Antimicrobial resistance and population genomics of emerging multidrug-resistant Salmonella 4,[5],12:i:- in Guangdong, China.

Salmonella 4,[5],12:i:-, a monophasic variant of Salmonella Typhimurium, has emerged as a global cause of multidrug-resistant salmonellosis and has become endemic in many developing and developed countries, especially in China. Here, we have sequenced 352 clinical isolates in Guangdong, China, during 2009-2019 and performed a large-scale collection of Salmonella 4,[5],12:i:- with whole genome sequencing (WGS) data across the globe, to better understand the population structure, antimicrobial resistance (AMR) genomic characterization, and transmission routes of Salmonella 4,[5],12:i:- across Guangdong. Salmonella 4,[5],12:i:- strains showed broad genetic diversity; Guangdong isolates were found to be widely distributed among the global lineages. Of note, we identified the formation of a novel Guangdong clade (Bayesian analysis of population structure lineage 1 [BAPS1]) genetically diversified from the global isolates and likely emerged around 1990s. BAPS1 exhibits unique genomic features, including large pan-genome, decreased ciprofloxacin susceptibility due to mutation in gyrA and carriage of plasmid-mediated quinolone resistance (PMQR) genes, and the multidrug-resistant IncHI2 plasmid. Furthermore, high genetic similarity was found between strains collected from Guangdong, Europe, and North America, indicating the association with multiple introductions from overseas. These results suggested that global dissemination and local clonal expansion simultaneously occurred in Guangdong, China, and horizontally acquired resistance to first-line and last-line antimicrobials at local level, underlying emergences of extensive drug and pan-drug resistance. Our findings have increased the knowledge of global and local epidemics of Salmonella 4,[5],12:i:- in Guangdong, China, and provided a comprehensive baseline data set essential for future molecular surveillance.IMPORTANCESalmonella 4,[5],12:i:- has been regarded as the predominant pandemic serotype causing diarrheal diseases globally, while multidrug resistance (MDR) constitutes great public health concerns. This study provided a detailed and comprehensive genome-scale analysis of this important Salmonella serovar in the past decade in Guangdong, China. Our results revealed the complexity of two distinct transmission modes, namely global transmission and local expansion, circulating in Guangdong over a decade. Using phylogeography models, the origin of Salmonella 4,[5],12:i:- was predicted from two aspects, year and country, that is, Salmonella 4,[5],12:i:- emerged in 1983, and was introduced from the UK, and subsequently differentiated into the local endemic lineage circa 1991. Additionally, based on the pan-genome analysis, it was found that the gene accumulation rate in local endemic BAPS 1 lineage was higher than in other lineages, and the horizontal transmission of MDR IncHI2 plasmid associated with high resistance played a major role, which showed the potential threat to public health.

XTransCT: ultra-fast volumetric CT reconstruction using two orthogonal x-ray projections for image-guided radiation therapy via a transformer network.

Objective.The aim of this study was to reconstruct volumetric computed tomography (CT) images in real-time from ultra-sparse two-dimensional x-ray projections, facilitating easier navigation and positioning during image-guided radiation therapy.Approach.Our approach leverages a voxel-sapce-searching Transformer model to overcome the limitations of conventional CT reconstruction techniques, which require extensive x-ray projections and lead to high radiation doses and equipment constraints.Main results.The proposed XTransCT algorithm demonstrated superior performance in terms of image quality, structural accuracy, and generalizability across different datasets, including a hospital set of 50 patients, the large-scale public LIDC-IDRI dataset, and the LNDb dataset for cross-validation. Notably, the algorithm achieved an approximately 300% improvement in reconstruction speed, with a rate of 44 ms per 3D image reconstruction compared to former 3D convolution-based methods.Significance.The XTransCT architecture has the potential to impact clinical practice by providing high-quality CT images faster and with substantially reduced radiation exposure for patients. The model's generalizability suggests it has the potential applicable in various healthcare settings.

Eight-month intensive meditation-based intervention improves refractory hallucinations and delusions and quality of life in male inpatients with schizophrenia: a randomized controlled trial.

AIM: This study investigated the impact of an 8-month daily-guided intensive meditation-based intervention (iMI) on persistent hallucinations/delusions and health-related quality of life (QoL) in male inpatients with schizophrenia with treatment-refractory hallucinations and delusions (TRHDs). METHODS: A randomized controlled trial assigned 64 male inpatients with schizophrenia and TRHD equally to an 8-month iMI plus general rehabilitation program (GRP) or GRP alone. Assessments were conducted at baseline and the third and eighth months using the Positive and Negative Syndrome Scale (PANSS), 36-Item Short Form-36 (SF-36), and Five Facet Mindfulness Questionnaire (FFMQ). Primary outcomes measured PANSS reduction rates for total score, positive symptoms, and hallucinations/delusions items. Secondary outcomes assessed PANSS, SF-36, and FFMQ scores for psychotic symptoms, health-related QoL, and mindfulness skills, respectively. RESULTS: In the primary outcome, iMI significantly improved the reduction rates of PANSS total score, positive symptoms, and hallucination/delusion items compared with GRP at both the third and eighth months. Treatment response rates (≥25% reduction) for these measures significantly increased in the iMI group at the eighth month. Concerning secondary outcomes, iMI significantly reduced PANSS total score and hallucination/delusion items, while increasing scores in physical activity and mindfulness skills at both the third and eighth months compared with GRP. These effects were more pronounced with an 8-month intervention compared with a 3-month intervention. CONCLUSIONS: An iMI benefits patients with TRHDs by reducing persistent hallucinations/delusions and enhancing health-related QoL. Longer iMI duration yields superior treatment outcomes.

QUIZ: An arbitrary volumetric point matching method for medical image registration.

Rigid pre-registration involving local-global matching or other large deformation scenarios is crucial. Current popular methods rely on unsupervised learning based on grayscale similarity, but under circumstances where different poses lead to varying tissue structures, or where image quality is poor, these methods tend to exhibit instability and inaccuracies. In this study, we propose a novel method for medical image registration based on arbitrary voxel point of interest matching, called query point quizzer (QUIZ). QUIZ focuses on the correspondence between local-global matching points, specifically employing CNN for feature extraction and utilizing the Transformer architecture for global point matching queries, followed by applying average displacement for local image rigid transformation.We have validated this approach on a large deformation dataset of cervical cancer patients, with results indicating substantially smaller deviations compared to state-of-the-art methods. Remarkably, even for cross-modality subjects, it achieves results surpassing the current state-of-the-art.

Endothelial DGKG promotes tumor angiogenesis and immune evasion in hepatocellular carcinoma.

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) is among the most prevalent and lethal cancers worldwide. The tumor microenvironment (TME) contributes to the poor response of patients with HCC to current therapies, while tumor vascular endothelial cells (ECs) are fundamental TME components that significantly contribute to tumor progression. However, the specific functions and mechanisms of tumor vascular ECs in HCC remain unclear. METHODS: We screened and validated diacylglycerol kinase gamma (DGKG) hyper-expression specifically in HCC tumor vascular ECs. Single-cell RNA-sequencing, cytometry by time-of-flight, and in vitro and in vivo studies were performed to investigate the functions of endothelial DGKG. Multiplexed immunohistochemistry staining and flow cytometry were used to evaluate changes in the TME. RESULTS: Functionally, endothelial DGKG promotes tumor angiogenesis and immunosuppressive regulatory T-cell differentiation in HCC. Of significance, we found that HIF-1α activates DGKG transcription by directly binding to its promoter region under hypoxia. Upregulated DGKG promotes HCC progression by recruiting ubiquitin specific peptidase 16 to facilitate ZEB2 deubiquitination, which increases TGF-ß1 secretion, thus inducing tumor angiogenesis and regulatory T-cell differentiation. Importantly, targeting endothelial DGKG potentiated the efficiency of dual blockade of PD-1 and VEGFR-2. CONCLUSION: Hypoxia-induced EC-specific DGKG hyper-expression promotes tumor angiogenesis and immune evasion via the ZEB2/TGF-ß1 axis, suggesting EC-specific DGKG as a potential therapeutic target for HCC. IMPACT AND IMPLICATIONS: Here, we reported that hypoxia-induced endothelial cell-specific DGKG hyper-expression promotes angiogenesis and immune evasion in HCC by recruiting USP16 for K48-linked deubiquitination and inducing the subsequent stabilization of ZEB2, leading to increased TGF-ß1 secretion. Most importantly, endothelial DGKG inhibition greatly improved the efficacy of the dual combination of anti-VEGFR2 and anti-PD-1 treatment in a mouse HCC model, significantly inhibiting the malignant progression of HCC and improving survival. This preclinical study supports the targeting of endothelial DGKG as a potential strategy for precision HCC treatment.

A Cucurbit[8]uril-Based Supramolecular Framework Material for Reversible Iodine Capture in the Vapor Phase and Solution.

The safe and efficient management of hazardous radioactive iodine is significant for nuclear waste reprocessing and environmental industries. A novel supramolecular framework compound based on cucurbit[8]uril (Q[8]) and 4-aminopyridine (4-AP) is reported in this paper. In the single crystal structure of Q[8]-(4-AP), two 4-AP molecules interact with the outer surface of Q[8] and the two other 4-AP molecules are encapsulated into the Q[8] cavity to form the self-assembly Q[8]-(4-AP). Iodine adsorption experiments show that the as-prepared Q[8]-(4-AP) not only has a high adsorption capacity (1.74 g· g-1) for iodine vapor but also can remove the iodine in the organic solvent and the aqueous solution with the removal efficiencies of 95% and 91%, respectively. The presence of a large number of hydrogen bonds between the iodine molecule and the absorbent, as seen in the single crystal structure of iodine-loaded Q[8]-(4-AP) (I2@Q[8]-(4-AP)), is thought to be responsible for the exceptional iodine adsorption capacity of the material. In addition, the adsorption-desorption tests reveal that the self-assembly material has no significant loss of iodine capture capacity after five cycles, indicating that it has sufficient reusability.

Hepatocyte Deubiquitinating Enzyme OTUD5 Deficiency is a Key Aggravator for Metabolic Dysfunction-Associated Steatohepatitis by Disturbing Mitochondrial Homeostasis.

BACKGROUND & AIMS: Metabolic dysfunction-associated steatohepatitis (MASH) is a common chronic liver disease worldwide. No effective pharmacologic therapies for MASH have been developed; to develop such promising drugs, the underlying mechanisms regulating MASH need to be elucidated. Here, we aimed to determine the role of ovarian tumor domain-containing protein 5 (OTUD5) in MASH progression and identify a specific mechanism. METHODS: The expression levels of OTUD subfamily under palmitic acid/oleic acid (PAOA) stimulation were screened. OTUD5 expression was assessed in human liver tissues without steatosis, those with simple steatosis, and those with MASH. MASH models were developed in hepatocyte-specific Otud5-knockout mice that were fed high-fat high-cholesterol and high-fat high-cholesterol plus high-fructose/sucrose diet for 16 weeks. RESULTS: The expression of OTUD5 was down-regulated in fatty liver and was negatively related to the progression of MASH. Lipid accumulation and inflammation were exacerbated by Otud5 knockdown but attenuated by Otud5 overexpression under PAOA treatment. Hepatocyte-specific Otud5 deletion markedly exacerbated steatosis, inflammation, and fibrosis in the livers of 2 MASH mouse models. We identified voltage-dependent anion channel 2 (VDAC2) as an OTUD5-interacting partner; OTUD5 cleaved the K48-linked polyubiquitin chains from VDAC2, and it inhibited subsequent proteasomal degradation. The anabolic effects of OTUD5 knockdown on PAOA-induced lipid accumulation were effectively reversed by VDAC2 overexpression in primary hepatocytes. Metabolomic results revealed that VDAC2 is required for OTUD5-mediated protection against hepatic steatosis by maintaining mitochondrial function. CONCLUSIONS: OTUD5 may ameliorate MASH progression via VDAC2-maintained mitochondrial homeostasis. Targeting OTUD5 may be a viable MASH-treatment strategy.

A statistical deformation model-based data augmentation method for volumetric medical image segmentation.

The accurate delineation of organs-at-risk (OARs) is a crucial step in treatment planning during radiotherapy, as it minimizes the potential adverse effects of radiation on surrounding healthy organs. However, manual contouring of OARs in computed tomography (CT) images is labor-intensive and susceptible to errors, particularly for low-contrast soft tissue. Deep learning-based artificial intelligence algorithms surpass traditional methods but require large datasets. Obtaining annotated medical images is both time-consuming and expensive, hindering the collection of extensive training sets. To enhance the performance of medical image segmentation, augmentation strategies such as rotation and Gaussian smoothing are employed during preprocessing. However, these conventional data augmentation techniques cannot generate more realistic deformations, limiting improvements in accuracy. To address this issue, this study introduces a statistical deformation model-based data augmentation method for volumetric medical image segmentation. By applying diverse and realistic data augmentation to CT images from a limited patient cohort, our method significantly improves the fully automated segmentation of OARs across various body parts. We evaluate our framework on three datasets containing tumor OARs from the head, neck, chest, and abdomen. Test results demonstrate that the proposed method achieves state-of-the-art performance in numerous OARs segmentation challenges. This innovative approach holds considerable potential as a powerful tool for various medical imaging-related sub-fields, effectively addressing the challenge of limited data access.

Volumetric tumor tracking from a single cone-beam X-ray projection image enabled by deep learning.

Radiotherapy serves as a pivotal treatment modality for malignant tumors. However, the accuracy of radiotherapy is significantly compromised due to respiratory-induced fluctuations in the size, shape, and position of the tumor. To address this challenge, we introduce a deep learning-anchored, volumetric tumor tracking methodology that employs single-angle X-ray projection images. This process involves aligning the intraoperative two-dimensional (2D) X-ray images with the pre-treatment three-dimensional (3D) planning Computed Tomography (CT) scans, enabling the extraction of the 3D tumor position and segmentation. Prior to therapy, a bespoke patient-specific tumor tracking model is formulated, leveraging a hybrid data augmentation, style correction, and registration network to create a mapping from single-angle 2D X-ray images to the corresponding 3D tumors. During the treatment phase, real-time X-ray images are fed into the trained model, producing the respective 3D tumor positioning. Rigorous validation conducted on actual patient lung data and lung phantoms attests to the high localization precision of our method at lowered radiation doses, thus heralding promising strides towards enhancing the precision of radiotherapy.

Screening of potential key genes in esophageal cancer based on RBP and expression verification of HENMT1.

To screen key biomarkers of esophageal cancer (ESCA) by bioinformatics and analyze the correlation between key genes and immune infiltration. Expression profile data of ESCA was downloaded from TCGA database, and DEGs in ESCA were screened with R software. After the RNA binding proteins (RBPs) in DEGs were screened, the protein interaction network was constructed using tools such as STRING and Cytoscape and the key genes (HENMT1) were screened. Survival analysis of HENMT1 was performed by Kaplan-Meier method. Functional enrichment analysis of HENMT1 interacting proteins was performed using the DAVID website, and GSEA predicted the signal pathways involved by HENMT1. CIBERSORT algorithm was used to analyze the infiltration of immune cells in ESCA. The expression of HENMT1 in ESCA was detected by immunohistochemistry. A total of 105 RNA binding proteins (RBPs) were differentially expressed in ESCA, and a PPI network was constructed to screen the key gene HENMT1. The expression level of hemmt1 gene was closely related to the infiltration of B cells naive, T cells regulatory (Tregs), neutrophils, T cells CD4 memory activated, master cells resting and dendritic cells resting in ESCA tissues (P < .05). Immunohistochemical results showed that HENMT1 was highly expressed in ESCA tissues and was positively correlated with the expression of MKI67. HENMT1 is related to the occurrence and prognosis of ESCA, and is also related to the infiltration of immune cells in ESCA tissue, which may provide a new idea for the targeted treatment of ESCA.

[Construction and application of virtual simulation teaching platform for in-hospital emergency nursing of craniofacial injury patients].

PURPOSE: To construct a virtual simulation teaching platform for in-hospital emergency nursing of craniofacial injury patients by virtual simulation technology, and to evaluate its application effect. METHODS: Through virtual reality, animation, human-computer interaction and other technologies, a 3D experiment scene based on high simulation virtual human was constructed to reproduce the virtual rescue scenes of craniofacial injury patients, such as emergency reception, first-aid cooperation, massive hemorrhage rescue cooperation, and tracheotomy cooperation in emergency rescue of sudden airway obstruction, and exercise modules and assessment modules were set. In the virtual simulation platform, the students used the holistic nursing theory and the PDCA cycle method to observe, evaluate and care for craniofacial injury patients. Preliminary evaluation of the platform was carried out in the training of 62 dental nurses. RESULTS: The virtual simulation platform could improve students' comprehensive first-aid ability for craniofacial injury patients. The item with the highest satisfaction rate for the virtual simulation platform was the consistency between the content of the virtual simulation platform and the theoretical course (the satisfaction rate was 91.9%), and the lowest satisfaction rate was the convenience of the virtual simulation platform operation and the page setting (the satisfaction rate was 80.6%). The evaluation module of the virtual simulation platform showed that the highest score of the comprehensive evaluation was 97, the lowest score was 56, and the average score was 80.2. CONCLUSIONS: The virtual simulation teaching platform for in-hospital first aid of craniofacial injury patients can create an immersive learning mode, provide an intuitive rescue experience to the students, and improve their comprehensive first-aid ability.

COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning.

The detection of Coronavirus disease 2019 (COVID-19) is crucial for controlling the spread of the virus. Current research utilizes X-ray imaging and artificial intelligence for COVID-19 diagnosis. However, conventional X-ray scans expose patients to excessive radiation, rendering repeated examinations impractical. Ultra-low-dose X-ray imaging technology enables rapid and accurate COVID-19 detection with minimal additional radiation exposure. In this retrospective cohort study, ULTRA-X-COVID, a deep neural network specifically designed for automatic detection of COVID-19 infections using ultra-low-dose X-ray images, is presented. The study included a multinational and multicenter dataset consisting of 30,882 X-ray images obtained from approximately 16,600 patients across 51 countries. It is important to note that there was no overlap between the training and test sets. The data analysis was conducted from 1 April 2020 to 1 January 2022. To evaluate the effectiveness of the model, various metrics such as the area under the receiver operating characteristic curve, receiver operating characteristic, accuracy, specificity, and F1 score were utilized. In the test set, the model demonstrated an AUC of 0.968 (95% CI, 0.956-0.983), accuracy of 94.3%, specificity of 88.9%, and F1 score of 99.0%. Notably, the ULTRA-X-COVID model demonstrated a performance comparable to conventional X-ray doses, with a prediction time of only 0.1 s per image. These findings suggest that the ULTRA-X-COVID model can effectively identify COVID-19 cases using ultra-low-dose X-ray scans, providing a novel alternative for COVID-19 detection. Moreover, the model exhibits potential adaptability for diagnoses of various other diseases.

Predicting fracture classification and prognosis with hounsfield units and femoral cortical index: A simple and cost-effective approach.

BACKGROUND: The relationship between bone density and fracture has been widely studied and recognized, and the role of cortical bone in proximal femoral fractures has also been increasingly studied. However, both the determination of bone mineral density (BMD) and the determination of cortical mass are expensive and cumbersome. The purpose of this study is to investigate whether two readily available indicators, Hounsfield Units (HUs) and femoral cortical index (FCI), can be used to predict hip fracture classification and prognosis. METHODS: A retrospective study was conducted on 110 patients with hip fragility fractures. Cortical index was calculated on fractured and contralateral femur FCI, with HUs calculated on the proximal femur. The correlation of the FCI and HU with diabetes, hypertension, and related indicators, such as albumin, creatinine, and urea nitrogen levels, were also analyzed in the study. RESULTS: Both the Evans classification of intertrochanteric fractures and the Garden and Pauwels classifications of femoral neck fractures showed that as the severity of the fracture increased, the HUs and FCI decreased. Age and albumin level also had a negative correlation with HUs and FCI. There was also a significant correlation between HUs and FCI. CONCLUSIONS: The HUs and FCI, which can be easily and quickly obtained, can be used to predict the classification and prognosis of hip fractures.

Walnut Protein Peptides Ameliorate DSS-Induced Ulcerative Colitis Damage in Mice: An in Silico Analysis and in Vivo Investigation.

Walnut (Juglans regia L.) is a food with food-medicine homology, whose derived protein peptides have been shown to have anti-inflammatory activity in vitro. However, the effects and mechanisms of walnut protein peptides on ulcerative colitis (UC) in vivo have not been systematically and thoroughly investigated. In this study, we applied virtual screening and network pharmacology screening of bioactive peptides to obtain three novel WPPs (SHTLP, HYNLN, and LGTYP) that may alleviate UC through TLR4-MAPK signaling. In vivo studies have shown that WPPs improve intestinal mucosal barrier dysfunction and reduce inflammation by inhibiting activation of the TLR4-MAPK pathway. In addition, WPPs restore intestinal microbial homeostasis by reducing harmful bacteria (Helicobacter and Bacteroides) and increasing the relative abundance of beneficial bacteria (Candidatus_Saccharimonas). Our study showed that the WPPs obtained by virtual screening were effective in ameliorating colitis, which has important implications for future screening of bioactive peptides from medicinal food homologues as drugs or dietary supplements.

Impact of fear of recurrence in patients with cancer on caregivers' psychological health: A meta-analysis.

Objective: This study aims to quantitatively investigate the relationship between fear of cancer recurrence in patients with cancer and their caregivers' psychological health, examining the extent of the impact of this fear on caregivers. Methods: We conducted a comprehensive search in PubMed, EMBASE, Cochrane Library, Web of Science, CINAHL, and PsycINFO databases from inception until May 2023 for relevant English publications. Pearson correlation coefficients (r) were utilized as effect sizes to assess the overall relationship between fear of recurrence and psychological outcomes among caregivers of patients with cancer. Results: A total of 19 eligible studies were included in the analysis. The findings revealed a moderate positive correlation between fear of recurrence in patients with cancer and caregivers' fear of recurrence and depression. A relatively weaker correlation was observed between patients' fear of recurrence and caregivers' anxiety. Due to considerable heterogeneity (I2 â€‹= â€‹91.99%, Q â€‹= â€‹212.23), the primary outcome of fear of recurrence in patients with cancer influencing caregivers' fear of recurrence was examined through subgroup analyses. Conclusions: Our meta-analysis established a significant positive correlation between fear of recurrence in patients with cancer and negative psychological consequences among caregivers, including recurrence fear, depression, and anxiety. Future research should explore the evolution of adverse psychological outcomes in both patients with cancer and their caregivers over time and delve into the bivariate psychological impact within the patient-caregiver dyads. Systematic review registration: PROSPERO, CRD42022383866.

Volumetric feature points integration with bio-structure-informed guidance for deformable multi-modal CT image registration.

Objective.Medical image registration represents a fundamental challenge in medical image processing. Specifically, CT-CBCT registration has significant implications in the context of image-guided radiation therapy (IGRT). However, traditional iterative methods often require considerable computational time. Deep learning based methods, especially when dealing with low contrast organs, are frequently entangled in local optimal solutions.Approach.To address these limitations, we introduce a registration method based on volumetric feature points integration with bio-structure-informed guidance. Surface point cloud is generated from segmentation labels during the training stage, with both the surface-registered point pairs and voxel feature point pairs co-guiding the training process, thereby achieving higher registration accuracy.Main results.Our findings have been validated on paired CT-CBCT datasets. In comparison with other deep learning registration methods, our approach has improved the precision by 6%, reaching a state-of-the-art status.Significance.The integration of voxel feature points and bio-structure feature points to guide the training of the medical image registration network has achieved promising results. This provides a meaningful direction for further research in medical image registration and IGRT.

An unsupervised dual contrastive learning framework for scatter correction in cone-beam CT image.

PURPOSE: Cone-beam computed tomography (CBCT) is widely utilized in modern radiotherapy; however, CBCT images exhibit increased scatter artifacts compared to planning CT (pCT), compromising image quality and limiting further applications. Scatter correction is thus crucial for improving CBCT image quality. METHODS: In this study, we proposed an unsupervised contrastive learning method for CBCT scatter correction. Initially, we transformed low-quality CBCT into high-quality synthetic pCT (spCT) and generated forward projections of CBCT and spCT. By computing the difference between these projections, we obtained a residual image containing image details and scatter artifacts. Image details primarily comprise high-frequency signals, while scatter artifacts consist mainly of low-frequency signals. We extracted the scatter projection signal by applying a low-pass filter to remove image details. The corrected CBCT (cCBCT) projection signal was obtained by subtracting the scatter artifacts projection signal from the original CBCT projection. Finally, we employed the FDK reconstruction algorithm to generate the cCBCT image. RESULTS: To evaluate cCBCT image quality, we aligned the CBCT and pCT of six patients. In comparison to CBCT, cCBCT maintains anatomical consistency and significantly enhances CT number, spatial homogeneity, and artifact suppression. The mean absolute error (MAE) of the test data decreased from 88.0623 ± 26.6700 HU to 17.5086 ± 3.1785 HU. The MAE of fat regions of interest (ROIs) declined from 370.2980 ± 64.9730 HU to 8.5149 ± 1.8265 HU, and the error between their maximum and minimum CT numbers decreased from 572.7528 HU to 132.4648 HU. The MAE of muscle ROIs reduced from 354.7689 ± 25.0139 HU to 16.4475 ± 3.6812 HU. We also compared our proposed method with several conventional unsupervised synthetic image generation techniques, demonstrating superior performance. CONCLUSIONS: Our approach effectively enhances CBCT image quality and shows promising potential for future clinical adoption.

Follow-Up Effect of Wechat Group on Discharged Patients with Colles Fracture.

This work explores the effect of a Wechat group on follow-up and continuation of nursing for discharged patients with Colles fracture. A total of 96 patients with Colles fracture are enrolled and randomized into two groups by the random number table method. The control group is followed up and guided by traditional methods. Based on the follow-up method adopted in the control group, a Wechat group is combined with Wechat constant tracking. The regular follow-up rate, a satisfaction of with continuing care, Gartland-Werley wrist score, the exercise of self-care agency score (ESCA score), and complications are compared and analyzed. The regular follow-up rate, satisfaction, Gartland-Werley wrist score, and self-care ability of patients in the Wechat group are significantly higher than those in the control group (p < 0.05). Statistical analysis of postoperative complications showed that although there is no significant difference in the incidence of median nerve irritation and incision infection (p > 0.05), the proportion of joint stiffness in the control group is significantly higher (p < 0.05). The establishment of Wechat groups to follow up and continue nursing for discharged patients with Colles fracture is helpful to achieve better clinical efficacy and improve the effective way for doctor-patient communication, which is worthy of active promotion.

Macrophage PTEN controls STING-induced inflammation and necroptosis through NICD/NRF2 signaling in APAP-induced liver injury.

BACKGROUND: Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) signaling has been known to play a critical role in maintaining cellular and tissue homeostasis, which also has an essential role in the inflammatory response. However, it remains unidentified whether and how the macrophage PTEN may govern the innate immune signaling stimulator of interferon genes (STING) mediated inflammation and hepatocyte necroptosis in APAP-induced liver injury (AILI). METHODS: Myeloid-specific PTEN knockout (PTENM-KO) and floxed PTEN (PTENFL/FL) mice were treated with APAP (400 mg/kg) or PBS. In a parallel in vitro study, bone marrow-derived macrophages (BMMs) were isolated from these conditional knockout mice and transfected with CRISPR/Cas9-mediated Notch1 knockout (KO) or CRISPR/Cas9-mediated STING activation vector followed by LPS (100 ng/ml) stimulation. RESULTS: Here, we report that myeloid-specific PTEN knockout (PTENM-KO) mice were resistant to oxidative stress-induced hepatocellular injury with reduced macrophage/neutrophil accumulation and proinflammatory mediators in AILI. PTENM-KO increased the interaction of nuclear Notch intracellular domain (NICD) and nuclear factor (erythroid-derived 2)-like 2 (NRF2) in the macrophage nucleus, reducing reactive oxygen species (ROS) generation. Mechanistically, it is worth noting that macrophage NICD and NRF2 co-localize within the nucleus under inflammatory conditions. Additionally, Notch1 promotes the interaction of immunoglobulin kappa J region (RBPjκ) with NRF2. Disruption of the Notch1 signal in PTEN deletion macrophages, reduced RBPjκ and NRF2 binding, and activated STING signaling. Moreover, PTENM-KO macrophages with STING activated led to ROS generation and TNF-α release, resulting in hepatocyte necroptosis upon co-culture with primary hepatocytes. CONCLUSIONS: Our findings demonstrate that the macrophage PTEN-NICD/NRF2-STING axis is critical to regulating oxidative stress-induced liver inflammation and necroptosis in AILI and implies the therapeutic potential for managing sterile liver inflammation. Video Abstract.