New insights into starch, lipid, and protein interactions - Colon microbiota fermentation

Starch, lipids, and proteins are essential biological macromolecules that play a crucial role in providing energy and nutrition to our bodies. Interactions between these macromolecules have been shown to impact starch digestibility. Understanding and controlling starch digestibility is a key area of research. Investigating the mechanisms behind the interactions of these three components and their influence on starch digestibility is of significant practical importance. Moreover, these interactions can result in the formation of resistant starch, which can be fermented by gut microbiota in the colon, leading to various health benefits. While current research has predominantly focused on the digestive properties of starch in the small intestine, there is a notable gap in understanding the colonic microbial fermentation phase of resistant starch. The benefits of fermentation of resistant starch in the colon may outweigh its glucose-lowering effect in the small intestine. Thus, it is crucial to study the fermentation behavior of resistant starch in the colon. This paper investigates the impact of interactions among starch, lipids, and proteins on starch digestion, with a specific focus on the fermentation phase of indigestible carbohydrates in the colon. Furthermore, valuable insights are offered for guiding future research endeavors.

The antagonism mechanism of astilbin against cadmium-induced injury in chicken lungs via Treg/Th1 balance signaling pathway

The purpose of this study was to investigate the effect of Treg/Th1 imbalance in cadmium-induced lung injury and the potential protective effect of astilbin against cadmium-induced lung injury in chicken. Cadmium exposure significantly decreased T-AOC and GSH-Px levels and SOD activity in the chicken lung tissues. In contrast, it significantly increased the MDA and NO levels. These results indicate that cadmium triggers oxidative stress in lungs. Histopathological analysis revealed that cadmium exposure further induced infiltration of lymphocytes in the chicken lungs, indicating that cadmium causes pulmonary damage. Further analysis revealed that cadmium decreased the expression of IL-4 and IL-10 but increased those of IL-17, Foxp3, TNF-α, and TGF-β, indicating that the exposure of cadmium induced the imbalance of Treg/Th1. Moreover, cadmium adversely affected chicken lung function by activating the NF-kB pathway and inducing expression of genes downstream to these pathways (COX-2, iNOS), associated with inflammatory injury in the lung tissue. Astilbin reduced cadmium-induced oxidative stress and inflammation in the lungs by increasing antioxidant enzyme activities and restoring Treg/Th1 balance. In conclusion, our results suggest that astilbin treatment alleviated the effects of cadmium-mediated lung injury in chickens by restoring the Treg/Th1 balance.

Comparative Analysis of Multimodal Large Language Model Performance on Clinical Vignette Questions

This study compares 2 large language models and their performance vs that of competing open-source models.

Study on numerical simulation and mechanical properties of anchor cable with C-shaped tube subjected to shearing

To examine the disparity in deformation behavior and mechanical qualities between anchor cables with C-shaped tubes and regular anchor cables under shear conditions. The double-sided shear tests of free-section anchor cables and anchor cables with C-shaped tubes were conducted utilizing the indoor large-scale double-shear test equipment with varying pretension loads. The indoor double-shear tests indicate that the inclusion of the C-shaped tube alters the stress distribution of the anchor cables inside the anchor cables with C-shaped tubes and mitigates the impact of stress concentration. Moreover, it facilitates the transition of the anchor cable's failure mode from a mix of tensile and shear breaking to mainly tensile breakage. In addition, ABAQUS finite element analysis software was used to establish a double shear test model of the anchor cable with C-shaped tube to accurately simulate the interaction and stress distribution among the anchor cable, C-shaped tube, and concrete block in the double shear test. The findings of the simulation results reveal that the numerical model can adequately depict the evolution of the stress distribution in the prestressed anchored structure and the damage of the concrete block with increasing shear displacement. The relational equation for the yield state of the anchor cable with C-shaped tube under combined tensile and shear loads is found by integrating the experimental and simulation data, the static beam theory, and the concept of minimal potential energy.

Schiff base fluorescent sensor with aggregation induced emission characteristics for the sensitive and specific Fe3+ detection

An aggregation induced emission based compound ((E)-4-((2-hydroxy-5-methoxybenzylidene)amino)benzoic acid) was synthesized through facile Schiff base condensation and characterized by various spectral techniques. The as-prepared compound represented a typical aggregation induced emission behavior in aqueous solution and exploited as a turn-off fluorescent sensor for Fe3+ detection in THF-H2O system (3:7, v/v) with high sensitivity and selectivity. The mechanism of the fluorescence quenching was intensively studied, which was attributed to both dynamic quenching and inner filter effect. The fluorescence probe displayed a highly broad dynamic response range (0.5-500 μM) for selective detection of Fe3+ with a limit of detection of 0.079 μM. The proposed method was successfully employed for detection and quantification of Fe3+ in human urine samples and proved to have potential for practical applications in biological field.

Phosphorylated SHMT2 Regulates Oncogenesis Through m6 A Modification in Lung Adenocarcinoma

Targeting cancer-specific metabolic processes is a promising therapeutic strategy. Here, this work uses a compound library that directly inhibits metabolic enzymes to screen the potential metabolic targets in lung adenocarcinoma (LUAD). SHIN1, the specific inhibitor of serine hydroxymethyltransferase 1/2 (SHMT1/2), has a highly specific inhibitory effect on LUAD cells, and this effect depends mainly on the overexpression of SHMT2. This work clarifies that mitogen-activated protein kinase 1 (MAPK1)-mediated phosphorylation at Ser90 is the key mechanism underlying SHMT2 upregulation in LUAD and that this phosphorylation stabilizes SHMT2 by reducing STIP1 homology and U-box containing protein 1 (STUB1)-mediated ubiquitination and degradation. SHMT2-Ser90 dephosphorylation decreases S-adenosylmethionine levels in LUAD cells, resulting in reduced N6 -methyladenosine (m6 A) levels in global RNAs without affecting total protein or DNA methylation. Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) and RNA sequencing (RNA-Seq) analyses further demonstrate that SHMT2-Ser90 dephosphorylation accelerates the RNA degradation of oncogenic genes by reducing m6 A modification, leading to the inhibition of tumorigenesis. Overall, this study elucidates a new regulatory mechanism of SHMT2 during oncogenesis and provides a theoretical basis for targeting SHMT2 as a therapeutic target in LUAD.

Large language models streamline automated machine learning for clinical studies

A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical data analysis. We investigated the potential of the ChatGPT Advanced Data Analysis (ADA), an extension of GPT-4, to bridge this gap and perform ML analyses efficiently. Real-world clinical datasets and study details from large trials across various medical specialties were presented to ChatGPT ADA without specific guidance. ChatGPT ADA autonomously developed state-of-the-art ML models based on the original study's training data to predict clinical outcomes such as cancer development, cancer progression, disease complications, or biomarkers such as pathogenic gene sequences. Following the re-implementation and optimization of the published models, the head-to-head comparison of the ChatGPT ADA-crafted ML models and their respective manually crafted counterparts revealed no significant differences in traditional performance metrics (p ≥ 0.072). Strikingly, the ChatGPT ADA-crafted ML models often outperformed their counterparts. In conclusion, ChatGPT ADA offers a promising avenue to democratize ML in medicine by simplifying complex data analyses, yet should enhance, not replace, specialized training and resources, to promote broader applications in medical research and practice.

Mechanism and role of mitophagy in the development of severe infection

Mitochondria produce adenosine triphosphate and potentially contribute to proinflammatory responses and cell death. Mitophagy, as a conservative phenomenon, scavenges waste mitochondria and their components in the cell. Recent studies suggest that severe infections develop alongside mitochondrial dysfunction and mitophagy abnormalities. Restoring mitophagy protects against excessive inflammation and multiple organ failure in sepsis. Here, we review the normal mitophagy process, its interaction with invading microorganisms and the immune system, and summarize the mechanism of mitophagy dysfunction during severe infection. We highlight critical role of normal mitophagy in preventing severe infection.

Open Distal Femur Fractures Treated with Bone Cement Intramedullary Support Combined with Locked Plate Fixation

Objective

The objective of this study was to assess the short-term clinical efficacy of the short-term clinical efficacy of bone cement intramedullary support combined with locked plate fixation in the treatment of such fractures.

Methods

A retrospective study including 21 patients was reviewed at an urban level one trauma center. There were 17 males and 4 females, with a mean age of 33.9 years. Gustilo grade was II (12 cases), III-A (6 cases), III-B (2 cases), and III-C (1 case). Two fractures were AO-OTA type 33A3, 9 cases were type 33C2, and 10 cases were type 33C3. After the first stage debridement and temporary external fixation, all patients received bone cement intramedullary support combined with locked plate fixation through an anterolateral incision at the second stage.. The perioperative complications, need for bone graft, alignment, and radiographic union were recorded. At 1-year follow-up, the range of knee motion was recorded, and functional results were evaluated by the Hospital for Special Surgery (HSS) knee score.

Results

All 21 patients were followed up for 12-36 months, with an average of 18.7 months. 1 case had superficial wound infection, and 2 cases had partial skin edge necrosis of the original open wound. After symptomatic dressing changes, they all healed well. 4 cases had autogenous bone grafting. 18 patients (85.7%) achieved radiographic union, with a mean union time of 6.2 months. Two patients underwent secondary operation 9 months after surgery due to nonunion and finally united after autologous bone grafting. One patient developed a deep infection 8 months after surgery and was successfully treated with Masquelet technique. Finally, bone union was achieved 7 months after surgery. The alignment was good in 17 patients (81.0%). No deep infection or hardware failure occurred during 1-year follow-up. The average range of knee extension and flexion was 5.2 ° and 106.8 °, respectively. The HSS score averaged 83.6.

Conclusions

Bone cement intramedullary support combined with locked plate fixation was an effective treatment modality of open distal femur fractures with high union rate, low complication, adequate alignment and satisfactory functional outcomes.

[Establishment and efficiency test of a clinical prediction model of bronchopulmonary dysplasia associated pulmonary hypertension in very premature infants]

Objective: To develop a risk prediction model for identifying bronchopulmonary dysplasia (BPD) associated pulmonary hypertension (PH) in very premature infants. Methods: This was a retrospective cohort study. The clinical data of 626 very premature infants whose gestational age <32 weeks and who suffered from BPD were collected from October 1st, 2015 to December 31st, 2021 of the Seventh Medical Center of the People's Liberation Army General Hospital as a modeling set. The clinical data of 229 very premature infants with BPD of Hunan Children's Hospital from January 1 st, 2020 to December 31st, 2021 were collected as a validation set for external verification. The very premature infants with BPD were divided into PH group and non PH group based on the echocardiogram after 36 weeks' corrected age in the modeling set and validation set, respectively. Univariate analysis was used to compare the basic clinical characteristics between groups, and collinearity exclusion was carried out between variables. The risk factors of BPD associated PH were further screened out by multivariate Logistic regression, and the risk assessment model was established based on these variables. The receiver operating characteristic (ROC) area under curve (AUC) and Hosmer-Lemeshow goodness-of-fit test were used to evaluate the model's discrimination and calibration power, respectively. And the calibration curve was used to evaluate the accuracy of the model and draw the nomogram. The bootstrap repeated sampling method was used for internal verification. Finally, decision curve analysis (DCA) to evaluate the clinical practicability of the model was used. Results: A total of 626 very premature infants with BPD were included for modeling set, including 85 very premature infants in the PH group and 541 very premature infants in the non PH group. A total of 229 very premature infants with BPD were included for validation set, including 24 very premature infants in the PH group and 205 very premature infants in the non PH group. Univariate analysis of the modeling set found that 22 variables, such as artificial conception, fetal distress, gestational age, birth weight, small for gestational age, 1 minute Apgar score ≤7, antenatal corticosteroids, placental abruption, oligohydramnios, multiple pulmonary surfactant, neonatal respiratory distress syndrome (NRDS)>stage Ⅱ, early pulmonary hypertension, moderate-severe BPD, and hemodynamically significant patent ductus arteriosus (hsPDA) all had statistically significant influence between the PH group and the non PH group (all P<0.05). Antenatal corticosteroids, fetal distress, NRDS >stage Ⅱ, hsPDA, pneumonia and days of invasive mechanical ventilation were identified as predictive variables and finally included to establish the Logistic regression model. The AUC of this model was 0.86 (95%CI 0.82-0.90), the cut-off value was 0.17, the sensitivity was 0.77, and the specificity was 0.84. Hosmer-Lemeshow goodness-of-fit test showed that P>0.05. The AUC for external validation was 0.88, and the Hosmer-Lemeshow goodness-of-fit test suggested P>0.05. Conclusions: A high sensitivity and specificity risk prediction model of PBD associated PH in very premature infants was established. This predictive model is useful for early clinical identification of infants at high risk of BPD associated PH.

Encrypted federated learning for secure decentralized collaboration in cancer image analysis

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.

A bibliometric analysis and visualization of retinopathy of prematurity from 2001 to 2021

OBJECTIVE

Retinopathy of prematurity (ROP) is an eye disease with the potential to cause blindness, primarily affecting premature infants with low birth weight. This study analyzed the etiology, primary location, and research advances in ROP.

MATERIALS AND METHODS

We used bibliometric techniques and searched the Web of Science Core Collection for "retinopathy of prematurity." We found 4,018 original articles and reviews with 69,819 references. We analyzed the data using HistCite (12.03.17), VOSviewer (1.6.16), CiteSpace (6.1. R5), and the Bibliometrix Package (4.1.0).

RESULTS

The amount of literature in this area has increased between 2001-2021. An analysis of references and journal co-citations highlights this field's most influential articles and related topics. Hellström, from the University of Gothenburg (Sweden), is the most prolific researcher; Harvard University is the most prolific research institution, and the USA is the most productive country. "Threshold ROP" and "cryotherapy" are the keywords with the highest burst strength. The future research hotspots are artificial intelligence, zone II, ROP development, ranibizumab, and type 1 retinopathy.

CONCLUSIONS

This article offers a comprehensive review of the present status of ROP research, along with insights into emerging concepts and potential international collaborations in this field.

Alzheimer's Disease and Aging Association: Identification and Validation of Related Genes

BACKGROUND

Aging is considered a key risk factor for Alzheimer's disease (AD). This study aimed to identify and validate potential aging-related genes associated with AD using bioinformatics analysis.

METHODS

Datasets GSE36980 and GSE5281 were selected to screen differentially expressed genes (DEGs), and the immune cell correlation analysis and GSEA analysis of DEGs were performed. The intersection with senescence genes was taken as differentially expressed senescence-related genes (DESRGs), and the GSE44770 dataset was used for further validation. The potential biological functions and signaling pathways were determined by GO and KEGG, and the hub genes were identified by 12 algorithms in Cytohubba. The expression of 10 hub genes in different brain regions was determined and single-cell sequencing analysis was performed, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed and targeted drug prediction was performed.

RESULTS

A total of 2137 DEGs were screened from the GSE36980 and GSE5281 datasets, and 278 SRGs were identified from the CellAge database. The overlapping DEGs and SRGs constituted 29 DESRGs, including 14 senescence suppressor genes and 15 senescence inducible genes. The top 10 hub genes, including MDH1, CKB, PSMD14, SMARCA4, PEBP1, DDB2, ITPKB, ATF7IP, YAP1, and EWSR1 were screened. Furthermore, four diagnostic genes were identified: PMSD14, PEBP1, ITPKB, and ATF7IP. The ROC analysis showed that the respective area under the curves (AUCs) of PMSD14, PEBP1, ITPKB, and ATF7IP were 0.732, 0.701, 0.747, and 0.703 in the GSE36980 dataset and 0.870, 0.817, 0.902, and 0.834 in the GSE5281 dataset. In the GSE44770 dataset, PMSD14 (AUC, 0.838) and ITPKB (AUC, 0.952) had very high diagnostic values in the early stage of AD. Finally, based on these diagnostic genes, we found that the drug Abemaciclib is a targeted drug for the treatment of age-related AD. Flutamide can aggravate aging-related AD.

CONCLUSION

The results of this study suggest that cellular SRGs might play an important role in AD. PMSD14, PEBP1, ITPKB, and ATF7IP have the potential as specific biomarkers for the early diagnosis of AD.

TNFSF14 mediates the impact of docosahexaenoic acid on atopic dermatitis: a Mendelian randomization study

OBJECTIVE

While current research suggests potential value for docosahexaenoic acid (DHA) in the prevention and management of atopic dermatitis (AD), the causal relationship between DHA and AD remains unclear, and the underlying mechanisms are not well understood.

MATERIALS AND METHODS

To investigate the potential causal relationship between DHA and AD, as well as to explore potential mediating mechanisms, we employed the Mendelian randomization (MR) methods. To study these potential relationships, we conducted MR analysis using publicly available Genome-Wide Association Studies (GWAS) data. Effect estimates were computed using the random-effects inverse-variance weighted method.

RESULTS

Our study demonstrates a negative correlation between DHA levels and AD risk (OR: 0.915, 95% CI: 0.858-0.975, p=0.007). Furthermore, in MR analysis using tumor necrosis factor ligand superfamily member 14 (TNFSF14) levels as an outcome, DHA levels also show a negative association with TNFSF14 levels (OR: 0.933, 95% CI: 0.879-0.990, p=0.022). Subsequently, we performed further analysis to explore the relationship between TNFSF14 and AD risk, revealing a positive correlation (OR: 1.069, 95% CI: 1.005-1.137, p=0.033). This suggests a potential mediating role of TNFSF14 in the impact of DHA on AD risk.

CONCLUSIONS

In summary, our study employs MR analysis to offer genetic evidence indicating a potential role of DHA in reducing the risk of AD, as well as opening avenues for further in-depth investigation into potential mechanisms. These findings emphasize the importance of ongoing research in this field.

SUCLG2 Regulates Mitochondrial Dysfunction through Succinylation in Lung Adenocarcinoma

Mitochondrial dysfunction and abnormal energy metabolism are major features of cancer. However, the mechanisms underlying mitochondrial dysfunction during cancer progression are far from being clarified. Here, it is demonstrated that the expression level of succinyl-coenzyme A (CoA) synthetase GDP-forming subunit β (SUCLG2) can affect the overall succinylation of lung adenocarcinoma (LUAD) cells. Succinylome analysis shows that the deletion of SUCLG2 can upregulate the succinylation level of mitochondrial proteins and inhibits the function of key metabolic enzymes by reducing either enzymatic activity or protein stability, thus dampening mitochondrial function in LUAD cells. Interestingly, SUCLG2 itself is also succinylated on Lys93, and this succinylation enhances its protein stability, leading to the upregulation of SUCLG2 and promoting the proliferation and tumorigenesis of LUAD cells. Sirtuin 5 (SIRT5) desuccinylates SUCLG2 on Lys93, followed by tripartite motif-containing protein 21 (TRIM21)-mediated ubiquitination through K63-linkage and degradation in the lysosome. The findings reveal a new role for SUCLG2 in mitochondrial dysfunction and clarify the mechanism of the succinylation-mediated protein homeostasis of SUCLG2 in LUAD, thus providing a theoretical basis for developing anti-cancer drugs targeting SUCLG2.

Multimodal Deep Learning for Integrating Chest Radiographs and Clinical Parameters: A Case for Transformers

Background Clinicians consider both imaging and nonimaging data when diagnosing diseases; however, current machine learning approaches primarily consider data from a single modality. Purpose To develop a neural network architecture capable of integrating multimodal patient data and compare its performance to models incorporating a single modality for diagnosing up to 25 pathologic conditions. Materials and Methods In this retrospective study, imaging and nonimaging patient data were extracted from the Medical Information Mart for Intensive Care (MIMIC) database and an internal database comprised of chest radiographs and clinical parameters inpatients in the intensive care unit (ICU) (January 2008 to December 2020). The MIMIC and internal data sets were each split into training (n = 33 893, n = 28 809), validation (n = 740, n = 7203), and test (n = 1909, n = 9004) sets. A novel transformer-based neural network architecture was trained to diagnose up to 25 conditions using nonimaging data alone, imaging data alone, or multimodal data. Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC) analysis. Results The MIMIC and internal data sets included 36 542 patients (mean age, 63 years ± 17 [SD]; 20 567 male patients) and 45 016 patients (mean age, 66 years ± 16; 27 577 male patients), respectively. The multimodal model showed improved diagnostic performance for all pathologic conditions. For the MIMIC data set, the mean AUC was 0.77 (95% CI: 0.77, 0.78) when both chest radiographs and clinical parameters were used, compared with 0.70 (95% CI: 0.69, 0.71; P < .001) for only chest radiographs and 0.72 (95% CI: 0.72, 0.73; P < .001) for only clinical parameters. These findings were confirmed on the internal data set. Conclusion A model trained on imaging and nonimaging data outperformed models trained on only one type of data for diagnosing multiple diseases in patients in an ICU setting. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Kitamura and Topol in this issue.

A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis

Although generative adversarial networks (GANs) can produce large datasets, their limited diversity and fidelity have been recently addressed by denoising diffusion probabilistic models, which have demonstrated superiority in natural image synthesis. In this study, we introduce Medfusion, a conditional latent DDPM designed for medical image generation, and evaluate its performance against GANs, which currently represent the state-of-the-art. Medfusion was trained and compared with StyleGAN-3 using fundoscopy images from the AIROGS dataset, radiographs from the CheXpert dataset, and histopathology images from the CRCDX dataset. Based on previous studies, Progressively Growing GAN (ProGAN) and Conditional GAN (cGAN) were used as additional baselines on the CheXpert and CRCDX datasets, respectively. Medfusion exceeded GANs in terms of diversity (recall), achieving better scores of 0.40 compared to 0.19 in the AIROGS dataset, 0.41 compared to 0.02 (cGAN) and 0.24 (StyleGAN-3) in the CRMDX dataset, and 0.32 compared to 0.17 (ProGAN) and 0.08 (StyleGAN-3) in the CheXpert dataset. Furthermore, Medfusion exhibited equal or higher fidelity (precision) across all three datasets. Our study shows that Medfusion constitutes a promising alternative to GAN-based models for generating high-quality medical images, leading to improved diversity and less artifacts in the generated images.

STUB1-mediated ubiquitination regulates the stability of GLUD1 in lung adenocarcinoma

The dysregulation of glutamine metabolism provides survival advantages for tumors by supplementing tricarboxylic acid cycle. Glutamate dehydrogenase 1 (GLUD1) is one of the key enzymes in glutamine catabolism. Here, we found that enhanced protein stability was the key factor for the upregulation of GLUD1 in lung adenocarcinoma. We discovered that GLUD1 showed a high protein expression in lung adenocarcinoma cells or tissues. We elucidated that STIP1 homology and U-box-containing protein 1 (STUB1) was the key E3 ligase responsible for ubiquitin-mediated proteasomal degradation of GLUD1. We further showed that lysine 503 (K503) was the main ubiquitination site of GLUD1, inhibiting the ubiquitination at this site promoted the proliferation and tumor growth of lung adenocarcinoma cells. Taken together, this study clarifies the molecular mechanism of GLUD1 in maintaining protein homeostasis in lung adenocarcinoma, which provides a theoretical basis for the development of anti-cancer drugs targeting GLUD1.

Quality analysis and metabolomic profiling of the effects of exogenous abscisic acid on rabbiteye blueberry

Blueberry is a characteristic berry fruit shrub of the genus Vaccinium in the Rhododendron family. The fruit is rich in anthocyanins and has a variety of nutritional and health functions. This study aimed to systematically study the effect of exogenous abscisic acid (ABA) application on ripening and metabolites in blueberry fruits. Blueberry fruit ripening was divided into six stages for further analysis. In this study, nontarget metabolomics was performed to demonstrate the effect on metabolite levels. The results showed that 1000 mg/L ABA significantly promoted fruit ripening and increased anthocyanin content. Moreover, exogenous ABA treatment can affect endogenous ABA levels and improve its antioxidant capacity. Important metabolites of the flavonoid pathway were detected, and the results showed that anthocyanin synthesis increased, and some other bioactive metabolite levels decreased. After comprehensive assessments, we believe that 1000 mg/L exogenous ABA application will have positive impacts on blueberry fruit quality and economic benefits.

Fabricating a photochromic benzonitrile Schiff base into a low-cost reusable paper-based wearable sensor for naked-eye dosimetry of UV radiations

We report in this study a photochromic benzonitrile Schiff base, (E)-4-((2-hydroxy-4-methoxybenzylidene)amino)benzonitrile (HMBAB). The molecular design, synthesis, aggregation-induced emission (AIE) as well as the quantum chemical calculations were outlined. In particular, HMBAB would undergo a reversible tautomerism in response to UV exposure, exhibiting remarkable changes in both absorption and emission: the compound shows yellow color and green-yellow luminescence; after UV exposure, the changes into orange-red while the luminescence is dramatically quenched, accompanied by a large bathochromic-shift. In addition, the photochromic state can be fully recovered via thermal treatment. Such reversible dual-channel photochromism was investigated using UV-vis reflectance spectroscopy and colorimeter, wherein a gradient change with time and a high fatigue resistance in cycle use was recorded. The photochromism is quantified by well-established RGB and Lab color space, in which the color change can be accurately analyzed by the chromatic aberration (ΔE*_Lab). Sensitivity test gives a two-stage linear relation between ΔE*_Lab and UV intensity, by which a limit of detection (LOD) as low as 67 μW/cm² is obtained. HMBAB was further fabricated into a paper-based wearable sensor, capable of being integrated into a chest card or a bracelet. It exhibits various degrees of color change in different sunlight environments, which can be readily observed by naked eyes, providing an early warning for high-dose UV radiations.

Medical transformer for multimodal survival prediction in intensive care: integration of imaging and non-imaging data

When clinicians assess the prognosis of patients in intensive care, they take imaging and non-imaging data into account. In contrast, many traditional machine learning models rely on only one of these modalities, limiting their potential in medical applications. This work proposes and evaluates a transformer-based neural network as a novel AI architecture that integrates multimodal patient data, i.e., imaging data (chest radiographs) and non-imaging data (clinical data). We evaluate the performance of our model in a retrospective study with 6,125 patients in intensive care. We show that the combined model (area under the receiver operating characteristic curve [AUROC] of 0.863) is superior to the radiographs-only model (AUROC = 0.811, p < 0.001) and the clinical data-only model (AUROC = 0.785, p < 0.001) when tasked with predicting in-hospital survival per patient. Furthermore, we demonstrate that our proposed model is robust in cases where not all (clinical) data points are available.

[Focusing on timing selection and whole-course management of liver transplantation treatment for patients with acute-on-chronic liver failure]

Acute-on-chronic liver failure (ACLF) is a clinical syndrome of acute decompensation accompanied by organ failure that occurs on the basis of chronic liver disease and has a high short-term mortality rate. Currently, there are still differences in relation to the definition of ACLF; thus, baseline characteristics and dynamic changes are important bases for clinical decision-making in patients with liver transplantation and others. The basic strategies for treating ACLF currently include internal medicine treatment, artificial liver support systems, and liver transplantation. Multidisciplinary active collaborative management throughout the whole course is of great significance for further improving the survival rate in patients with ACLF.

Inflammation Modifies miR-21 Expression Within Neuronal Extracellular Vesicles to Regulate Remyelination Following Spinal Cord Injury

Cell‒cell communication following spinal cord injury (SCI) plays a key role in remyelination and neurological recovery. Although communication between neuron-neural stem cells (NSCs) affects remyelination, its precise mechanism remains unclear. The present study investigated the biological effects of extracellular vesicles (EVs) derived from neurons on the differentiation of NSCs and the remyelination of axons in a rat model for SCI. We found that that EVs derived from neurons promoted the differentiation of NSCs into oligodendrocytes and the remyelination of axons in SCI rats. However, neuron-derived EVs lost their biological effects after inflammatory stimulation of these neurons from which they originate. Further analysis demonstrated that the inflammatory stimulation on neurons upregulated miR-21 within EVs, which targeted SMAD 7 and upregulated the TGF-β/SMAD2 signaling pathway, resulting in an excess of astrocytic scar boundaries and in remyelination failure. Moreover, these effects could be abolished by miR-21 inhibitors/antagomirs. Considered together, these results indicate that inflammatory stimulation of neurons prevents remyelination following SCI via the upregulation of miR-21 expression within neuron-derived EVs, and this takes place through SMAD 7-mediated activation of the TGF-β/SMAD2 signaling pathway. Graphical Astract.

Correlation of pericoronary adipose tissue CT attenuation values of plaques and periplaques with plaque characteristics

AIM

To investigate the relationship between different plaque characteristics and pericoronary adipose tissue (PCAT) computed tomography (CT) attenuation values for plaques and periplaques.

MATERIALS AND METHODS

The data from 188 eligible patients with stable coronary heart disease (280 lesions) who underwent coronary CT angiography between March 2021 and November 2021 were collected retrospectively. All PCAT CT attenuation values of plaques and periplaques (the area within 5 and 10 mm proximal and distal to the plaque) were calculated, and multiple linear regression was used to assess their correlation with different plaque characteristics.

RESULTS

PCAT CT attenuation of plaques and periplaques was higher in non-calcified plaques (-73.38 ± 10.41 HU, -76.77 ± 10.86 HU, 79.33 ± 11.13 HU, -75.67 ± 11.24 HU, -78.63 ± 12.09 HU) and mixed plaques (-76.83 ± 8.11 HU, -79 [-85, -68.5] HU, -78.55 ± 11 HU, -78.76 ± 9.9 HU, -78.79 ± 11.06 HU) than in calcified plaques (-86.96 ± 10 HU, -84 [-92, -76] HU, -84.14 ± 11.08 HU, -84.91 ± 11.41 HU, -84.59 ± 11.69 HU; all p<0.05) and higher in distal segment plaques than in proximal segment plaques (all p<0.05). Plaque PCAT CT attenuation was lower in plaques with minimal stenosis than in plaques with mild or moderate stenosis (p<0.05). The significant determinants of PCAT CT attenuation values of plaques and periplaques were non-calcified plaques, mixed plaques, and plaques located in the distal segment (all p<0.05).

CONCLUSIONS

PCAT CT attenuation values in both plaques and periplaques were related to plaque type and location.

Denoising diffusion probabilistic models for 3D medical image generation

Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen, and Stable Diffusion. However, their use in medicine, where imaging data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy-preserving artificial intelligence and can also be used to augment small datasets. We show that diffusion probabilistic models can synthesize high-quality medical data for magnetic resonance imaging (MRI) and computed tomography (CT). For quantitative evaluation, two radiologists rated the quality of the synthesized images regarding "realistic image appearance", "anatomical correctness", and "consistency between slices". Furthermore, we demonstrate that synthetic images can be used in self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (Dice scores, 0.91 [without synthetic data], 0.95 [with synthetic data]).

Amino acid metabolism regulated by lncRNAs: the propellant behind cancer metabolic reprogramming

Metabolic reprogramming is one of the main characteristics of cancer cells and plays pivotal role in the proliferation and survival of cancer cells. Amino acid is one of the key nutrients for cancer cells and many studies have focused on the regulation of amino acid metabolism, including the genetic alteration, epigenetic modification, transcription, translation and post-translational modification of key enzymes in amino acid metabolism. Long non-coding RNAs (lncRNAs) are composed of a heterogeneous group of RNAs with transcripts of more than 200 nucleotides in length. LncRNAs can bind to biological molecules such as DNA, RNA and protein, regulating the transcription, translation and post-translational modification of target genes. Now, the functions of lncRNAs in cancer metabolism have aroused great research interest and significant progress has been made. This review focuses on how lncRNAs participate in the reprogramming of amino acid metabolism in cancer cells, especially glutamine, serine, arginine, aspartate, cysteine metabolism. This will help us to better understand the regulatory mechanism of cancer metabolic reprogramming and provide new ideas for the development of anti-cancer drugs. Video Abstract.

Inflammatory stimulation of astrocytes affects the expression of miRNA-22-3p within NSCs-EVs regulating remyelination by targeting KDM3A

BACKGROUND

Endogenous neural stem cells (NSCs) are critical for the remyelination of axons following spinal cord injury (SCI). Cell-cell communication plays a key role in the regulation of the differentiation of NSCs. Astrocytes act as immune cells that encounter early inflammation, forming a glial barrier to prevent the spread of destructive inflammation following SCI. In addition, the cytokines released from astrocytes participate in the regulation of the differentiation of NSCs. The aim of this study was to investigate the effects of cytokines released from inflammation-stimulated astrocytes on the differentiation of NSCs following SCI and to explore the influence of these cytokines on NSC-NSC communication.

RESULTS

Lipopolysaccharide stimulation of astrocytes increased bone morphogenetic protein 2 (BMP2) release, which not only promoted the differentiation of NSCs into astrocytes and inhibited axon remyelination in SCI lesions but also enriched miRNA-22-3p within extracellular vesicles derived from NSCs. These miRNA-22 molecules function as a feedback loop to promote NSC differentiation into oligodendrocytes and the remyelination of axons following SCI by targeting KDM3A.

CONCLUSIONS

This study revealed that by releasing BMP2, astrocytes were able to regulate the differentiation of NSCs and NSC-NSC communication by enriching miRNA-22 within NSC-EVs, which in turn promoted the regeneration and remyelination of axons by targeting the KDM3A/TGF-beta axis and the recovery of neurological outcomes following SCI.

Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images

Background Reducing the amount of contrast agent needed for contrast-enhanced breast MRI is desirable. Purpose To investigate if generative adversarial networks (GANs) can recover contrast-enhanced breast MRI scans from unenhanced images and virtual low-contrast-enhanced images. Materials and Methods In this retrospective study of breast MRI performed from January 2010 to December 2019, simulated low-contrast images were produced by adding virtual noise to the existing contrast-enhanced images. GANs were then trained to recover the contrast-enhanced images from the simulated low-contrast images (approach A) or from the unenhanced T1- and T2-weighted images (approach B). Two experienced radiologists were tasked with distinguishing between real and synthesized contrast-enhanced images using both approaches. Image appearance and conspicuity of enhancing lesions on the real versus synthesized contrast-enhanced images were independently compared and rated on a five-point Likert scale. P values were calculated by using bootstrapping. Results A total of 9751 breast MRI examinations from 5086 patients (mean age, 56 years ± 10 [SD]) were included. Readers who were blinded to the nature of the images could not distinguish real from synthetic contrast-enhanced images (average accuracy of differentiation: approach A, 52 of 100; approach B, 61 of 100). The test set included images with and without enhancing lesions (29 enhancing masses and 21 nonmass enhancement; 50 total). When readers who were not blinded compared the appearance of the real versus synthetic contrast-enhanced images side by side, approach A image ratings were significantly higher than those of approach B (mean rating, 4.6 ± 0.1 vs 3.0 ± 0.2; P < .001), with the noninferiority margin met by synthetic images from approach A (P < .001) but not B (P > .99). Conclusion Generative adversarial networks may be useful to enable breast MRI with reduced contrast agent dose. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bahl in this issue.

[Risk factors of gastrointestinal polypectomy concurrent with bleeding in patients with liver cirrhosis]

Objective: To investigate and analyze the occurrence and the related risk factors of gastrointestinal polypectomy accompanied by bleeding in patients with liver cirrhosis. Methods: 127 cases of gastrointestinal polyps with cirrhosis who had endoscopy at the Endoscopic Center of Tianjin Third Central Hospital between November 2017 and November 2020 were collected. At the same time, 127 cases of gastrointestinal polyps with non-cirrhosis that were treated by endoscopy were collected for comparison. The occurrence of hemorrhagic complications between the two groups was compared. The effects of age, sex, liver function, peripheral blood leukocytes, hemoglobin, platelets, blood glucose, the international normalized ratio (INR), polyp resection method, polyp location, size, number, endoscopic morphology, pathology, the presence or absence of diabetes, portal vein thrombosis, and esophageal varices on polypectomy bleeding in the cirrhosis group were analyzed. The measurement data between groups were compared using the t-test and rank sum test. The χ (2) test or Fisher's exact probability method, and multivariate logistic regression analysis were used for the comparison of categorical data between groups. Results: The number of polypectomy bleeding cases in the cirrhotic group was 21, with a bleeding rate of 16.5%. The number of bleeding cases in the non-cirrhotic group was 3, with a bleeding rate of 2.4%. The bleeding rate was higher in the cirrhosis group when polypectomy was performed (χ (2) = 14.909, P < 0.001). A univariate analysis of the risk factors for gastrointestinal polypectomy associated with bleeding in patients with liver cirrhosis showed that liver function grading, platelets, INR, hemoglobin, degree of esophageal and gastric varices, and the location, shape, size, and pathology of the polyps had a statistically significant impact on bleeding (P < 0.05). Multivariate logistic regression analysis showed that liver function grade, degree of varicose veins, and polyp location were independent risk factors for bleeding. Patients with Child-Pugh B or C grade liver function were more likely to bleed than those with Child-Pugh A grade (OR = 4.102, 95% CI 1.133 ~ 14.856), gastric polyps were more likely to bleed than colorectal polyps (OR = 27.763, 95% CI 5.567 ~ 138.460), and severe esophagogastric varices were more likely to bleed than no varices or mild to moderate varices (OR = 7.183, 95% CI 1.384 ~ 37.275). Conclusion: Cirrhotic population has higher risk of bleeding during endoscopic gastrointestinal polypectomy than the non-cirrhotic population. Cirrhotic patients with Child-Pugh grades B or C liver function, polyps located in the stomach, severe esophagogastric varices, and other high-risk factors should be listed as a relative contraindication for endoscopic polypectomy.

4EBP1 senses extracellular glucose deprivation and initiates cell death signaling in lung cancer

Nutrient-limiting conditions are common during cancer development. The coordination of cellular glucose levels and cell survival is a fundamental question in cell biology and has not been completely understood. 4EBP1 is known as a translational repressor to regulate cell proliferation and survival by controlling translation initiation, however, whether 4EBP1 could participate in tumor survival by other mechanism except for translational repression function, especially under glucose starvation conditions remains unknown. Here, we found that protein levels of 4EBP1 was up-regulated in the central region of the tumor which always suffered nutrient deprivation compared with the peripheral region. We further discovered that 4EBP1 was dephosphorylated by PTPMT1 under glucose starvation conditions, which prevented 4EBP1 from being targeted for ubiquitin-mediated proteasomal degradation by HERC5. After that, 4EBP1 translocated to cytoplasm and interacted with STAT3 by competing with JAK and ERK, leading to the inactivation of STAT3 in the cytoplasm, resulting in apoptosis under glucose withdrawal conditions. Moreover, 4EBP1 knockdown increased the tumor volume and weight in xenograft models by inhibiting apoptosis in the central region of tumor. These findings highlight a novel mechanism for 4EBP1 as a new cellular glucose sensor in regulating cancer cell death under glucose deprivation conditions, which was different from its classical function as a translational repressor.

Artificial Intelligence for Clinical Interpretation of Bedside Chest Radiographs

Background Supine chest radiography for bedridden patients in intensive care units (ICUs) is one of the most frequently ordered imaging studies worldwide. Purpose To evaluate the diagnostic performance of a neural network-based model that is trained on structured semiquantitative radiologic reports of bedside chest radiographs. Materials and Methods For this retrospective single-center study, children and adults in the ICU of a university hospital who had been imaged using bedside chest radiography from January 2009 to December 2020 were reported by using a structured and itemized template. Ninety-eight radiologists rated the radiographs semiquantitatively for the severity of disease patterns. These data were used to train a neural network to identify cardiomegaly, pulmonary congestion, pleural effusion, pulmonary opacities, and atelectasis. A held-out internal test set (100 radiographs from 100 patients) that was assessed independently by an expert panel of six radiologists provided the ground truth. Individual assessments by each of these six radiologists, by two nonradiologist physicians in the ICU, and by the neural network were compared with the ground truth. Separately, the nonradiologist physicians assessed the images without and with preliminary readings provided by the neural network. The weighted Cohen κ coefficient was used to measure agreement between the readers and the ground truth. Results A total of 193 566 radiographs in 45 016 patients (mean age, 66 years ± 16 [SD]; 61% men) were included and divided into training (n = 122 294; 64%), validation (n = 31 243; 16%), and test (n = 40 029; 20%) sets. The neural network exhibited higher agreement with a majority vote of the expert panel (κ = 0.86) than each individual radiologist compared with the majority vote of the expert panel (κ = 0.81 to ≤0.84). When the neural network provided preliminary readings, the reports of the nonradiologist physicians improved considerably (aided vs unaided, κ = 0.87 vs 0.79, respectively; P < .001). Conclusion A neural network trained with structured semiquantitative bedside chest radiography reports allowed nonradiologist physicians improved interpretations compared with the consensus reading of expert radiologists. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Wielpütz in this issue.

Adversarial attacks and adversarial robustness in computational pathology

Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential to quantify and mitigate this risk before widespread clinical use. Here, we show that convolutional neural networks (CNNs) are highly susceptible to white- and black-box adversarial attacks in clinically relevant weakly-supervised classification tasks. Adversarially robust training and dual batch normalization (DBN) are possible mitigation strategies but require precise knowledge of the type of attack used in the inference. We demonstrate that vision transformers (ViTs) perform equally well compared to CNNs at baseline, but are orders of magnitude more robust to white- and black-box attacks. At a mechanistic level, we show that this is associated with a more robust latent representation of clinically relevant categories in ViTs compared to CNNs. Our results are in line with previous theoretical studies and provide empirical evidence that ViTs are robust learners in computational pathology. This implies that large-scale rollout of AI models in computational pathology should rely on ViTs rather than CNN-based classifiers to provide inherent protection against perturbation of the input data, especially adversarial attacks.

Artificial Intelligence can support diagnostic workflows in oncology, but they are vulnerable to adversarial attacks. Here, the authors show that convolutional neural networks are highly susceptible to white- and black-box adversarial attacks in clinically relevant classification tasks.

GNIP1 functions both as a scaffold protein and an E3 ubiquitin ligase to regulate autophagy in lung cancer

Background

Glycogen-Interacting Protein 1 (GNIP1), an E3 ligase, is a member of the tripartite motif (TRIM) family proteins. Current studies on GNIP1 mainly focus on glycogen metabolism. However, the function and molecular mechanisms of GNIP1 in regulating autophagy still remains unclear. This study aimed to investigate the regulatory mechanism of GNIP1 in regulating autophagy in non-small cell lung cancer (NSCLC).

Methods

Crystal violet staining assays were used to evaluate the ability of cell growth and proliferation. Transwell and scratch wound healing assays were used to evaluate the cell migration ability. The protein expressions were measured by western blot and immunohistochemistry. Co-immunoprecipitation assays determined the protein–protein interactions. The in vivo effect of GNIP1 on tumor growth was determined by xenograft assay.

Results

We found that GNIP1 was overexpressed in tumor tissues and the expression level of GNIP1 was related to the poor prognosis and the survival time of NSCLC patients. In non-small cell lung cancer (NSCLC), GNIP1 increased proliferation and migration of cancer cells by promoting autophagy. Mechanistic studies indicated that GNIP1, as a scaffold protein, recruited BECN1 and LC3B to promote the formation of autophagosomes. Besides, GNIP1 mediated the degradation of 14-3-3ζ, the negative regulator of VPS34 complex, thus promoting autophagy. Overexpressing GNIP1 promoted tumorigenesis and enhanced autophagy in xenograft models.

Conclusion

GNIP1 promotes proliferation and migration of NSCLC cells through mediating autophagy, which provides theoretical basis for targeting GNIP1 as anti-cancer drugs.

Discrimination between microcystic meningioma and atypical meningioma using whole-lesion apparent diffusion coefficient histogram analysis

AIM

To explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in discriminating microcystic meningioma (MCM) from atypical meningioma (AM).

MATERIALS AND METHODS

Clinical and preoperative MRI data of 20 patients with MCM and 26 patients with AM were analysed retrospectively. Whole-lesion apparent diffusion coefficient (ADC) histogram analysis was performed on each patient's lesion to obtain histogram parameters, including mean, variance, skewness, kurtosis, the 1st (ADCp1), 10th (ADCp10), 50th (ADCp50), 90th (ADCp90), and 99th (ADCp99) percentiles of ADC. The differences between the ADC histogram parameters of the two tumours were compared, and the receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of statistically significant parameters in distinguishing the two tumours.

RESULTS

The mean, ADCp1, ADCp10, ADCp50, and ADCp90 of MCM were greater than those of AM, and significant differences were observed in these parameters between MCM and AM (all p<0.05). ROC analysis showed that the mean had the highest area under the curve value (AUC) in distinguishing the two tumours (AUC = 0.852), when using 120.46 × 10^-6 mm²/s as the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for discriminating the two groups were 84.6%, 75%, 80.4%, 81.5%, and 78.9%, respectively.

CONCLUSION

Histogram analysis based on whole-lesion ADC maps was useful for discriminating between MCM from AM preoperatively, with the mean being the most promising potential parameter.

Deacetylation of Glutaminase by HDAC4 contributes to Lung Cancer Tumorigenesis

Inhibiting cancer metabolism via glutaminase (GAC) is a promising strategy to disrupt tumor progression. However, mechanism regarding GAC acetylation remains mostly unknown. In this study, we demonstrate that lysine acetylation is a vital post-translational modification that inhibits GAC activity in non-small cell lung cancer (NSCLC). We identify that Lys311 is the key acetylation site on GAC, which is deacetylated by HDAC4, a class II deacetylase. Lys311 acetylation stimulates the interaction between GAC and TRIM21, an E3 ubiquitin ligase of the tripartite motif (TRIM) family, therefore promoting GAC K63-linked ubiquitination and inhibiting GAC activity. Furthermore, GAC^K311Q mutation in A549 cells decreases cell proliferation and alleviates tumor malignancy. Our findings reveal a novel mechanism of GAC regulation by acetylation and ubiquitination that participates in non-small cell lung cancer tumorigenesis.

Application of exposure enhancement technique combined with femoral condyle pushing technique in repairing the posterior horn of the medial meniscus under knee arthroscopy

Objectives:

To investigate the clinical efficacy of exposure enhancement technique and femoral condyle pushing technique applying in the posterior horn of the medial meniscus of the knee.

Methods:

From January 2016 to June 2019, 52 patients with injury in the medial meniscus treated in our department were selected. The horizontal tear of the posterior horn of the medial meniscus was repaired by exposure enhancement technique and femoral condyle pushing technique using the meniscus suture system. Postoperatively, the efficacy was evaluated using the Lysholm scoring system.

Results:

These 52 patients were all followed up for 3~18 months, with an average of 12.5 ± 7.3 months. The pain and activity of all patients were significantly improved compared with those before surgery.

Conclusion:

Exposure enhancement technique and femoral condyle pushing technique in the repair of the posterior horn of the medial meniscus presents satisfactory efficacy. It can improve the pain and activity of the knee, and enhance the stability of residual meniscus. Therefore, it is worth promoting.

Efficacy of mirror therapy and virtual reality therapy in alleviating phantom limb pain: a meta-analysis and systematic review

INTRODUCTION

Amputations result from trauma, war, conflict, vascular diseases and cancer. Phantom limb pain (PLP) is a potentially debilitating form of chronic pain affecting around 100 million amputees across the world. Mirror therapy and virtual reality (VR) are two commonly used treatments, and we evaluated their respective success rates.

METHODS

A meta-analysis and systematic review was undertaken to investigate mirror therapy and VR in their ability to reduce pain levels. A mean difference (MD) model to compare group pain levels pretreatment and post-treatment via aggregating these results from numerous similar studies was employed. Meta-analysis was conducted using RevMan (V.5.4) and expressed in MD for visual analogue scale (VAS) score.

RESULTS

A total of 15 studies met our search criteria; they consisted of eight mirror therapy with 214 participants and seven VR including 86 participants, totalling 300 participants. Mean age ranged from 36 to 63 years, 77% male, of which 61% were lower body amputees. Both led to a VAS reduction (mirror therapy mean reduction VAS score was 2.54, 95% CI 1.42 to 3.66; p<0.001; VR 2.24, 95% CI 1.28 to 3.20; p<0.001). There was no statistically significant difference in pain alleviation between mirror therapy and VR (p=0.69).

CONCLUSIONS

Mirror therapy and VR are both equally efficacious in alleviating PLP, but neither is more effective than the other. However, due to small sample size and limited number of studies, factors such as gender, cause of amputation, site of limb loss or length of time from amputation, which may influence treatment success, could not be explored.

Micro-RNA let-7a-5p Derived From Mesenchymal Stem Cell-Derived Extracellular Vesicles Promotes the Regrowth of Neurons in Spinal-Cord-Injured Rats by Targeting the HMGA2/SMAD2 Axis

Spinal cord injury (SCI) often causes neuronal and axonal damage, resulting in permanent neurological impairments. Mesenchymal stem cells (MSCs) and extracellular vesicles (EVs) are promising treatments for SCI. However, the underlying mechanisms remain unclear. Herein, we demonstrated that EVs from bone marrow-derived MSCs promoted the differentiation of neural stem cells (NSCs) into the neurons and outgrowth of neurites that are extending into astrocytic scars in SCI rats. Further study found that let-7a-5p exerted a similar biological effect as MSC-EVs in regulating the differentiation of NSCs and leading to neurological improvement in SCI rats. Moreover, these MSC-EV-induced effects were attenuated by let-7a-5p inhibitors/antagomirs. When investigating the mechanism, bioinformatics predictions combined with western blot and RT-PCR analyses showed that both MSC-EVs and let-7a-5p were able to downregulate the expression of SMAD2 by inhibiting HMGA2. In conclusion, MSC-EV-secreted let-7a-5p promoted the regrowth of neurons and improved neurological recovery in SCI rats by targeting the HMGA2/SMAD2 axis.

A dual-channel indicator of fish spoilage based on a D-π-A luminogen serving as a smart label for intelligent food packaging

Advances in food monitoring benefit tremendously from the naked-eye observation and device-miniaturization of colorimetric and fluorometric methods. Intelligent food packaging, containing a built-in sensor inside food bags, is capable of real-time monitoring of food quality by visibly discernible out-put signals, which effectively ensures food safety. We synthesized a donor-π-acceptor (D-π-A) compound DPABA, and disclosed its fluorescence response to amines. According to quantum chemical calculations, DPABA is apt to D-A coupling in aggregated state, causing the formation of exciplex/excimer together with intermolecular charge/energy transfer to the disadvantage of light emission; while the evasion of amine vapors would decouple the intermolecular D-A interactions to induce stronger emission with shorter wavelength. Utilizing the amine vapor generated by fish, DPABA can serve as an indicator for freshness monitoring. To create an intelligent food package, the compound was made into cellulose film, which was further cut into smart labels to be encapsulated into food bags. The as-prepared smart label exhibits red color under ambient light and glows weak red emission under UV light, while it turns into faint yellow color in response to putrid fish, and its emission changes to bright cyan. The output signals can be accurately recorded by instrument, and detected by naked eye, suggesting high signal contrast. In addition, the smart label exhibits different changing scope in response to different degree of freshness, showing high potential for in-field detection.

[Changes of serum uric acid levels in patients with chronic hepatitis C after using direct antiviral agents therapy]

Objective: To observe the changes of serum uric acid levels and clinical characteristic in patients with chronic hepatitis C combined with hyperuricemia after direct antiviral agents (DAA) therapy. Methods: A prospective cohort study was used to investigate the risk of hyperuricemia in patients with chronic hepatitis C who received DAA treatment to obtain sustained virological response. The changes and factors influencing serum uric acid levels after 12 weeks of DAA treatment were observed. Comparisons between groups were performed using χ (2) test or Fisher's exact test, analysis of variance, Student's t test, or the non-parametric Mann-Whitney U test. Serum uric acid (SUA) changes, liver and kidney function indexes before and after treatment were compared by repeated measurement and paired t-test. Uric acid reduction was defined as a decrease in SUA from baseline at 12 weeks after treatment. Rates of change in eGFR, aspartate aminotransferase/platelet ratio, alanine aminotransferase and controlled attenuation parameter were defined from baseline (baseline to 12 weeks after treatment). Binary logistic regression analysis was used to compare the risk factors and factors influencing high and low uric acid level. Results: 161 cases with chronic hepatitis C who received DAA treatment were included, of which 19.3% patients were hyperuricemic. eGFR < 60 ml/(min·1.73 m(2)) and body mass index were independent risk factors for hyperuricemia in patients with chronic hepatitis C (eGFR: OR = 0.123, P = 0.002; body mass index: OR = 1.220, P = 0.002). SUA levels was changed significantly before treatment, at the end of treatment and at 12 weeks after treatment (327.96 vs. 320.76 vs. 314.92, F = 3.272, P = 0.042). At 12 weeks after treatment, SUA, liver stiffness, alanine aminotransferase and control attenuation parameters were all significantly lower than baseline (P < 0.05). The rate of increase in eGFR from baseline and the rate of decrease in controlled attenuation parameter during treatment were the factors influencing SUA reduction (eGFR: OR = 5124, P = 0.000; controlled attenuation index: OR = 0.010, P = 0.039). Conclusion: In chronic hepatitis C, reduced eGFR and body mass index are the risk factors for the development of hyperuricemia and a significant reduction in serum uric acid levels after DAA treatment can eradicate the virus.