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Wolfberry, known as Goji berry, is the fruit of Lycium barbarum L. (LB). As a famous functional food and TCM, the cost and efficacy of LB are closely linked to its geographical origin. The present study aimed to establish an effective method for distinguishing LB from different geographical origins. By employing UHPLC-QTOF-MS/MS combined with multivariate analysis, the metabolite profiling of LB (199 batches) obtained from Ningxia, Gansu, Qinghai, and Xinjiang, was evaluated. The results demonstrated that the method effectively distinguished LB from the four regions, with a total of 148 different metabolites being detected. Subsequent assessment using heat maps, Venn analysis, receiver operating characteristics curves and dot plots revealed 21 of these metabolites exhibited exceptional sensitivity and specificity, with under-curve values approaching 1, thus indicating their potential as biomarkers for LB. These findings strongly support the suitability of UHPLC-QTOF-MS/MS-based metabolomics as an effective approach to identify the source of LB.

Abstract Objective To identify the risk factors for moderate and severe contrast media extravasation and provide effective guidance to reduce the degree of extravasation injuries. Methods We observed 224 adult patients who underwent contrast media extravasation at Xiangya Hospital of Central South University, Hunan Provincial Maternal and Child Healthcare Hospital, and Xiangya Changde Hospital, Hunan Province between January 1, 2018 and December 31, 2022. Risk factors for moderate extravasation injuries were evaluated using univariate and multivariate logistic regression. Results Among 224 patients, 0 (0%) had severe, 18 (8.0%) had moderate, and 206 (92.0%) had mild contrast media extravasation injury. Multivariate logistic regression analysis revealed malignant tumors (odds ratio [OR] = 6.992, 95% confidence interval [CI]: 1.674–29.208), Iohexol (OR = 9.343, 95% CI 1.280–68.214), large-volume (> 50 mL) extravasation (OR = 5.773, 95% CI 1.350‒24.695), and injection site (back of the hand) (OR = 13.491, 95% CI 3.056–59.560) as independent risk factors for moderate injury. Conclusion Risk factors for moderate contrast media extravasation injury are malignant tumors, iohexol, large-volume (> 50 mL) extravasation, and back-of-the-hand injection. Analysis of these risk factors can help reduce the degree of injury after extravasation. Clinical relevance statement High-risk patients with extravasation support should choose the appropriate contrast media type, avoiding back-of-the-hand injections. We recommend that patients with cancer be implanted with a high-pressure resistant central venous catheter and receive effective measures to timely detect and reduce extravasation.

Abstract Improving diabetes self-management (DSM) is facing real-world challenges among people with type 2 diabetes mellitus (T2DM) who have a low education level in resource-limited areas. This study aimed to investigate whether diabetes knowledge could predict glycemic levels in people with T2DM in rural China. This analytical cross-sectional study recruited 321 people with T2DM from eight villages by purposive sampling at baseline. After 10 months, 206 patients completed the follow-up survey and HbA1c tests, with a response rate of 64.17% (206/321). Multiple regression analysis was employed to explore the correlation between diabetes knowledge and HbA1c levels. The patient's diabetes knowledge was significantly negatively correlated with HbA1c levels before and after controlling for covariates in both hierarchical multiple regression and multiple logistic regression (p

Abstract Background Curcumin (Cur), a bioactive component of Chinese traditional medicine, has demonstrated inhibitory properties against cancer cell proliferation while synergistically enhancing the anticancer efficacy of erlotinib (Er). However, the individual limitations of both drugs, including poor aqueous solubility, lack of targeting ability, short half-life, etc., and their distinct pharmacokinetic profiles mitigate or eliminate their combined antitumor potential. Results In this study, we developed a molybdenum disulfide (MoS2)-based delivery system, functionalized with polyethylene glycol (PEG) and biotin, and co-loaded with Cur and Er, to achieve efficient cancer therapy. The MoS2-PEG-Biotin-Cur/Er system effectively converted near-infrared (NIR) light into heat, thereby inducing direct photothermal ablation of cancer cells and promoting controlled release of Cur and Er. Biotin-mediated tumor targeting facilitated the selective accumulation of MoS2-PEG-Biotin-Cur/Er at the tumor site, thus enhancing the synergistic antitumor effects of Cur and Er. Remarkably, MoS2-PEG-Biotin-Cur/Er achieved the combination of synergistic chemotherapy and photothermal therapy (PTT) upon NIR irradiation, effectively suppressing lung cancer cell proliferation and inhabiting tumor growth in vivo. Conclusions The as-synthesized MoS2-PEG-Biotin-Cur/Er, featuring high targeting ability, NIR light-responsive drug release, and the integration of synergistic chemotherapy and PTT, may provide a promising strategy for the treatment of lung cancer in clinical practice. Graphical Abstract

The aim of this experiment was to investigate the differences in metabolites in perirenal fat (PF) between Chinese Simmental cattle and Angus cattle. Six healthy 18-month-old male Angus cattle and Chinese Simmental cattle were selected, and the perirenal adipose tissue was collected after slaughtering. HE staining, a triglyceride assay kit, and liquid chromatography–tandem mass spectrometry (LC-MS/MS) technology were used to compare and analyze the differences in the cell morphology, lipid accumulation, and metabolites of the two types of PF. The results showed that the PF of Angus cattle had a larger cell area and stronger lipid deposition ability than that of Simmental cattle. A total of 567 metabolites were detected by LC-MS/MS technology, of which 119 were significantly upregulated in Angus cattle PF and 129 were significantly upregulated in Simmental cattle PF. Differential metabolites were enriched in pathways such as fatty acid biosynthesis, polyunsaturated fatty acid biosynthesis, regulation of adipocyte lipolysis, and oxidative phosphorylation. Finally, 12 metabolites that may cause phenotypic differences between the two types of perirenal adipose tissue were screened out from these pathways. This study has preliminarily screened out biomarkers that may affect lipid metabolism in PF, providing basic data for the further exploration of the metabolic characteristics of PF.

Based on C-band weather radar and ground precipitation data from the Helan Mountain area in Yinchuan between 2017 to 2020, we evaluated the forecasting performances of 15 mainstream deep learning models used in recent years, including recurrent-based and recurrent-free models. The critical success index (CSI), probability of detection (POD), false alarm rate (FAR), mean square error (MSE), mean absolute error (MAE), and learned perceptual image patch similarity (LPIPS), were used to evaluate the forecasting abilities. The results showed that (1) recurrent-free models have significant parameter quantity and computing power advantages, especially the SimVP model. Among the recurrent-based models, PredRNN and PredRNN++ demonstrate good predictive capabilities for changes in echolocation and intensity, PredRNN++ performs better in predicting long sequences (1 h); (2) SimVP uses Inception to extract temporal features, which cannot capture the complex physical changes in radar echo images and fails to extract spatial–temporal correlations and accurately predict heavy rainfall areas effectively. Therefore, we constructed the SimVP-GMA model, replacing the temporal prediction module in SimVP and modifying the spatial encoder part. Compared with SimVP, the MSE and LPIPS indicators were improved by 0.55 and 0.0193, respectively. It can be seen from the forecast images that the forecast details have been significantly improved, especially in the forecasting of heavy rainfall weather.

We study an ecosystem of three keystone species: salmon, bears and vegetation. Bears consume salmon and vegetation for energy and nutrient intake, but the food quality differs significantly due to the nutritional level difference between salmon and vegetation. We propose a stoichiometric predator-prey model that not only tracks the energy flow from one trophic level to another but also nutrient recycling in the system. Analytical results show that bears may coexist with salmon and vegetation at a steady state, but the abundance of salmon may differ under different regimes. Numerical simulations reveal that a smaller vegetation growth rate may drive the vegetation population to extinction, whereas a large vegetation growth rate may drive the salmon population to extinction. Moreover, a large vegetation growth rate may stabilize the system where the bear, salmon and vegetation populations oscillate periodically.

Abstract Few studies have provided data on the metabolomics characteristics of metabolic diseases such as hyperuricemia and hyperbilirubinemia in the Tibetan plateau. In the current study, we sought to investigate the serum metabolomics characteristics of hyperbilirubinemia and hyperuricemia in the Tibetan plateau, with the aim to provide a basis for further research on their pathogenesis, prevention, and treatment. The study participants were born in low-altitude areas below 1000 m and had no prior experience living in a high-altitude area before entering Golmud, Tibet (average elevation: 3000 m) and Yushu, Qinghai (average elevation: 4200 m). Thirty-four participants with hyperbilirubinemia (18 in Golmud and 16 in Yushu), 24 participants with hyperuricemia, and 22 healthy controls were enrolled. The serum samples of subjects were separated and then sent to a local tertiary hospital for biochemical examination. Serum widely targeted technology, based on the ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) platform, was used to detect serum metabolites and differential metabolites. Compared to the healthy controls, hyperbilirubinemia patients from Golmud showed 19 differential metabolites, hyperbilirubinemia patients from Yushu showed 12 differential metabolites, and hyperuricemia patients from Yushu showed 23 differential metabolites. Compared to the hyperbilirubinemia patients from Golmud that is at a low altitude, the Yushu groups had 33 different metabolites. Differential metabolites are primarily classified into amino acids and their derivatives, nucleotides and their derivatives, organic acids and their derivatives, and lipids/fatty acids. These are related to metabolic pathways such as caffeine metabolism, arachidonic acid metabolism, and tyrosine metabolism. Hyperbilirubinemia and hyperuricemia in the Tibetan plateau have unique serum metabolomics characteristics. Glycine derivatives and arachidonic acid and its derivatives were associated with plateau hyperbilirubinemia, and vanillic acid and pentadecafluorooctanoic acid were associated with plateau hyperuricemia.

PURPOSEThis study aimed to investigate the effect of using a deep neural network (DNN) in breast cancer (BC) detection.METHODSIn this retrospective study, a DNN-based model was constructed from a total of 880 mammograms that 220 patients underwent between April and June 2020. The mammograms were reviewed by two senior and two junior radiologists with and without the aid of the DNN model. The performance of the network was assessed by comparing the area under the curve (AUC) and receiver operating characteristic curves for the detection of four features of malignancy (masses, calcifications, asymmetries, and architectural distortions), with and without the aid of the DNN model and by the senior and junior radiologists. Additionally, the effect of utilizing the DNN on diagnosis time for both the senior and junior radiologists was evaluated.RESULTSThe AUCs of the model for the detection of mass and calcification were 0.877 and 0.937, respectively. In the senior radiologist group, the AUC values for evaluation of mass, calcification, and asymmetric compaction were significantly higher with the DNN model than those obtained without the model. Similar effects were observed in the junior radiologist group, but the increase in the AUC values was even more dramatic. The median mammogram assessment time of the junior and senior radiologists was 572 (357–951) s, and 273.5 (129–469) s, respectively, with the DNN model, and the corresponding assessment time without the model, was 739 (445–1003) s and 321 (195–491) s, respectively.CONCLUSIONThe DNN model exhibited high accuracy in detecting the four named features of BC and effectively shortened the review time by both senior and junior radiologists.

Abstract Background To compare the 4-year visual outcomes of implantable collamer lens V4c (EVO-ICL) implantation and small incision lenticule extraction (SMILE) for high myopia and astigmatism. Methods This retrospective case study included 64 eyes of 40 patients. These patients with preoperative manifest refraction spherical equivalent (SE) between − 6.00 and − 10.00 diopters (D) were screened from the database of SMILE and EVO-ICL implantation procedures in 2015. The ICL group [32 eyes of 19 patients (mean age, 29.6 ± 6.3 years); mean SE, -8.71 ± 1.06 D] and SMILE group [32 eyes of 21 patients (mean age, 27.7 ± 5.6 years); mean SE, -8.35 ± 0.65D] were compared. All patients were then prospectively examined at a four-year follow-up for routine postoperative examinations, higher-order ocular aberrations, retinal image quality and a questionnaire. Results The safety indexes were 1.15 ± 0.14 and 1.22 ± 0.21 (P = 0.36) for the SMILE and ICL groups, respectively. No eyes lost two or more lines of CDVA in either group. The efficacy indexes were 0.97 ± 0.16 and 0.96 ± 0.19 (P = 0.87), respectively. Twenty-three eyes (72%) in ICL and 26 eyes (81%) in SMILE groups were within ± 0.5 D of the attempted SE (P

Abstract Introduction The prevalence of myopia in young generations has dramatically increased over the years, especially in China. This study aims to understand Chinese parents’ perspectives on myopia for further improvement of treatment compliance and informing future health planning and policy. Methods This was a prospective cross-sectional survey study. A self-administrated, internet-based questionnaire was distributed to 2545 parents in China. Detailed information about the demographics, the awareness of myopia, related complications, and practices of myopia prevention and control of the respondents were collected. The distribution of answers was compared among different groups of children’s age, children’s refractions, and parents’ residential locations. Relationships between parental cognition and behavior were also analyzed. Results Eligible responses were returned by 2500 parents. A total of 55.1% of the respondents considered myopia as a disease and more than 70% of respondents did not realize pathological changes related to myopia. Most parents thought myopia could be prevented (82.0%) and controlled (75.2%), and these parents were more likely to take measures than the parents who did not think so (P

PurposeTo develop deep-learning radiomics model for predicting biochemical recurrence (BCR) of advanced prostate cancer (PCa) based on pretreatment apparent diffusion coefficient (ADC) maps.MethodsData were collected retrospectively from 131 patients diagnosed with advanced PCa, randomly divided into training (n = 93) and test (n = 38) datasets. Pre-treatment ADC images were segmented using a pre-trained artificial intelligence (AI) model to identify suspicious PCa areas. Three models were constructed, including a clinical model, a conventional radiomics model and a deep-radiomics model. The receiver operating characteristic (ROC), precision-recall (PR) curve and decision curve analysis (DCA) were used to assess predictive performance in test dataset. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were employed to compare the performance enhancement of the deep-radiomics model in relation to the other two models.ResultsThe deep-radiomics model exhibited a significantly higher area under the curve (AUC) of ROC than the other two (P = 0.033, 0.026), as well as PR curve (AUC difference 0.420, 0.432). The DCA curve demonstrated superior performance for the deep-radiomics model across all risk thresholds than the other two. Taking the clinical model as reference, the NRI and IDI was 0.508 and 0.679 for the deep-radiomics model with significant difference. Compared with the conventional radiomics model, the NRI and IDI was 0.149 and 0.164 for the deep-radiomics model without significant difference.ConclusionThe deep-radiomics model exhibits promising potential in predicting BCR in advanced PCa, compared to both the clinical model and the conventional radiomics model.

BackgroundMyopia poses a global health concern and is influenced by both genetic and environmental factors. The incidence of myopia tends to increase during infectious outbreaks, such as the COVID-19 pandemic. This study examined the screen-time behaviors among Chinese children and adolescents and investigated the efficacy of artificial intelligence (AI)-based alerts in modifying screen-time practices.MethodsA cross-sectional analysis was performed using data from 6,716 children and adolescents with AI-enhanced tablets that monitored and recorded their behavior and environmental light during screen time.ResultsThe median daily screen time of all participants was 58.82 min. Among all age groups, elementary-school students had the longest median daily screen time, which was 87.25 min and exceeded 4 h per week. Children younger than 2 years engaged with tablets for a median of 41.84 min per day. Learning accounted for 54.88% of participants’ screen time, and 51.03% (3,390/6,643) of the participants used tablets for 1 h at an average distance

Purpose: To investigate the rate of vault changes after implantation of ICL V4c and the correlation with anterior segment parameters. Methods: A total of 64 eyes were studied from the 37 myopic subjects recruited. CASIA2 was used to record the vault at 1 week, 1 month, 3 months and 6 months after the operation as well as the preoperative anterior segment parameters including pupil diameter, lens vault, anterior chamber depth, anterior chamber width, iris area (IA), iris thickness, angle opening distance, angle recess area, trabecular iris space area, and trabecular iris angle. The rates of vault change in different time intervals were compared and the correlations between the rates and anterior segment parameters were analyzed. P 0.05). Conclusion: The rate of decreasing vault significantly slowed down from 3 months post-operation. A larger IA may imply a lower decreasing rate of vault change.

Abstract The interplay between immune cells/macrophages and fibroblast‐like synoviocytes (FLSs) plays a pivotal role in initiating synovitis; however, their involvement in metabolic disorders, including diabetic osteoarthritis (DOA), is largely unknown. In this study, single‐cell RNA sequencing (scRNA‐seq) is employed to investigate the synovial cell composition of DOA. A significant enrichment of activated macrophages within eight distinct synovial cell clusters is found in DOA synovium. Moreover, it is demonstrated that increased glycolysis in FLSs is a key driver for DOA patients’ synovial macrophage infiltration and polarization. In addition, the yes‐associated protein 1 (YAP1)/thioredoxin‐interacting protein (TXNIP) signaling axis is demonstrated to play a crucial role in regulating glucose transporter 1 (GLUT1)‐dependent glycolysis in FLSs, thereby controlling the expression of a series of adhesion molecules such as intercellular adhesion molecule‐1 (ICAM‐1) which may subsequently fine‐tune the infiltration of M1‐polarized synovial macrophages in DOA patients and db/db diabetic OA mice. For treatment, M1 macrophage membrane‐camouflaged Verteporfin (Vt)‐loaded PLGA nanoparticles (MVPs) are developed to ameliorate DOA progression by regulating the YAP1/TXNIP signaling axis, thus suppressing the synovial glycolysis and the infiltration of M1‐polarized macrophages. The results provide several novel insights into the pathogenesis of DOA and offer a promising treatment approach for DOA.

Abstract This study aimed to investigate the awareness of high and pathological myopia among patients with high myopia. A cross‐sectional multicenter survey involving both physicians and patients was conducted between February and April 2021. The outpatient education status, doctors' expectations toward patient education, patients' awareness of high/pathological myopia, and their access to relevant knowledge were inquired, and the proportions of patients with myopia, including high and pathological myopia, were assessed. In total, the survey involved 6975 physicians and patients with myopia from 161 medical institutions in China. The prevalence of high myopia among patients was 11.3%, with 22.59% having pathological myopia. Among those with pathological myopia, 22.45% experienced high myopia complications. Approximately 48.7% of the physicians believed that patients had only a vague idea about high/pathological myopia, with certain misconceptions. Additionally, most outpatients (61.4%) had no access to specialized staff for patient education. Patients' preferences and requirements for online ophthalmology information varied based on family background, medical history, and cognition toward myopia. This study demonstrated that the awareness level among patients with myopia regarding high and pathological myopia is currently insufficient. Therefore, it is important to enhance education efforts to reduce the incidence of adverse outcomes.

Abstract Background Implantable collamer lens (ICL) has been widely accepted for its excellent visual outcomes for myopia correction. It is a new challenge in phakic IOL power calculation, especially for those with low and moderate myopia. This study aimed to establish a novel stacking machine learning (ML) model for predicting postoperative refraction errors and calculating EVO-ICL lens power. Methods We enrolled 2767 eyes of 1678 patients (age: 27.5 ± 6.33 years, 18–54 years) who underwent non-toric (NT)-ICL or toric-ICL (TICL) implantation during 2014 to 2021. The postoperative spherical equivalent (SE) and sphere were predicted using stacking ML models [support vector regression (SVR), LASSO, random forest, and XGBoost] and training based on ocular dimensional parameters from NT-ICL and TICL cases, respectively. The accuracy of the stacking ML models was compared with that of the modified vergence formula (MVF) based on the mean absolute error (MAE), median absolute error (MedAE), and percentages of eyes within ± 0.25, ± 0.50, and ± 0.75 diopters (D) and Bland-Altman analyses. In addition, the recommended spheric lens power was calculated with 0.25 D intervals and targeting emmetropia. Results After NT-ICL implantation, the random forest model demonstrated the lowest MAE (0.339 D) for predicting SE. Contrarily, the SVR model showed the lowest MAE (0.386 D) for predicting the sphere. After TICL implantation, the XGBoost model showed the lowest MAE for predicting both SE (0.325 D) and sphere (0.308 D). Compared with MVF, ML models had numerically lower values of standard deviation, MAE, and MedAE and comparable percentages of eyes within ± 0.25 D, ± 0.50 D, and ± 0.75 D prediction errors. The difference between MVF and ML models was larger in eyes with low-to-moderate myopia (preoperative SE > − 6.00 D). Our final optimal stacking ML models showed strong agreement between the predictive values of MVF by Bland-Altman plots. Conclusion With various ocular dimensional parameters, ML models demonstrate comparable accuracy than existing MVF models and potential advantages in low-to-moderate myopia, and thus provide a novel nomogram for postoperative refractive error prediction and lens power calculation.

Abstract Background AI-based software may improve the performance of radiologists when detecting clinically significant prostate cancer (csPCa). This study aims to compare the performance of radiologists in detecting MRI-visible csPCa on MRI with and without AI-based software. Materials and methods In total, 480 multiparametric MRI (mpMRI) images were retrospectively collected from eleven different MR devices, with 349 csPCa lesions in 180 (37.5%) cases. The csPCa areas were annotated based on pathology. Sixteen radiologists from four hospitals participated in reading. Each radiologist was randomly assigned to 30 cases and diagnosed twice. Half cases were interpreted without AI, and the other half were interpreted with AI. After four weeks, the cases were read again in switched mode. The mean diagnostic performance was compared using sensitivity and specificity on lesion level and patient level. The median reading time and diagnostic confidence were assessed. Results On lesion level, AI-aided improved the sensitivity from 40.1% to 59.0% (18.9% increased; 95% confidence interval (CI) [11.5, 26.1]; p

Objective New vertebral compression fracture (NVCF) occurring after bone cement injection in middle‐aged and elderly patients with vertebral compression fracture is very common. Preoperative baseline characteristics and surgical treatment parameters have been widely studied as a risk factor, but the importance of the patients' laboratory indicators has not been thoroughly explored. We aimed to explore the relationship between laboratory indicators and NVCF, and attempt to construct a clinical prediction model of NVCF together with other risk factors. Methods Retrospective analysis was performed for 200 patients who underwent bone cement injection (percutaneous kyphoplasty or vertebroplasty) for vertebral compression fractures between January 2019 and January 2020. We consulted the relevant literature and collated the factors affecting the occurrence of NVCF. Feature selection of patients with NVCF was optimized using the least absolute shrinkage and selection operator regression model, which was used to conduct multivariable logistic regression analysis, to create a predictive model incorporating the selected features. The discrimination, calibration, and clinical feasibility of the predictive model were assessed using the concordance index (C‐index), calibration plots, and decision curve analysis. Internal validation was performed using Bootstrap resampling verification. Results Time from injury to surgery exceeding 7 days, low osteocalcin levels, elevated homocysteine levels, osteoporosis, mode of operation (percutaneous vertebroplasty), lack of postoperative anti‐osteoporosis treatment, and poor diffusion of bone cement were independent risk factors for NVCF in middle‐aged and elderly patients with vertebral compression fracture after bone cement injection. The C‐index of the nomogram constructed using these seven factors was 0.895, indicating good discriminatory ability. The calibration plot showed that the model was well calibrated. Bootstrap resampling verification yielded a significant C‐index of 0.866. Decision curve analysis demonstrated that the greatest clinical net benefit for predicting NVCF after bone cement injection could be achieved with a threshold of 1%–91%. Conclusion This nomogram can effectively predict NVCF incidence after bone cement injection in middle‐aged and elderly patients with vertebral compression fracture, thus aiding clinical decision‐making and postoperative management, promoting effective postoperative rehabilitation, and improving the quality of life.

Abstract Introduction The aim of this study was to evaluate the measurements of corneal higher-order aberrations (HOAs) obtained by a new anterior segment optical coherence tomography (OCT) technique combined with a Placido topographer (the MS-39 device) in eyes with prior small-incision lenticule extraction (SMILE) and compare them to the measurements obtained by a Scheimpflug camera combined with a Placido topographer (the Sirius device). Methods A total of 56 eyes (56 patients) were included in this prospective study. Corneal aberrations were analyzed for the anterior, posterior, and total cornea surfaces. Within-subject standard deviation (S w), test–retest repeatability (TRT), and intraclass correlation coefficient (ICC) were used to assess the intraobserver repeatability and interobserver reproducibility. The differences were evaluated by paired t-test. Bland–Altman plots and 95% limits of agreement (95% LoA) were used to evaluate the agreement. Results High repeatability was observed for anterior and total corneal parameters, with S w value 0.893, but not trefoil. For the posterior corneal parameters, ICCs varied from 0.088 to 0.966. Regarding interobserver reproducibility, all S w values were ≤ 0.04 and TRT ≤ 0.11. ICCs ranged from 0.846 to 0.989, from 0.432 to 0.972, and from 0.798 to 0.985 for the anterior, total, and posterior corneal aberrations parameters, respectively. The mean difference in all aberrations was ≤ 0.05 μm. All parameters showed a narrow 95% LoA. Conclusion The MS-39 device achieved high precision in both anterior and total corneal measurements; the precision of posterior corneal higher-order RMS, astigmatism II, coma, and trefoil was lower. The two technologies used by the MS-39 and Sirius devices can be used interchangeably for measuring corneal HOAs after SMILE.

Abstract Background The incidence of stroke is a challenge in China, as stroke imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) is an essential indicator of utilization of medical services and is usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on a machine learning algorithm to predict ischemic stroke patients’ LOS. Methods A total of 18,195 ischemic stroke patients’ electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. The prediction of LOS was regarded as a multi classification problem, and LOS was divided into three categories: 1–7 days, 8–14 days and more than 14 days. After preprocessing the data and feature selection, the XGBoost algorithm was used to build a machine learning model. Ten fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the XGBoost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature importance. Results Compared with the naive Bayesian algorithm, logistic region algorithm, decision tree classifier algorithm and ADaBoost classifier algorithm, the XGBoot algorithm has higher ACC, RE and F1 measure. The average ACC, RE and F1 measure were 0.89, 0.89 and 0.89 under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features in terms of hemiplegia aphasia, MRS, NIHSS, TIA, Operation or not, coma index etc. were found to be the top features in importance in predicting the LOS of ischemic stroke patients. Conclusions The XGBoost algorithm was an appropriate machine learning method for predicting the LOS of patients with ischemic stroke. Based on the prediction model, an intelligent medical management prediction system could be developed to predict the LOS based on ischemic stroke patients’ electronic medical records.

Hyperforatums A–D (1–4), four new polyprenylated acylphloroglucinols, together with 13 known compounds were isolated and identified from the aerial parts of Hypericum perforatum L. (St. John’s wort). Their structures were confirmed with a comprehensive analysis comprising spectroscopic methods, including 1D and 2D NMR, HRESIMS, and electronic circular dichroism (ECD) calculations. Hyperforatum A featured an unusual chromene-1,4-dione bicyclic system, and hyperforatums B and C were two rare monocyclic PPAPs with five-membered furanone cores. Compound 1 exhibited a moderate inhibition effect on NO production in BV-2 microglial cells stimulated by LPS.

Fluorescent sensors with single reading are generally subject to unpredictable disturbs from environmental and artificial factors. In order to overcome this barrier of detection reliability, a paper-based optical sensor with proportional fluorescence was established and further combined with a smartphone for visual, on-site and quantitative detection of Fe3+, which affects the color, smell and taste of water, and endangers the health of plants and animals. The ratio fluorescent probe was fabricated by rhodamine B and carbon quantum dots derived from xylan. The red fluorescence of rhodamine B was inert to Fe3+, which was referred to as background. And blue emitting carbon quantum dots functioned as signal report units, which would be quenched by Fe3+ and make the fluorescence of the ratio probe change from purple to red. The quantitative detection of Fe3+ was conducted by investigating the RGB value of fluorescent images with a smartphone. With the increase of Fe3+ concentration, the R/B (red/blue) value of the fluorescent paper gradually increased. The linear detection range was 10–180 μM, and the limit of detection was 198.2 nM. The application of ratio fluorescent paper with a smartphone provides a facile method for the rapid detection of ions.

In this work, in order to elucidate the three-dimensional (3D) resonant sloshing dynamics of the oil–water interface in an offshore cylindrical wet storage tank, a series of model experiments are conducted in a completely filled cylindrical tank containing two immiscible liquids. To begin with, a series of free damping tests are performed to experimentally determine the viscous damping rate of the system and to examine the corresponding theoretical solutions. Subsequently, the separation surface wave responses at a series of excitation frequencies including the natural frequencies of first five modes are examined. Finally, the rotary sloshing dynamics at the natural frequencies of the first and second natural modes are systematically explored. Interestingly, it is found that the separation surface rotary sloshing in a two-layer liquid system is much more intricate than one-layer liquid rotary sloshing due to the generation of multitudinous short waves in the long wave. As far as we know, this is the first investigation of 3D separation surface rotary wave motion in a two-layer liquid system without a free surface.

African swine fever virus (ASFV) and porcine reproductive and respiratory syndrome virus (PRRSV) infections lead to severe respiratory diseases in pigs, resulting in significant economic losses for the global swine industry. While numerous studies have focused on specific gene functions or pathway activities during infection, an investigation of shared immune responses in porcine alveolar macrophages (PAMs) after ASFV and PRRSV infections was lacking. In this study, we conducted a comparison using two single-cell transcriptomic datasets generated from PAMs under ASFV and PRRSV infection. Pattern recognition receptors (PRRs) RIG-I (DDX58), MDA5 (IFIH1), and LGP2 (DHX58) were identified as particularly recognizing ASFV and PRRSV, triggering cellular defense responses, including the upregulation of four cytokine families (CCL, CXCL, IL, and TNF) and the induction of pyroptosis. Through weighted gene co-expression network analysis and protein–protein interaction analysis, we identified thirteen gene and protein interactions shared by both scRNA-seq analyses, suggesting the ability to inhibit both ASFV and PRRSV viral replication. We discovered six proteins (PARP12, PARP14, HERC5, DDX60, RSAD2, and MNDA) in PAMs as inhibitors of ASFV and PRRSV replication. Collectively, our findings showed detailed characterizations of the immune responses in PAMs during ASFV and PRRSV infections, which may facilitate the treatments of these viral diseases.

Abstract Recovery of skin function remains a significant clinical challenge for deep burns owing to the severe scar formation and poor appendage regeneration, and stem cell therapy has shown great potential for injured tissue regeneration. Here, a cell‐free therapy system for deep burn skin was explored using mesenchymal stem cell paracrine proteins (MSC‐PP) and polyethylene glycol (PEG) temperature‐sensitive hydrogels. A three‐dimensional (3D) dynamic culture system for MSCs' large‐scale expansion was established using a porous gelatin microcarrier crosslinked with hyaluronic acid (PGM‐HA), and the purified MSC‐PP from culture supernatant was characterized by mass spectrometric analysis. The results showed the 3D dynamic culture system regulated MSCs cell cycle, reduced apoptosis, and decreased lactic acid content, and the MSC‐PP produced in 3D group can promote cell proliferation, migration, and adhesion. The MSC‐PP + PEG system maintained stable release in 28 days of observation in vitro. The in vivo therapeutic efficacy was investigated in the rabbit's third‐degree burn model, and saline, PEG, MSC‐PP, and MSC‐PP + PEG treatments groups were set. The in vivo results showed that the MSC‐PP + PEG group significantly improved wound healing, inhibited scar formation, and facilitated skin appendage regeneration. In conclusion, the MSC‐PP + PEG sustained‐release system provides a potentially effective treatment for deep burn skin healing.

The popularity of online social networks (OSNs) promotes the co-propagation of multiple types of information. And there exist inevitably competitive interactions between these information, which will significantly affect the spreading trend of each information. Besides, the coupled topology of multi-layer interconnects exhibited in OSNs will also increase the research complexity of information propagation dynamics. To effectively address these challenges, we propose a novel competitive information propagation model on multi-layer interconnected networks, where the tendency of an individual to become a positive or negative spreader depends on the weighted consideration of local and global prevalence. Then the basic reproduction number is calculated via next-generation matrix method. And under the critical conditions of the basic reproduction number, the asymptotic stability of information-free and information-endemic equilibria is theoretically proven through Lyapunov stability theory. Besides, an optimal control problem involving two heterogeneous controls is formulated, aiming at achieving the best suppression performance of negative information with the minimum control cost. According to Cesari theorem and Pontryagin minimum principle, the existence and analytical formulation of optimal solutions are derived. Extensive numerical experiments are conducted to prove the correctness of our theoretical results, and evaluate the effectiveness of our proposed control strategies. This study can provide useful insights into the modeling and control of multiple information propagation considering multi-layer network topology and individual adaptive behavior.

Soy isoflavones (SI) undergo biotransformation into equol (EQ) by gut microbiota and equol exhibits higher bioactivity than soy isoflavones. As a result, the endogenous equol concentration becomes a crucial indicator for assessing the bioavailability of soy isoflavones. This study screened prebiotics and probiotics capable of promoting equol production and constructed a human flora-associated (HFA) mice model with both high- and low-equol-producing levels to investigate the effects of synbiotic preparation on soy isoflavones transformation by gut microbiota. In vitro fermentation revealed that lactulose and Lactobacillus rhamnosus ATCC 7469 significantly enhanced equol production. Furthermore, synbiotic preparation intervention substantially increased plasma equol concentration in low-producing HFA mice. Through the integration of metagenomics and metatranscriptomics analyses, we identified EC 2.7.1.40 in the ko00010 pathway as a crucial auxiliary factor facilitating equol production. In conclusion, this study presents a novel model for enhancing endogenous equol levels through synbiotic preparation intervention.

Introduction: Although various therapies have been adopted to treat cancer, metastasis of tumor cells still is a big challenge that compromises therapeutic benefits.Methods: We herein report an injectable drug-loaded hybrid hydrogel that can achieve sonodynamic therapy (SDT) and chemodyanmic therapy (CDT) combined action and suppression of tumor metastasis. This alginate (ALG)-based hydrogel (termed as AMPS) contains manganese dioxide (MnO2) nanoparticles as the CDT agents, an organic polymer as the sonosensitizer, and a SIS3 drug as metastasis inhibitor.Results: AMPS is formed via the chelation of ALG by Ca2+ in tumor microenvironment, in which MnO2 nanoparticles mediate CDT via Fenton-like reaction and the organic polymers enable SDT under ultrasound (US) irradiation by generating singlet oxygen (1O2), allowing for combinational action of CDT and SDT. In addition, SIS3 is released from AMPS hydrogels to inhibit the metastasis of tumor cells. As such, the AMPS enables a combinational action of SDT and CDT to greatly inhibit the growths of subcutaneous tumors in living mice and also completely suppress the tumor metastasis in lungs and livers.Conclusion: This study thus offers a hybrid hydrogel platform for combinational therapy and metastasis suppression simultaneously.

Abstract Introduction This study aimed to investigate anterior segment parameters of eyes, with anterior chamber depth (ACD) less than 2.8 mm and axial length greater than 25.0 mm. Methods This cross-sectional study included 180 myopic eyes of 180 consecutive patients with axial length greater than 25.0 mm. Patients were divided into low ACD (ACD

Single-cell multi-omics and deep learning could lead to the inference of biological networks across specific cell types. Here, the authors develop DeepMAPS, a deep learning, graph-based approach for cell-type specific network inference from single-cell multi-omics data that is tested on healthy and tumour tissue datasets.

Objective: To study the molecular mechanism of Phedimus aizoon (Linnaeus)'t Hart. on anti-inflammatory effect by network pharmacology, molecular docking, and experiment research. Methods: The potential anti-inflammatory effect ingredients and targets were acquired from TCMSP, HERB, and other databases under the screening condition of oral bioavailability ≥30%. The inflammation-related targets were collected from GeneCards, OMIM, and other databases, and the protein-protein interaction (PPI) analysis to obtain the key anti-inflammatory effect targets. Meanwhile, GO annotation and KEGG signal pathway enrichment analysis of the key targets was analyzed using the David database. Moreover, this study verified the key targets predicted by AutoDock molecular docking. The key anti-inflammatory effect ingredients of Phedimus aizoon (Linnaeus)'t Hart. were analyzed using high-performance liquid chromatography (HPLC). The anti-inflammatory effect of the key ingredients was verified on the LPS-induced RAW264.7 cell model. Results: The results showed that there were five potent components corresponding to 130 related targets, and 421 inflammation-related targets, with 22 intersection anti-inflammatory targets, among which the key targets were PPARG, EGFR, TP53, etc. β-sitosterol and gallic acid (3,4,5-trihydroxy benzoic acid) were the main ingredients that can be anti-inflammatory. GO annotation and KEGG signal pathway enrichment analysis indicated that β-sitosterol and gallic acid played an anti-inflammatory role through multiple pathways such as transcription from RNA polymerase II promoter, positive regulation of neuron apoptotic process, and response to lipopolysaccharide, etc., as well as signal pathways including pathways in cancer, hepatitis B, TNF signaling pathway, MAPK signaling pathway, and other signaling pathways. Furthermore, the molecular docking analysis revealed that the key targets had good binding affinities with the key active ingredients. β-sitosterol and gallic acid as active ingredients in Phedimus aizoon (Linnaeus)'t Hart. were analyzed by HPLC. The experimental results showed that β-sitosterol and gallic acid could significantly reduce NO content, increase the content of IL-10 (P

Abstract Background Pulse pressure variation (PPV) has been widely used in hemodynamic assessment. Nevertheless, PPV is limited in low tidal volume ventilation. We conducted this systematic review and meta-analysis to evaluate whether the tidal volume challenge (TVC) could improve the feasibility of PPV in patients ventilated at low tidal volumes. Methods PubMed, Embase and Cochrane Library inception to October 2022 were screened for diagnostic researches relevant to the predictability of PPV change after TVC in low tidal volume ventilatory patients. Summary receiving operating characteristic curve (SROC), pooled sensitivity and specificity were calculated. Subgroup analyses were conducted for possible influential factors of TVC. Results Ten studies with a total of 429 patients and 457 measurements were included for analysis. The predictive performance of PPV was significantly lower than PPV change after TVC in low tidal volume, with mean area under the receiving operating characteristic curve (AUROC) of 0.69 ± 0.13 versus 0.89 ± 0.10. The SROC of PPV change yielded an area under the curve of 0.96 (95% CI 0.94, 0.97), with overall pooled sensitivity and specificity of 0.92 (95% CI 0.83, 0.96) and 0.88 (95% CI 0.76, 0.94). Mean and median cutoff value of the absolute change of PPV (△PPV) were 2.4% and 2%, and that of the percentage change of PPV (△PPV%) were 25% and 22.5%. SROC of PPV change in ICU group, supine or semi-recumbent position group, lung compliance less than 30 cm H2O group, moderate positive end-expiratory pressure (PEEP) group and measurements devices without transpulmonary thermodilution group yielded 0.95 (95%0.93, 0.97), 0.95 (95% CI 0.92, 0.96), 0.96 (95% CI 0.94, 0.97), 0.95 (95% CI 0.93, 0.97) and 0.94 (95% CI 0.92, 0.96) separately. The lowest AUROCs of PPV change were 0.59 (95% CI 0.31, 0.88) in prone position and 0.73 (95% CI 0.60, 0.84) in patients with spontaneous breathing activity. Conclusions TVC is capable to help PPV overcome limitations in low tidal volume ventilation, wherever in ICU or surgery. The accuracy of TVC is not influenced by reduced lung compliance, moderate PEEP and measurement tools, but TVC should be cautious applied in prone position and patients with spontaneous breathing activity. Trial registration PROSPERO (CRD42022368496). Registered on 30 October 2022.

The patterns of interaction between terrestrial vegetation and the atmosphere are complex, and some are poorly understood. Linear or general linear methods have been widely used to explore the dynamics of vegetation and climate changes. However, linear thinking may hinder our understanding of complex nonlinear systems, and it is difficult to extract the underlying causality of linear correlations directly from observational data. In this study, we aimed to quantify the interactions between vegetation and climate, using nonlinear dynamical methods based on state-space reconstruction and datasets from Chinese meteorological stations and remote sensing data during 1982–2015 in Northeast China (NEC). Specifically, we identified the causal links between meteorological factors (temperature and precipitation) and the vegetation index (NDVI) by reconstructing the state space from historical records. During the study period, vegetation exhibited a strong bidirectional causal relationship with both temperature and precipitation across Northeast China. The NDVI can be accurately reconstructed from the state information of meteorological factors (temperature and precipitation). The results of the multivariate EDM scenarios reveal varying sensitivities of different vegetation types to meteorological factors. Overall, slight temperature changes have a stronger impact on vegetation compared to precipitation. Mixed forest and broad-leaved forest demonstrate lower sensitivity to precipitation changes compared to other vegetation types. This study on the causal relationship between vegetation and meteorological factors in Northeast China contributes to a deeper understanding of climate change and vegetation feedback in middle and high latitudes. Our work demonstrates that the EDM-based convergent cross-mapping nonlinear causal analysis method is valuable for comprehending the interactions within complex systems in earth science.

PURPOSETo evaluate interreader agreement on pelvic multiparametric magnetic resonance imaging (mpMRI) interpretation among radiologists using a structured reporting tool based on the METastasis Reporting and Data System for Prostate Cancer (MET-RADS-P) guidelines.METHODSA structured report for follow-up pelvic mpMRI for advanced prostate cancer (APC) patients was formulated based on MET-RADS-P guidelines. In total, 163 paired pelvic mpMRI examinations were performed from December 2017 to February 2021 on 105 patients with APC. These were retrospectively reviewed by two senior and two junior radiologists for metastatic lesion detection and were categorized by these readers using primary/secondary response assessment categories (RACs), with and without the structured report. Interreader agreement regarding metastasis detection and RAC scores was evaluated with Cohen’s kappa and weighted Cohen’s kappa statistics (K), respectively.RESULTSThe two senior radiologists showed higher agreement with the reference standard for metastasis detection using the structured report (S1: K = 0.83; S2: K = 0.73) compared with the conventional report (S1: K = 0.72; S2: K = 0.61). Junior radiologists showed similar results (J1: 0.66 vs. 0.59; J2: 0.65 vs. 0.57). The overall agreement between the two senior radiologists was excellent for the primary RAC pattern using the structured reports (K = 0.81) and was substantial for secondary RAC categorization (K = 0.75). The interreader agreement of the two junior radiologists was substantial for both primary and secondary RAC values (K = 0.76, 0.68).CONCLUSIONGood interreader agreement was found for the follow-up assessment of APC patients between radiologists, where the pelvic mpMRI was reported using MET-RADS-P guidelines. This improvement applied to both metastatic lesion detection and qualitative RAC assessment.

Abstract Background The evaluation of treatment response according to METastasis Reporting and Data System for Prostate Cancer (MET-RADS-P) criteria is an important but time-consuming task for patients with advanced prostate cancer (APC). A deep learning-based algorithm has the potential to assist with this assessment. Objective To develop and evaluate a deep learning-based algorithm for semiautomated treatment response assessment of pelvic lymph nodes. Methods A total of 162 patients who had undergone at least two scans for follow-up assessment after APC metastasis treatment were enrolled. A previously reported deep learning model was used to perform automated segmentation of pelvic lymph nodes. The performance of the deep learning algorithm was evaluated using the Dice similarity coefficient (DSC) and volumetric similarity (VS). The consistency of the short diameter measurement with the radiologist was evaluated using Bland–Altman plotting. Based on the segmentation of lymph nodes, the treatment response was assessed automatically with a rule-based program according to the MET-RADS-P criteria. Kappa statistics were used to assess the accuracy and consistency of the treatment response assessment by the deep learning model and two radiologists [attending radiologist (R1) and fellow radiologist (R2)]. Results The mean DSC and VS of the pelvic lymph node segmentation were 0.82 ± 0.09 and 0.88 ± 0.12, respectively. Bland–Altman plotting showed that most of the lymph node measurements were within the upper and lower limits of agreement (LOA). The accuracies of automated segmentation-based assessment were 0.92 (95% CI: 0.85–0.96), 0.91 (95% CI: 0.86–0.95) and 75% (95% CI: 0.46–0.92) for target lesions, nontarget lesions and nonpathological lesions, respectively. The consistency of treatment response assessment based on automated segmentation and manual segmentation was excellent for target lesions [K value: 0.92 (0.86–0.98)], good for nontarget lesions [0.82 (0.74–0.90)] and moderate for nonpathological lesions [0.71 (0.50–0.92)]. Conclusion The deep learning-based semiautomated algorithm showed high accuracy for the treatment response assessment of pelvic lymph nodes and demonstrated comparable performance with radiologists.

Traditional Chinese medicine (TCM) has been successfully applied worldwide in the treatment of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the pharmacological mechanisms underlying this success remain unclear. Hence, the aim of this review is to combine pharmacological assays based on the theory of TCM in order to elucidate the potential signaling pathways, targets, active compounds, and formulas of herbs that are involved in the TCM treatment of COVID-19, which exhibits combatting viral infections, immune regulation, and amelioration of lung injury and fibrosis. Extensive reports on target screening are elucidated using virtual prediction via docking analysis or network pharmacology based on existing data. The results of these reports indicate that an intricate regulatory mechanism is involved in the pathogenesis of COVID-19. Therefore, more pharmacological research on the natural herbs used in TCM should be conducted in order to determine the association between TCM and COVID-19 and account for the observed therapeutic effects of TCM against COVID-19.

Abstract Background Evaluation of treated tumors according to Response Evaluation Criteria in Solid Tumors (RECIST) criteria is an important but time-consuming task in medical imaging. Deep learning methods are expected to automate the evaluation process and improve the efficiency of imaging interpretation. Objective To develop an automated algorithm for segmentation of liver metastases based on a deep learning method and assess its efficacy for treatment response assessment according to the RECIST 1.1 criteria. Methods One hundred and sixteen treated patients with clinically confirmed liver metastases were enrolled. All patients had baseline and post-treatment MR images. They were divided into an initial (n = 86) and validation cohort (n = 30) according to the examined time. The metastatic foci on DWI images were annotated by two researchers in consensus. Then the treatment responses were assessed by the two researchers according to RECIST 1.1 criteria. A 3D U-Net algorithm was trained for automated liver metastases segmentation using the initial cohort. Based on the segmentation of liver metastases, the treatment response was assessed automatically with a rule-based program according to the RECIST 1.1 criteria. The segmentation performance was evaluated using the Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD). The area under the curve (AUC) and Kappa statistics were used to assess the accuracy and consistency of the treatment response assessment by the deep learning model and compared with two radiologists [attending radiologist (R1) and fellow radiologist (R2)] in the validation cohort. Results In the validation cohort, the mean DSC, VS, and HD were 0.85 ± 0.08, 0.89 ± 0.09, and 25.53 ± 12.11 mm for the liver metastases segmentation. The accuracies of R1, R2 and automated segmentation-based assessment were 0.77, 0.65, and 0.74, respectively, and the AUC values were 0.81, 0.73, and 0.83, respectively. The consistency of treatment response assessment based on automated segmentation and manual annotation was moderate [K value: 0.60 (0.34–0.84)]. Conclusion The deep learning-based liver metastases segmentation was capable of evaluating treatment response according to RECIST 1.1 criteria, with comparable results to the junior radiologist and superior to that of the fellow radiologist.

Abstract Recurrent implantation failure (RIF) is a rather thorny problem in the clinical practice of assisted reproductive technology. Due to the complex aetiology of RIF, its pathogenesis is far from fully understood, and there is no effective treatment available. Here, We explored the regulatory mechanism of the four half-domains of LIM domain 1 (FHL1), which is significantly downregulated in the endometrium of RIF patients, in blastocyst-epithelial adhesion. Indeed, FHL1 expression was dramatically increased in normal female mid-secretory endometrial epithelial cells and was abnormally reduced in RIF patients. Furthermore, FHL1 overexpression promoted blastocyst-epithelial adhesion, and interfering with FHL1 expression in the mouse uterus significantly inhibited embryo implantation. Mechanistically, FHL1 did not regulate HOXA10 mRNA expression but increased HOXA10 protein stability and activated HOXA10, thereby promoting its regulation of downstream gene expression and the β3 integrin/FAK pathway. Meanwhile, FHL1 regulates HOXA10 function by increasing HOXA10 deacetylation through enhanced binding of HOXA10 and SIRT2. SIRT2-specific inhibitors can significantly inhibit this effect. In the endometrial epithelial cells of RIF patients, the correlation between FHL1 and HOXA10 and its downstream target genes has also been verified. Finally, our data indicated FHL1 is a regulatory molecule that promotes blastocyst-epithelial adhesion. Altogether, downstream dysfunction due to aberrant FHL1 expression is an important molecular basis for embryo implantation failure in patients with RIF and to provide new potential therapeutic targets.

Abstract Background With the rapid development of deep learning algorithms and the rapid improvement of computer hardware in the past few years, AI-assisted diagnosis software for bone age has achieved good diagnostic performance. The purpose of this study was to investigate the effect of AI-assisted software on residents’ inter-observer agreement and intra-observer reproducibility for the X-ray bone age assessment of preschool children. Methods This prospective study was approved by the Institutional Ethics Committee. Six board-certified residents interpreted 56 bone age radiographs ranging from 3 to 6 years with structured reporting by the modified TW3 method. The images were interpreted on two separate occasions, once with and once without the assistance of AI. After a washout period of 4 weeks, the radiographs were reevaluated by each resident in the same way. The reference bone age was the average bone age results of the three experts. Both TW3-RUS and TW3-Carpal were evaluated. The root mean squared error (RMSE), mean absolute difference (MAD) and bone age accuracy within 0.5 years and 1 year were used as metrics of accuracy. Interobserver agreement and intraobserver reproducibility were evaluated using intraclass correlation coefficients (ICCs). Results With the assistance of bone age AI software, the accuracy of residents’ results improved significantly. For interobserver agreement comparison, the ICC results with AI assistance among 6 residents were higher than the results without AI assistance on the two separate occasions. For intraobserver reproducibility comparison, the ICC results with AI assistance were higher than results without AI assistance between the 1st reading and 2nd reading for each resident. Conclusions For preschool children X-ray bone age assessment, in addition to improving diagnostic accuracy, bone age AI-assisted software can also increase interobserver agreement and intraobserver reproducibility. AI-assisted software can be an effective diagnostic tool for residents in actual clinical settings.

Abstract Background To investigate the effects of lighting conditions and accommodation on the three-dimensional position of Visian implantable collamer lens (ICL V4c). Methods This observational study recruited 62 eyes of 31 myopia patients underwent ICL V4c implantation. Anterior segment optical coherence tomography (AS-OCT) assessed the anterior chamber depth (ACD), ACD-ICL (distance from the corneal endothelium to anterior surface of the ICL V4c), vault (distance between the posterior ICL V4c surface and anterior crystalline lens surface), and crystalline lens tilt under various lighting conditions and accommodation relative to the corneal topographic axis at one year after ICL V4c implantation. Baseline was defined as the scotopic condition, which was also the non-accommodative stimulus condition. The ICL V4c tilt was analyzed using MATLAB. The significance level was set at P

Abstract Background Preoperative pelvic lymph node metastasis (PLNM) prediction can help clinicians determine whether to perform pelvic lymph node dissection (PLND). The purpose of this research is to explore the feasibility of diffusion-weighted imaging (DWI)-based radiomics for preoperative PLNM prediction in PCa patients at the nodal level. Methods The preoperative MR images of 1116 pathologically confirmed lymph nodes (LNs) from 84 PCa patients were enrolled. The subjects were divided into a primary cohort (67 patients with 192 positive and 716 negative LNs) and a held-out cohort (17 patients with 43 positive and 165 negative LNs) at a 4:1 ratio. Two preoperative pelvic lymph node metastasis (PLNM) prediction models were constructed based on automatic LN segmentation with quantitative radiological LN features alone (Model 1) and combining radiological and radiomics features (Model 2) via multiple logistic regression. The visual assessments of junior (Model 3) and senior (Model 4) radiologists were compared. Results No significant difference was found between the area under the curve (AUCs) of Models 1 and 2 (0.89 vs. 0.90; P = 0.573) in the held-out cohort. Model 2 showed the highest AUC (0.83, 95% CI 0.76, 0.89) for PLNM prediction in the LN subgroup with a short diameter ≤ 10 mm compared with Model 1 (0.78, 95% CI 0.70, 0.84), Model 3 (0.66, 95% CI 0.52, 0.77), and Model 4 (0.74, 95% CI 0.66, 0.88). The nomograms of Models 1 and 2 yielded C-index values of 0.804 and 0.910, respectively, in the held-out cohort. The C-index of the nomogram analysis (0.91) and decision curve analysis (DCA) curves confirmed the clinical usefulness and benefit of Model 2. Conclusions A DWI-based radiomics nomogram incorporating the LN radiomics signature with quantitative radiological features is promising for PLNM prediction in PCa patients, particularly for normal-sized LNM.

Background: Burns are a common concern around the world, with the majority of cases happening in low- and middle-income nations. China is the largest developing country. With the unremitting efforts of domestic colleagues, China has taken the lead in the treatment of burn in the world. With the change of times, we have observed some noteworthy changes in the types of patients that have admitted our Burns and Plastic Surgery, the Affiliated Hospital of Yangzhou University. Methods: This retrospective observational study included brought into; all patients reached to our burn unit during 2013–2021. The gathered data were descriptively examined and statistically contrasted with each other year. Results: Of 4407 cases admitted to burn unit during 2013–2021, men constituted 56 % of such cases, with an average age of 47.3 ± 19.3 years. Moreover, among the patients hospitalized, January and February usually admit fewer than other months. Between 2013 and 2021, both the number of patients admitted to burn unit and the expense of their hospitalization rose yearly. The percentage of burn patients admitted to burn ward of our hospital is decreasing, especially during the period of serious Coronavirus disease (COVID-19) epidemic. We also observed that during the COVID-19 pandemic, patients with superficial masses also dropped off a cliff because of government controls. Conclusion: The diseases in the department show the trend of maximizing marginal disciplines, burn surgeons are facing a more complex challenge. Further research addressing the relationship between the change of patient types and economic and social development in burn department will help to foster better pinpoint hospitalization patients need, fine service for hospitalized patients.

Abstract Background Pyroptosis is a form of programmed cell death involved in the pathophysiological progression of hypoxic pulmonary hypertension (HPH). Emerging evidence suggests that N6-methyladenosine (m6A)-modified transcripts of long noncoding RNAs (lncRNAs) are important regulators that participate in many diseases. However, whether m6A modified transcripts of lncRNAs can regulate pyroptosis in HPH progression remains unexplored. Methods The expression levels of FENDRR in hypoxic pulmonary artery endothelial cells (HPAECs) were detected by using quantitative real-time polymerase chain reaction (qRT-PCR) and fluorescence in situ hybridization (FISH). Western blot, Lactate dehydrogenase (LDH) release assay, Annexin V-FITC/PI double staining, Hoechst 33342/PI fluorescence staining and Caspase-1 activity assay were used to detect the role of FENDRR in HPAEC pyroptosis. The relationship between FENDRR and dynamin-related protein 1 (DRP1) was explored using bioinformatics analysis, Chromatin Isolation by RNA Purification (CHIRP), Electrophoretic mobility shift assay (EMSA) and Methylation-Specific PCR (MSP) assays. RNA immunoprecipitation (RIP) and m6A dot blot were used to detect the m6A modification levels of FENDRR. A hypoxia-induced mouse model of pulmonary hypertension (PH) was used to test preventive effect of conserved fragment TFO2 of FENDRR. Results We found that FENDRR was significantly downregulated in the nucleus of hypoxic HPAECs. FENDRR overexpression inhibited hypoxia-induced HPAEC pyroptosis. Additionally, DRP1 is a downstream target gene of FENDRR, and FENDRR formed an RNA–DNA triplex with the promoter of DRP1, which led to an increase in DRP1 promoter methylation that decreased the transcriptional level of DRP1. Notably, we illustrated that the m6A reader YTHDC1 plays an important role in m6A-modified FENDRR degradation. Additionally, conserved fragment TFO2 of FENDEE overexpression prevented HPH in vivo. Conclusion In summary, our results demonstrated that m6A-induced decay of FENDRR promotes HPAEC pyroptosis by regulating DRP1 promoter methylation and thereby provides a novel potential target for HPH therapy.

Single-cell RNA-seq data provide the opportunity to predict drug response in cancer while considering intratumour heterogeneity. Here, the authors develop a deep transfer learning framework - scDEAL - to predict single-cell drug responses in cancer by integrating single-cell and bulk RNA-seq data.

ObjectiveTo evaluate the effect of long-term rotation on astigmatism following Evolution-toric intraocular collamer lens (EVO-TICL) implantation.MethodsForty eyes of 22 patients were enrolled in this prospective study. Visual acuity, refractive parameters, and axial position of the EVO-TICL by OPD-Scan III aberrometer were measured preoperatively, 1 month and 3 years postoperatively.ResultsLast visit, the safety index was 1.32 ± 0.15 and the efficacy index was 1.01 ± 0.23. The best-fitting curve of the attempted versus achieved correction was y = 0.9751x + 0.001. The mean spherical equivalent (SE) decreased from −8.94 ± 2.72D preoperatively to 0.06 ± 0.24D and − 0.36 ± 0.46D 1 month and 3 years postoperatively, respectively. The mean target and surgical induced astigmatism were 1.55 ± 0.61D and 1.67 ± 0.94D 3 years postoperatively. The average expected axis of the TICL was-1.15 ± 9.07 (−21–19°). One month and 3 years postoperatively, the average actual axis was −0.70 ± 9.86 (−20–20°) and − 0.35 ± 11.72 (−25–30°), respectively. The absolute rotation of the TICL was 3.70 ± 4.42 (0–22°) and 6.00 ± 6.70 (0–32°) 1 month and 3 years postoperatively, respectively (p

Objective.This study aims to evaluate the morphometrics of normal adrenal glands in adult patients semiautomatically using a deep learning-based segmentation model.Materials and Methods.A total of 520 abdominal CT image series with normal findings, from January 1, 2016, to March 14, 2019, were retrospectively collected for the training of the adrenal segmentation model. Then, 1043 portal venous phase image series of inpatient contrast-enhanced abdominal CT examinations with normal adrenal glands were included for analysis and grouped by every 10-year gap. A 3D U-Net-based segmentation model was used to predict bilateral adrenal labels followed by manual modification of labels as appropriate. Quantitative parameters (volume, CT value, and diameters) of the bilateral adrenal glands were then analyzed.Results.In the study cohort aged 18–77 years old (554 males and 489 females), the left adrenal gland was significantly larger than the right adrenal gland [all patients, 2867.79 (2317.11–3499.89) mm3 vs. 2452.84 (1983.50–2935.18) mm3, P

AimTo investigate the long-term safety, efficacy, and binocular balance of monovision surgery using Implantable Collamer Lens (ICL) V4c implantation and Femtosecond Laser-Assisted in situ Keratomileusis (FS-LASIK) for the treatment of myopic patients with presbyopia.MethodsThis case series study involved 90 eyes of 45 patients (male/female = 19/26; average age:46.27 ± 5.54 years; average follow-up time:48.73 ± 14.65 months) who underwent the aforementioned surgery to treat myopic presbyopes. Data on manifest refraction, corrected distance visual acuity, dominant eye, presbyopic addition, intraocular pressure, and anterior segment biometric parameters were collected. The visual outcomes and binocular balance at 0.4 m, 0.8 m, and 5 m were documented.ResultsThe safety index for the ICL V4c and FS-LASIK groups were 1.24 ± 0.27 and 1.04 ± 0.20 (p = 0.125), respectively. Binocular visual acuity (logmar) for 0.4 m, 0.8 m, and 5 m were −0.03 ± 0.05, −0.03 ± 0.02, and 0.10 ± 0.03 for the ICL V4c group, and −0.02 ± 0.09, −0.01 ± 0.02, and 0.06 ± 0.04 for the FS-LASIK group, respectively. The proportions of all patients with imbalanced vision at 0.4 m, 0.8 m, and 5 m distances were 68.89, 71.11, and 82.22%, respectively (all p > 0.05 between the two groups). There were significant differences in refraction between the balanced and imbalanced vision for patients at 0.4 m distance (for non-dominant eye spherical equivalent [SE]: −1.14 ± 0.17D and −1.47 ± 0.13D, p