Browse Articles

Elevated plasma level of PAI-1 is associated with severe COVID-19

Abstract Evidence indicates endothelial dysfunction in severe coronavirus disease (COVID-19). Plasminogen activator inhibitor-1 (PAI-1) is a marker of endothelial injury and could be a prognostic marker for COVID-19-related hospitalization and outcomes. The association between PAI-1 levels and the severity of COVID-19-related outcomes was investigated in this study. This single-center retrospective chart review included 113 hospitalized adults from 6.29.2020 to 8.1.2021 with confirmed COVID-19. Plasma PAI-1 levels were measured by ELISA. The primary endpoint was the difference in PAI-1 levels between severe and non-severe COVID-19 groups. Severe COVID-19 was defined as the need for ventilator assistance and/or death. The mean age was 60.78 (22 to 103, SD ± 16.93), and 52 were female and 63 male. There was a significant positive correlation between age and PAI-1 levels. PAI-1 levels were significantly higher in patients with hyperlipidemia. PAI-1 levels in patients requiring ventilator assistance and who died versus those who did not are significantly higher. High plasma PAI-1 levels are associated with severe COVID-19, defined as requiring ventilator use and/or death. Thus, PAI-1 may be a biological marker for severe COVID-19.

Frontispiece: Molecular Design and Synthesis of Narrowband Near‐Ultraviolet and Pure Deep‐Blue Thermally Activated Delayed Fluorescence Materials by an Ether Group Strategy

Light‐Driven Ligand Exchange for Palladium‐Catalyzed Regiodivergent Transfer Hydrothiocarbonylation

Abstract Transfer hydrocarbonylation of alkenes could streamline the synthesis of value‐added carbonylated molecules by avoiding the use of pressurized toxic CO. Existing methods, however, are limited to the use of alcohol‐derived formates or anhydrides as CO and nucleophile‐surrogates, because the parallel mechanism does not work for other transfer carbonylation reagents comprising nucleophilic fragments with higher coordination affinities to transition metals. Herein, we present a novel visible‐light‐driven strategy to address this challenge by promoting the ligand exchange via facilitating the dissociation of CO instead of nucleophiles, upon which was also an efficient transfer hydrothiocarbonylation of olefins with thioformates successfully established. The protocol features mild reaction conditions, excellent regioselectivities, broad functional group compatibilities, and vital synthetic efficacy. The kinetic analysis unveiled a striking linear correlation between the product concentration and the time‐squared. Furthermore, the kinetic behaviors observed for the substrates were all consistent with the rate law derived from the proposed mechanism. These results demonstrated the pivotal importance of rapid CO dissociation via light excitation to suppress the side reaction of premature reductive elimination.

Non-linear relationship between serum iron levels and 28-day mortality in sepsis patients: a retrospective study

A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature

Abstract This research paper delves into the realm of quantum machine learning (QML) by conducting a comprehensive study on time-series data. The primary objective is to compare the results and time complexity of classical machine learning algorithms on traditional hardware to their quantum counterparts on quantum computers. As the amount and complexity of time-series data in numerous fields continues to expand, the investigation of advanced computational models becomes critical for efficient analysis and prediction. We employ a time-series dataset that include temperature records from different nations throughout the world spanning the previous half of the century. The study compares the performance of classical machine learning algorithms to quantum algorithms, which use the concepts of superposition and entanglement to handle subtle temporal patterns in time-series data. This study attempts to reveal the different benefits and drawbacks of quantum machine learning in the time-series domain through rigorous empirical analysis. The findings of this study not only help to comprehend the applicability of quantum algorithms in real-world contexts, but they also open the way for future advances in utilizing quantum computing for increased time-series analysis and prediction. This study’s findings could have ramifications in industries ranging from finance to healthcare, where precise forecasting using time-series data is critical for informed decision-making.

Patch‐Clamp‐Like Micropipette Electrode with Exterior and Interior Interfaces for Simultaneous Monitoring of Extracellular and Intracellular Serotonin Across Neuron

Abstract Simultaneous monitoring of intracellular and extracellular serotonin (5‐HT) across five orders of magnitude is crucial for understanding 5‐HT‐related diseases. Here, we created a patch‐clamp‐like micropipette electrode with double sensing interfaces, in which gold nanoparticles were coated onto exterior surface and conductive hydrogel was in situ polymerized by designed monomer to form interior interface. Furthermore, we engineered serotonin binding protein (SerBP) as recognition element to develop open‐circuit potentiometric 5‐HT sensor and modulated SerBP to enhance dissociation constant toward 5‐HT through key residue mutation to broaden the linear ranges by five orders of magnitude for both intracellular and extracellular 5‐HT recording. Meanwhile, the designed noninsertion mode together with zero potential detection caused minimal cell damage, thus significantly prolonged the recording duration. Using this powerful tool, we first discovered 5‐HT transporter reversed function to release 5‐HT before intracellular exocytosis enhanced, and together, through the release–reuptake–metabolism cycle, dynamically regulate the concentration of 5‐HT with the development of seizure.

RETRACTED ARTICLE: Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques

Synthesis of Versatile DNA‐Conjugated Aldehydes by Controlled Oxidation of Amines

Abstract Aldehyde‐functionalized oligonucleotides have found diverse applications in chemical biology and material science. However, due to the electrophilic nature of aldehydes, incorporating aldehyde functionalities directly into DNA is challenging, particularly for highly reactive alkyl aldehydes. Inspired by natural oxidases, we herein developed a controlled oxidation strategy to generate aldehyde‐functionalized DNAs from synthetically accessible DNA‐conjugated amines in situ. A broad range of DNA‐conjugated alkyl and aryl aldehydes were efficiently produced from the corresponding amines using O 2 /laccase/TEMPO, with feasible micromole‐scale preparation. Moreover, combining oxidative cleavage of DNA‐conjugated secondary and tertiary amines with reductive amination enabled switchable amine–aldehyde transformation and reversible solid‐phase bioconjugation of DNA probes. Furthermore, the reactivity “umpolung” from nucleophilic amines to electrophilic aldehydes highlights its potential for synthesizing chemically diverse DNA‐encoded libraries (DELs). In summary, the presented controlled oxidation strategy expands the current toolbox to introduce aldehyde functionalities into DNAs within a chemical biological context.

Force fluctuations regulation and the role of neurophysiological mechanisms throughout different isometric contraction intensities

Abstract Force complexity is a key indicator of the neuromuscular system’s adaptability and motor control. Although an inverted U-shaped relationship between force complexity and contraction intensity is established, its underlying mechanisms remain unclear. To investigate whether changes in motor unit behaviour (recruitment and firing rate) would accompany and explain this relationship, 25 young male adults performed a 30-second knee extensors’ hold-isometric task at 50%, 75%, 100%, 150% and 175% of their End-Test Torque (ETT), at individual’s optimal angle. Force complexity and motor unit behaviour were assessed through Sample Entropy (SampEn) and high-density surface electromyography, respectively. We demonstrated a trend for an inverted U-shaped relationship between force complexity and contraction intensity, with SampEn at ETT and 150%ETT being significantly higher than at 50%ETT and 75%ETT (all p < 0.05). This pattern was accompanied by an increase in motor unit actions potentials and firing rate as the intensity increased up to 150%ETT (all p < 0.05). A multiple linear regression analysis showed that force complexity was explained in 18% by the vastus lateralis’ motor unit behaviour. The findings suggest that changes in force complexity depend on contraction intensity and are partly explained by alterations in motor unit behaviour, influencing the neuromuscular system’s adaptability to meet task demands.

Real-time facial recognition via multitask learning on raspberry Pi

Continuous Intermediates Spillover Boosts Electrochemical Nitrate Conversion to Ammonia over Dual Single‐Atom Alloy

Abstract Electrochemical nitrate conversion to ammonia driven by sustainable green electricity is regarded as a promising supplement to the traditional Haber–Bosch process. However, it is still restricted by the low NH 3 yield rate and Faradaic efficiency. Here, we propose a continuous intermediates spillover strategy by constructing dual single‐atom alloy to boost ammonia yield rate and Faradaic efficiency. The intermediates continuously spill over back and forth on the atomically dispersed Mo and Fe sites in Pd lattice, which adaptively experiences low energy barrier for each elementary step in nitrate conversion. As a result, the synthesized dual single‐atom alloy metallene delivered an NH 3 yield rate of 13.4 mol g cat. −1  h −1 , and Faradaic efficiency of 94.6%, as well as remarkable cycling stability of 300 h. Furthermore, the dual single‐atom alloy metallene was assembled into a zinc‐nitrate battery as the cathode, which delivered an output voltage of 1.477 V, and the maximum output power density of 13.4 mW cm −2 .

Stroke burden analysis attributable to ambient and household PM2.5 in China from 1990 to 2021 based on GBD 2021

Modulation of Lanthanide Luminescence with the Mechanical Bond: Antenna‐Emitter Confinement in a Compact [2]Rotaxane

Abstract Luminescent emitters based on lanthanide ions are of ubiquitous importance in the biological sciences, but typically need sensitization from a covalently attached adjacent chromophore – an “antenna” – to have suitable emission intensities. Here we show that the mechanical bond can be used to connect the antenna to the emitter, providing dynamic features and stimuli‐responsiveness to the resulting assemblies. We outline a strategy to synthesize [2]rotaxanes capped with strong chelating groups, and establish that post‐functionalization of the interlocked scaffold by lanthanide ion insertion is modular, high‐yielding and straightforward. Photophysical studies revealed effective antenna‐emitter energy transfer within the [2]rotaxane, and the sensitization mechanism as well as ring‐thread dynamics were studied with spectroscopic and computational methods. The rotaxane was shown to have high selectivity toward Cu(II) ions, acting as an efficient turn‐off sensor. This study validates the mechanical bond as a conjugation method between antennas and emitters, yielding otherwise hard‐to‐access and beneficial features to the resulting molecular systems.

Initial production prediction for horizontal wells in tight sandstone gas reservoirs based on data-driven methods

Abstract Accurate prediction of the initial production in horizontal wells targeting tight sandstone gas reservoirs (IPHTSG) is critical for assessing the exploitation potential of well locations and identifying reservoir sweet spots. Traditional methods for estimating horizontal well productivity exhibit limited applicability due to reservoir heterogeneity and unfavourable petrophysical properties; therefore, this study proposes the use of machine learning for IPHTSG forecasting by systematically analysing the engineering parameters and production metrics. First, an IPHTSG database is established by categorizing and compiling the collected engineering and production parameters in addition to the classified initial production data. Second, on the basis of the IPHTSG database, prediction models for the IPHTSG are developed by employing various machine learning algorithms. The dimensionality of the input data is reduced via correlation analysis of the feature parameters, and the parameters of each prediction model are optimized using a grid search and 10-fold cross-validation. Finally, the models are applied to make predictions on a test set to validate their reliability, forming a set of methods and procedures for IPHTSG prediction. Then, this work describes a case study that was conducted on the tight gas reservoir of the H8 Member in the Sulige Southeast Field (Ordos Basin). The effective reservoir length, vertical thickness, open-flow capacity, bottom hole pressure, and amount of sand inclusion from 155 horizontal wells were selected as feature parameters, with data from 140 wells used as the training set and data from 15 wells used as the test set. Six machine learning algorithms were utilized to establish models, and the relevant calculation indicators of different models are compared. Ultimately, the XGBoost prediction model, which exhibits superior performance, is selected. This model achieves a training accuracy of 95% and a testing accuracy of 93.33%, with precision, recall, and F1-score values of 95%, 94.12%, and 93.14%, respectively, and it also has a relatively short training time. The method proposed in this paper successfully realizes IPHTSG prediction, providing a decision-making basis for formulating reasonable development plans and optimizing production parameters. This interdisciplinary methodology provides a replicable template for data-intelligent decision-making in tight gas reservoir management.

Relay‐Enhanced Electron Transfer in Triple‐Layer Ru@Ir@Pt Core–Shell Nanoparticles for the Ammonia Oxidation Reaction

Abstract Ammonia oxidation reaction (AOR) is important for enabling the efficient use of NH 3 as a promising carrier for hydrogen storage and transportation. However, the catalytic activity of state‐of‐the‐art Pt‐based catalysts decreases significantly due to the strong adsorption of N species. In this study, a triple‐layer core–shell structured Ru@Ir@Pt model catalyst was employed to demonstrate that the relay electron transfer strategy can decelerate the adsorption of N species and increase AOR activity, a process facilitated by the built‐in electric field (BEF) induced by differing work functions that drive the sequential relay of charge transfer across the interfaces between different metals. In situ Fourier Transform Infrared (FTIR) spectroscopy revealed that AOR proceeds primarily via the N 2 H 4 pathway of the G–M mechanism. Both the experimental and theoretical simulation results confirm that relay electron transfer induced by a built‐in electric field enables the outermost Pt electron‐rich state to reduce the intensity of N adsorbed species and lower the energy barrier of rate‐determining step in the AOR, resulting in excellent activity with a mass activity reaching up to 363.5 A g −1 . This value is 5.24 times higher than that of 20% Pt/C and significantly surpasses most previously reported catalysts. This work presents a novel material design approach for developing high‐performance advanced ammonia oxidation electrocatalysts.

Diagnostic performance of MUAC and MUACZ in screening acute malnutrition among children aged 6–23 months in Amhara Region, Ethiopia

Operando Spectroscopic Insights into CO ₂ Reduction at Electrode/Polyelectrolyte Interfaces

Abstract The electrode/polyelectrolyte interface is a notable feature in modern electrochemical technologies that utilize membrane electrode assembly (MEA) configurations. However, its interfacial structure and catalytic behavior remain poorly understood. Here, we developed an integrated operando Raman spectroscopy and mass spectrometry (MS) method, to directly investigate the CO 2 reduction mechanism at the electrode/polyelectrolyte interface within a practical MEA electrolyzer operating at high current densities. Combined with isotope labeling experiments and ab initio molecular dynamics (AIMD) simulations, we provide, for the first time, the direct spectroscopic evidence of *CCO, a crucial intermediate for C 2 product formation, which has been frequently hypothesized but rarely detected in previous studies. By contrast, the linearly adsorbed *CO L intermediate, typically observed in conventional liquid electrolytes, was absent. These distinct behaviors arise from the unique structure of the electrode/polyelectrolyte interface, which shifts the rate‐determining step from the usual C–C coupling to the *CCO hydrogenation in the conversion of CO 2 to C 2 products. This study not only deepens our understanding of electrode/polyelectrolyte interfacial characteristics but also offers valuable insights for advancing the performance of CO 2 MEA electrolyzers.

Modeling and simulation of optical wireless communication channels in IoUT considering water types turbulence and transmitter selection

Abstract The Internet of Underwater Things (IoUT) is revolutionizing underwater communication by enabling real-time data exchange, environmental monitoring, and exploration in aquatic environments. Among emerging technologies, optical wireless communication (OWC) has gained prominence due to its high-speed data rates and superior efficiency compared to traditional acoustic and radio frequency (RF) methods. This paper presents a comprehensive study of OWC channel modeling and simulation tailored for IoUT applications. The research investigates the physical characteristics of underwater optical channels, focusing on the effects of absorption, scattering, turbulence, and various noise sources on light propagation across diverse water types, including pure seawater, clear coastal waters, and turbid harbor waters. A central aspect of the study is the comparative evaluation of two transmitter types—light-emitting diode photo sources (LED-PS) and laser diode photo sources (LD-PS)—both operating at a 520 nm wavelength (green light). Their performance is assessed under varying environmental conditions, incorporating three turbulence models: log-normal, generalized gamma, and Weibull distributions. Simulation models are developed and implemented using MATLAB and Python to analyze key parameters such as transmission distance, water type, transmitter characteristics, wavelength, and turbulence intensity. Performance metrics, including received optical power, signal-to-noise ratio (SNR), and bit error rate (BER), are evaluated to provide in-depth insights into system behavior. Results show that LD-PS consistently outperforms LED-PS across all scenarios. For instance, at a received power threshold of − 53.4 dBm, LD-PS achieves a communication distance of up to 68.39 m in pure seawater (compared to 27.36 m for LED-PS), while in turbid harbor, the range is reduced to 3.08 m. At a BER of 10−5, LD-PS reaches 67.69 m in pure seawater and 3.18 m in turbid harbor conditions. Under a fixed SNR of 50 dB, LD-PS achieves a maximum range of 73.34 m in pure sea. The minimum SNR required to maintain a BER of 10−5 is 12.19 dB in pure seawater and rises to 91.94 dB in turbid harbor conditions. These findings advance the development of OWC systems by providing practical guidelines for optimizing underwater communication performance. The insights presented serve as a foundation for designing robust and efficient IoUT networks capable of reliable data transmission across a range of aquatic environments.

Single‐Specie Selectivity via Subnanometer Hierarchical Porous Channel Metal‐Organic Gels Toward Ultra‐stable Anodes for Zn Metal Batteries

Abstract Gel polymer electrolytes demonstrate substantial potential for aqueous zinc‐ion batteries, yet obtaining exquisite balance between molecular/ion sieving precision enhancement and ion conduction pathway optimization remains a persistent scientific challenge. Here, to assure long‐term stabilized zinc electrodeposition, a polyacrylonitrile (PAN)‐Zn(ClO 4 ) 2 ‐DMF metal‐organic gel (MOG) embedded with hierarchical sub‐nanochannels was strategically designed to facilitate single‐specie selectivity toward Zn 2+ . In accordance with the ion‐sieving transition state theory, the MOG accomplished directional discrimination of DMF solvent and ClO 4 − species via synergistic size exclusion effect and interaction force (dipole–dipole interaction, electrostatic interaction and vdW force), which specifically reduces the sieving activation energy of Zn 2+ . Furthermore, ClO 4 − anions with optimized microstructural dimensions and strong coordination ability have stronger interactions with PAN chain segments and promote the dissociation of Zn 2+ from PAN chain segments, which facilitates a smooth Zn 2+ flux through distinctive ion transport pathways. Besides, the PAN@ClO 4 − complex demonstrates sustainable epitaxial stacking characteristics of crystallographically oriented Zn (002) planes, which promote flat and compact Zn deposition. Consequently, the zinc symmetric battery demonstrates an exceptional cycle life exceeding 7000 h at 0.2 mA cm −2 , with extremely lower overpotential (20 mV). Furthermore, the Zn||I 2 cell maintains a capacity retention rate of 81.7% after 10 000 cycles at 0.5 A g −1 .

Prevalence, molecular characterization, and histopathological impact of Trichomonas gallinae in domestic pigeons from Northeastern Egypt

Abstract Trichomonas gallinae (T. gallinae) is a single-celled flagellate protozoan that causes trichomoniasis, a serious and widespread infectious disease primarily affecting Columbiformes. This study investigated the prevalence, molecular characterization, and histopathological effects of T. gallinae in domestic pigeons (Columba livia domestica) from different environments and regions in Egypt. A total of 685 pigeons were examined from markets, dovecotes, and houses across Cairo, Giza, and Qalubyya Governorates from February 2022 to November 2024. Microscopic examination confirmed an infection in 533 pigeons, yielding an overall infection rate of 77.8%. Markets exhibited the highest prevalence (91.8%), followed by dovecotes (72.1%) and houses (58.4%). Regionally, Cairo recorded the highest infection rate (80.7%), followed by Giza (76.0%) and Qalubyya (76.0%). Seasonal variation indicated that summer accounted for the highest number of cases (48.2%), while winter had the lowest (11.4%). Age distribution revealed a strong predominance of squabs (70.7%) compared to adults (29.3%), and gender analysis showed a significantly higher prevalence in females (83.4%) than in males (16.6%). Histopathological examination of oropharyngeal mucosa, proventriculus, and gizzard showed distinct pathological changes, including severe necrosis, caseation, and granulomatous tissue reactions, which are pathognomonic for T. gallinae infection. Molecular analysis confirmed the presence of T. gallinae, with PCR amplification of the ITS1/5.8S/ITS2 gene revealing two novel strains, were deposited in GenBank with accession numbers (OR498119) and (OR498120). These strains exhibited high nucleotide sequence identity with isolates from China, Germany, and Spain, indicating a high degree of genetic conservation and a widespread global distribution of T. gallinae. These findings highlight the widespread prevalence of T. gallinae in domestic pigeons, particularly in urban and commercial environments, with seasonal, age, and gender-related variations influencing infection rates. The detection of novel genetic variants and severe tissue damage emphasizes the need for enhanced surveillance, control measures, and further research on the pathogenicity and epidemiology of T. gallinae in pigeons and potential spillovers to other avian hosts.