Gut microbiome features and metabolites in non-alcoholic fatty liver disease among community-dwelling middle-aged and older adults.

BACKGROUND: The specific microbiota and associated metabolites linked to non-alcoholic fatty liver disease (NAFLD) are still controversial. Thus, we aimed to understand how the core gut microbiota and metabolites impact NAFLD. METHODS: The data for the discovery cohort were collected from the Guangzhou Nutrition and Health Study (GNHS) follow-up conducted between 2014 and 2018. We collected 272 metadata points from 1546 individuals. The metadata were input into four interpretable machine learning models to identify important gut microbiota associated with NAFLD. These models were subsequently applied to two validation cohorts [the internal validation cohort (n = 377), and the prospective validation cohort (n = 749)] to assess generalizability. We constructed an individual microbiome risk score (MRS) based on the identified gut microbiota and conducted animal faecal microbiome transplantation experiment using faecal samples from individuals with different levels of MRS to determine the relationship between MRS and NAFLD. Additionally, we conducted targeted metabolomic sequencing of faecal samples to analyse potential metabolites. RESULTS: Among the four machine learning models used, the lightGBM algorithm achieved the best performance. A total of 12 taxa-related features of the microbiota were selected by the lightGBM algorithm and further used to calculate the MRS. Increased MRS was positively associated with the presence of NAFLD, with odds ratio (OR) of 1.86 (1.72, 2.02) per 1-unit increase in MRS. An elevated abundance of the faecal microbiota (f__veillonellaceae) was associated with increased NAFLD risk, whereas f__rikenellaceae, f__barnesiellaceae, and s__adolescentis were associated with a decreased presence of NAFLD. Higher levels of specific gut microbiota-derived metabolites of bile acids (taurocholic acid) might be positively associated with both a higher MRS and NAFLD risk. FMT in mice further confirmed a causal association between a higher MRS and the development of NAFLD. CONCLUSIONS: We confirmed that an alteration in the composition of the core gut microbiota might be biologically relevant to NAFLD development. Our work demonstrated the role of the microbiota in the development of NAFLD.

Prediction differences and implications of acute kidney injury with and without urine output criteria in adult critically ill patients.

BACKGROUND: Due to the convenience of serum creatinine (SCr) monitoring and the relative complexity of urine output (UO) monitoring, most studies have predicted acute kidney injury (AKI) only based on SCr criteria. This study aimed to compare the differences between SCr alone and combined UO criteria in predicting AKI. METHODS: We applied machine learning methods to evaluate the performance of 13 prediction models composed of different feature categories on 16 risk assessment tasks (half used only SCr criteria, half used both SCr and UO criteria). The area under receiver operator characteristic curve (AUROC), the area under precision recall curve (AUPRC) and calibration were used to assess the prediction performance. RESULTS: In the first week after ICU admission, the prevalence of any AKI was 29% under SCr criteria alone and increased to 60% when the UO criteria was combined. Adding UO to SCr criteria can significantly identify more AKI patients. The predictive importance of feature types with and without UO was different. Using only laboratory data maintained similar predictive performance to the full feature model under only SCr criteria [e.g. for AKI within the 48-h time window after 1 day of ICU admission, AUROC (95% confidence interval) 0.83 (0.82, 0.84) vs 0.84 (0.83, 0.85)], but it was not sufficient when the UO was added [corresponding AUROC (95% confidence interval) 0.75 (0.74, 0.76) vs 0.84 (0.83, 0.85)]. CONCLUSIONS: This study found that SCr and UO measures should not be regarded as equivalent criteria for AKI staging, and emphasizes the importance and necessity of UO criteria in AKI risk assessment.

hUC-MSCs exosomal miR-451 alleviated acute lung injury by modulating macrophage M2 polarization via regulating MIF-PI3K-AKT signaling pathway.

In the previous study, we have proved that exosomal miR-451 from human umbilical cord mesenchymal stem cells (hUC-MSCs) attenuated burn-induced acute lung injury (ALI). However, the mechanism of exosomal miR-451 in ALI remains unclear. Therefore, this study aimed to study the molecular mechanism of hUC-MSCs-derived exosomal miR-451 on ALI by regulating macrophage polarization. Exosomes were isolated and identified by transmission electron microscope (TEM) and nanoparticle tracking analysis (NTA). The expression of miR-451, macrophage migration inhibitory factor (MIF) and PI3K/AKT signaling pathway proteins were detected by qRT-PCR and western blot. Flow cytometry was used to detect the CD80 and CD206 positive cells. Severe burn rat model was established and HE was used to detect the inflammatory cell infiltration and inflammatory injury. Dual luciferase reporter system was used to detect the regulation of miR-451 to MIF. The contents of cytokines were detected by ELISA. The results showed that hUC-MSCs exosomes promoted macrophage M1 to M2 polarization. Furthermore, hUC-MSCs-derived exosomal miR-451 alleviated ALI development and promoted macrophage M1 to M2 polarization. Moreover, MIF was a direct target of miR-451. Downregulation of MIF regulated by miR-451 alleviated ALI development promoted macrophage M1 to M2 polarization. In addition, we found that MIF and hUC-MSCs-derived exosomal miR-451 participated in ALI by regulating PI3K/AKT signaling pathway. In conclusion, we indicated that hUC-MSCs-derived exosomal miR-451 alleviated ALI by modulating macrophage M2 polarization via regulating MIF-PI3K-AKT signaling pathway, which provided great scientific significance and clinical application value for the treatment of burn-induced ALI.

Analysis of the therapeutic effect of artificial leather embedding combined with fascial sleeve flap transplantation on chronic wounds of lower limbs with bone and plate exposure.

BACKGROUND: After severe trauma of lower limbs, bone, tendon or plate graft exposure is common. The traditional repair method is to use a variety of skin flap transplantation to cover the exposed part, but the wound often can not heal after operation, or the wound is cracked, ulcer, sinus, bone and steel plate are exposed again after wound healing. The reason for this result is that when the flap is covered, the space around the bone plate is not well closed, forming a dead cavity, blood and exudate accumulation, hematoma formation or infection, and finally the wound ruptures again. In addition, due to the swelling and contracture of the flap after operation, the suture tension between the flap and the receiving area becomes larger, the skin becomes thinner and broken, and then the wound is formed. In order to solve the above problems, we carried out the study of artificial true skin embedding combined with fascial sleeve flap transplantation in the treatment of chronic bone plate exposed wounds of lower limbs. METHODS: In this paper, 11 cases of chronic wounds with bone exposure and skin necrosis after steel plate implantation were selected. First stage is the wound bed preparation including primary wound expansion, removal of necrotic tissue and incision of sinus wall, removal of deep necrotic bone and fibrotic scarred skin on the outer wall of steel plate to normal tissue on the outer edge of the wound, removal of precipitated peptone and purulent fur in the hole, periphery and bone space of the steel plate, and removal of tendon tissue with basal necrosis and disintegration of the wound. After vacuum sealing drainage (VSD) 1-2 weeks, the peritraumatic basal granulation tissue grew well and there was no necrotic tissue in the wound. In the second stage, the exposed bone was covered with artificial dermis, the steel plate hole or the periphery and the basal space were filled, and the exposed steel plate was completely embedded, and then the fascia sleeve flap was transplanted to cover the wound. The sural neurovascular flap was performed in nine cases and the lateral superior malleolar artery perforator flap in two case. RESULTS: The flap survived well in all 11 cases. During the follow-up of 6 months to the removal of the plate, there was no case of rupture, exposure and sinus formation. CONCLUSIONS: Artificial dermal covering combined with fascial sleeve flap transplantation can effectively avoid wound dehiscence or sinus formation caused by foreign body retention, infection and flap contracture. It has good effect in repairing chronic wounds with bone plate exposure after severe trauma of lower limbs.

A hybrid adaptive approach for instance transfer learning with dynamic and imbalanced data.

Machine learning has demonstrated success in clinical risk prediction modeling with complex electronic health record data. However, the evolving nature of clinical practices can dynamically change the underlying data distribution over time, leading to model performance drift. Adopting an outdated model is potentially risky and may result in unintentional losses. In this paper, we propose a novel Hybrid Adaptive Boosting approach (HA-Boost) for transfer learning. HA-Boost is characterized by the domain similarity-based and class imbalance-based adaptation mechanisms, which simultaneously address two critical limitations of the classical TrAdaBoost algorithm. We validated HA-Boost in predicting hospital-acquired acute kidney injury using real-world longitudinal electronic health records data. The experiment results demonstrate that HA-Boost stably outperforms the competing baselines in terms of both AUROC and AUPRC across a 7-year time span. This study has confirmed the effectiveness of transfer learning as a superior model updating approach in dynamic environment.

Nearly-Unity Quantum Yield and 12-Hour Afterglow from a Transparent Perovskite of Cs2 NaScCl6 :Tb.

Unlike afterglow phosphors, transparent crystals free of scattering issues hold great potential in applications such as volumetric displays and three-dimensional encryption. Here, a double-perovskite host of ca. 85 % transparency, a Cs2 NaScCl6 single crystal, was grown in aqueous solution. Through heavy doping of Tb ions, the crystal exhibited a high photoluminescence quantum yield (PL QY) up to 98.2 %. Both steady-state and transient PL spectroscopic investigation revealed a cross-relaxation efficiency (ηCR ≈99 %) between Tb ions, greatly mitigated the electron saturation on the 5 D3 level and encouraged the radiative route against quenching. The doped crystal displayed an intense afterglow of up to 12 h after ceasing the X-ray excitation, allowing it to be used in a radiation-storage battery which has a linear scaling capacity with loading weight. Our work opens possibilities in photo-stimulated volumetric display and three-dimensional information storage.

Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes.

BACKGROUND: Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and comprehensive transcriptome data resource can be used to build a better DDI prediction model is still unclear. Therefore, we developed and validated a novel deep learning model for predicting DDI using 89,970 known DDIs extracted from the DrugBank database (version 5.1.4). RESULTS: The proposed model consists of a graph convolutional autoencoder network (GCAN) for embedding drug-induced transcriptome data from the L1000 database of the LINCS project; and a long short-term memory (LSTM) for DDI prediction. Comparative evaluation of various machine learning methods demonstrated the superior performance of our proposed model for DDI prediction. Many of our predicted DDIs were revealed in the latest DrugBank database (version 5.1.7). In the case study, we predicted drugs interacting with sulfonylureas to cause hypoglycemia and drugs interacting with metformin to cause lactic acidosis, and showed both to induce effects on the proteins involved in the metabolic mechanism in vivo. CONCLUSIONS: The proposed deep learning model can accelerate the discovery of new DDIs. It can support future clinical research for safer and more effective drug co-prescription.

Solution-Grown Chloride Perovskite Crystal of Red Afterglow.

We report the growth of a halide-based double perovskite, Cs2 Nax Ag1-x InCl6 :y%Mn, via a facile hydrothermal reaction at 180 °C. Through a co-doping strategy of both Na+ and Mn2+ , the as-prepared crystals exhibited a red afterglow featuring a high color purity (ca. 100 %) and a long duration time (>5400 s), three orders of magnitude longer than those solution-processed organic afterglow crystals. The energy transfer (ET) process between self-trapped excitons (STE) and activators was investigated through time-resolved spectroscopy, which suggested an ET efficiency up to 41 %. Importantly, the nominal concentration of dopants, especially in the case of Na+ , was found a useful tool to control both energy level and number distribution of traps. Cryogenic afterglow measurements suggested that the afterglow phenomenon was likely governed by thermal-activated exciton diffusion and electron tunneling process.

Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study.

BACKGROUND: Likelihood of developing acute kidney injury (AKI) increases with age. We aimed to explore whether the predictability of AKI varies between age groups and assess the volatility of risk factors using electronic medical records (EMR). METHODS: We constructed a retrospective cohort of adult patients from all inpatient units of a tertiary care academic hospital and stratified it into four age groups: 18-35, 36-55, 56-65, and > 65. Potential risk factors collected from EMR for the study cohort included demographics, vital signs, medications, laboratory values, past medical diagnoses, and admission diagnoses. AKI was defined based on the Kidney Disease Improving Global Outcomes (KDIGO) serum creatinine criteria. We analyzed relative importance of the risk factors in predicting AKI using Gradient Boosting Machine algorithm and explored the predictability of AKI across age groups using multiple machine learning models. RESULTS: In our cohort, older patients showed a significantly higher incidence of AKI than younger adults: 18-35 (7.29%), 36-55 (8.82%), 56-65 (10.53%), and > 65 (10.55%) (p < 0.001). However, the predictability of AKI decreased with age, where the best cross-validated area under the receiver operating characteristic curve (AUROC) achieved for age groups 18-35, 36-55, 56-65, and > 65 were 0.784 (95% CI, 0.769-0.800), 0.766 (95% CI, 0.754-0.777), 0.754 (95% CI, 0.741-0.768), and 0.725 (95% CI, 0.709-0.737), respectively. We also observed that the relative risk of AKI predictors fluctuated between age groups. CONCLUSIONS: As complexity of the cases increases with age, it is more difficult to quantify AKI risk for older adults in inpatient population.

Effect of Netrin-1 Anti-Inflammatory Factor on Acute Lung Injury in Sepsis Rats.

BACKGROUND Acute lung injury (ALI) often occurs early and seriously in the progress of sepsis. Netrin-1 is demonstrated to be an effective anti-inflammatory agent. However, whether netrin-1 can relieve sepsis-induced ALI remains unknown. MATERIAL AND METHODS The sepsis rat model was built with the method of cecal ligation and puncture (CLP). The lung tissue changes were represented as the results of hematoxylin-eosin (HE) staining, wet-to-dry (W/D) ratio, Western blot analysis, and immunohistochemistry. An in vitro lung injury model was simulated with LPS-induced BEAS-2B cells. The cell transfection effects were evaluated by Western blot analysis and RT-qPCR analysis. TNF-alpha, IL-1ß, and IL-6 levels were detected by Western blot analysis in LPS-induced BEAS-2B cells. RESULTS Obvious inflammation caused by sepsis appeared in lung tissues with the increase of the W/D ratio and expression of inflammatory cytokines. Netrin-1 and its receptor UNC5B were reduced in sepsis. However, upregulation of netrin-1 alleviated the levels of inflammation and increased the UNC5B levels in BEAS-2B cells. CONCLUSIONS Netrin-1 protects against ALI in sepsis rats through its anti-inflammation effect and may provide a novel treatment to prevent lung injury caused by sepsis.

Causal risk factor discovery for severe acute kidney injury using electronic health records.

BACKGROUND: Acute kidney injury (AKI), characterized by abrupt deterioration of renal function, is a common clinical event among hospitalized patients and it is associated with high morbidity and mortality. AKI is defined in three stages with stage-3 being the most severe phase which is irreversible. It is important to effectively discover the true risk factors in order to identify high-risk AKI patients and allow better targeting of tailored interventions. However, Stage-3 AKI patients are very rare (only 0.2% of AKI patients) with a large scale of features available in EHR (1917 potential risk features), yielding a scenario unfeasible for any correlation-based feature selection or modeling method. This study aims to discover the key factors and improve the detection of Stage-3 AKI. METHODS: A causal discovery method (McDSL) is adopted for causal discovery to infer true causal relationship between information buried in EHR (such as medication, diagnosis, laboratory tests, comorbidities and etc.) and Stage-3 AKI risk. The research approach comprised two major phases: data collection, and causal discovery. The first phase is propose to collect the data from HER (includes 358 encounters and 891 risk factors). Finally, McDSL is employed to discover the causal risk factors of Stage-3 AKI, and five well-known machine learning models are built for predicting Stage-3 AKI with 10-fold cross-validation (predictive accuracy were measured by AUC, precision, recall and F-score). RESULTS: McDSL is useful for further research of EHR. It is able to discover four causal features, all selected features are medications that are modifiable. The latest research of machine learning is employed to compare the performance of prediction, and the experimental result has verified the selected features are pivotal. CONCLUSIONS: The features selected by McDSL, which enable us to achieve significant dimension reduction without sacrificing prediction accuracy, suggesting potential clinical use such as helping physicians develop better prevention and treatment strategies.

Transparent and Planar Cs3Cu2Cl5 Crystals for Micrometer-Resolution X-ray Imaging Screen.

Copper-based (I) halide perovskites have emerged as a promising candidate for scintillation screens in X-ray inspection and imaging areas due to their solution processability and high light yield. Here, a centimeter-sized Cs3Cu2Cl5 single crystal was grown by a slow-cooling method. The planar orientation was controlled in a space-confined chamber, generating a planar crystal which is readily used for a scintillation screen without any further shaping. The crystal exhibited a unity photoluminescence quantum yield and superior scintillation performance. The Cs3Cu2Cl5 single crystal exhibited a high light yield up to 95,000 photons/MeV, which enabled an X-ray detector of a detection limit down to 2.7 µGyair/s. The homemade imager demonstrated a spatial resolution of 105 lp/mm, representing an unprecedented micrometer resolution in laboratory. Importantly, the stability of Cs3Cu2Cl5 was significantly improved by a new surface passivation procedure, whereby the passivated crystal reserved its phase after 6 months' storage in a vial. This work introduced a new solution-based synthetic method for two-dimensional scintillating crystals, opening many avenues to high-performance X-ray imaging applications.

Pulsed laser induced plasma and thermal effects on molybdenum carbide for dry reforming of methane.

Dry reforming of methane (DRM) is a highly endothermic process, with its development hindered by the harsh thermocatalytic conditions required. We propose an innovative DRM approach utilizing a 16 W pulsed laser in combination with a cost-effective Mo2C catalyst, enabling DRM under milder conditions. The pulsed laser serves a dual function by inducing localized high temperatures and generating *CH plasma on the Mo2C surface. This activates CH4 and CO2, significantly accelerating the DRM reaction. Notably, the laser directly generates *CH plasma from CH4 through thermionic emission and cascade ionization, bypassing the traditional step-by-step dehydrogenation process and eliminating the rate-limiting step of methane cracking. This method maintains a carbon-oxygen balanced environment, thus preventing the deactivation of the Mo2C catalyst due to CO2 oxidation. The laser-catalytic DRM achieves high yields of H2 (14300.8 mmol h-1 g-1) and CO (14949.9 mmol h-1 g-1) with satisfactory energy efficiency (0.98 mmol kJ-1), providing a promising alternative for high-energy-consuming catalytic systems.

Mixed traffic flow of human-driven vehicles and connected autonomous vehicles: String stability and fundamental diagram.

The introduction of connected autonomous vehicles (CAVs) gives rise to mixed traffic flow on the roadway, and the coexistence of human-driven vehicles (HVs) and CAVs may last for several decades. CAVs are expected to improve the efficiency of mixed traffic flow. In this paper, the car-following behavior of HVs is modeled by the intelligent driver model (IDM) based on actual trajectory data. The cooperative adaptive cruise control (CACC) model from the PATH laboratory is adopted for the car-following model of CAVs. The string stability of mixed traffic flow is analyzed for different market penetration rates of CAVs, showing that CAVs can effectively prevent stop-and-go waves from forming and propagating. In addition, the fundamental diagram is obtained from the equilibrium state, and the flow-density chart indicates that CAVs can improve the capacity of mixed traffic flow. Furthermore, the periodic boundary condition is designed for numerical simulation according to the infinite length platoon assumption in the analytical approach. The simulation results are consistent with the analytical solutions, suggesting the validity of the string stability and fundamental diagram analysis of mixed traffic flow.

Long Non-Coding RNA ZSCAN16-AS1 Promotes the Malignant Progression of Melanoma Through Regulating the miR-503-5p/ARL2 Axis.

Background: LncRNA zinc finger and SCAN domain containing 16 antisense RNA 1 (ZSCAN16-AS1), a newly identified lncRNA, has been proven to accelerate hepatocellular carcinoma progression. However, the function and molecular mechanism of ZSCAN16-AS1 in melanoma are still unknown. Methods: The level of ZSCAN16-AS1 in melanoma tissues was detected and reported in The Cancer Genome Atlas (TCGA) and GEO#GSE15605. CCK-8, Transwell and flow cytometry assays were used to explore the role of ZSCAN16-AS1 in melanoma cells. Luciferase reporter assays and RNA pull-down assays were used to verify the molecular mechanism of ZSCAN16-AS1. Results: Here, we found that ZSCAN16-AS1 expression was increased in melanoma. We confirmed that ZSCAN16-AS1 promotes the growth and metastasis of melanoma. ZSCAN16-AS1 exerts its pro-tumour role through sponging of miR-503-5p to liberate ADP-ribosylation factor-like protein 2 (ARL2) mRNA transcripts. Conclusion: These results demonstrated the role and molecular mechanism of ZSCAN16-AS1 in the occurrence and development of melanoma. Therefore, ZSCAN16-AS1 may be used as a specific biomarker in the diagnosis and treatment of melanoma patients.

Afterglow Phosphor Goes Transparent.

Recently, transparent afterglow phosphors have attracted increasing interest due to the mitigated self-absorption and the ensuing improved light output, which have inspired many advanced applications, including volumetric display and three-dimensional optical encryption. To date, the most successful afterglow phosphors remain those traditional oxide, nitride, or sulfide powders which are not transparent due to a severe scattering effect. By reduction of the number of interfaces and engineering the refractive index, the scattering effect could be circumvented effectively. To this end, four material systems, including transparent afterglow single crystals, transparent phosphorescent organics, transparent afterglow glass, and luminescent nanocomposites, were reviewed in this Perspective. We started with the discussion of the nontransparency origin. Through a careful inspection of Rayleigh scattering theory, a general solution involving both refractive index and particle size was proposed to reduce the scattering effect. Many representative works on transparent afterglow phosphors were systematically reviewed, where the typical synthesis methods and the advantages and disadvantages of each system were critically presented. In the last part, bottlenecks, prospects, and future development directions based on transparent afterglow phosphors are proposed.

Protein-ligand binding affinity prediction with edge awareness and supervised attention.

Accurate prediction of protein-ligand binding affinity is crucial in structure-based drug design but remains some challenges even with recent advances in deep learning: (1) Existing methods neglect the edge information in protein and ligand structure data; (2) current attention mechanisms struggle to capture true binding interactions in the small dataset. Herein, we proposed SEGSA_DTA, a SuperEdge Graph convolution-based and Supervised Attention-based Drug-Target Affinity prediction method, where the super edge graph convolution can comprehensively utilize node and edge information and the multi-supervised attention module can efficiently learn the attention distribution consistent with real protein-ligand interactions. Results on the multiple datasets show that SEGSA_DTA outperforms current state-of-the-art methods. We also applied SEGSA_DTA in repurposing FDA-approved drugs to identify potential coronavirus disease 2019 (COVID-19) treatments. Besides, by using SHapley Additive exPlanations (SHAP), we found that SEGSA_DTA is interpretable and further provides a new quantitative analytical solution for structure-based lead optimization.

Trap-tuning in afterglow perovskite crystals through alkali metal ion doping.

Herein, we report a trap-tuning strategy involving alkali metal ions of Li+ and K+, which enabled a fine tuning of afterglow traps at 250 K. Through Li+/K+ doping, a red afterglow from Cs2AgInCl6:Mn was activated at room temperature, which was extended up to 3600 s at low temperature. Thermoluminescence measurement revealed a shallow trap at ∼0.50 eV below the conduction band, which could be shifted up- and down-ward by dopant species.

Mn2+-Activated Afterglow in a Transparent Perovskite Crystal.

Transparent crystals of long afterglow are keenly desired for advanced applications involving three-dimensional information storage and volumetric display. Here, we report the growth of a perovskite crystal, Cs2NaInCl6:Mn, via a facile hydrothermal reaction. The crystal featured a high transparency up to 90% in a broad range from 400 to 750 nm. Upon ultraviolet (UV) excitation, the crystal exhibited an overlapped emission from both self-trapped exciton (STE) and Mn ions. An unusual energy transfer from Mn ion to STE was revealed through steady-state and transient photoluminescence (PL) spectra. Through concentration tuning, the afterglow duration was extended to 2500 s with a signal-to-noise ratio over 20. The single dopant of Mn ions was found to play two roles in afterglow mechanism, including both electron traps and emitting activators. This work provided a facile method to synthesize transparent crystals of persistent luminescence, opening many avenues for three-dimensional information storage and volumetric display.

Flexible and Transparent Ceramic Nanocomposite for Laboratory X-ray Imaging of Micrometer Resolution.

Transparent nanocomposites have attracted considerable attention in many areas including X-ray imaging, wearable electronics, and volumetric display. However, both the transparency and the flexibility were largely jeopardized by the loading content of functional nanoparticles (NPs), posing a major challenge to material engineering. Herein, an ultra-high-loading-ceramic nanocomposite film was fabricated by a blade-coating technique. The film exhibited a high transparency over ∼89% in the whole visible region even with a fluoride-ceramic content up to ∼83 wt %. Based on a real-time investigation on the formation process of the film, the refractive-index difference between the nanoparticles and matrix was identified as the dominating factor to transparency. The transmittance spectra based on Rayleigh scattering theory were simulated to screen both nanoparticle radius and loading content, leading to the discovery of a transparency zone for film making. As a proof-of-concept experiment, the transparent film was used as an X-ray scintillation screen, which exhibited a comparable light yield to that of LYSO owing to the mitigated self-absorption effect. The homemade imager demonstrated a spatial resolution of 122 lp/mm, representing a record resolution of 4.1 µm for laboratory X-ray photography. Our work not only provided an experimental procedure to make high-loading functional films but also demonstrated a theoretical model to guide the search for gradients of transparent composites.