Teclistamab in relapsed refractory multiple myeloma: multi-institutional real-world study.

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).

XB2Bi2 (X = Si, Ge, Sn, Pb): Penta-Atomic Planar Tetracoordinate Si/Ge/Sn/Pb Clusters with 20 Valence Electrons.

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.

A liquid chromatographic-mass spectrometric procedure for analysis of pentaerythrityl tetranitrate metabolites - Development, validation and application to ovine serum and human plasma samples.

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.

Extended Dynamic Mode Decomposition with Invertible Dictionary Learning.

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.

osl-dynamics, a toolbox for modeling fast dynamic brain activity.

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.

Block Sequence Effects on the Self-Assembly Behaviors of Polypeptide-Based Penta-Block Copolymer Hydrogels.

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.

Contrast response function estimation with nonparametric Bayesian active learning.

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.

Safety and Efficacy of Teclistamab in Patients with Relapsed/Refractory Multiple Myeloma: A Real-World Experience.

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.

Perspectives on pulsed field ablation: how to judge endpoints.

PURPOSE OF REVIEW: This review highlights pulse field ablation's (PFA) significance in treating atrial fibrillation. PFA uses short-pulsed electrical fields, offering safety advantages over thermal methods. Multicenter studies' findings on PFA's safety, efficiency, and efficacy, compared with thermal techniques, are discussed. RECENT FINDINGS: The review encompasses major PFA systems utilized in multicenter studies: penta-spline, circular, and lattice catheters. These studies affirm PFA's safety, with minimal complications like esophageal injury, phrenic nerve complications, and pulmonary vein stenosis. PFA also demonstrates procedural efficiency benefits because of rapid pulse delivery. However, PFA's efficacy appears on par with thermal ablation, showing similar rates of atrial arrhythmia recurrence during follow-up periods. The studies explore diverse postablation monitoring strategies, underscoring the necessity for standardized monitoring or consistent transformation of arrhythmia data. SUMMARY: In conclusion, PFA marks a promising era for atrial fibrillation treatment with improved safety and efficiency. Efficacy is comparable to thermal methods, though technology advancements could alter this. PFA's potential as a safer and faster alternative positions it as a dominant atrial fibrillation ablation technology. Careful analysis and standardized monitoring are vital to assess PFA's potential and clinical implications.

Snakebite Clinics and Pathogenesis: From Preclinical to Resource Mapping Studies.

Amidst the global healthcare landscape, the menace of snakebite envenoming (SBE) has persisted, silently afflicting millions and annually claiming tens of thousands of lives [...].

Rapid auxin signaling: Unknowns old and new.

To respond to auxin, the chief orchestrator of their multicellularity, plants evolved multiple receptor systems and signal transduction cascades. Despite decades of research, however, we are still lacking a satisfactory synthesis of various auxin signaling mechanisms. The chief discrepancy and historical controversy of the field is that of rapid and slow auxin effects on plant physiology and development. How is it possible that ions begin to trickle across the plasma membrane as soon as auxin enters the cell, even though the best-characterized transcriptional auxin pathway can take effect only after tens of minutes? Recently, unexpected progress has been made in understanding this and other unknowns of auxin signaling. We provide a perspective on these exciting developments and concepts whose general applicability might have ramifications beyond auxin signaling.

Amplification-Free Ratiometric Electrochemical Aptasensor for Point-of-Care Detection of Therapeutic Monoclonal Antibodies.

The rapid quantification of therapeutic monoclonal antibodies (mAbs) is of great significance to their pharmacokinetics/pharmacodynamics (PK/PD) research and the personalized medication for disease treatment. Taking advantage of the direct decoration of tens of redox tags to the target of interest, we illustrate herein an amplification-free ratiometric electrochemical aptasensor for the point-of-care (POC) detection of trace amounts of therapeutic mAbs. The POC detection of therapeutic mAbs involved the use of the methylene blue (MB)-conjugated aptamer as the affinity element and the decoration of therapeutic mAbs with ferrocene (Fc) tags via the boronate crosslinking, in which the MB-derived peak current was used as the reference signal, and the peak current of the Fc tag was used as the output signal. As each therapeutic mAb carries tens of diol sites for the site-specific decoration of the Fc output tags, the boronate crosslinking enabled the amplification-free detection, which is cost-effective and quite simple in operation. In the presence of bevacizumab (BevMab) as the target, the resulting ratiometric signal (i.e., the IFc/IMB value) exhibited a good linear response over the range of 0.025-2.5 µg/mL, and the limit of detection (LOD) of the electrochemical aptasensor was 6.5 ng/mL. Results indicated that the aptamer-based affinity recognition endowed the detection of therapeutic mAbs with high selectivity, while the ratiometric readout exhibited satisfactory reproducibility and robustness. Moreover, the ratiometric electrochemical aptasensor is applicable to the detection of therapeutic mAbs in serum samples. Taking together, the amplification-free ratiometric electrochemical aptasensor holds great promise in the POC detection of therapeutic mAbs.

Long-lived SERS Matrix for Real-Time Biochemical Detection Using "Frozen" Transition State.

For the long-time tracking of biological events, maintaining the bioactivity of the analytes during the detection process is essential. Here, we show a versatile surface-enhanced Raman Scattering (SERS) platform, termed a superwettable-omniphobic lubricous porous SERS (SOLP-SERS) substrate. The SOLP-SERS substrate could generate a three-dimensional liquid "hotspots" matrix with an ultra-long lifetime (tens of days) by confining tiny amounts of liquids within the gaps between nanoparticles. Then, the analytes are trapped in the uniform liquid "hotspots", whose bioactivity can be well maintained over a long period of time during SERS detection. Limits of detection down to femtomolar levels were achieved for various molecules. More importantly, SERS signals were uniform within the substrate and remained stable for more than 30 days. As a proof-of-concept experiment, the dynamic detection of the polymerization of Aß peptides into amyloids was monitored by the SOLP-SERS substrate within 48 h. Moreover, the exosomes secreted by breast cancer cells, an important biomarker of cancer, were also measured. These results demonstrate that the SOLP-SERS platform will provide new insights into the development of real-time biochemical sensors with ultrahigh sensitivity.

Genomic Mysteries of Giant Bacteria: Insights and Implications.

Bacteria and Archaea are traditionally regarded as organisms with a simple morphology constrained to a size of 2-3 µm. Nevertheless, the history of microbial research is rich in the description of giant bacteria exceeding tens and even hundreds of micrometers in length or diameter already from its early days, for example, Beggiatoa spp., to the present, for example, Candidatus Thiomargarita magnifica. While some of these giants are still being studied, some were lost to science, with merely drawings and photomicrographs as evidence for their existence. The physiology and biogeochemical role of giant bacteria have been studied, with a large focus on those involved in the sulfur cycle. With the onset of the genomic era, no special emphasis has been given to this group, in an attempt to gain a novel, evolutionary, and molecular understanding of the phenomenon of bacterial gigantism. The few existing genomic studies reveal a mysterious world of hyperpolyploid bacteria with hundreds to hundreds of thousands of chromosomes that are, in some cases, identical and in others, extremely different. These studies on giant bacteria reveal novel organelles, cellular compartmentalization, and novel mechanisms to combat the accumulation of deleterious mutations in polyploid bacteria. In this perspective paper, we provide a brief overview of what is known about the genomics of giant bacteria and build on that to highlight a few burning questions that await to be addressed.

Bacteria isolated from explosive contaminated environments transform pentaerythritol tetranitrate (PETN) under aerobic and anaerobic conditions.

Pentaerythritol tetranitrate (PETN) is a nitrate ester explosive that may be persistent with scarce reports on its environmental fate and impacts. Our main objective was to isolate and characterize bacteria that transform PETN under aerobic and anaerobic conditions. Biotransformation of PETN (100 mg L-1) was evaluated using mineral medium with (M + C) and without (M - C) additional carbon sources under aerobic conditions and with additional carbon sources under anaerobic conditions. Here, we report on the isolation of 12 PETN-transforming cultures (4 pure and 8 co-cultures) from environmental samples collected at an explosive manufacturing plant. The highest transformation of PETN was observed for cultures in M + C under aerobic conditions, reaching up to 91% ± 2% in 2 d. Under this condition, PETN biotransformation was observed in conjunction with the release of nitrites and bacterial growth. No substantial transformation of PETN (<45%) was observed during 21 d in M - C under aerobic conditions. Under anaerobic conditions, five cultures could transform PETN (up to 52% ± 13%) as the sole nitrogen source, concurrent with the formation of two unidentified metabolites. PETN-transforming cultures belonged to Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, and Actinobacteria. In conclusion, we isolated 12 PETN-transforming cultures belonging to diverse taxa, suggesting that PETN transformation is phylogenetically widespread.

Hyperspectral signature-band extraction and learning: an example of sugar content prediction of Syzygium samarangense.

This study proposes a method to extract the signature bands from the deep learning models of multispectral data converted from the hyperspectral data. The signature bands with two deep-learning models were further used to predict the sugar content of the Syzygium samarangense. Firstly, the hyperspectral data with the bandwidths lower than 2.5 nm were converted to the spectral data with multiple bandwidths higher than 2.5 nm to simulate the multispectral data. The convolution neural network (CNN) and the feedforward neural network (FNN) used these spectral data to predict the sugar content of the Syzygium samarangense and obtained the lowest mean absolute error (MAE) of 0.400° Brix and 0.408° Brix, respectively. Secondly, the absolute mean of the integrated gradient method was used to extract multiple signature bands from the CNN and FNN models for sugariness prediction. A total of thirty sets of six signature bands were selected from the CNN and FNN models, which were trained by using the spectral data with five bandwidths in the visible (VIS), visible to near-infrared (VISNIR), and visible to short-waved infrared (VISWIR) wavelengths ranging from 400 to 700 nm, 400 to 1000 nm, and 400 to 1700 nm. Lastly, these signature-band data were used to train the CNN and FNN models for sugar content prediction. The FNN model using VISWIR signature bands with a bandwidth of ± 12.5 nm had a minimum MAE of 0.390°Brix compared to the others. The CNN model using VISWIR signature bands with a bandwidth of ± 10 nm had the lowest MAE of 0.549° Brix compared to the other CNN models. The MAEs of the models with only six spectral bands were even better than those with tens or hundreds of spectral bands. These results reveal that six signature bands have the potential to be used in a small and compact multispectral device to predict the sugar content of the Syzygium samarangense.

Review of Applications of Density Functional Theory (DFT) Quantum Mechanical Calculations to Study the High-Pressure Polymorphs of Organic Crystalline Materials.

Since its inception, chemistry has been predominated by the use of temperature to generate or change materials, but applications of pressure of more than a few tens of atmospheres for such purposes have been rarely observed. However, pressure is a very effective thermodynamic variable that is increasingly used to generate new materials or alter the properties of existing ones. As computational approaches designed to simulate the solid state are normally tuned using structural data at ambient pressure, applying them to high-pressure issues is a highly challenging test of their validity from a computational standpoint. However, the use of quantum chemical calculations, typically at the level of density functional theory (DFT), has repeatedly been shown to be a great tool that can be used to both predict properties that can be later confirmed by experimenters and to explain, at the molecular level, the observations of high-pressure experiments. This article's main goal is to compile, analyze, and synthesize the findings of works addressing the use of DFT in the context of molecular crystals subjected to high-pressure conditions in order to give a general overview of the possibilities offered by these state-of-the-art calculations.

Increased Cesarean Section Rates during the COVID-19 Pandemic: Looking for Reasons through the Robson Ten Group Classification System.

OBJECTIVE: To compare cesarean section (CS) rates according to the Robson Ten Group Classification System (RTGCS) and its indications in pregnant women admitted for childbirth during the first wave of the coronavirus disease 2019 (COVID-19) pandemic with those of the previous year. MATERIALS AND METHODS: We conducted a cross-sectional study to compare women admitted for childbirth from April to October 2019 (before the pandemic) and from March to September 2020 (during the pandemic). The CSs and their indications were classified on admission according to the RTGCS, and we also collected data on the route of delivery (vaginal or CS). Both periods were compared using the Chi-squared (χ2) test or the Fisher exact test. RESULTS: In total, 2,493 women were included, 1,291 in the prepandemic and 1,202 in the pandemic period. There was a a significant increase in the CS rate (from 39.66% to 44.01%; p = 0.028), mostly due to maternal request (from 9.58% to 25.38%; p < 0.01). Overall, groups 5 and 2 contributed the most to the CS rates. The rates decreased among group 1 and increased among group 2 during the pandemic, with no changes in group 10. CONCLUSION: There was an apparent change in the RTGSC comparing both periods, with a significant increase in CS rates, mainly by maternal request, most likely because of changes during the pandemic and uncertainties and fear concerning COVID-19.

OBJETIVO: Comparar as taxas de cesárea segundo a Classificação de Robson, assim como suas indicações, em mulheres admitidas para parto durante a primeira onda de doença do coronavírus 2019 (coronavirus disease 2019, COVID-19, em inglês), com as do ano anterior. MATERIAIS E MéTODOS: Conduzimos um estudo transversal que comparou as mulheres admitidas para parto entre abril e outubro de 2019 (pré-pandemia) e entre março e setembro de 2020 (durante a pandemia). As cesarianas e as suas indicações foram classificadas conforme o sistema proposto por Robson, e obteve-se a via de parto (vaginal ou cesárea). Ambos os períodos foram comparados usando-se os testes do Qui quadrado ou o exato de Fisher. RESULTADOS: Ao todo, 2.943 mulheres foram incluídas, das quais 1.291 antes da pandemia e 1.202 durante a pandemia. A taxa de cesárea aumentou significativamente (de 39.66% para 44,01%; p = 0,028), principalmente devido a desejo materno (de 9,58% para 25,38%; p < 0,01). Os grupos 5 e 2 foram os que mais contribuíram para as taxas de cesárea. Durante a pandemia, o grupo 1 reduziu sua frequência, enquanto o grupo 2 a aumentou. CONCLUSãO: Houve uma aparente mudança nas características da população conforme a classificação de Robson. Observou-se significativo aumento nas taxas de cesárea, principalmente por desejo materno, o que reflete possíveis incertezas e medos relacionados à COVID-19.