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Abstract Respiratory syncytial virus (RSV) and human metapneumovirus (hMPV) cause human respiratory diseases and are major targets for vaccine development. In this study, we design uncleaved prefusion-closed (UFC) trimers for the fusion protein (F) of both viruses by examining mutations critical to F metastability. For RSV, we assess four previous prefusion F designs, including the first and second generations of DS-Cav1, SC-TM, and 847A. We then identify key mutations that can maintain prefusion F in a native-like, closed trimeric form (up to 76%) without introducing any interprotomer disulfide bond. For hMPV, we develop a stable UFC trimer with a truncated F2-F1 linkage and an interprotomer disulfide bond. Dozens of UFC constructs are characterized by negative-stain electron microscopy (nsEM), x-ray crystallography (11 RSV-F structures and one hMPV-F structure), and antigenic profiling. Using an optimized RSV-F UFC trimer as bait, we identify three potent RSV neutralizing antibodies (NAbs) from a phage-displayed human antibody library, with a public NAb lineage targeting sites Ø and V and two cross-pneumovirus NAbs recognizing site III. In mouse immunization, rationally designed RSV-F and hMPV-F UFC trimers induce robust antibody responses with high neutralizing titers. Our study provides a foundation for future prefusion F-based RSV and hMPV vaccine development.

Abstract The non-vanishing neutrino mass strongly hints the existence of right-handed neutrinos (RHNs), singlets of the standard model (SM). However, they are highly decoupled from the SM and difficult to probe. In this work, we consider the Majorana RHNs from the type-I seesaw mechanism may well mix with the heavy neutral lepton dwelling in certain vector-like lepton (VLL), thus acquiring a sizable electroweak charge. Such a simple scenario yields many interesting consequences, and the imprint on oblique corrections, well expected from the mass splitting between components of VLL by virtue of VLL-RHN mixing, is our focus here. We analytically calculate the Peskin-Takeuchi parameters S, T and U with full details, carefully treating the Majorana loop to obtain the self consistent expressions free of divergence. Then, we constrain on the VLL-RHN system which only gives a sizable T parameter using the PDG-2021 data and CDF-II data, separately, by imposing T ≲ O $$ \mathcal{O} $$ (0.1). It is found that for the RHN and VLL below the TeV scale, with a properly large mixing, stands in the frontier of the electroweak precision test such as W-boson mass.

Metasurfaces, composed of planar arrays of intricately designed meta-atom structures, possess remarkable capabilities in controlling electromagnetic waves in various ways. A critical aspect of metasurface design involves selecting suitable meta-atoms to achieve target functionalities such as phase retardation, amplitude modulation, and polarization conversion. Conventional design processes often involve extensive parameter sweeping, a laborious and computationally intensive task heavily reliant on designer expertise and judgement. Here, we present an efficient genetic algorithm assisted meta-atom optimization method for high-performance metasurface optics, which is compatible to both single- and multi-objective device design tasks. We first employ the method for a single-objective design task and implement a high-efficiency Pancharatnam-Berry phase based metalens with an average focusing efficiency exceeding 80% in the visible spectrum. We then employ the method for a dual-objective metasurface design task and construct an efficient spin-multiplexed structural beam generator. The device is capable of generating zeroth-order and first-order Bessel beams respectively under right-handed and left-handed circular polarized illumination, with associated generation efficiencies surpassing 88%. Finally, we implement a wavelength and spin co-multiplexed four-channel metahologram capable of projecting two spin-multiplexed holographic images under each operational wavelength, with efficiencies over 50%. Our work offers a streamlined and easy-to-implement approach to meta-atom design and optimization, empowering designers to create diverse high-performance and multifunctional metasurface optics.

Abstract Interannual global mean surface temperature (GMST) forecast provides critical insights into the economic and societal implications of climate variability. The pronounced GMST elevation in 2023–2024 indicates that the Earth may have accumulated enough heat to cause widespread disasters, underscoring the necessity for establishing accurate short-term GMST predictions to offer timely and sustainable public service. However, capturing high-frequency annual variability (ANV) component of GMST poses challenges due to its susceptibility to intraseasonal-to-interannual (ISI) noises, particularly across the Northern Hemisphere’s mid-to-high latitudes. Averaging these ISI variations in November and December effectively enhances signal clarity, especially over oceans, and masks unpredictable noises on land. By forecasting the average GMST for November and December to extract ANV predictability, a strategy for annual GMST prediction was established. This approach successfully advanced precise GMST hindcasts by up to 2-months during 1980–2022, exceeding performance of existing climate models and boosting early warning for interannual GMST shifts.

Abstract Implementing the strongly coupled data assimilation (SCDA) in coupled earth system models remains big challenging, primarily due to accurately estimating the coupled cross background-error covariance. In this work, through simplified two-variable one-dimensional assimilation experiments focusing on the air–sea interactions over the tropical pacific, we aim to clarify that SCDA based on the variance–covariance correlation, such as the ensemble-based SCDA, is limited in handling the inherent nonlinear relations between cross-sphere variables and provides a background matrix containing linear information only. These limitations also lead to the analysis distributions deviating from the truth and miscalculating the strength of rare extreme events. However, free from linear or Gaussian assumptions, the application of the data-driven machine learning (ML) method, such as multilayer perceptron, on SCDA circumvents the expensive matrix operations by avoiding the explicit calculation of background matrix. This strategy presents comprehensively superior performance than the conventional ensemble-based assimilation strategy, particularly in representing the strongly nonlinear relationships between cross-sphere variables and reproducing long-tailed distributions, which help capture the occurrence of small probability events. It is also demonstrated to be cost-effective and has great potential to generate a more accurate initial condition for coupled models, especially in facilitating prediction tasks of the extreme events.

Abstract Background Plant traits and plant adaptive strategies have been affected by the increasing intensity and severity of environmental changes. Given the uncertainty surrounding future environmental conditions, investigating plant trait variations under various stresses is crucial for unraveling plant survival strategies. Leaf anatomical traits are closely responsible for plants’ photosynthesis, respiration and transpiration. However, knowledge of how the multi-species leaf anatomical traits varied in extremely and moderately stressful environments is limited. Our objective was to compare the variation of leaf anatomic traits and adaptation strategies in two different stressful regions of the Qinghai-Tibet Plateau (TP) and Mongolian Plateau (MP) of China. Methods We sampled ten sites in each of the two regions (MP and TP) along an environmental gradient. Seven leaf anatomical traits and two leaf economic traits were measured for all leaf samples. Leaf anatomical traits include the traits related to leaf physiological processes (mesophyll thickness (MT), palisade tissue thickness (PT), spongy tissue thickness (ST), palisade-spongy tissue thickness ratio (PST) and epidermal thickness (ET)) and the traits related to trait construction investment (epiderm-leaf thickness ratio (ET/LT) and mesophyll-leaf thickness ratio (MT/LT)). Leaf economic traits include specific leaf area (SLA) and leaf nitrogen content (LN). Results The results revealed that leaf anatomical traits in the TP exhibited greater phylogenetic conservation with thicker structures, being less susceptible to environmental impacts than those in the MP. Additionally, the leaf anatomical and economic traits decoupled both in the MP and TP. Conclusion These findings highlight that plants adopt diverse strategies to cope with extremely and moderately environmental stresses, but multidimensional trait patterns are generally favored in stressful environments.

Abstract Paleoclimate model simulations provide reference data to help interpret the geological record and offer a unique opportunity to evaluate the performance of current models under diverse boundary conditions. Here, we present a dataset of 35 climate model simulations of the warm early Eocene Climatic Optimum (EECO; ~ 50 million years ago) and corresponding preindustrial reference experiments. To streamline the use of the data, we apply standardised naming conventions and quality checks across eight modelling groups that have carried out coordinated simulations as part of the Deep-Time Model Intercomparison Project (DeepMIP). Gridded model fields can be downloaded from an online repository or accessed through a new web application that provides interactive data exploration. Local model data can be extracted in CSV format or visualised online for streamlined model-data comparisons. Additionally, processing and visualisation code templates may serve as a starting point for advanced analysis. The dataset and online platform aim to simplify accessing and handling complex data, prevent common processing issues, and facilitate the sharing of climate model data across disciplines.

Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.

Abstract Background Schistosomiasis is still one of the most serious parasitic diseases. Evidence showed that the metabolite profile in serum can potentially act as a marker for parasitic disease diagnosis and evaluate disease progression and prognosis. However, the serum metabolome in patients with Schistosoma japonicum infection is not well defined. In this study, we investigated the metabolite profiles of patients with chronic and with advanced S. japonicum infection. Methods The sera of 33 chronic S. japonicum patients, 15 patients with advanced schistosomiasis and 17 healthy volunteers were collected. Samples were extracted for metabolites and analyzed with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Results We observed significant differences in metabolite profiles in positive and negative ion modes between patients with advanced and chronic S. japonicum infection. In patients with chronic S. japonicum infection, 199 metabolites were significantly upregulated while 207 metabolites were downregulated in advanced infection. These differential metabolites were mainly concentrated in steroid hormone biosynthesis, cholesterol metabolism and bile secretion pathways. We also found that certain bile acid levels were significantly upregulated in the progression from chronic to advanced S. japonicum infection. In receiver operator characteristic (ROC) analysis, we identified three metabolites with area under the curve (AUC) > 0.8, including glycocholic (GCA), glycochenodeoxycholate (GCDCA) and taurochenodeoxycholic acid (TCDCA) concentrated in cholesterol metabolism, biliary secretion and primary bile acid biosynthesis. Conclusions This study provides evidence that GCA, GCDCA and TCDCA can potentially act as novel metabolite biomarkers to distinguish patients in different stages of S. japonicum infection. This study will contribute to the understanding of the metabolite mechanisms of the transition from chronic to advanced S. japonicum infection, although more studies are needed to validate this potential role and explore the underlying mechanisms. Graphical Abstract

Abstract The paleoclimate record provides a test-bed in which climate models can be evaluated under conditions of substantial CO2 change; however, these data are typically under-used in the process of model development and evaluation. Here, we use a set of metrics based on paleoclimate proxy observations to evaluate climate models under three past time periods. We find that the latest CMIP6/PMIP4 ensemble mean does a remarkably good job of simulating the global mean surface air temperatures of these past periods, and is improved on CMIP5/PMIP3, implying that the modern climate sensitivity of the CMIP6/PMIP4 model ensemble mean is consistent with the paleoclimate record. However, some models, in particular those with very high or very low climate sensitivity, simulate paleo temperatures that are outside the uncertainty range of the paleo proxy temperature data; in this regard, the paleo data can provide a more stringent constraint than data from the historical record. There is also consistency between models and data in terms of polar amplification, with amplification increasing with increasing global mean temperature across all three time periods. The work highlights the benefits of using the paleoclimate record in the model development and evaluation cycle, in particular for screening models with too-high or too-low climate sensitivity across a range of CO2 concentrations.

Abstract High-quality ocean in situ profile observations are fundamental for ocean and climate research and operational oceanographic applications. Here we describe a new global ocean subsurface temperature profile database named the Chinese Academy of Science (CAS) Oceanography Data Center version 1 (CODC-v1). This database contains over 17 million temperature profiles between 1940–2023 from all available instruments. The major data source is the World Ocean Database (WOD), but CODC-v1 also includes some data from some Chinese institutes which are not available in WOD. The data are quality-controlled (QC-ed) by a new QC system that considers the skewness of local temperature distributions, topographic barriers, and the shift of temperature distributions due to climate change. Biases in Mechanical Bathythermographs (MBTs), eXpendable Bathythermographs (XBTs), and Bottle data (OSD) are all corrected using recently proposed correction schemes, which makes CODC-v1 a bias-corrected dataset. These aspects ensure the data quality of the CODC-v1 database, making it suitable for a wide spectrum of ocean and climate research and applications.

BackgroundCystic Echinococcosis (CE) is a zoonotic disease causing fibrosis and necrosis of diseased livers caused by infection with Echinococcus granulosus (E.g). There is evidence that E.g is susceptible to immune escape and tolerance when host expression of immunoinflammation and fibrosis is suppressed, accelerating the progression of CE. Ghrelin has the effect of suppressing immunoinflammation and fibrosis, and whether it is involved in regulating the progression of E.g-infected liver lesions is not clear.MethodsSerum and hepatic Ghrelin levels were observed in E.g-infected mice (4, 12 and 36 weeks) and compared with healthy control groups. Co-localization analysis is performed between protein expression of Ghrelin in and around the hepatic lesions of E.g-infected 12-week mice and protein expression of different hepatic histiocytes by mIHC. HepG2 cells and protoscoleces (PSCs) protein were co-cultured in vitro, as well as PSCs were alone in vitro, followed by exogenously administered of Ghrelin and its receptor blocker, [D-Lys3]-GHRP-6, to assess their regulatory effects on immunoinflammation, fibrosis and survival rate of PSCs.ResultsSerum Ghrelin levels were increased in E.g-infected 4- and 12-week mice, and reduced in 36-week mice. E.g-infected mice consistently recruited Ghrelin in and around the hepatic lesions, which was extremely strongly co-localized with the protein expression of hepatic stellate cells (HSCs), T cells and the TGF-β1/Smad3 pathway. The secretion of Ghrelin was increased with increasing concentrations of PSCs protein in HepG2 cells culture medium. Moreover, Ghrelin could significantly inhibit the secretion of IL-2, INF-γ and TNF-α, as well as the expression of Myd88/NF-κB and TGF-β1/Smad3 pathway protein, and promoted the secretion of IL-4 and IL-10. Blocking Ghrelin receptor could significantly inhibit PSCs growth in in vitro experiment.ConclusionGhrelin is highly expressed in the early stages of hepatic E.g infection and may be involved in regulating the progression of liver lesions by suppression immunoinflammation and fibrosis.

Adenomatoid tumors are rare, specific, benign tumors of the reproductive tract that originate from mesenchymal tissue. A patient was diagnosed with uterine fibroids 1 year previously when a mass of approximately 30 mm was found in the left adnexal region during a physical examination. At 1 year of follow-up, ultrasound showed that the mass in the left adnexal area had greatly increased to 61 × 45 × 50 mm. Contrast-enhanced pelvic magnetic resonance imaging (MRI) was performed before surgery and suggested a borderline tumor. Histopathology suggested signet ring cell carcinoma, and an immunohistochemical examination suggested a uterine adenomatoid tumor. Our suspicion of a borderline tumor was based mainly on the following features: the mass had increased in size within 1 year, the cancer antigen 125 concentration had increased, and several lymph nodes in the pelvic and groin regions showed positive signals on MRI enhancement. Uterine adenomatoid tumors are challenging to diagnose, especially adenomatosis with signet ring cells. However, the accuracy of diagnosing this disease can be greatly improved by combining ultrasound and MRI. This article describes the most comprehensive and reliable imaging features of ultrasound and MRI, which play an important role in diagnosing uterine adenomatoid tumors and provide useful information for clinicians.

Abstract The link between mantle plumes and the formation of large igneous provinces (LIPs) is well established although the anatomy of these remains equivocal. Recent experimental studies and geophysical data suggest that the mantle plume head is more likely to be irregular and asymmetric, rather than an axisymmetric flattened disk. The Emeishan large igneous province (ELIP) provides a unique opportunity to test this hypothesis. According to robust petrographic, geochronologic, and geochemical evidence from the late Permian basalts in the Sichuan Basin, and in conjunction with a comprehensive compilation of geologic maps and published geochemical data from the ELIP, we identified several giant radial “fingering” structures. Based on the shallow mantle source from the center to margin in the ELIP and relief of the lithosphere‐asthenosphere boundary, we propose a new mantle plume model to explain the evolution of the Emeishan plume periphery, where narrow finger‐like protrusions and plumelets developed outwards from the main body of the plume to the edges of the flattened plume head. Dragged fingers might have been torn apart into some plumelets, which dispersed and were trapped beneath the thinnest lithosphere relief, and eventually erupted to form small‐scale flood basalt in the Outer Zone of the ELIP.

Abstract Background Post-ERCP pancreatitis is one of the most common adverse events in ERCP-related procedures. The purpose of this study is to construct an online model to predict the risk of post-ERCP pancreatitis in non-elderly patients with common bile duct stones through screening of relevant clinical parameters. Methods A total of 919 cases were selected from 7154 cases from a major Chinese tertiary hospital. Multivariable logistic regression model was fitted using the variables selected by the LASSO regression from 28 potential predictor variables. The internal and external validation was assessed by evaluating the receiver operating characteristic curve and the area under curve. Restricted cubic spline modelling was used to explore non-linear associations. The interactive Web application developed for risk prediction was built using the R “shiny” package. Results The incidence of post-ERCP pancreatitis was 5.22% (48/919) and significantly higher in non-elderly patients with female, high blood pressure, the history of pancreatitis, difficult intubation, endoscopic sphincterotomy, lower alkaline phosphatase and smaller diameter of common bile duct. The predictive performance in the test and external validation set was 0.915 (95% CI, 0.858–0.972) and 0.838 (95% CI, 0.689–0.986), respectively. The multivariate restricted cubic spline results showed that the incidence of pancreatitis was increased at 33–50 years old, neutrophil percentage > 58.90%, hemoglobin > 131 g/L, platelet 241.40 × 109/L, total bilirubin > 18.39 umol / L, aspartate amino transferase

Abstract Spinal Cord Injury (SCI) is a condition characterized by complete or incomplete motor and sensory impairment, as well as dysfunction of the autonomic nervous system, caused by factors such as trauma, tumors, or inflammation. Current treatment methods primarily include traditional approaches like spinal canal decompression and internal fixation surgery, steroid pulse therapy, as well as newer techniques such as stem cell transplantation and brain-spinal cord interfaces. However, the above methods have limited efficacy in promoting axonal and neuronal regeneration. The challenge in medical research today lies in promoting spinal cord neuron regeneration and regulating the disrupted microenvironment of the spinal cord. Studies have shown that gas molecular therapy is increasingly used in medical research, with gasotransmitters such as hydrogen sulfide, nitric oxide, carbon monoxide, oxygen, and hydrogen exhibiting neuroprotective effects in central nervous system diseases. The gas molecular protect against neuronal death and reshape the microenvironment of spinal cord injuries by regulating oxidative, inflammatory and apoptotic processes. At present, gas therapy mainly relies on inhalation for systemic administration, which cannot effectively enrich and release gas in the spinal cord injury area, making it difficult to achieve the expected effects. With the rapid development of nanotechnology, the use of nanocarriers to achieve targeted enrichment and precise control release of gas at Sites of injury has become one of the emerging research directions in SCI. It has shown promising therapeutic effects in preclinical studies and is expected to bring new hope and opportunities for the treatment of SCI. In this review, we will briefly outline the therapeutic effects and research progress of gasotransmitters and nanogas in the treatment of SCI. Graphical Abstract

Abstract Inflammation plays a key role in pathogenesis and rupture of aneurysms. Non-invasively and dynamically monitoring aneurysm inflammation is critical. This study evaluated myeloperoxidase (MPO) as an imaging biomarker and therapeutic target for aneurysm inflammation using an elastase-induced rabbit model treated with or without 4-aminobenzoic acid hydrazide (ABAH), an irreversible inhibitor of MPO. Myeloperoxidase-sensitive magnetic resonance imaging (MRI) using Mn-TyrEDTA, a peroxidase activity-dependent contrast agent, revealed weak contrast enhancement in contralateral arteries and decreased contrast enhancement in aneurysm walls with ABAH treatment, indicating MPO activity decreased and inflammation mitigated. This was supported by reduced immune cell infiltration, matrix metalloproteinases (MMP-2 and − 9) activity, ROS production and arterial wall destruction on histology. Finally, the aneurysm expansion rate remained

Abstract Idealized general circulation models (GCMs) suggest global‐mean precipitation ceases to increase with warming in hot climates because evaporation is limited by the available solar radiation at the surface. We investigate the extent to which this generalizes in comprehensive GCMs. We find that in the Community Atmosphere Model, global‐mean precipitation increases approximately linearly with global‐mean surface temperatures up to about 330 K, where it peaks at 5 mm day−1. Beyond 330 K, global‐mean precipitation decreases substantially despite increasing surface temperatures because of increased atmospheric shortwave absorption by water vapor, which decreases the shortwave radiation available for evaporation at the surface. Precipitation decreases in the tropics and subtropics but continues to increase in the extratropics because of continuously strengthening poleward moisture transport. Precipitable water increases everywhere, resulting in longer water‐vapor residence times and implying more episodic precipitation. Other GCMs indicate global‐mean precipitation might exhibit a smaller maximum rate and begin to decrease at lower surface temperatures.

In the past few decades, great progress has been made in prenatal diagnosis of congenital heart disease (CHD). Fetal echocardiography is recognized as the main prenatal screening and diagnostic tool that can accurately detect approximately 85 % of fetal cardiac abnormalities. Evaluation of the fetal heart remains a major challenge in prenatal ultrasound screening and diagnosis due to fetal position, involuntary movement, small and complex fetal cardiac anatomy, maternal abdominal wall conditions, and lack of expertise in fetal echocardiography by some physicians engaged in obstetric ultrasound. Artificial intelligence (AI) can automate and standardize the display of each diagnostic section of the fetal heart and thus contribute to accurate diagnosis, which significantly optimizes the clinical application of fetal echocardiography. In this review, we not only clarify the role of AI but also highlight its significance and future solutions in the field of fetal echocardiography.

With the improvement of national health awareness and the popularization of a series of screening methods, the number of patients with early colorectal cancer is gradually increasing, and accurate prediction of lymph node metastasis of T1 colorectal cancer is the key to determining the optimal therapeutic solutions. Whether patients with T1 colorectal cancer undergoing endoscopic resection require additional surgery and regional lymph node dissection is inconclusive in current guidelines. However, we can be sure that in early colorectal cancer without lymph node metastasis, endoscopic resection alone does not affect the prognosis, and it greatly improves the quality of life and reduces the incidence of surgical complications while preserving organ integrity. Therefore, it is vital to discriminate patients without lymph node metastasis in T1 colorectal cancer, and this requires accurate predictors. This paper briefly explains the significance and shortcomings of traditional pathological factors, then extends and states the new pathological factors, clinical test factors, molecular biomarkers, and the risk assessment models of lymph node metastasis based on artificial intelligence.

Ischemic heart disease invariably leads to devastating damage to human health. Nicotinamide ribose (NR), as one of the precursors of NAD+ synthesis, has been discovered to exert a protective role in various neurological and cardiovascular disorders. Our findings demonstrated that pretreatment with 200 mg/kg NR for 3 h significantly reduced myocardial infarct area, decreased levels of CK-MB and LDH in serum, and improved cardiac function in the rats during myocardial ischemia-reperfusion (I/R) injury. Meanwhile, 0.5 mM NR also effectively increased the viability and decreased the LDH release of H9c2 cells during OGD/R. We had provided evidence that NR pretreatment could decrease mitochondrial reactive oxygen species (mtROS) production and MDA content, and enhance SOD activity, thereby mitigating mitochondrial damage and inhibiting apoptosis during myocardial I/R injury. Further investigations revealed that NR increased NAD+ content and upregulated SIRT3 protein expression in myocardium. Through using of SIRT3 small interfering RNA and the SIRT3 deacetylase activity inhibitor 3-TYP, we had confirmed that the cardioprotective effect of NR on cardiomyocytes was largely dependent on the inhibition of mitochondrial oxidative stress via SIRT3-SOD2 axis. Overall, our study suggested that exogenous supplementation with NR mitigated mitochondrial damage and inhibited apoptosis during myocardial I/R injury by reducing mitochondrial oxidative stress via SIRT3-SOD2-mtROS pathway.

Abstract The study focuses on the control of nonlinear dynamic systems in the presence of parameter uncertainties, unmodeled dynamics, and external disturbances. The lumped perturbation is assumed to be bounded within a polynomial in the system state with the polynomial parameters and degrees unknown a priori such that it accommodates a quite wider range dynamic systems. Based on the studies in recent super-twisting algorithm designs and the idea from adaptive sliding mode control for nonlinear systems with uncertainties, we propose a novel adaptive super-twisting algorithm with exponential reaching law, or exponential super-twisting algorithm (ESTA), for the high-stability and acceptable accuracy control of the aimed nonlinear dynamics. The stability analysis and practical finite-time (PFT) convergence are proven using Lyapunov theory and an intuitive analysis of the control behaviour. Simulations are performed to compare the proposed ESTA with the existing super-twisting method and the traditional proportional integral differential control. The simulation results demonstrate the effectiveness of the proposed ESTA in terms of the fastest settling time and the smallest overshoot.

Many designs of in-memory logic computing using ReRAM have been proposed and even a few of them have been experimentally demonstrated. However, the ability to cascade and extend the designed circuits is limited. In this work, we elaborate a design called time-sum-logic based on 2T1R memristor arrays and implement arithmetic computing circuits using memristors by integrating both time and space dimension. The core method of our design is to use sum of minterms to transfer logic to time sequence by 2T1R memristor arrays. This method is extensible by extending the number of layers to realize multi-bit arithmetic computing. Remarkably, cascading between different layers is elaborated carefully and pipelines are adopted to manage the sequence of memristors’ writing and reading and lower the complexity and latency. In this way, the proposed circuits will be more efficient and can support high data throughout. Based on our design, 4-bit full adder and $4\times 4$ multiplier are designed with pipelines, and n-bit full adders and m $\times $ n multipliers could be easily constructed. The low latency and high efficiency confirm the advantages we have illustrated and show a promised application prospect.

The Gaussian noise assumption is usually made for slowly time-varying underwater acoustic communication (UWAC) systems, which is entirely adequate for most cases. However, certain human activities such as geophysical surveys, multi-beam echosounders and iceberg breakup generate impulsive noise. Consequently, the impulsive noise degrades the designed receiver based on the Gaussian noise model significantly. An optimization formulation which takes the slowly time-varying channel impulse response (CIR), the group-sparse impulsive noise and the encoder structure of the data symbol into account is proposed. To tackle the optimization problem, we propose a joint channel estimation, data decoding and impulsive noise estimation (JCDI) algorithm consisting of two modules named module A and module B, where module A performs the joint channel estimation, data estimation and impulsive noise estimation, while module B uses the low density parity check (LDPC) decoder to decode the encoded data symbols. In particular, the block coordinate descent (BCD) algorithm is proposed, and a smoothed fast iterative threshold soft algorithm (FISTA) is proposed to estimate the group-sparse impulsive noise. Substantial numerical simulations and real experiments are conducted to show the effectiveness of JCDI under impulsive noise, i.e., JCDI achieves lower coded BER than conventional receivers.

The intelligent operation mode decision scheme has been proved to be a promising solution for enhancing the cleaning performance of cleaning robots. In this paper, we propose a lightweight operation mode decision method for cleaning robots, driven by garbage attributes perception. The method aims to enable cleaning robots to intelligently select the most appropriate operation mode when dealing with different types of garbage, thereby improving their cleaning efficiency. Specifically, we propose a lightweight garbage attributes extraction network (LGAE-Net) based on inverted residuals, which adopts the proposed deep dynamic attention convolution (DDA-Conv) as its basic structure and adaptively extracts share features of attributes while reducing computational complexity. Then, the network adopts a multi-label architecture to predict multiple attributes, and a dynamic weighting joint learning strategy is introduced to alleviate the problem of imbalanced difficulty in attribute learning. Finally, based on the extraction of attributes, a decision module for operation mode is constructed. This module enables fast decision from the perception of attributes to the selection of an operation mode. Our proposed method achieves 94.26% decision accuracy on the test dataset, and the single-sheet recognition rate is only 1.63 msec. In addition, it maintains a parameter count (Params(M)) of approximately 2.97M and floating-point operations (FLOPs(M)) of only 94.91M, which reflects the excellent balance of accuracy and efficiency of the method, and can meet the real-time requirements of cleaning robots.

Abstract Background Chemotherapy is an important approach for lung cancer patients. The study was designed to evaluate the feasibility of the compound probiotic supplements in improving the quality of life for lung cancer patients undergoing chemotherapy. Methods This randomized, double‐blind, placebo‐controlled trial enrolled chemotherapy‐naive patients with lung cancer who were scheduled to receive platinum‐based doublet chemotherapy. All eligible patients were randomly administered (1:1) compound probiotic supplements (group BP‐1) or placebo (group C) for two chemotherapy cycles. The EORTC QLQ C30 questionnaire scores were evaluated before the first, second, and third cycles of chemotherapy. The primary endpoint was the difference in the EROTC QLQ C30 questionnaire score between the two groups after two cycles of chemotherapy. Results A total of 110 patients were recruited from March 2021 to January 2022. After undergoing two cycles of chemotherapy, group BP‐1 were significantly better in various dimensions of the overall quality of life, role function, nausea and vomiting, appetite loss, constipation, and diarrhea relative to group C (76.90 ± 18.31 vs. 58.89 ± 17.17; 93.33 ± 11.58 vs. 85.93 ± 15.06; 0.00 ± 0.00 vs. 27.04 ± 29.15; 6.67 ± 13.53 vs. 22.22 ± 18.80; 0.95 ± 5.63 vs. 28.15 ± 22.42; 2.86 ± 9.47 vs. 15.56 ± 16.82; p

Abstract Progress in immunotherapy for prostate cancer (PCa) lags that for other cancers, mainly because of limited immune infiltration in PCa. This study aimed to assess the feasibility of NSD2 as an immunotherapeutic target in PCa. Immunohistochemistry was performed to evaluate the expression pattern of NSD2 in 34 cases of benign prostatic hyperplasia (BPH), 36 cases of prostatic intraepithelial neoplasia (PIN), and 57 cases of PCa, including 19 cases of metastatic castration-resistant prostatic cancer (mCRPC). Single-cell RNA sequencing and gene set enrichment analysis (GSEA) were used to correlate NSD2 with certain downstream pathways. Furthermore, the Immuno-Oncology-Biological-Research (IOBR) software package was used to analyze the potential roles of NSD2 in the tumor microenvironment. We found that the positive expression rate of NSD2 increased progressively in BPH, PIN and PCa. mCRPC had the highest staining intensity for NSD2. High NSD2 expression was positively correlated with the infiltration level of CD4+ tumor-infiltrating lymphocytes (TILs) and negatively correlated with that of CD8+ TILs. Importantly, a new immune classification based on NSD2 expression and CD4+ TILs and CD8+ TILs was successfully used to stratify PCa patients based on OS.PSA and CD4+ TILs are independent risk factors for PCa bone metastasis. This study demonstrates a novel role for NSD2 in defining immune infiltrate on in PCa and highlights the great potential for its application in immunotherapy response evaluation for prostate malignancies.

The Geostationary Interferometric InfraRed Sounder (GIIRS) provides a novel opportunity to acquire high-spatiotemporal-resolution atmospheric information. Previous studies have demonstrated the positive impacts of assimilating GIIRS radiances from either long-wave temperature or middle-wave water vapor bands on modeling high-impact weather processes. However, the impact of assimilating both bands on forecast skill has been less investigated, primarily due to the non-identical geolocations for both bands. In this study, a locally cloud-resolving global model is utilized to assess the impact of assimilating GIIRS observations from both long-wave and middle-wave bands. The findings indicate that the GIIRS observations exhibit distinct inter-channel error correlations. Proper inflation of these errors can compensate for inaccuracies arising from the treatment of the geolocation of the two bands, leading to a significant enhancement in the usage of GIIRS observations from both bands. The assimilation of GIIRS observations not only markedly reduces the normalized departure standard deviations for most channels of independent instruments, but also improves the atmospheric states, especially for temperature forecasting, with a maximum reduction of 42% in the root-mean-square error in the lower troposphere. These improvements contribute to better performance in predicting heavy rainfall.

IntroductionMagnetic resonance imaging (MRI)-guided wire localization can be applied to assist to remove suspected breast lesions accurately. This study aimed to evaluate the clinical application value of this technique in Chinese women.MethodsA total of 126 patients (131 lesions) who had underwent such technique in our hospital from April 2017 to June 2023 were enrolled. 1.5T MRI system and a wire localization device were used. Image characteristics, clinical features and postoperative pathology were collected and analyzed.ResultsAll of 126 patients (131 lesions) were successfully localized by MRI and excised for biopsy. There were 39 malignant lesions (29.77%) and 92 benign lesions (70.23%). There was no significant correlation between the morphology of DCE-MRI and the ratio of malignant lesions (P=0.763), while there was a statistical correlation between the BPE, TIC curve and the malignancy rate (P

Key Clinical Message Unlike most cases, the lesions were localized to the dorsum of the hand, lacked pruritus (itching), and did not exhibit “sperm‐like blood vessels,” which are typically pathognomonic to classical MF. Abstract The study presents a rare case involving a 44‐year‐old woman who developed a skin condition on the base of her left thumb. Initially misdiagnosed as pigmented purpura, the need for further investigation arose to determine the nature of the condition accurately. The medical evaluation encompassed a comprehensive analysis of the patient's skin ailment. A series of diagnostic examinations were conducted to ascertain the underlying cause. Although routine blood tests yielded unremarkable results, the distinct characteristics of the rash prompted a more thorough investigation. Subsequent assessment revealed that the skin condition was not pigmented purpura, as initially presumed, but rather a manifestation of cutaneous T‐cell lymphoma (CTCL) known as mycosis fungoides (MF). MF is an infrequent lymphoma predominantly affecting individuals aged 45–65, exhibiting a male‐to‐female sex ratio of 2:1. The annual incidence of MF ranges from 0.3 to 0.96 cases per 100,000 individuals. The woman's skin exhibited discrete patches adorned with colored dots, progressively thickening and pigmentation. Notably, the absence of pruritus did not dispel suspicion. This case underscores the significance of accurately diagnosing uncommon dermatological disorders to facilitate appropriate medical intervention. The unique appearance of the rash and its distinctive features, despite normal blood results, enabled the identification of MF. The patient's treatment encompassed a combination of steroids and narrowband UV therapy. Vigilance, continued research, and heightened awareness are paramount for early intervention and improved patient outcomes. Such efforts contribute to an enhanced understanding of the complexities of this condition.

Objective To evaluate the effectiveness of oral probiotic supplements in patients undergoing immune checkpoint inhibitors (ICIs) for the treatment of advanced lung cancer. Methods This prospective real-world study enrolled patients with advanced lung cancer who were receiving ICIs as part of their treatment. The patients were divided into 2 groups: Group OPS received oral probiotic supplements along with ICIs, while Group C did not. The primary endpoint was progression-free survival (PFS). The secondary outcome measure was the objective response rate (ORR). Results A total of 253 patients were included in the study, with 71 patients in Group OPS and 182 patients in the control group (Group C). No significant differences were observed in the median PFS between the 2 groups for all patients. However, for small cell lung cancer (SCLC) patients, the median PFS was significantly better in the Group OPS compared to the Group C (11.1 months vs 7.0 months, P = .049). No significant differences were observed in median PFS for the non-small cell lung cancer (NSCLC) cohort between the 2 groups, but a trend towards better median PFS in Group OPS was noticed (16.5 months vs 12.3 months, P = .56). The ORR for the entire cohort was 58.0%. Conclusion Oral probiotics supplements in combination with ICIs included regimen may improve the outcome in patients with advanced SCLC. The above points should be proved by further study.

One-step hydrothermal method has been used to synthesize YMnO3@NiO (YMO@NO) photocatalysts with high photocatalytic activity for the degradation of oil and gas field wastewater under simulated solar irradiation. Through various characterization methods, it has been confirmed that the YMO@NO photocatalyst comprises only YMO and NO, without any other impurities. The microstructure characterization confirmed that the YMO@NO photocatalyst was composed of large squares and fine particles, and heterojunction was formed at the interface of YMO and NO. The optical properties confirm that the YMO@NO photocatalyst has high UV-vis optical absorption coefficient, suggesting that it has high UV-vis photocatalytic activity. Taking oil and gas field wastewater as degradation object, YMO@NO photocatalyst showed the highest photocatalytic activity (98%) when the catalyst content was 1.5 g/L, the mass percentage of NO was 3%, and the irradiation time was 60 min. Capture and stability experiments confirm that the YMO@NO photocatalyst is recyclable and electrons, holes, hydroxyl radicals and superoxide radicals play major roles in the photocatalysis process. Based on experiments and theoretical calculations, a reasonable photocatalytic mechanism of the YMO@NO photocatalyst is proposed.

Abstract Testicular microcirculation is closely related to spermatogenic function and seminiferous tubular function. The diagnosis and monitoring of testicular diseases can be associated with testicular microcirculation; however, there are currently no effective non‐invasive methods for super‐resolution imaging of testicular microcirculation. In this study, we introduced state‐of‐the‐art graph‐based tracking with the Kalman motion model algorithm to non‐invasively image human testicular microcirculation for the first time with a regular frame‐rate clinical ultrasound imaging system (37 Hz). Two distinct testicular vessels with an 81 µm separation were resolved in the testicular vasculature, surpassing all other imaging modalities. In a retrospective study, we performed contrast‐enhanced ultrasound examinations(CEUS) and ultrasound localization microscopy (ULM) processing on the included 76 infertile patients and 15 healthy controls from August 2021 to May 2023 and obtained super‐resolution images of testicular microcirculation with sub‐diffraction resolution. Through the results of one‐way analysis of variance tests and receiver operating characteristic analyses, we found that the ULM‐based parameters hold promise as clinical guidance for differentiating between non‐obstructive and obstructive male infertility. The mean vessel diameter achieved an area under the curve (AUC) of 0.920 (95% confidence interval [CI]: 0.847–0.994, p

The development of an effective and broadly protective influenza vaccine against circulating and emerging strains remains elusive. In this study, we evaluated a potentially universal influenza vaccine based on single-component self-assembling protein nanoparticles (1c-SApNPs) presenting the conserved matrix protein 2 ectodomain (M2e) from influenza A and B viruses (IAV and IBV, respectively). We previously designed a tandem antigen comprising three IAV M2e domains of human, avian/swine, and human/swine origins (termed M2ex3). The M2ex3-presenting 1c-SApNPs conferred complete protection in mice against sequential lethal challenges with H1N1 and H3N2. To broaden this protection to cover IBVs, we designed a series of antigens incorporating different arrangements of three IAV M2e domains and three copies of IBV M2e. Tandem repeats of IAV and IBV (termed influenza A-B) M2e arrayed on the I3-01v9a 60-mer 1c-SApNP, when formulated with an oil-in-water emulsion adjuvant, generated greater M2e-specific immunogenicity and protective efficacy than the soluble influenza A-B M2e trimer, indicated by higher survival rates and reduced weight loss post-challenge. Importantly, one of the influenza A-B M2e SApNP constructs elicited 100% protection against a lethal influenza A/Puerto Rico/8/1934 (H1N1) challenge in mice and 70% protection against a lethal influenza B/Florida/4/2006 (Yamagata lineage) challenge, the latter of which has not been reported in the literature to date. Our study thus provides a promising M2e-based single-component universal vaccine candidate against the two major types of influenza virus circulating in humans.

Cystic echinococcosis (CE) is a zoonotic disease caused by the parasite Echinococcus granulosus (E. granulosus), which can lead to the formation of liver lesions. Research indicates that E. granulosus releases both Toll-like receptor 2 (TLR2) and Interleukin-9 (IL-9), which can potentially impair the body’s innate immune defenses and compromise the liver’s ability to fight against diseases. To investigate the role of TLR2 and IL-9 in liver damage caused by E. granulosus infection, samples were initially collected from individuals diagnosed with CE. Subsequently, BALB/c mice were infected with E. granulosus at multiple time points (4 weeks, 12 weeks, 32 weeks) and the expression levels of these markers was then assessed at each of these phases. Furthermore, a BALB/c mouse model was generated and administered anti-IL-9 antibody via intraperitoneal injection. The subsequent analysis focused on the TLR2/MyD88/NF-κB signaling pathway and the expression of IL-9 in E. granulosus was examined. A co-culture experiment was conducted using mouse mononuclear macrophage cells (RAW264.7) and hepatic stellate cells (HSCs) in the presence of E. granulosus Protein (EgP). The findings indicated elevated levels of IL-9 and TLR2 in patients with CE, with the activation of the signaling pathway significantly increased as the duration of infection progressed. Administration of anti-IL-9 in mice reduced the activation of the TLR2/MyD88/NF-κB signaling pathway, exacerbating liver injury. Moreover, EgP stimulates the TLR2/MyD88/NF-κB signaling pathway, resulting in the synthesis of α-SMA and Collagen I. The data suggest that infection with E. granulosus may stimulate the production of IL-9 through the activation of the TLR2/MyD88/NF-κB signaling pathway, which is mediated by TLR2. This activation stimulates RAW264.7 and HSCs, exacerbating liver injury and fibrosis.

In this paper, we present a general theory for fractional-order sequential differential equations with Riemann–Liouville nabla derivatives and Caputo nabla derivatives on time scales. The explicit solution, in the case of constant coefficients, for both the homogeneous and the non-homogeneous problems, are given using the ∇-Mittag-Leffler function, Laplace transform method, operational method and operational decomposition method. In addition, we also provide some results about a solution to a new class of fractional-order sequential differential equations with convolutional-type variable coefficients using the Laplace transform method.

A core transcription regulatory circuitry (CRC) is an interconnected self-regulatory circuitry that is formed by a group of core transcription factors (TFs). These core TFs collectively regulate gene expression by binding not only to their own super enhancers (SEs) but also to the SEs of one another. For most human tissue/cell types, a global view of CRCs and core TFs has not been generated. Here, we identified numerous CRCs using two identification methods and detailed the landscape of the CRCs driven by SEs in large cell/tissue samples. The comprehensive biological analyses, including sequence conservation, CRC activity and genome binding affinity were conducted for common TFs, moderate TFs, and specific TFs, which exhibit different biological features. The local module located from the common CRC network highlighted the essential functions and prognostic performance. The tissue-specific CRC network was highly related to cell identity. Core TFs in tissue-specific CRC networks exhibited disease markers, and had regulatory potential for cancer immunotherapy. Moreover, a user-friendly resource named CRCdb (http://www.licpathway.net/crcdb/index.html) was developed, which contained the detailed information of CRCs and core TFs used in this study, as well as other interesting results, such as the most representative CRC, frequency of TFs, and indegree/outdegree of TFs.

Microorganisms are important sources of various natural products that have been commercialized for human medicine and animal healthcare. Bacitracin is an important antibacterial natural product predominantly produced by Bacillus licheniformis and Bacillus subtilis, and it is characterized by a broad antimicrobial spectrum, strong activity and low resistance, thus bacitracin is extensively applied in animal feed and veterinary medicine industries. In recent years, various strategies have been proposed to improve bacitracin production. Herein, we systematically describe the regulation of bacitracin biosynthesis in genus Bacillus and its associated mechanism, to provide a theoretical basis for bacitracin overproduction. The metabolic engineering strategies applied for bacitracin production are explored, including improving substrate utilization, using an enlarged precursor amino acid pool, increasing ATP supply and NADPH generation, and engineering transcription regulators. We also present several approaches of fermentation process optimization to facilitate the industrial large-scale production of bacitracin. Finally, the challenges and prospects associated with microbial bacitracin synthesis are discussed to facilitate the establishment of high-yield and low-cost biological factories.

Abstract Spatiotemporal forecasting involves generating temporal forecasts for system state variables across spatial regions. Data‐driven methods such as Convolutional Long Short‐Term Memory (ConvLSTM) are effective in capturing both spatial and temporal correlations, but they suffer from error accumulation and accuracy loss as forecasting time increases due to the nonlinearity and uncertainty in physical processes. To address this issue, we propose to combine data‐driven and data assimilation (DA) methods for spatiotemporal forecasting. The accuracy of the data‐driven ConvLSTM model can be improved by periodically assimilating real‐time observations using the ensemble Kalman filter (EnKF) approach. This proposed hybrid ConvLSTM‐EnKF method is demonstrated through PM2.5 forecasting in China, which is a challenging task due to the complexity of topographical and meteorological conditions in the region, the need for high‐resolution forecasting over a large study area, and the scarcity of observations. The results show that the ConvLSTM‐EnKF method outperforms conventional methods and can provide satisfactory operational PM2.5 forecasts for up to 1 month with spatially averaged RMSE below 20 μg/m3 and correlation coefficient (R) above 0.8. In addition, the ConvLSTM‐EnKF method shows a substantial reduction in CPU time when compared to the commonly used NAQPMS‐EnKF method, up to three orders of magnitude. Overall, the use of data‐driven models provides efficient forecasts and speeds up DA. This hybrid ConvLSTM‐EnKF is a novel operational forecasting technique for spatiotemporal forecasting and is used in real spatiotemporal forecasting for the first time.

Abstract Pig gastrointestinal tracts harbor a heterogeneous and dynamic ecosystem populated with trillions of microbes, enhancing the ability of the host to harvest energy from dietary carbohydrates and contributing to host adipogenesis and fatness. However, the microbial community structure and related mechanisms responsible for the differences between the fatty phenotypes and the lean phenotypes of the pigs remained to be comprehensively elucidated. Herein, we first found significant differences in microbial composition and potential functional capacity among different gut locations in Jinhua pigs with distinct fatness phenotypes. Second, we identified that Jinhua pigs with lower fatness exhibited higher levels of short‐chain fatty acids in the colon, highlighting their enhanced carbohydrate fermentation capacity. Third, we explored the differences in expressed carbohydrate‐active enzyme (CAZyme) in pigs, indicating their involvement in modulating fat storage. Notably, Clostridium butyricum might be a representative bacterial species from Jinhua pigs with lower fatness, and a significantly higher percentage of its genome was dedicated to CAZyme glycoside hydrolase family 13 (GH13). Finally, a subsequent mouse intervention study substantiated the beneficial effects of C. butyricum isolated from experimental pigs, suggesting that it may possess characteristics that promote the utilization of carbohydrates and hinder fat accumulation. Remarkably, when Jinhua pigs were administered C. butyricum, similar alterations in the gut microbiome and host fatness traits were observed, further supporting the potential role of C. butyricum in modulating fatness. Taken together, our findings reveal previously overlooked links between C. butyricum and CAZyme function, providing insight into the basic mechanisms that connect gut microbiome functions to host fatness.

Conclusions: mRIPC decreased the incidence of myocardial and lung injury in MVR surgery, providing new evidence for the clinical application of RIPC in valve surgery.

Ghrelin widely exists in the central nervous system and peripheral organs, and has biological activities such as maintaining energy homeostasis, regulating lipid metabolism, cell proliferation, immune response, gastrointestinal physiological activities, cognition, memory, circadian rhythm and reward effects. In many benign liver diseases, it may play a hepatoprotective role against steatosis, chronic inflammation, oxidative stress, mitochondrial dysfunction, endoplasmic reticulum stress and apoptosis, and improve liver cell autophagy and immune response to improve disease progression. However, the role of Ghrelin in liver Echinococcosis is currently unclear. This review systematically summarizes the molecular mechanisms by which Ghrelin regulates liver growth metabolism, immune-inflammation, fibrogenesis, proliferation and apoptosis, as well as its protective effects in liver fibrosis diseases, and further proposes the role of Ghrelin in liver Echinococcosis infection. During the infectious process, it may promote the parasitism and survival of parasites on the host by improving the immune-inflammatory microenvironment and fibrosis state, thereby accelerating disease progression. However, there is currently a lack of targeted in vitro and in vivo experimental evidence for this viewpoint.

Abstract Previous studies have noticed that the Coupled Model Intercomparison Project Phase 6 (CMIP6) models with a stronger cooling from aerosol‐cloud interactions (ACI) also have an enhanced warming from positive cloud feedback, and these two opposing effects are counter‐balanced in simulations of the historical period. However, reasons for this anti‐correlation are less explored. In this study, we perturb the cloud ice microphysical processes to obtain cloud liquid of varying amounts in two Earth System Models (ESMs). We find that the model simulations with a larger liquid water path (LWP) tend to have a stronger cooling from ACI and a stronger positive cloud feedback. More liquid clouds in the mean‐state present more opportunities for anthropogenic aerosol perturbations and also weaken the negative cloud feedback at middle to high latitudes. This work, from a cloud state perspective, emphasizes the influence of the mean‐state LWP on effective radiative forcing due to ACI (ERFACI).

Objective Chronic kidney disease (CKD) poses a major global health burden. Early CKD risk prediction enables timely interventions, but conventional models have limited accuracy. Machine learning (ML) enhances prediction, but interpretability is needed to support clinical usage with both in diagnostic and decision-making. Methods A cohort of 491 patients with clinical data was collected for this study. The dataset was randomly split into an 80% training set and a 20% testing set. To achieve the first objective, we developed four ML algorithms (logistic regression, random forests, neural networks, and eXtreme Gradient Boosting (XGBoost)) to classify patients into two classes—those who progressed to CKD stages 3–5 during follow-up (positive class) and those who did not (negative class). For the classification task, the area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate model performance in discriminating between the two classes. For survival analysis, Cox proportional hazards regression (COX) and random survival forests (RSFs) were employed to predict CKD progression, and the concordance index (C-index) and integrated Brier score were used for model evaluation. Furthermore, variable importance, partial dependence plots, and restrict cubic splines were used to interpret the models’ results. Results XGBOOST demonstrated the best predictive performance for CKD progression in the classification task, with an AUC-ROC of 0.867 (95% confidence interval (CI): 0.728–0.100), outperforming the other ML algorithms. In survival analysis, RSF showed slightly better discrimination and calibration on the test set compared to COX, indicating better generalization to new data. Variable importance analysis identified estimated glomerular filtration rate, age, and creatinine as the most important predictors for CKD survival analysis. Further analysis revealed non-linear associations between age and CKD progression, suggesting higher risks in patients aged 52–55 and 65–66 years. The association between cholesterol levels and CKD progression was also non-linear, with lower risks observed when cholesterol levels were in the range of 5.8–6.4 mmol/L. Conclusions Our study demonstrated the effectiveness of interpretable ML models for predicting CKD progression. The comparison between COX and RSF highlighted the advantages of ML in survival analysis, particularly in handling non-linearity and high-dimensional data. By leveraging interpretable ML for unraveling risk factor relationships, contrasting predictive techniques, and exposing non-linear associations, this study significantly advances CKD risk prediction to enable enhanced clinical decision-making.

Abstract The Eocene (56–34 million years ago) is characterized by declining sea surface temperatures (SSTs) in the low latitudes (∼4°C) and high southern latitudes (∼8–11°C), in accord with decreasing CO2 estimates. However, in the mid‐to‐high northern latitudes there is no evidence for surface water cooling, suggesting thermal decoupling between northern and southern hemispheres and additional non‐CO2 controls. To explore this further, we present a multi‐proxy (Mg/Ca, δ18O, TEX86) SST record from Bass River in the western North Atlantic. Our compiled multi‐proxy SST record confirms a net decline in SSTs (∼4°C) between the early Eocene Climatic Optimum (53.3–49.1 Ma) and mid‐Eocene (∼44–41 Ma), supporting declining atmospheric CO2 as the primary mechanism of Eocene cooling. However, from the mid‐Eocene onwards, east‐west North Atlantic temperature gradients exhibit different trends, which we attribute to incursion of warmer waters into the eastern North Atlantic and inception of Northern Component Water across the early‐middle Eocene transition.

IntroductionArtificial intelligence (AI) technology has made rapid progress for disease diagnosis and triage. In the field of ophthalmic diseases, image-based diagnosis has achieved high accuracy but still encounters limitations due to the lack of medical history. The emergence of ChatGPT enables human-computer interaction, allowing for the development of a multimodal AI system that integrates interactive text and image information.ObjectiveTo develop a multimodal AI system using ChatGPT and anterior segment images for diagnosing and triaging ophthalmic diseases. To assess the AI system's performance through a two-stage cross-sectional study, starting with silent evaluation and followed by early clinical evaluation in outpatient clinics.Methods and analysisOur study will be conducted across three distinct centers in Shanghai, Nanjing, and Suqian. The development of the smartphone-based multimodal AI system will take place in Shanghai with the goal of achieving ≥90% sensitivity and ≥95% specificity for diagnosing and triaging ophthalmic diseases. The first stage of the cross-sectional study will explore the system's performance in Shanghai's outpatient clinics. Medical histories will be collected without patient interaction, and anterior segment images will be captured using slit lamp equipment. This stage aims for ≥85% sensitivity and ≥95% specificity with a sample size of 100 patients. The second stage will take place at three locations, with Shanghai serving as the internal validation dataset, and Nanjing and Suqian as the external validation dataset. Medical history will be collected through patient interviews, and anterior segment images will be captured via smartphone devices. An expert panel will establish reference standards and assess AI accuracy for diagnosis and triage throughout all stages. A one-vs.-rest strategy will be used for data analysis, and a post-hoc power calculation will be performed to evaluate the impact of disease types on AI performance.DiscussionOur study may provide a user-friendly smartphone-based multimodal AI system for diagnosis and triage of ophthalmic diseases. This innovative system may support early detection of ocular abnormalities, facilitate establishment of a tiered healthcare system, and reduce the burdens on tertiary facilities.Trial registrationThe study was registered in ClinicalTrials.gov on June 25th, 2023 (NCT 05930444).

To discover novel multifunctional agents for the treatment of Parkinson’s disease, a series of 2-(4-(benzyloxy)-5-(hydroxyl) phenyl) benzothiazole derivatives was designed, synthesized and evaluated. The results revealed that representative compound 3h possessed potent and selective MAO-B inhibitory activity (IC50 = 0.062 µM), and its inhibitory mode was competitive and reversible. Additionally, 3h also displayed excellent anti-oxidative effect (ORAC = 2.27 Trolox equivalent), significant metal chelating ability and appropriate BBB permeability. Moreover, 3h exhibited good neuroprotective effect and anti-neuroinflammtory ability. These results indicated that compound 3h was a promising candidate for further development against PD.

Abstract Background Atopic dermatitis (AD) is one of the most common immune and inflammatory skin disorders, leading to insufferable itching and skin abnormalities that seriously affect life quality of patients. There are still huge unmet needs for long-term and effective disease control, despite currently available therapies. Evidenced by some preclinical and clinical studies of AD treatment with stem cells, stem cell treatment could significantly and effectively ameliorate AD symptoms. Objectives To elucidate underlying mechanisms of how stem cells therapy alleviates AD-like symptoms. Methods An AD-like mouse model was constructed and treated with mesenchymal stem cells (MSCs) subcutaneously or subcutaneously combined with intravenously. The differentially expressed genes were sorted out from RNA sequencing results of dorsal skin and blood. Results Two injection routes of MSCs could alleviate AD-like symptoms and pathologic changes of the skin and immune organs. RNA sequencing of dorsal skin sections and blood provided gene expression signatures for amelioration of skin defects, inflammatory and immune modulation by MSCs, as well as common AD molecular markers for the skin and blood, which may benefit for clinical diagnosis. IL-1β and its signaling pathway were specifically found to be associated with the development of AD-like dermatitis lesions. MSC treatment effectively inhibited the JAK-STAT pathway and receptors of IL-4, IL-13, IL-17, and IgE. Conclusions MSC therapy could regulate abnormal immune and inflammatory status in AD. Mechanistic exploration will contribute to the development of personalized AD treatment based on MSCs.