The environmental effects of microplastics and microplastic derived dissolved organic matter in aquatic environments: A review.

Currently, microplastics (MPs) have ubiquitously distributed in different aquatic environments. Due to the unique physicochemical properties, MPs exhibit a variety of environmental effects with the coexisted contaminants. MPs can not only alter the migration of contaminants via vector effect, but also affect the transformation process and fate of contaminants via environmental persistent free radicals (EPFRs). The aging processes may enhance the interaction between MPs and co-existed contaminants. Thus, it is of great significance to review the aging mechanism of MPs and the influence of coexisted substances, the formation mechanism of EPFRs, environmental effects of MPs and relevant mechanism. Moreover, microplastic-derived dissolved organic matter (MP-DOM) may also influence the elemental biogeochemical cycles and the relevant environmental processes. However, the environmental implications of MP-DOM are rarely outlined. Finally, the knowledge gaps on environmental effects of MPs were proposed.

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

A meta-analysis of diabetes risk prediction models applied to prediabetes screening.

AIM: To provide a systematic overview of diabetes risk prediction models used for prediabetes screening to promote primary prevention of diabetes. METHODS: The Cochrane, PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for a comprehensive search period of 30 August 30, 2023, and studies involving diabetes prediction models for screening prediabetes risk were included in the search. The Quality Assessment Checklist for Diagnostic Studies (QUADAS-2) tool was used for risk of bias assessment and Stata and R software were used to pool model effect sizes. RESULTS: A total of 29 375 articles were screened, and finally 20 models from 24 studies were included in the systematic review. The most common predictors were age, body mass index, family history of diabetes, history of hypertension, and physical activity. Regarding the indicators of model prediction performance, discrimination and calibration were only reported in 79.2% and 4.2% of studies, respectively, resulting in significant heterogeneity in model prediction results, which may be related to differences between model predictor combinations and lack of important methodological information. CONCLUSIONS: Numerous models are used to predict diabetes, and as there is an association between prediabetes and diabetes, researchers have also used such models for screening the prediabetic population. Although it is a new clinical practice to explore, differences in glycaemic metabolic profiles, potential complications, and methods of intervention between the two populations cannot be ignored, and such differences have led to poor validity and accuracy of the models. Therefore, there is no recommended optimal model, and it is not recommended to use existing models for risk identification in alternative populations; future studies should focus on improving the clinical relevance and predictive performance of existing models.

A BIGRU-Based Stacked Attention Network for Biomedical Named Entity Recognition with Chinese EMRs.

Biomedical named entity recognition (BNER) is an effective method to structure the medical text data. It is an important basic task for building the medical application services such as the medical knowledge graphs and the intelligent auxiliary diagnosis systems. Existing medical named entity recognition methods generally leverage the word embedding model to construct text representation, and then integrate multiple semantic understanding models to enhance the semantic understanding ability of the model to achieve high-performance entity recognition. However, in the medical field, there are many professional terms that rarely appear in the general field, which cannot be represented well by the general domain word embedding model. Second, existing approaches typically only focus on the extraction of global semantic features, which generate a loss of local semantic features between characters. Moreover, as the word embedding dimension becomes much higher, the standard single-layer structure fails to fully and deeply extract the global semantic features. We put forward the BIGRU-based Stacked Attention Network (BSAN) model for biomedical named entity recognition. Firstly, we use the large-scale real-world medical electronic medical record (EMR) data to fine-tune BERT to build the proprietary embedding representations of the medical terms. Second, we use the Convolutional Neural Network model to extract semantic features. Finally, a stacked BIGRU is constructed using a multi-layer structure and a novel stacking method. It not only enables comprehensive and in-depth extraction of global semantic features, but also requires less time. Experimentally validated on the real-world datasets in Chinese EMRs, the proposed BSAN model achieves 90.9% performance on F1-values, which is stronger than the BNER performance of other state-of-the-art models.

Fractionation of neural reward processing into independent components by novel decoding principle.

How to retrieve latent neurobehavioural processes from complex neurobiological signals is an important yet unresolved challenge. Here, we develop a novel approach, orthogonal-Decoding multi-Cognitive Processes (DeCoP), to reveal underlying latent neurobehavioural processing and show that its performance is superior to traditional non-orthogonal decoding in terms of both false inference and robustness. Processing value and salience information are two fundamental but mutually confounded pathways of reward reinforcement essential for decision making. During reward/punishment anticipation, we applied DeCoP to decode brain-wide responses into spatially overlapping, yet functionally independent, evaluation and readiness processes, which are modulated differentially by meso­limbic vs nigro-striatal dopamine systems. Using DeCoP, we further demonstrated that most brain regions only encoded abstract information but not the exact input, except for dorsal anterior cingulate cortex and insula. Furthermore, we anticipate our novel analytical principle to be applied generally in decoding multiple latent neurobehavioral processes and thus advance both the design and hypothesis testing for cognitive tasks.

Measurement of the [Formula: see text]Ta([Formula: see text]) cross sections up to stellar s-process temperatures at the CSNS Back-n.

The neutron capture cross section of [Formula: see text]Ta is relevant to s-process of nuclear astrophysics, extraterrestrial samples analysis in planetary geology and new generation nuclear energy system design. The [Formula: see text]Ta([Formula: see text]) cross section had been measured between 1 eV and 800 keV at the back-streaming white neutron facility (Back-n) of China spallation neutron source(CSNS) using the time-of-flight (TOF) technique and [Formula: see text] liquid scintillator detectors. The experimental results are compared with the data of several evaluated libraries and previous experiments in the resolved and unresolved resonance region. Resonance parameters are extracted using the R-Matrix code SAMMY in the 1-700 eV region. The astrophysical Maxwell average cross section(MACS) from kT = 5 to 100 keV is calculated over a sufficiently wide range of neutron energies. For the characteristic thermal energy of an astrophysical site, at kT = 30keV the MACS value of [Formula: see text]Ta is 834 ± 75 mb, which shows an obvious discrepancy with the Karlsruhe Astrophysical Database of Nucleosynthesis in Stars (KADoNiS) recommended value 766 ± 15 mb. The new measurements strongly constrain the MACS of [Formula: see text]Ta([Formula: see text]) reaction in the stellar s-process temperatures.

Yeast ß-glucan modulates macrophages and improves antitumor NK-cell responses in cancer.

As the largest proportion of myeloid immune cells in tumors, macrophages play an important role in tumor growth and regression according to their different phenotypes, thus reprogramming macrophages has become a new research direction for cancer immunotherapy. Yeast-derived whole ß-glucan particles (WGPs) can induce M0 macrophages to differentiate into M1 macrophages and convert M2 macrophages and tumor-associated macrophages (TAMs) into M1 macrophages. In vitro, studies have confirmed that WGP-treated macrophages increase the activating receptors in natural killer cells (NK cells) and enhance the cytotoxicity of NK cells. The extracellular regulated protein kinases (ERK) signaling pathway is involved in WGP-mediated regulation of the macrophage phenotype. Further in vivo studies show that oral WGP can significantly delay tumor growth, which is related to the increased proportion of macrophages and NK cells, the macrophage phenotype reversal, and the enhancement of NK cell immune function. NK-cell depletion reduces the therapeutic efficacy of WGP in tumor-bearing mice. These findings revealed that in addition to T cells, NK cells also participate in the antitumor process of WGP. It was confirmed that WGP regulates the macrophage phenotype to regulate NK-cell function.

Performance of a prediabetes risk prediction model: A systematic review.

Backgrounds: The prediabetes population is large and easily overlooked because of the lack of obvious symptoms, which can progress to diabetes. Early screening and targeted interventions can substantially reduce the rate of conversion of prediabetes to diabetes. Therefore, this study systematically reviewed prediabetes risk prediction models, performed a summary and quality evaluation, and aimed to recommend the optimal model. Methods: We systematically searched five databases (Cochrane, PubMed, Embase, Web Of Science, and CNKI) for published literature related to prediabetes risk prediction models and excluded preprints, duplicate publications, reviews, editorials, and other studies, with a search time frame of March 01, 2023. Data were categorized and summarized using a standardized data extraction form that extracted data including author; publication date; study design; country; demographic characteristics; assessment tool name; sample size; study type; and model-related indicators. The PROBAST tool was used to assess the risk of bias profile of included studies. Findings: 14 studies with a total of 15 models were eventually included in the systematic review. We found that the most common predictors of models were age, family history of diabetes, gender, history of hypertension, and BMI. Most of the studies (83.3%) had a high risk of bias, mainly related to under-reporting of outcome information and poor methodological design during the development and validation of models. Due to the low quality of included studies, the evidence for predictive validity of the available models is unclear. Interpretation: We should pay attention to the early screening of prediabetes patients and give timely pharmacological and lifestyle interventions. The predictive performance of the existing model is not satisfactory, and the model building process can be standardized and external validation can be added to improve the accuracy of the model in the future.

"Shutter" Effects Enhance Protein Diffusion in Dynamic and Rigid Molecular Networks.

Hydrogels have been widely applied to understand the fundamental functions and mechanism of a natural extracellular matrix (ECM). However, revealing the high permeability of ECM through synthetic hydrogels is still challenged by constructing analogue networks with rigid and dynamic properties. Here, in this study, taking advantage of the rigidity and dynamic binding of DNA building blocks, we have designed a model hydrogel system with structural similarity to ECM, leading to enhanced diffusion for proteins compared with a synthetic polyacrylamide (PAAm) hydrogel. The molecular diffusion behaviors in such a rigid and dynamic network have been investigated both in experiments and simulations, and the dependence of diffusion coefficients with respect to molecular size exhibits a unique transition from a power law to an exponential function. A "shutter" model based on the rigid and dynamic molecular network has been proposed, which has successfully revealed how the rigidity and dynamic bond exchange determine the diffusion mechanism, potentially providing a novel perspective to understand the possible mechanism of enhanced diffusion behaviors in ECM.

Knowledge Guided Feature Aggregation for the Prediction of Chronic Obstructive Pulmonary Disease With Chinese EMRs.

The automatic disease diagnosis utilizing clinical data has been suffering from the issues of feature sparse and high probability of missing values. Since the graph neural network is a effective tool to model the structural information and infer the missing values, it is becoming the dominant method for the predictive model construction from electronic medical records. Existing graph neural network based solutions usually adopt the medical concepts (e.g., symptoms) the feature representation of clinical data without considering their underlying semantic relations. The limited discriminative capability of the medical concept cannot provide sufficient indicative information about the disease. This paper proposes a knowledge- guided graph attention network for the disease prediction. Beside extracting the attribute-value structure as a large-size medical concept, the mutual information between multiple medical concepts mentioned in the electronic medical records are taken into account in the graph construction. Meanwhile, the defined diseases and their associations with the medical concepts in the medical knowledge graph are incorporated into the graph, which provides the potentials to enhance the indicative impacts of the medical concepts directly related to a target disease. Then, the spatial and attention based graph encoders are employed to aggregate information from directly neighbor nodes to generate node embeddings as the compact features to be used for disease diagnosis. The approach itself is a general one that can utilized to build the predictive model using Chinese EMRs for different diseases. The empirical experiments for its performance evaluation are conducted on the real-world COPD EMR dataset. The comparison study results show that the proposed model outperforms baseline methods, which illustrates the effectiveness of our proposed model.

Inhibition of TFEB promotes tumor-educated dendritic cells activation to enhance antitumor immune responses.

Tumors can induce the generation and accumulation of immunosuppressive cells in -the tumor microenvironment (TME). Among them, tumor-educated dendritic cells (TEDCs) involved in tolerance induction contribute greatly to the progression of tumors. However, the mechanisms governing the immunosuppressive function of dendritic cells in the TME are unclear. In this study, we found that the expression of transcription factor EB (TFEB) was significantly increased in TEDCs induced by cancer cell supernatant. TFEB knockdown significantly promoted the differentiation and maturation of TEDCs, with upregulated expression of CD11c and costimulatory molecules (CD86 and MHC-II) but reduced expression of the inhibitory molecule PD-L1, and enhanced their ability to induce Th1 proliferation and differentiation. Moreover, TEDCs with TFEB knockdown significantly reduced tumor growth with increased infiltration of CD11c+MHC-II+ dendritic cells and effector T cells in tumor masses, thus leading to a delay in tumor progression. These findings demonstrate a critical role of TFEB in regulating the immunosuppression of TEDCs, with potential implications as an antitumor immune therapeutic approach.

Oral administration of a whole glucan particle (WGP)-based therapeutic cancer vaccine targeting macrophages inhibits tumor growth.

Although therapeutic cancer vaccines have been gaining substantial ground, the development of cancer vaccines is impeded because of the undegradability of delivery systems, ineffective delivery of tumor antigens and weak immunogenicity of adjuvants. Here, we made use of a whole glucan particle (WGP) to encapsulate ovalbumin (OVA), thereby formulating a novel cancer vaccine. Results from in vitro experiments showed that WGP-OVA not only induced the activation of bone marrow-derived macrophages (BMDMs) including driving M0 BMDM polarization to the M1 phenotype, upregulating the costimulatory molecules and inducing the generation of cytokines, but also facilitated antigen presentation. After oral administration of the WGP-OVA formulation to mice with OVA-expressing tumors, these particles can increase tumor-infiltrating OVA-specific CD8+ CTLs and repolarize tumor-associated macrophages (TAMs) toward M1-like phenotype, which led to delayed tumor progression. These findings revealed that WGP could serve as both an antigen delivery system and an adjuvant system for promising cancer vaccines.

Highly sensitive X-ray detector based on a ß-Ga2O3:Fe single crystal.

ß-Ga2O3 semiconductor crystal is of wide band gap and high radiation resistance, which shows great potential for applications such as medical imaging, radiation detections, and nuclear physical experiments. However, developing ß-Ga2O3-based X-ray radiation detectors with high sensitivity, fast response speed, and excellent stability remains a challenge. Here we demonstrate a high-performance X-ray detector based on a Fe doped ß-Ga2O3 (ß-Ga2O3:Fe) crystal grown by the float-zone growth method, which consists of two vertical Ti/Au electrodes and a ß-Ga2O3:Fe crystal with high resistivity. The resistivity of the ß-Ga2O3:Fe crystal exceeds 1012 Ω cm owed to the compensation of the Fe ions and the free electrons. The detector shows short response time (0.2 s), high sensitivity (75.3 µC Gyair -1 cm-2), and high signal-to-noise ratio (100), indicating great potential for X-ray radiation detection.

Light output enhancement of scintillators by using mixed-scale microstructures.

Scintillators play an important role in the field of nuclear radiation detection. However, the light output of the scintillators is often limited by total internal reflection due to the high refractive indices of the scintillators. Furthermore, the light emission from scintillators typically has an approximately Lambertian profile, which is detrimental to the collection of the light. In this paper, we demonstrate a promising method to achieve enhancement of the light output from scintillators through use of mixed-scale microstructures that are composed of a photonic crystal slab and a microlens array. Simulations and experimental results both show significant improvements in the scintillator light output. The X-ray imaging characteristics of scintillators are improved by the application of the mixed-scale microstructures. The results presented here suggest that the application of the proposed mixed-scale microstructures to scintillators will be beneficial in the nuclear radiation detection field.

Ginkgo biloba extracts inhibit post-ischemic LTP through attenuating EPSCs in rat hippocampus.

Ginkgo biloba extract 761 (EGb761), a standardized extract from the Ginkgo biloba leaf, is purported to inhibit NMDA receptor-mediated neuronal excitotoxicity and protect neurons form ischemic injury. However, the specific signal pathway involved in the effects of EGb761 on synaptic plasticity is still in dispute. In this article, effects of EGb761 and its monomer component ginkgolide A (GA), ginkgolide B (GB), ginkgolide C (GC) and quercetin on rat hippocampal synaptic plasticity were studied. The evoked Excitatory postsynaptic currents (EPSCs) and miniature EPSCs were recorded on hippocampal slices from SD rats (14-21 days of age) by whole-cell patch-clamp recording and long-term potentiation (LTP) was induced by theta-burst stimulation. Acutely applied EGb761 inhibited the LTP, but bilaterally affect the evoked EPSCs. The evoked EPSCs were increased by incubation of lower concentration of EGb761, then the evoked EPSCs were decreased by incubation of higher concentration of EGb761. EGb761 monomer component GA, GB and GC could also inhibit the TBS-induced LTP and EPSC amplitude but not paired-pulse ratio (PPR). But quercetin, another monomer component of EGb761, led to increase in EPSC amplitude and decrease in PPR. Simultaneously, EGb761 and its monomer component ginkgolides inhibited the post-ischemic LTP (i-LTP) by inhibiting the EPSCs and the AMPA receptor subunit GluA1 expression on postsynaptic membrane. The results indicated that high concentration of EGb761 might inhibit LTP and i-LTP through inhibition effects of GA, GB and GC on AMPA receptors.

Highly Permeable DNA Supramolecular Hydrogel Promotes Neurogenesis and Functional Recovery after Completely Transected Spinal Cord Injury.

Regeneration after severe spinal cord injury cannot occur naturally in mammals. Transplanting stem cells to the injury site is a highly promising method, but it faces many challenges because it relies heavily on the microenvironment provided by both the lesion site and delivery material. Although mechanical properties, biocompatibility, and biodegradability of delivery materials have been extensively explored, their permeability has rarely been recognized. Here, a DNA hydrogel is designed with extremely high permeability to repair a 2 mm spinal cord gap in Sprague-Dawley rats. The rats recover basic hindlimb function with detectable motor-evoked potentials, and a renascent neural network is formed via the proliferation and differentiation of both implanted and endogenous stem cells. The signal at the lesion area is conveyed by, on average, 15 newly formed synapses. This hydrogel system offers great potential in clinical trials. Further, it should be easily adaptable to other tissue regeneration applications.

Improved light output from thick ß-Ga2O3 scintillation crystals via graded-refractive-index photonic crystals.

ß-Ga2O3 is a promising candidate as a fast scintillation crystal for radiation detection in fast X-ray imaging and high-energy physics experiments. However, total internal reflection severely limits its light output. Conventional photonic crystals can improve the light output, but such improvement decreases dramatically with increased scintillator thickness due to the strong backward reflection by the photonic crystals. Here, graded-refractive-index photonic crystals composed of nanocone arrays are designed and fabricated on the surfaces of ß-Ga2O3 crystals with various thicknesses. Compared to the conventional photonic crystals, there is still an obvious light output improvement by using the graded-refractive-index photonic crystals when the thickness of the crystals is increased by three times. The effect of thickness on the improved light output is investigated with numerical simulations and experiments. Overall, the graded-refractive-index photonic crystals are beneficial to the improvement of light output from thick scintillators.

Hollow nanosphere arrays with a high-index contrast for enhanced scintillating light output from ß-Ga2O3 crystals.

ß-Ga2O3 is a new type of fast scintillator with potential applications in medical imaging and nuclear radiation detection with high count-rate situations. Because of the severe total internal reflection with its high refractive index, the light extraction efficiency of ß-Ga2O3 crystals is rather low, which would limit the performance of detection systems. In this paper, we use hollow nanosphere arrays with a high-index contrast to enhance the light extraction efficiency of ß-Ga2O3 crystals. We can increase the transmission diffraction efficiency and reduce the reflection diffraction efficiency through controlling the refractive index and the thickness of the shell of the hollow nanospheres, which can lead to a significant increase in the light extraction efficiency. The relationships between the light extraction efficiency and the refractive index and thickness of the shell of the hollow nanospheres are investigated by both numerical simulations and experiments. It is found that when the refractive index of the shell of the hollow nanospheres is higher than that of ß-Ga2O3, the light extraction efficiency is mainly determined by the diffraction efficiency of light transmitted from the surface with the hollow nanosphere arrays. When the refractive index of the shell is less than that of ß-Ga2O3, the light extraction efficiency is determined by the ratio of the diffraction efficiency of the light transmitted from the surface with the hollow nanosphere arrays to the diffraction efficiency of the light that can escape from the lateral surface.

Nuclear resonance fluorescence drug inspection.

There is an increasing challenge to prevent illicit drug smuggling across borders and seaports. However, the existing techniques in-and-of-themselves are not sufficient to identify the illicit drugs rapidly and accurately. In the present study, combining nuclear resonance fluorescence (NRF) spectroscopy and the element (or isotope) ratio approach, we present a novel inspection method that can simultaneously reveal the elemental (or isotopic) composition of the illicit drugs, such as widely abused methamphetamine, cocaine, heroin, ketamine and morphine. In the NRF spectroscopy, the nuclei are excited by the induced photon beam, and measurement of the characteristic energies of the emitted [Formula: see text] rays from the distinct energy levels in the excited nuclei provides "fingerprints" of the interested elements in the illicit drugs. The element ratio approach is further used to identify drug elemental composition in principle. Monte Carlo simulations show that four NRF peaks from the nuclei [Formula: see text]C, [Formula: see text]N and [Formula: see text]O can be detected with high significance of 7-24[Formula: see text] using an induced photon beam flux of [Formula: see text]. The ratio of [Formula: see text]/[Formula: see text] and/or [Formula: see text]/[Formula: see text] for illicit drugs inspected are then extracted using the element ratio approach. It is found that the present results of simulations are in good agreement with the theoretical calculations. The feasibility to detect the illicit drugs, inside the 15-mm-thick iron shielding, or surrounded by thin benign materials, is also discussed. It is indicated that, using the state-of-the-art [Formula: see text]-ray source of high intensity and energy-tunability, the proposed method has a great potential for identifying drugs and explosives in a realistic measurement time.

High-Performance X-ray Detector Based on Single-Crystal ß-Ga2O3:Mg.

X-ray detection plays an important role in medical imaging, scientific research, and security inspection. Recently, the ß-Ga2O3 single-crystal-based X-ray detector has attracted extensive attention due to its excellent intrinsic properties such as good absorption for X-ray photons, a high breakdown electric field, high stability, and low cost. However, developing a high-performance ß-Ga2O3-based X-ray detector remains a challenge because of the large dark current and the high oxygen vacancy concentration in the crystals. In this paper, we report a high-performance Mg-doped ß-Ga2O3 single-crystal-based X-ray detector with a sandwich structure. The reduced dark current enables the detector to have a high sensitivity of 338.9 µC Gy-1 cm-2 under 50 keV X-ray irradiation with a dose rate of 69.5 µGy/s. The sensitivity is 16-fold higher than that of the commercial amorphous selenium detector. Furthermore, the reduced oxygen vacancy concentration can improve the response speed (<0.2 s) of the detector. The present studies provide a promising method to obtain the high performances for the X-ray detector based on ß-Ga2O3 single crystals.