Planar tetracoordinate silicon, germanium, tin, and lead (ptSi/Ge/Sn/Pb) species are scarce and exotic. Here, we report a series of penta-atomic ptSi/Ge/Sn/Pb XB2Bi2 (X = Si, Ge, Sn, Pb) clusters with 20 valence electrons (VEs). Ternary XB2Bi2 (X = Si, Ge, Sn, Pb) clusters possess beautiful fan-shaped structures, with a Bi-B-B-Bi chain surrounding the central X core. The unbiased density functional theory (DFT) searches and high-level CCSD(T) calculations reveal that these ptSi/Ge/Sn/Pb species are the global minima on their potential energy surfaces. Born-Oppenheimer molecular dynamics (BOMD) simulations indicate that XB2Bi2 (X = Si, Ge, Sn, Pb) clusters are robust. Bonding analyses indicate that 20 VEs are perfect for the ptX XB2Bi2 (X = Si, Ge, Sn, Pb): two lone pairs of Bi atoms; one 5c-2e π, and three σ bonds (two Bi-X 2c-2e and one B-X-B 3c-2e bonds) between the ligands and X atom; three 2c-2e σ bonds and one delocalized 4c-2e π bond between the ligands. The ptSi/Ge/Sn/Pb XB2Bi2 (X = Si, Ge, Sn, Pb) clusters possess 2π/2σ double aromaticity, according to the (4n + 2) Hückel rule.

This is a follow-up study to the pentaerythrityl tetranitrate randomized controlled multicenter trial that reports neonatal outcome data of newborns admitted to neonatal intensive care units and outcome data of the offspring at 12 months of age.
We present data on adverse events reported during the study to document the safety of pentaerythrityl tetranitrate treatment during pregnancy. To further evaluate the effects of pentaerythrityl tetranitrate on neonatal and long-term outcomes, we present follow up data from of 240 children at 12 months of age, including information on height, weight, head circumference, developmental milestones, and the presence of chronic disease and of 144 newborns admitted to the neonatal intensive care unit during the trial.
The pentaerythrityl tetranitrate trial was a randomized, double-blind, placebo-controlled study designed to assess the efficacy and safety of the nitric oxide-donor pentaerythrityl tetranitrate in the prevention of fetal growth restriction and perinatal death in pregnancies complicated by abnormal placental perfusion.
Results at 12 months demonstrated that significantly more children were age appropriately developed without impairments in the pentaerythrityl tetranitrate group (P=.018). In addition, the presence of chronic disease was lower in the pentaerythrityl tetranitrate group (P=.041). Outcome data of the 144 newborns admitted to the neonatal intensive care unit did not reveal differences between the treatment and placebo groups. There were no differences in the number or nature of reported adverse events between the study groups.
The analysis shows that study children born in the pentaerythrityl tetranitrate cohort have a clear advantage compared with the placebo group at the age of 12 months, as evidenced by the increased incidence of normal development without the presence of chronic disease. Although safety has been proven, further follow-up studies are necessary to justify pentaerythrityl tetranitrate treatment during pregnancies complicated by impaired uterine perfusion.

The objective of our study was to report real-world data on the safety and efficacy of standard-of-care teclistamab in patients with relapsed/refractory multiple myeloma (MM). This is a multi-institutional retrospective cohort study and included all consecutive patients that received at least one dose of teclistamab up until August 2023. One hundred and ten patients were included, of whom, 86% had triple-class refractory disease, 76% penta-refractory disease, and 35% had prior exposure to B-cell maturation antigen (BCMA)-targeting therapies. The overall response rate (ORR) in our cohort was 62%, with a ≥ very good partial remission (VGPR) rate of 51%. The ORR in patients with and without prior BCMA-targeted therapies was 54% vs 67%, respectively (p = 0.23). At a median follow-up of 3.5 months (range, 0.39-10.92), the estimated 3 month and 6 month progression free survival (PFS) was 57% (95% CI, 48%, 68%) and 52% (95% CI, 42%, 64%) respectively. The incidence of cytokine release syndrome (CRS) and immune effector cell associated neurotoxicity syndrome (ICANS) was 56% and 11% respectively, with grade ≥3 CRS and ICANS noted in 3.5% and 4.6% of patients respectively. 78 unique infections were diagnosed in 44 patients, with the incidence of all-grade and grade ≥3 infections being 40% vs 26% respectively. Primary prophylaxis with intravenous immunoglobulin (IVIG) was associated with a significantly lower infection risk on multivariate analysis (Hazard ratio [HR] 0.33; 95% CI 0.17, 0.64; p = 0.001).

The Koopman operator has received attention for providing a potentially global linearization representation of the nonlinear dynamical system. To estimate or control the original system, the invertibility problem is introduced into the data-driven modeling, i.e., the observables are required to be reconstructed the original system\'s states. Existing methods cannot solve this problem perfectly. Only linear or nonlinear but lossy reconstruction can be achieved. This paper proposed a novel data-driven modeling approach, denoted as the Extended Dynamic Mode Decomposition with Invertible Dictionary Learning (EDMD-IDL) to address this issue, which can be interpreted as a further extension of the classical Extended Dynamic Mode Decomposition (EDMD). The Invertible Neural Network (INN) is introduced in the proposed method, where its inverse process provides the explicit inverse on the dictionary functions, thus allowing the nonlinear and lossless reconstruction. An iterative algorithm is designed to solve the extended optimization problem defined by the Koopman operator and INN by combining the optimization algorithm based on the gradient descent and the classical EDMD method, making the method successfully obtain the finite-dimensional approximation of the Koopman operator. The method is tested on various canonical nonlinear dynamical systems and is shown that the predictions obtained in a linear fashion and the ground truth match well over the long-term, where only the initial status is provided. Comparison experiments highlight the superiority of the proposed method over the other EDMD-based methods. Notably, a typical example in fluid dynamics, cylinder wake, illustrates the potential of the method to be further extended to the high-dimensional system with tens of thousands of states. By combining the Proper Orthogonal Decomposition technique, nontrivial Kármán vortex sheet phenomenon is perfectly reconstructed. Our proposed method provides a new paradigm for solving the finite-dimensional approximation of the Koopman operator and applying it to data-driven modeling.

Pentaerythrityl tetranitrate (PETN) is an established drug in the treatment of coronary heart disease and heart failure. It is assumed, that the vasodilative and vasoprotective effects of PETN also have a positive impact on pregnant patients with impaired placental perfusion and studies evaluating the effect of PETN in risk pregnancies have been carried out. In the context of these clinical trials, measuring of serum levels of PETN and its metabolites pentaerythrityl trinitrate (PETriN), pentaerythrityl dinitrate (PEDN), pentaerythrityl mononitrate (PEMN) and pentaerythritol (PE) were required. To evaluate the transfer of PETN and its metabolites (PEXN) from the mother to the fetus using samples from a human clinical trial and animal study, the present work aimed to develop a rapid and simple method to simultaneously analyze PEXN in human and ovine samples. A method employing a rapid and simple liquid-liquid extraction followed by reversed-phase (C18) liquid chromatography coupled to high-resolution mass spectrometry with negative electrospray ionization was developed and validated for the detection of PETN and PEXN in human and ovine samples. PE could only be qualitatively detected at higher concenrations. Method validation requirements, including accuracy, repeatability and intermediate precision were fulfilled in ovine and human samples for all other PEXN with exception PETriN in human samples. The recovery (RE) in ovine samples was 76.7 % ± 12 % for PEMN, 98 % ± 23 % for PEDN, 94 % ± 22 % for PETriN, in human samples RE was 59 % ± 16 % for PEMN, 67 % ± 19 % for PEDN, 71 % ± 17 %. The matrix effects (ME) in ovine samples were 90 % ± 11 % for PEMN, 70 % ± 30 % for PEDN, 107 % ± 17 % for PETriN, in human samples the ME were 93 % ± 13 % for PEMN, 84 % ± 17 % for PEDN, 98 % ± 16 % for PETriN. The limits of quantification (LOQ) in ovine samples were 1.0 ng/mL for PETriN and 0.1 ng/mL for PEMN and PEDN. The LOQs in human samples were 5.0 ng/mL for PETriN and 0.3 ng/mL for PEMN und PEDN. The newly developed method was used to analyze 184 ovine serum samples and 18 human plasma samples. In ovine maternal samples, the highest observed PEDN concentration was 3.5 ng/mL and the highest PEMN concentration was 10 ng/mL, the respective concentrations in fetal serum samples were 4.9 ng/mL for PEDN and 5.4 ng/mL for PEMN. PETriN was only detected in traces in maternal and fetal samples, whereas PETN could not be detected at all. In human maternal samples, the highest concentration for PEDN was 27 ng/mL and for PEMN 150 ng/mL. In umbilical cord plasma, concentrations of 2.3 ng/mL for PEDN and 73 ng/mL for PEMN were detected. Although the PEMN and PEDN concentrations in the human samples were several times higher than in ovine samples, neither PETN nor PETriN signals could be detected. These results demonstrated that the metabolites were transferred from mother to fetus with a slight time delay.

Peptide-based hydrogels have great potential for applications in tissue engineering, drug delivery, and so on. We systematically synthesize, characterize, and investigate the self-assembly behaviors of a series of polypeptide-based penta-block copolymers by varying block sequences and lengths. The copolymers contain hydrophobic blocks of poly(γ-benzyl-l-glutamate) (PBG, Bx) and two kinds of hydrophilic blocks, poly(l-lysine) (PLL, Ky) and poly(ethylene glycol) (PEG, EG34), where x and y are the number of repeating units of each block, where PBG and PLL blocks have unique functions for nerve regeneration and cell adhesion. It shows that a sufficient length of the middle hydrophilic segment capped with hydrophobic end PBG blocks is required. They first self-assemble into flower-like micelles and sequentially form transparent hydrogels (as low as 2.3 wt %) with increased polymer concentration. The hydrogels contain a microscale porous structure, a desired property for tissue engineering to facilitate the access of nutrient flow for cell growth and drug delivery systems with high efficiency of drug storage. We hypothesize that the structure of Bx-Ky-EG34-Ky-Bx agglomerates is beyond micron size (transparent), while that of Ky-Bx-EG34-Bx-Ky is on the submicron scale (opaque). We establish a working strategy to synthesize a polypeptide-based block copolymer with a wide window of sol-gel transition. The study offers insight into rational polypeptide hydrogel design with specific morphology, exploring the novel materials as potential candidates for neural tissue engineering.

Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes.

Background  Multiplexing tissue imaging is developing as a complement for single cell analysis, bringing the spatial information of cells in tissue in addition to multiple parameters measurements. More and more commercial or home-made systems are available. These techniques allow the imaging of tens of fluorescent reporters, where the spectral overlap is solved by imaging by cycles the fluorophores using microfluidics to change the reporters between each cycle. Methods  For several systems, the acquisition system coupled to the microfluidic system is a wide field microscope, and the acquisition process is done by mosaicking to cover a large field of view, relying on image processing to obtain the data set to be analysed in intensity. The processed data set allows the identification of different populations, quite similarly to cytometry analysis, but with spatial information in addition. To obtain the final image for analysis from the raw acquisitions, several preprocessing steps are needed for inter-cycle registration, tissue autofluorescence correction or mosaicking. We propose a workflow for this preprocessing, implemented as an open source software (as a library, command line tool and standalone). Results  We exemplify the workflow on the commercial system PhenoCycler TM (formerly named CODEX®) and provide a reduced size data set for testing. Conclusions  We compare our processor with the commercially provided processor and show that we solve some problems also reported by other users.

Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to provide an adjustable balance between accuracy and efficiency. Contrast sensitivity functions (CSFs) are behaviorally estimated curves that provide insight into both peripheral and central visual function. Because estimation can be impractically long, current clinical workflows must make compromises such as limited sampling across spatial frequency or strong assumptions on CSF shape. This article describes the development of the machine learning contrast response function (MLCRF) estimator, which quantifies the expected probability of success in performing a contrast detection or discrimination task. A machine learning CSF can then be derived from the MLCRF. Using simulated eyes created from canonical CSF curves and actual human contrast response data, the accuracy and efficiency of the machine learning contrast sensitivity function (MLCSF) was evaluated to determine its potential utility for research and clinical applications. With stimuli selected randomly, the MLCSF estimator converged slowly toward ground truth. With optimal stimulus selection via Bayesian active learning, convergence was nearly an order of magnitude faster, requiring only tens of stimuli to achieve reasonable estimates. Inclusion of an informative prior provided no consistent advantage to the estimator as configured. MLCSF achieved efficiencies on par with quickCSF, a conventional parametric estimator, but with systematically higher accuracy. Because MLCSF design allows accuracy to be traded off against efficiency, it should be explored further to uncover its full potential.

Teclistamab is a B cell maturation antigen (BCMA)-directed bispecific antibody approved for relapsed/refractory multiple myeloma (RRMM) on the basis of the phase I/II MajesTEC-1 trial. Here we report clinical outcomes with standard-of-care teclistamab in a real-world RRMM population. A total of 106 patients from 5 academic centers who received teclistamab from August 2022 to August 2023 were included in this retrospective analysis, 83% of whom would have been considered ineligible for the MajesTEC-1 trial. All patients were triple-class exposed, 64% were penta-class refractory, and 53% had received prior BCMA-directed therapy. Cytokine release syndrome was observed in 64% of patients, and only 1 event was grade ≥3, whereas immune effector cell-associated neurotoxicity syndrome was observed in 14% of patients (3 events were grade 3 or 4). One-third (31%) of patients experienced at least 1 infection, with nearly half of these infections graded as severe (grade ≥3). The overall response rate (ORR) was 66%, and the complete or better response rate was 29%. The ORR was 47% for patients with extramedullary disease (EMD), 59% for patients with prior BCMA-directed therapy exposure, and 68% for patients with penta-refractory disease. At a median follow-up of 3.8 months, the median progression-free survival (PFS) was 5.4 months (95% CI, 3.4 months to not reached), while median overall survival was not reached. Patients with Eastern Cooperative Oncology Group Performance Status ≥2, EMD, and age ≤70 years had inferior PFS on multivariable analysis. Our study demonstrates reasonable safety and good efficacy of teclistamab in patients with RRMM treated in a real-world setting.