Browse Articles

Positive association between chronic hepatitis B virus infection and anemia in pregnancy in Southern China

Abstract This observational investigation aimed to explore potential risk factors for anemia in pregnancy. Firstly, a cross-sectional study was conducted, encompassing a review of clinical data of 43,201 pregnant women admitted to the Hainan Women and Children’s Medical Center between January 2017 and December 2020. Comparison between women with and without anemia in pregnancy revealed significant differences between the two groups concerning age, gestational diabetes, hypothyroidism, hyperthyroidism, chronic hepatitis B virus infection, syphilis infection, and human immunodeficiency virus infection. Multivariable logistic regression analysis showed that chronic hepatitis B virus infection was significantly associated with anemia during pregnancy (AOR   2.97, 95% CI 2.57–3.44, p < 0.0001). Subsequently, a retrospective cohort comprising 86 cases with chronic hepatitis B virus infection and 129 control subjects recruited from the Hainan Women and Children’s Medical Center from November 2021 and January 2023 was examined. Results of the examination revealed a corroborative association between chronic hepatitis B virus infection and anemia in pregnancy (OR  2.13, 95% CI 1.20–3.79, p = 0.0092), particularly manifesting in the third trimester of gestation. Further analysis unveiled distinctive hematological alterations among cases with chronic hepatitis B virus infection, characterized by diminished erythrocyte size and reduced levels of corpuscular hemoglobin. Collectively, these findings underscore a positive association of chronic hepatitis B virus infection with anemia during pregnancy.

Pervasive glacier retreats across Svalbard from 1985 to 2023

Abstract A major uncertainty in predicting the behaviour of marine-terminating glaciers is ice dynamics driven by non-linear calving front retreat, which is poorly understood and modelled. Using 124919 calving front positions for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, generated with deep learning, we identify pervasive calving front retreats for non-surging glaciers over the past 38 years. We observe widespread seasonal cycles in calving front position for over half of the glaciers. At the seasonal timescale, peak retreat rates exhibit a several-month phase lag, with changes on the west coast occurring before those on the east coast, coincident with regional ocean warming. This spatial variability in seasonal patterns is linked to different timings of warm ocean water inflow from the West Spitsbergen Current, demonstrating the dominant role of ice-ocean interaction in seasonal front changes. The interannual variability of calving front retreat shows a strong sensitivity to both atmospheric and oceanic warming, with immediate responses to large air and ocean temperature anomalies in 2016 and 2019, likely driven by atmospheric blocking that can influence extreme temperature variability. With more frequent blocking occurring and continued regional warming, future calving front retreats will likely intensify, leading to more significant glacier mass loss.

Integrated bioinformatics analysis identified cuproptosis-related hub gene Mpeg1 as potential biomarker in spinal cord injury

Observation of momentum-gap topology of light at temporal interfaces in a time-synthetic lattice

Stability of small incision lenticule extraction over laser in situ keratomileusis at an altitude of 3874 m

Chimeric antigen receptor macrophages (CAR-M) sensitize HER2+ solid tumors to PD1 blockade in pre-clinical models

Gender inventorship equity in patent prosecution

Construction of N−E bonds via Lewis acid-promoted functionalization of chromium-dinitrogen complexes

Abstract Direct conversion of dinitrogen (N 2 ) into N-containing compounds beyond ammonia under ambient conditions remains a longstanding challenge. Herein, we present a Lewis acid-promoted strategy for diverse nitrogen-element bonds formation from N 2 using chromium dinitrogen complex [Cp*(I i Pr 2 Me 2 )Cr(N 2 ) 2 ]K ( 1 ). With the help of Lewis acids AlMe 3 and BF 3 , we successfully trap a series of fleeting diazenido intermediates and synthesize value-added compounds containing N−B, N−Ge, and N−P bonds with 3 d metals, offering a method for isolating unstable intermediates. Furthermore, the formation of N−C bonds is realized under more accessible conditions that avoid undesired side reactions. DFT calculations reveal that Lewis acids enhance the participation of dinitrogen units in the frontier orbitals, thereby promoting electrophilic functionalization. Moreover, Lewis acid replacement and a base-induced end-on to side-on switch of [NNMe] unit in [(Cp*(I i Pr 2 Me 2 )CrNN(BEt 3 )(Me)] ( 8 ) are achieved.

Algae

Publisher Correction: HER2-related biomarkers predict clinical outcomes with trastuzumab deruxtecan treatment in patients with HER2-expressing metastatic colorectal cancer: biomarker analyses of DESTINY-CRC01

N-acetylated sugars in clownfish and damselfish skin mucus as messengers involved in chemical recognition by anemone host

Two dimensional confinement induced discontinuous chain transitions for augmented electrocaloric cooling

Mutated IL-32θ (A94V) inhibits COX2, GM-CSF and CYP1A1 through AhR/ARNT and MAPKs/NF-κB/AP-1 in keratinocytes exposed to PM10

Syntalos: a software for precise synchronization of simultaneous multi-modal data acquisition and closed-loop interventions

Abstract Complex experimental protocols often require multi-modal data acquisition with precisely aligned timing, as well as state- and behavior-dependent interventions. Tailored solutions are mostly restricted to individual experimental setups and lack flexibility and interoperability. We present an open-source, Linux-based integrated software solution, called ‘Syntalos’, for simultaneous acquisition and synchronization of data from an arbitrary number of sources, including multi-channel electrophysiological recordings and different live imaging devices, as well as closed-loop, real-time interventions with different actuators. Precisely matching timestamps for all inputs are ensured by continuous statistical analysis and correction of individual devices’ timestamps. New data sources can be integrated with minimal programming skills. Data is stored in a comprehensively structured format to facilitate pooling or sharing data between different laboratories. Syntalos enables precisely synchronized multi-modal recordings as well as closed-loop interventions for multiple experimental approaches. Preliminary neuroscientific experiments on mice with different research questions show the successful performance and easy-to-learn structure of the software suite.

Development and validation of nomograms for predicting pentafecta outcomes before and after robot-assisted radical prostatectomy: a retrospective study

Pressure treatment enables white-light emission in Zn-IPA MOF via asymmetrical metal-ligand chelate coordination

Association between air pollution and lifestyle with the risk of developing mild cognitive impairment and dementia in individuals with cardiometabolic diseases

Abstract Lifestyle factors and ambient air pollution are linked to dementia and CMDs, yet few studies have investigated their impact on dementia risk in CMDs patients at the same time. The Cox proportional hazards model was used to evaluate the influence of lifestyle and ambient air pollution on the dementia risk of the CMDs population among 438,681 participants in the UK Biobank. It is found that the risk of developing mild cognitive impairment and dementia in the population seems to increase with the increase in the number of CMDs. There appears to be a statistically significant association between high levels of ambient air pollution, unhealthy lifestyles, and a higher risk of developing mild cognitive impairment and dementia in the CMDs population. It is found that a healthy lifestyle may have an effect modifier role in the association between ambient air pollution and the risk of mild cognitive impairment and the development of dementia in patients with CMDs. Therefore, maybe people with CMDs can lessen the impact of ambient air pollution on their risk of developing mild cognitive impairment and dementia by improving their lifestyle.

Morphological and functional convergence of visual projection neurons from diverse neurogenic origins in Drosophila

Hardware-efficient preparation of architecture-specific graph states on near-term quantum computers

Abstract Highly entangled quantum states are an ingredient in numerous applications in quantum computing. However, preparing these highly entangled quantum states on currently available quantum computers at high fidelity is limited by ubiquitous errors. Besides improving the underlying technology of a quantum computer, the scale and fidelity of these entangled states in near-term quantum computers can be improved by specialized compilation methods. In this work, the compilation of quantum circuits for the preparation of highly entangled architecture-specific graph states is addressed by defining and solving a formal model, i.e., a form of discrete constraint optimization. Our model incorporates information about gate cancellations, gate commutations, and accurate gate timing to determine an optimized graph state preparation circuit. Up to now, these aspects have only been considered independently of each other, typically applied to arbitrary quantum circuits. We quantify the quality of a generated state by performing stabilizer measurements and determining its fidelity. We show that our new method reduces the error when preparing a seven-qubit graph state by 3.5x on average compared to the state-of-the-art Qiskit solution. For a linear eight-qubit graph state, the error is reduced by 6.4x on average. The presented results highlight the ability of our approach to prepare higher fidelity or larger-scale graph states on gate-based quantum computing hardware.

Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping

Abstract Estimation of the energy of quantum many-body systems is a paradigmatic task in various research fields. In particular, efficient energy estimation may be crucial in achieving a quantum advantage for a practically relevant problem. For instance, the measurement effort poses a critical bottleneck for variational quantum algorithms. We aim to find the optimal strategy with single-qubit measurements that yields the highest provable accuracy given a total measurement budget. As a central tool, we establish tail bounds for empirical estimators of the energy. They are helpful for identifying measurement settings that improve the energy estimate the most. This task constitutes an NP-hard problem. However, we are able to circumvent this bottleneck and use the tail bounds to develop a practical, efficient estimation strategy, which we call ShadowGrouping. As the name indicates, it combines shadow estimation methods with grouping strategies for Pauli strings. In numerical experiments, we demonstrate that ShadowGrouping improves upon state-of-the-art methods in estimating the electronic ground-state energies of various small molecules, both in provable and practical accuracy benchmarks. Hence, this work provides a promising way, e.g., to tackle the measurement bottleneck associated with quantum many-body Hamiltonians.