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Transition metal oxides represent a promising category of pseudocapacitive materials for potassium-ion hybrid supercapacitors (PIHCs) characterized by high energy density. Nevertheless, their utility is hindered by intrinsic low conductivity, restricted electrochemical sites, and notable volume expansion, all of which directly contribute to the degradation of their electrochemical performance, thereby limiting their practical applicability in supercapacitor systems. In this study, we present a facile synthesis approach to fabricate nitrogen-doped carbon-supported oxygen vacancy-rich Co2NiO4 nanoflowers (Ov-Co2NiO4/NC NFs) featuring tunable surface layering and electron distribution. The nanoflower structure augments the contact area between the material and the electrolyte. Density functional theory (DFT) calculations reveal oxygen vacancies could bring an enhanced charge density across the entire Fermi level in Co2NiO4 and expand the interatomic distances between adjacent cobalt and nickel atoms to 3.370 Å. N-doped carbon carriers further accelerate charge transfer, increase the electrostatic energy storage and inhibit the structural collapse of Co2NiO4. These structural modifications serve to improve electrochemical reaction kinetics, augment the binding energy of K+ (-2.87 eV), and mitigate structural variations during K+ storage. In a 6 M KOH electrolyte, Ov-Co2NiO4/NC NF exhibits a specific capacitance of 1104 F g-1 at a current density of 0.5 A g-1, with a remarkable capacitance retention rate of 91.48 % after 6500 cycles. Furthermore, the assembled PIHCs demonstrate an energy density of 47.8 Wh kg-1 and an ultra-high power density of 376 W kg-1, alongside notable cycle stability, retaining 90.13 % of its capacitance after 8000 cycles in a 6 M KOH electrolyte.

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Previous methods for robustness evaluation relies on dose calculation for a number of uncertainty scenarios, which either fails to provide statistical meaning when the number is too small (e.g., ~8) or becomes unfeasible in daily clinical practice when the number is sufficiently large (e.g., > 100). Our proposed deep learning-based method addressed this issue by avoiding the intermediate dose calculation step and instead directly predicting the percentile dose distribution from the nominal dose distribution using a deep learning (DL) model. In this study, we sought to validate this a deep learning (DL)-based statistical robustness evaluation (SRE) method for efficient and accurate robustness quantification in head and neck (H&N) intensity-modulated proton therapy with diverse beam configurations and multifield optimization. APPROACH: A dense, dilated 3D U-net was trained to predict the 5th and 95th percentile distributions of planning dose plan using the nominal dose and planning CT images. The data set comprised proton therapy plans for 582 H&N cancer patients. Ground truth percentile values were estimated for each patient through 600 dose recalculations, representing randomly sampled uncertainty scenarios. The comprehensive comparisons of different models were conducted for H&N cancer patients, considering those with and without a beam mask (BM) and diverse beam configurations, including varying beam angles, couch angles, and beam numbers. The performance of our model trained based on a mixture of patients with H&N and prostate cancer was also assessed in contrast with models trained based on data specific for patients with cancer at either site. RESULTS: The DL-based model's predictions of percentile dose distributions exhibited excellent agreement with the ground truth dose distributions. The average gamma index with 2 mm/2%, consistently exceeded 97% for both 5th and 95th percentile dose volumes. Mean dose-volume histogram error analysis revealed that predictions from the combined training set yielded mean errors and standard deviations that were generally similar to those in the specific patient training data sets. SIGNIFICANCE: Our proposed DL-based method for evaluation of the robustness of proton therapy plans provides precise, rapid predictions of percentile dose for a given confidence level regardless of the beam arrangement and cancer site. This versatility positions our model as a valuable tool for evaluating the robustness of proton therapy across various cancer sites.

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Pneumonia involves complex immunological and pathological processes leading to pulmonary dysfunction, which can be life-threatening yet lacks effective specialized medications. Natural enzymes can be used as biological agents for the treatment of oxidative stress-related diseases, but limiting to catalytic and environmental stability as well as high cost. Herein, an artificial enzyme, gold nanoclusters (Au NCs) with excellent stability, bioactivity, and renal clearance can be used as the next-generation biological agents for acute lung injury (ALI) and allergic lung disease (ALD). The Au25 clusters can mimic catalase (CAT) and glutathione peroxidase (GPx), and the Km of Au24Er1 with H2O2 reaches 1.28 mM, about 22 times higher than natural CAT (≈28.8 mM). The clusters inhibit the oxidative stress in the mitochondria and promote the synthesis of adenosine triphosphate (ATP). The molecular mechanism shows that the TLR4/MyD88/NF-κB pathway and M1 macrophage-mediated inflammatory response are suppressed in ALI and the Th1/Th2 imbalance in ovalbumin (OVA)-induced ALD is rescued. Further, the clusters can notably improve lung function in both ALI and ALD models which paves the way for immunomodulation and intervention for lung injury and can be used as a substitute for natural enzymes and potential biopharmaceuticals in the treatment of various types of pneumonia.

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Background and Objective: Exoskeleton robot control should ideally be based on human voluntary movement intention. The readiness potential (RP) component of the motion-related cortical potential is observed before movement in the electroencephalogram and can be used for intention prediction. However, its single-trial features are weak and highly variable, and existing methods cannot achieve high cross-temporal and cross-subject accuracies in practical online applications. Therefore, this work aimed to combine a deep convolutional neural network (CNN) framework with a transfer learning (TL) strategy to predict the lower limb voluntary movement intention, thereby improving the accuracy while enhancing the model generalization capability; this would also provide sufficient processing time for the response of the exoskeleton robotic system and help realize robot control based on the intention of the human body. Methods: The signal characteristics of the RP for lower limb movement were analyzed, and a parameter TL strategy based on CNN was proposed to predict the intention of voluntary lower limb movements. We recruited 10 subjects for offline and online experiments. Multivariate empirical-mode decomposition was used to remove the artifacts, and the moment of onset of voluntary movement was labeled using lower limb electromyography signals during network training. Results: The RP features can be observed from multiple data overlays before the onset of voluntary lower limb movements, and these features have long latency periods. The offline experimental results showed that the average movement intention prediction accuracy was 95.23% ± 1.25% for the right leg and 91.21% ± 1.48% for the left leg, which showed good cross-temporal and cross-subject generalization while greatly reducing the training time. Online movement intention prediction can predict results about 483.9 ± 11.9 ms before movement onset with an average accuracy of 82.75%. Conclusion: The proposed method has a higher prediction accuracy with a lower training time, has good generalization performance for cross-temporal and cross-subject aspects, and is well-prioritized in terms of the temporal responses; these features are expected to lay the foundation for further investigations on exoskeleton robot control.

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The description and analysis of chemical bonds have been difficult following the popularization of electronic structure calculations. Although many attempts have been made from the perspective of electronic structure, the sheer volume of information in the electronic structure has left contemporary chemical bond analysis methods grappling with an inescapable "Trilemma" where the model briefness, generality, and descriptiveness (descriptive power) cannot be obtained simultaneously. To push the generality and descriptiveness to their extremes, herein a general machine learning-based framework is introduced to compact chemical bonds into a detailed residue-by-residue "genome" with matched encoding/decoding tools. The framework fuses the quantum mechanical aspects, auto feature extraction, nanostructures and/or simulations, and generative models. The encoded genomes are information-dense and decodable, where 100% generality is guaranteed. The descriptiveness of genomes appears to be broader than most known models. As a proof of concept, the realization presented in this work compacts the complete information regarding two critical chemical bonds in thiolate-protected gold nanoclusters, the S-Au and Au-Au bonds, from a Bosonic-Fermionic character perspective into 8-valued genomes. The machine learning component is trained based on 26,528 density functional theory simulated electron localization function images. With an exploration of the space span for the genome, bond polarization, hybridization, intrusion of other atoms, alignments, crystal orientation, atomic motions, and more details are observed. Furthermore, it has emerged from extensive generation tests that molecules and solids can be integrated in such a concise manner than is typically achieved with purely geometric representations. To showcase the intraclass complexity of S-Au and Au-Au bonds visually, a roadmap is plotted by summarizing and correlating the similarities of 8-value-genomes. Furthermore, genomes can be associated with realistic indices easily with a simple multilayer perception architecture as a simple calculating tool. Besides, there are 3 sets of applications, including a set of chemisorption, a set of molecular dynamical analysis, and a set of ultrafast processes, showcasing the interpretability potentials of interatomic genomes in the geometric structures, kinetic properties, and vibration characteristics of molecular systems. As the framework rose to the challenge of nanoclusters from a complicated mesoscopic family of material, the displayed generality and comprehensiveness indicate that the model may "understand" chemical bonds in a machine's way.

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OBJECTIVE: To compare the sex differences of Helicobacter pylori (HP) infection rate and 1-year recurrence rate. METHODS: A cross-sectional study was conducted on the prevalence of HP infection in 81,754 people who underwent physical examination in physical examination centers and outpatient clinics of the Affiliated Hospital of Gansu University of Traditional Chinese Medicine, the Second People's Hospital of Zhangye City, Tianshui City Hospital of Integrated Chinese and Western Medicine, the First and Second Department of The First Hospital of Lanzhou University Physical Examination Center, from March 2010 to December 2019. Among them, 53,771 (65.77%) were males (18-91 years old) and 27,983 (34.23%) were females (18-94 years old). According to age, they were divided into young group, middle-aged group and old group. 1448 asymptomatic infected patients were selected and treated with bismuth-containing quadruple drug eradication therapy for 2 weeks. The eradication rate and recurrence rate after 1 year were compared between males and females. RESULTS: The overall infection rate was 49.59%, including 49.74% in males and 49.3% in females. The risk of infection in young women was lower than that in men (OR = 0.908, 95%CI: 0.868-0.95, P < 0.01), the risk of infection in older women was higher than that in men (OR = 1.137, 95%CI: 1.041-1.243, P < 0.01). The female infection rate was positively correlated with age from 18 to 60, and Spearman's correlation coefficient was 0.825 (P < 0.01). The overall eradication rate was 84.67% in intention-to-treat analysis (ITT) and 88.46% in protocol analysis (PP). The eradication rates of ITT and PP in the older group were 78.38% and 82.27%, respectively, which were lower than 87.25% and 89.39% in the male group (P < 0.05). The 1-year overall recurrence rate was 3.86%, including 2.82% in males and 5.44% in females (P < 0.05), female was a risk factor for recurrence after eradication after controlling for age (OR = 2.177, 95%CI 1.166-4.066, P < 0.05). There were no obvious adverse reactions during the treatment. CONCLUSION: There is a positive linear correlation between HP infection rate and age increase in women. Older women have the characteristics of high HP infection rate, low eradication rate and high recurrence rate.

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Background: This study conducts a systematic review through meta-analysis, comparing the composition and diversity of the gut microbiome in patients with esophageal cancer and healthy individuals, and explores the relationship between risk factors and related factors of esophageal cancer. Methods: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we comprehensively searched the databases of PubMed, Web of Science, Embase, Cochrane Library. In addition, we applied the R programming language version 4.0.3 and Stata 15.1 software for data analysis. We also implemented the Newcastle-Ottawa Scale (NOS), funnel plot analysis, Egger's test, and Begg's test to assess the risk of bias. Results: In this study, a total of 328 studies were identified through the literature search. Among them, 117 duplicate studies were removed, and 202 studies were excluded based on inclusion and exclusion criteria. Finally, 9 studies were included in the analysis, involving a total of 216 patients with esophageal carcinoma and 352 healthy controls. Four studies provided Chao1 index for quantitative consolidation (ES = 637.41, 95% CI: 549.16 to 725.66, p = 0.000, I2 = 98.2%). Two studies [27, 29] reported ACE index (ES = 438.89, 95% CI: 362.42 to 515.35, p = 0.000, I2 = 97%). Seven studies [26,27,29,30,32] reported the Shannon index for quantitative consolidation (ES = 4.38, 95% CI: 3.95 to 4.81, p = 0.000, I2 = 99%). At the phylum level, the abundance of Bacteroidetes(ES = 37.8, 95% CI: 25.75 to 49.85, p = 0.000, I2 = 87.2%) and Proteobacteria(ES = 7.48, 95% CI: 5.02 to 8.85, p = 0.04, I2 = 2.4%) have statistical difference between ESCC and HC. There was no significant difference between ESCC and HC in the abundance of genera(p>0.05). Conclusions: This observational meta-analysis revealed that changes in the GM were correlated with esophageal carcinoma, and variations in some advantageous GM might involve regional differences. Additionally, the study aims to facilitate early diagnosis of esophageal cancer and improve screening and diagnostic efficiency.

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Background: Oesophagogastroduodenoscopy (OGD) quality and identification of the early upper gastrointestinal (UGI) neoplasm play an important role in detecting the UGI neoplasm. However, the optimal method for quality control in daily OGD procedures is currently lacking. We aimed to evaluate the efficacy of a real-time intelligent quality-control system (IQCS), which combines OGD quality control with lesion detection of early UGI neoplasms. Methods: We performed a multicentre, single-blinded, randomised controlled trial at 6 hospitals in China. Patients aged 40-80 years old who underwent painless OGD were screened for enrolment in this study. Patients with a history of advanced UGI cancer, stenosis, or obstruction in UGI tract were excluded. Eligible subjects were randomly assigned (1:1) to either the routine or IQCS group to undergo standard OGD examination and OGD examination aided by IQCS, respectively. Patients were masked to the randomisation status. The primary outcome was the detection of early UGI neoplasms. All analyses were done on a per-protocol basis. This trial is registered with ClinicalTrials.gov, NCT04720924. Findings: Between January 16, 2021 and December 23, 2022, 1840 patients were randomised (IQCS group: 919, routine group: 921). The full analysis set consisted of 914 in the IQCS group and 915 in the routine group. The early UGI neoplasms detection rate in the IQCS group (6.1%, 56/914) was significantly higher than in the routine group (2.3%, 21/915; P = 0.0001). The IQCS group had fewer blind spots (2.3 vs. 6.2, P < 0.0001). The IQCS group had higher stomach cleanliness on cardia or fundus (99.5% vs. 87.9%, P < 0.0001), body (98.9% vs. 88.0%, P < 0.0001), angulus (99.8% vs. 88.4%, P < 0.0001) and antrum or pylorus (100.0% vs. 87.4%, P < 0.0001). The inspection time (576.2 vs. 574.5s, P = 0.91) and biopsy rate (57.2% vs. 56.6%, P = 0.83) were not different between the groups. The early UGI neoplasms detection rate in the IQCS group increased in both non-academic centres (RR = 3.319, 95% CI 1.277-9.176; P = 0.0094) and academic centres (RR = 2.416, 95% CI 1.301-4.568; P = 0.0034). The same improvements were observed for both less-experienced endoscopists (RR = 2.650, 95% CI 1.330-5.410; P = 0.0034) and experienced endoscopists (RR = 2.710, 95% CI 1.226-6.205; P = 0.010). No adverse events or serious adverse events were reported in the two groups. Interpretation: The IQCS improved the OGD quality and increased early UGI neoplasm detection in different hospital types and endoscopist experiences. IQCS could play an important role in primary basic hospitals and non-expert endoscopists to improve the diagnostic accuracy of early UGI neoplasms. The effectiveness of IQCS in real-world clinical settings needs a larger population validation. Funding: Key R&D Program of Shandong Province, China (Major Scientific and Technological Innovation Project), National Natural Science Foundation of China, the Taishan Scholars Program of Shandong Province, the National Key Research and Development Program of China, and the Shandong Provincial Natural Science Foundation.

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OBJECTIVES: Accurate segmentation of focal cortical dysplasia (FCD) lesions from MR images plays an important role in surgical planning and decision but is still challenging for radiologists and clinicians. In this study, we introduce a novel transformer-based model, designed for the end-to-end segmentation of FCD lesions from multi-channel MR images. METHODS: The core innovation of our proposed model is the integration of a convolutional neural network-based encoder-decoder structure with a multiscale transformer to augment the feature representation of lesions in the global field of view. Transformer pathways, composed of memory- and computation-efficient dual-self-attention modules, leverage feature maps from varying depths of the encoder to discern long-range interdependencies among feature positions and channels, thereby emphasizing areas and channels relevant to lesions. The proposed model was trained and evaluated on a public-open dataset including MR images of 85 patients using both subject-level and voxel-level metrics. RESULTS: Experimental results indicate that our model offers superior performance both quantitatively and qualitatively. It successfully identified lesions in 82.4% of patients, with a low false-positive lesion cluster rate of 0.176 ± 0.381 per patient. Furthermore, the model achieved an average Dice coefficient of 0.410 ± 0.288, outperforming five established methods. CONCLUSION: Integration of the transformer could enhance the feature presentation and segmentation performance of FCD lesions. The proposed model has the potential to serve as a valuable assistive tool for physicians, enabling rapid and accurate identification of FCD lesions. The source code and pre-trained model weights are available at https://github.com/zhangxd0530/MS-DSA-NET . CRITICAL RELEVANCE STATEMENT: This multiscale transformer-based model performs segmentation of focal cortical dysplasia lesions, aiming to help radiologists and clinicians make accurate and efficient preoperative evaluations of focal cortical dysplasia patients from MR images. KEY POINTS: The first transformer-based model was built to explore focal cortical dysplasia lesion segmentation. Integration of global and local features enhances the segmentation performance of lesions. A valuable benchmark for model development and comparative analyses was provided.

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Designing biomimetic materials with high activity and customized biological functions by mimicking the central structure of biomolecules has become an important avenue for the development of medical materials. As an essential electron carrier, the iron-sulfur (Fe-S) clusters have the advantages of simple structure and high electron transport capacity. To rationally design and accurately construct functional materials, it is crucial to clarify the electronic structure and conformational relationships of Fe-S clusters. However, due to the complex catalytic mechanism and synthetic process in vitro, it is hard to reveal the structure-activity relationship of Fe-S clusters accurately. This review introduces the main structural types of Fe-S clusters and their catalytic mechanisms first. Then, several typical structural design strategies of biomimetic Fe-S clusters are systematically introduced. Furthermore, the development of Fe-S clusters in the biocatalytic field is enumerated, including tumor treatment, antibacterial, virus inhibition and plant photoprotection. Finally, the problems and development directions of Fe-S clusters are summarized. This review aims to guide people to accurately understand and regulate the electronic structure of Fe-S at the atomic level, which is of great significance for designing biomimetic materials with specific functions and expanding their applications in biocatalysis.

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AIM: We aimed to explore the impact of DNA methylation alterations on the DNA damage response (DDR) in melanoma prognosis and immunity. MATERIAL & METHODS: Different melanoma cohorts with molecular and clinical data were included. RESULTS: Hierarchical clustering utilizing different combinations of DDR-relevant CpGs yielded distinct melanoma subtypes, which were characteristic of different prognoses, transcriptional function profiles of DDR, and immunity and immunotherapy responses but were associated with similar tumor mutation burdens. We then constructed and validated a clinically applicable 4-CpG risk-score signature for predicting survival and immunotherapy response. CONCLUSION: Our study describes the close interrelationship among DNA methylation, DDR machinery, local tumor immune status, melanoma prognosis, and immunotherapy response.

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This study evaluated the ability of triethyl benzyl ammonium chloride/lactic acid deep eutectic solvent extracted lignin (TEBAC/LA-DES-L) to adsorb methylene blue (MB) without additional functional group modification. The structure and morphology of TEBAC/LA-DES-L were characterized using SEM, BET, FT-IR, and TGA techniques. Various factors influencing MB adsorption, such as extraction temperature, solution pH, adsorbent dose, initial MB concentration, adsorption time, and reaction temperature, were investigated. The Redlich-Peterson isotherm displayed a good fit for the experimental data, with a maximum adsorption capacity of 85.16 mg/g. Kinetic analysis suggested that the adsorption process followed the pseudo-second-order model, with adsorption occurring in <100 min on DES-L-4 h. The mechanism of MB adsorption on DES-L-4 h was attributed to electrostatic attraction, hydrophobic interactions, and hydrogen bonding forces. Overall, DES-L-4 h demonstrated high adsorption capacity and rapid adsorption rate, making it a promising adsorbent for effectively removing cationic dyes from wastewater.

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Stratification strategies for chemotherapy plus PD-1 inhibitors in advanced non-small-cell lung cancer (NSCLC) are critically demanded. We performed high-throughput panel-based deep next-generation sequencing and low-pass whole genome sequencing on prospectively collected circulating tumor DNA (ctDNA) specimens from 460 patients in the phase 3 CHOICE-01 study at different time points. We identified predictive markers for chemotherapy plus PD-1 inhibitor, including ctDNA status and genomic features such as blood-based tumor mutational burden, intratumor heterogeneity, and chromosomal instability. Furthermore, we established an integrated ctDNA-based stratification strategy, blood-based genomic immune subtypes (bGIS) scheme, to distinguish patients who benefit from the addition of PD-1 inhibitor to first-line chemotherapy. Moreover, we demonstrated potential applications for the dynamic monitoring of ctDNA. Overall, we proposed a potential therapeutic algorithm based on the ctDNA-based stratification strategy, shedding light on the individualized management of immune-chemotherapies for patients with advanced NSCLC.

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Background: The diagnosis of acute myocardial infarction (AMI) using high-sensitivity cardiac troponin T (hs-cTnT) remains challenging in patients with kidney dysfunction. Methods: In this large, multicenter cohort study, a total of 20 912 adults who underwent coronary angiography were included. Kidney function-specific cut-off values of hs-cTnT were determined to improve the specificity without sacrificing sensitivity, as compared with that using traditional cut-off value (14 ng/L) in the normal kidney function group. The diagnostic accuracy of the novel cut-off values was validated in an independent validation cohort. Results: In the derivation cohort (n = 12 900), 3247 patients had an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. Even in the absence of AMI, 50.2% of participants with eGFR <60 mL/min/1.73 m2 had a hs-cTnT concentration ≥14 ng/L. Using 14 ng/L as the threshold of hs-cTnT for diagnosing AMI led to a significantly reduced specificity and positive predictive value in patients with kidney dysfunction, as compared with that in patients with normal kidney function. The kidney function-specific cut-off values were determined as 14, 18 and 48 ng/L for patients with eGFR >60, 60-30 and <30 mL/min/1.73 m2, respectively. Using the novel cut-off values, the specificities for diagnosing AMI in participants with different levels of kidney dysfunction were remarkably improved (from 9.1%-52.7% to 52.8-63.0%), without compromising sensitivity (96.6%-97.9%). Similar improvement of diagnostic accuracy was observed in the validation cohort (n = 8012). Conclusions: The kidney function-specific cut-off values of hs-cTnT may help clinicians to accurately diagnose AMI in patients with kidney dysfunction and avoid the potential overtreatment in practice.

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In modern optics, birefringent materials that can manipulate light polarization play important roles in lasers and information fields. The search for ultraviolet (UV) crystals with large birefringence is the focus of attention in the field of optical materials. In this work, we synthesized two birefringent crystals, C2H12N6C4O4·2H2O and Na2C4O4·3H2O, containing planar π-conjugated [C4O4]2- groups. Attributed to the large structural anisotropy and relatively ordered arrangement of the [C4O4]2- groups, C2H12N6C4O4·2H2O and Na2C4O4·3H2O possess large birefringence of 0.20-0.21 at 1064 nm. Meanwhile, they exhibit short ultraviolet cutoff edges at about 280-300 nm, corresponding to the large band gaps of 4.35 and 4.24 eV, respectively. Using structural analysis and first-principles calculations, the origins of such large birefringence are investigated and discussed. This work provides two potential UV birefringent crystals and prompts the search for novel birefringent materials.

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OBJECTIVE: To develop and externally validate a binary classification model for lumbar vertebral body fractures based on CT images using deep learning methods. METHODS: This study involved data collection from two hospitals for AI model training and external validation. In Cohort A from Hospital 1, CT images from 248 patients, comprising 1508 vertebrae, revealed that 20.9% had fractures (315 vertebrae) and 79.1% were non-fractured (1193 vertebrae). In Cohort B from Hospital 2, CT images from 148 patients, comprising 887 vertebrae, indicated that 14.8% had fractures (131 vertebrae) and 85.2% were non-fractured (756 vertebrae). The AI model for lumbar spine fractures underwent two stages: vertebral body segmentation and fracture classification. The first stage utilized a 3D V-Net convolutional deep neural network, which produced a 3D segmentation map. From this map, region of each vertebra body were extracted and then input into the second stage of the algorithm. The second stage employed a 3D ResNet convolutional deep neural network to classify each proposed region as positive (fractured) or negative (not fractured). RESULTS: The AI model's accuracy for detecting vertebral fractures in Cohort A's training set (n = 1199), validation set (n = 157), and test set (n = 152) was 100.0 %, 96.2 %, and 97.4 %, respectively. For Cohort B (n = 148), the accuracy was 96.3 %. The area under the receiver operating characteristic curve (AUC-ROC) values for the training, validation, and test sets of Cohort A, as well as Cohort B, and their 95 % confidence intervals (CIs) were as follows: 1.000 (1.000, 1.000), 0.978 (0.944, 1.000), 0.986 (0.969, 1.000), and 0.981 (0.970, 0.992). The area under the precision-recall curve (AUC-PR) values were 1.000 (0.996, 1.000), 0.964 (0.927, 0.985), 0.907 (0.924, 0.984), and 0.890 (0.846, 0.971), respectively. According to the DeLong test, there was no significant difference in the AUC-ROC values between the test set of Cohort A and Cohort B, both for the overall data and for each specific vertebral location (all P>0.05). CONCLUSION: The developed model demonstrates promising diagnostic accuracy and applicability for detecting lumbar vertebral fractures.

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(1) Background: Weaning is a challenging and stressful event in the pig's life, which disrupts physiological balance and induces oxidative stress. Microbiota play a significant role during the weaning process in piglets. Therefore, this study aimed to investigate key gut microbiota and metabolites associated with weaning stress in piglets. (2) Methods: A total of ten newborn piglet littermates were randomly assigned to two groups: S (suckling normally) and W (weaned at 21 d; all euthanized at 23 d). Specimens of the cecum were dehydrated with ethanol, cleared with xylene, embedded in paraffin, and cut into 4 mm thick serial sections. After deparaffinization, the sections were stained with hematoxylin and eosin (H&E) for morphometric analysis. Cecal metagenomic and liver LC-MS-based metabolomics were employed in this study. Statistical comparisons were performed by a two-tailed Student's t-test, and p < 0.05 indicated statistical significance. (3) Results: The results showed that weaning led to intestinal morphological damage in piglets. The intestinal villi of suckling piglets were intact, closely arranged in an orderly manner, and finger-shaped, with clear contours of columnar epithelial cells. In contrast, the intestines of weaned piglets showed villous atrophy and shedding, as well as mucosal bleeding. Metagenomics and metabolomics analyses showed significant differences in composition and function between suckling and weaned piglets. The W piglets showed a decrease and increase in the relative abundance of Bacteroidetes and Proteobacteria (p < 0.05), respectively. The core cecal flora in W piglets were Campylobacter and Clostridium, while those in S piglets were Prevotella and Lactobacillus. At the phylum level, the relative abundance of Bacteroidetes significantly decreased (p < 0.05) in weaned piglets, while Proteobacteria significantly increased (p < 0.05). Significant inter-group differences were observed in pathways and glycoside hydrolases in databases, such as the KEGG and CAZymes, including fructose and mannose metabolism, salmonella infection, antifolate resistance, GH135, GH16, GH32, and GH84. We identified 757 differential metabolites between the groups through metabolomic analyses-350 upregulated and 407 downregulated (screened in positive ion mode). In negative ion mode, 541 differential metabolites were identified, with 270 upregulated and 271 downregulated. Major differential metabolites included glycerophospholipids, histidine, nitrogen metabolism, glycine, serine, threonine, ß-alanine, and primary bile acid biosynthesis. The significant differences in glycine, serine, and threonine metabolites may be potentially related to dysbiosis caused by weaning stress. Taken together, the identification of microbiome and metabolome signatures of suckling and weaned piglets has paved the way for developing health-promoting nutritional strategies, focusing on enhancing bacterial metabolite production in early life stages.

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Background: Proton therapy (PT) has unique biologic properties with excellent clinical outcomes for the management of localized prostate cancer. Here, we aim to characterize the toxicity of PT for patients with localized prostate cancer and propose mitigation strategies using a large institutional database. Methods: We reviewed medical records of 2772 patients with localized prostate cancer treated with definitive PT between May 2006 through January 2020. Disease risk was stratified according to National Comprehensive Cancer Network guidelines as low [LR, n = 640]; favorable-intermediate [F-IR, n = 849]; unfavorable-intermediate [U-IR, n = 851]; high [HR, n = 315]; or very high [VHR, n = 117]. Descriptive statistics and Kaplan-Meier estimates assessed toxicity and freedom from biochemical relapse (FFBR). Results: Median follow-up was 7.0 years. The median dose was 78 Gy(RBE)(range: 72-79.2 Gy) in 2.0 Gy(RBE) fractions; 63 % of patients received 78 Gy(RBE) in 39 fractions, and 29 % received 76 Gy(RBE) in 38 fractions. Overall rates of late grade ≥3 GU and GI toxicity were 0.87 % and 1.01 %, respectively. Two patients developed grade 4 late GU toxicity and seven patients with grade 4 late GI toxicity. All patients experiencing severe late grade 4 toxicities were treated to 78 Gy(RBE) in 39 fractions with 80 Gy(RBE) dose to the anterior rectal wall and/or bladder neck. The 10-year FFBR rates for patients with LR to U-IR disease were compared between those treated with 76 and 78 Gy(RBE); the rates were 94.5 % (95 % confidence interval [CI] 92.4-96.0 %) and 93.2 % (95 % CI 91.3-95.7 %), respectively (log-rank p = 0.22). Conclusions: Proton therapy is associated with low rates of late grade ≥3 GU and GI toxicity. While rare, late grade 4 toxicities occurred in nine (0.3 %) patients. De-escalation to a total dose of 76 Gy(RBE) yields excellent clinical outcomes for patients with LR to U-IR disease with the potential for significant reductions in grade ≥3 late toxicity.

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Augmented Reality (AR) offers a rich business format, convenient applications, great industrial potential, and strong commercial benefits. The integration of AR technology with online shopping has brought tremendous changes to e-commerce. The Technology Acceptance Model (TAM) is a mature model for assessing consumer acceptance of new technologies, and applying it to evaluate the impact of AR online shopping experiences on consumer purchase intention is an urgently needed area of research. Firstly, the typical applications of AR in online shopping were reviewed, and the connotations and experiences of AR online shopping were summarized. Secondly, using the five types of AR online shopping experiences as antecedent variables, and perceived ease of use and perceived usefulness as intermediate variables, a theoretical model was constructed to explore the impact of AR online shopping experiences on customer purchase intentions, followed by an empirical study. Finally, suggestions were proposed for optimizing the online shopping experience to enhance purchase intentions. The article expands the application scenarios of the Technology Acceptance Model and enriches the theory of consumer behavior in Metaverse e-commerce.

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In the title mol-ecule, C11H11BrO3, the di-hydro-indene moiety is essentially planar but with a slight twist in the saturated portion of the five-membered ring. The meth-oxy groups lie close to the above plane. In the crystal, π-stacking inter-actions between six-membered rings form stacks of mol-ecules extending along the a-axis direction, which are linked by weak C-H⋯O and C-H⋯Br hydrogen bonds. A Hirshfeld surface analysis was performed showing H⋯H, O⋯H/H⋯O and Br⋯H/H⋯Br contacts make the largest contributions to inter-molecular inter-actions in the crystal.