received melphalan doses < 200 mg/m2 (22% vs. 10%; P = .001), and had had lower rates of very good partial response or better after SCT (58% vs. 79%; P = .001). On multivariable analysis, no differences were found in median OS (90 vs. 84 months; P = .093), PFS to first relapse (40 vs. 37 months; P = .552), or PFS to second relapse (54 vs. 52 months; P = .488) between the early and delayed groups. CONCLUSION: Delaying SCT did not affect OS or even PFS to second relapse in our cohort of patients with newly diagnosed myeloma who had received current era induction therapy.