MYO3B promotes cancer progression in endometrial cancer by mediating the calcium ion-RhoA/ROCK1 signaling pathway.

PURPOSE: This study aimed to investigate the effect of MYO3B on endometrial cancer (EC) proliferation and invasion. METHODS: The expression of MYO3B in EC tissues and cells was analyzed using TCGA database, immunohistochemical staining, real-time PCR, and western blot (WB). Cell proliferation was detected by CCK8, Annexin V-APC/PI flow cytometry was used to detect apoptosis, intracellular calcium ion (Ca2+) was detected by flow cytometry with Fluo-4 AM fluorescent probe, cell migration by scratch assay, and cell invasion by Transwell assay, and the expression of proteins related to Ca2+ homeostasis and RhoA/ROCK1 signaling pathway was detected by WB and immunofluorescence staining. RESULTS: The expression of MYO3B was an influential factor in EC recurrence, and the expression of MYO3B was significantly up-regulated in EC tissues and cells, but down-regulated in KLE cells, and MYO3B knockdown inhibited the proliferation, migration, and invasion ability of EC cells and promoted apoptosis, suggesting that MYO3B plays a tumor-promoting role in EC. Furthermore, MYO3B knockdown decreased Ca2+ concentration in EC cells and the RhoA/ROCK1 signaling pathway was inhibited, and the effect of MYO3B knockdown on RhoA/ROCK1 signaling was reversed by treatment with the Calmodulin agonist CALP-2, and the effects of MYO3B knockdown on cell proliferation, migration, and invasion were reversed after treatment with the RhoA agonist U-46,619. CONCLUSION: MYO3B promotes the proliferation and migration of endometrial cancer cells via Ca2+-RhoA/ROCK1 signaling pathway. High expression of MYO3B may be a biomarker for EC metastasis.

Analysis of Th1/Th2 Balance and Related Cytokine Changes in the Peripheral Blood of Patients with Multiple Myeloma.

BACKGROUND: This study aimed to explore the changes in Th cells and cytokines in the peripheral blood of patients with multiple myeloma before and after treatment and at the time of the bacterial infection. METHODS: In total, 23 newly diagnosed MM patients admitted to the Hospital and 23 healthy individuals were selected as the study group and the control group, respectively. Flow cytometry was used to detect the Th1 and Th2 lymphocytes and cytokines, such as IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, INF-γ, IL-17A, IL-1b, TNF-α, TNF-ß, and IL-12P70, in the peripheral blood of the patients at initial diagnosis, before and after treatment, and at the time of the bacterial infection. RESULTS: The Th1% and Th1/Th2 ratio at the time of the initial diagnosis were lower in the MM patients than in the control group, whereas the Th2% at initial diagnosis was higher in the MM patients than in the control group. The levels of IL-6, IL-8, IL-10, and IL-17A at initial diagnosis were higher in the MM patients than in the control group. After 4 cycles of treatment, the Th2% in the patients was lower than before the treatment and the Th1/Th2 ratio in the patients was higher than before the treatment. The Th1% and the levels of IL-6, IL-8, IL-10, and INF-γ increased, while the level of IL-12P70 decreased, when MM patients got a bacterial infection. The abovementioned differences were statistically significant (p < 0.05). CONCLUSIONS: The Th1/Th2 deviation affects the immune function of the MM patients. There were significant changes in the Th1 and Th2 lymphocytes and cytokines in newly diagnosed MM patients after the treatment. The changes in the Th lymphocytes and cytokines may be an indicator of bacterial infection.

Protein corona dynamicity contributes to biological destiny disparities of nanoparticles.

Extracellular protein coronas (exPCs), which have been identified in various biofluids, are recognized for their pivotal role in mediating the interaction between nanoparticles and the cytomembrane. However, it is still unclear whether various exPCs can induce different levels of intracellular proteostasis, which is of utmost importance in preserving cellular function, and eliciting distinct intracellular biological behaviors. To investigate this, two types of exPC-coated iron oxide nanoparticles (IONPs) are prepared and used to investigate the influence of exPCs on extracellular and intracellular biological outcomes. The results demonstrate that the formation of exPCs promotes the colloidal stability of IONPs, and the discrepancies in the components of the two exPCs, including opsonin, dysopsonin, and lipoprotein, are responsible for the disparities in cellular uptake and endocytic pathways. Moreover, the differential evolution of the two exPCs during cellular internalization leads to distinct autophagy and glycolysis activities, which can be attributed to the altered depletion of angiopoietin 1 during the formation of intracellular protein coronas, which ultimately impacts the PI3K/AKT-mTOR signaling. These findings offer valuable insights into the dynamic characteristics of exPCs during cellular internalization, and their consequential implications for cellular internalization and intracellular metabolism activity, which may facilitate the comprehension of PCs on biological effects of NPs and expedite the design and application of biomedical nanoparticles.

Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images.

BACKGROUND: Adult-type diffuse gliomas are among the central nervous system's most aggressive malignant primary neoplasms. Despite advancements in systemic therapies and technological improvements in radiation oncology treatment delivery, the survival outcome for these patients remains poor. Fast and accurate assessment of tumor response to oncologic treatments is crucial, as it can enable the early detection of recurrent or refractory gliomas, thereby allowing timely intervention with life-prolonging salvage therapies. PURPOSE: Radiomics is a developing field with great potential to improve medical image interpretation. This study aims to apply a radiomics-based predictive model for classifying response to radiotherapy within the first 3 months post-treatment. METHODS: Ninety-five patients were selected from the Burdenko Glioblastoma Progression Dataset. Tumor regions were delineated in the axial plane on contrast-enhanced T1(CE T1W) and T2 fluid-attenuated inversion recovery (T2_FLAIR) magnetic resonance imaging (MRI). Hand-crafted radiomic (HCR) features, including first- and second-order features, were extracted using PyRadiomics (3.7.6) in Python (3.10). Then, recursive feature elimination with a random forest (RF) classifier was applied for feature dimensionality reduction. RF and support vector machine (SVM) classifiers were built to predict treatment outcomes using the selected features. Leave-one-out cross-validation was employed to tune hyperparameters and evaluate the models. RESULTS: For each segmented target, 186 HCR features were extracted from the MRI sequence. Using the top-ranked radiomic features from a combination of CE T1W and T2_FLAIR, an optimized classifier achieved the highest averaged area under the curve (AUC) of 0.829 ± 0.075 using the RF classifier. The HCR features of CE T1W produced the worst outcomes among all models (0.603 ± 0.024 and 0.615 ± 0.075 for RF and SVM classifiers, respectively). CONCLUSIONS: We developed and evaluated a radiomics-based predictive model for early tumor response to radiotherapy, demonstrating excellent performance supported by high AUC values. This model, harnessing radiomic features from multi-modal MRI, showed superior predictive performance compared to single-modal MRI approaches. These results underscore the potential of radiomics in clinical decision support for this disease process.

Characterization of Mild Acid Stress Response in an Engineered Acid-Tolerant Escherichia coli Strain.

Engineering acid-tolerant microbial strains is a cost-effective approach to overcoming acid stress during industrial fermentation. We previously constructed an acid-tolerant strain (Escherichia coli SC3124) with enhanced growth robustness and productivity under mildly acidic conditions by fine-tuning the expression of synthetic acid-tolerance module genes consisting of a proton-consuming acid resistance system (gadE), a periplasmic chaperone (hdeB), and ROS scavengers (sodB, katE). However, the precise acid-tolerance mechanism of E. coli SC3124 remained unclear. In this study, the growth of E. coli SC3124 under mild acid stress (pH 6.0) was determined. The final OD600 of E. coli SC3124 at pH 6.0 was 131% and 124% of that of the parent E. coli MG1655 at pH 6.8 and pH 6.0, respectively. Transcriptome analysis revealed the significant upregulation of the genes involved in oxidative phosphorylation, the tricarboxylic acid (TCA) cycle, and lysine-dependent acid-resistance system in E. coli SC3124 at pH 6.0. Subsequently, a weighted gene coexpression network analysis was performed to systematically determine the metabolic perturbations of E. coli SC3124 with mild acid treatment, and we extracted the gene modules highly associated with different acid traits. The results showed two biologically significant coexpression modules, and 263 hub genes were identified. Specifically, the genes involved in ATP-binding cassette (ABC) transporters, oxidative phosphorylation, the TCA cycle, amino acid metabolism, and purine metabolism were highly positively associated with mild acid stress responses. We propose that the overexpression of synthetic acid-tolerance genes leads to metabolic changes that confer mild acid stress resistance in E. coli. Integrated omics platforms provide valuable information for understanding the regulatory mechanisms of mild acid tolerance in E. coli and highlight the important roles of oxidative phosphorylation and ABC transporters in mild acid stress regulation. These findings offer novel insights to better the design of acid-tolerant chasses to synthesize value-added chemicals in a green and sustainable manner.

DeepMineLys: Deep mining of phage lysins from human microbiome.

Vast shotgun metagenomics data remain an underutilized resource for novel enzymes. Artificial intelligence (AI) has increasingly been applied to protein mining, but its conventional performance evaluation is interpolative in nature, and these trained models often struggle to extrapolate effectively when challenged with unknown data. In this study, we present a framework (DeepMineLys [deep mining of phage lysins from human microbiome]) based on the convolutional neural network (CNN) to identify phage lysins from three human microbiome datasets. When validated with an independent dataset, our method achieved an F1-score of 84.00%, surpassing existing methods by 20.84%. We expressed 16 lysin candidates from the top 100 sequences in E. coli, confirming 11 as active. The best one displayed an activity 6.2-fold that of lysozyme derived from hen egg white, establishing it as the most potent lysin from the human microbiome. Our study also underscores several important issues when applying AI to biology questions. This framework should be applicable for mining other proteins.

A new paradigm in intracellular immunology: Mitochondria emerging as leading immune organelles.

Mitochondria, traditionally recognized as cellular 'powerhouses' due to their pivotal role in energy production, have emerged as multifunctional organelles at the intersection of bioenergetics, metabolic signaling, and immunity. However, the understanding of their exact contributions to immunity and inflammation is still developing. This review first introduces the innovative concept of intracellular immunity, emphasizing how mitochondria serve as critical immune signaling hubs. They are instrumental in recognizing and responding to pathogen and danger signals, and in modulating immune responses. We also propose mitochondria as the leading immune organelles, drawing parallels with the broader immune system in their functions of antigen presentation, immune regulation, and immune response. Our comprehensive review explores mitochondrial immune signaling pathways, their therapeutic potential in managing inflammation and chronic diseases, and discusses cutting-edge methodologies for mitochondrial research. Targeting a broad readership of both experts in mitochondrial functions and newcomers to the field, this review sets forth new directions that could transform our understanding of intracellular immunity and the integrated immune functions of intracellular organelles.

Systematic Evaluation of Guidelines for the Diagnosis and Treatment of Hepatitis E Virus Infection.

Background and Aims: The hepatitis E virus (HEV) is a zoonotic disease, and infection with HEV in humans primarily causes acute infections and can progress to chronic manifestation in immunocompromised individuals. Over the past decade, guidelines for diagnosing and treating HEV infection have been developed. This study aimed to systematically assess the quality of current guidelines for diagnosing and treating HEV infection, and we analyzed the differences in guideline quality and primary recommendations and explored possible reasons for these differences. Methods: Guidelines published between 2013 and 2022 were searched, and studies were identified using selection criteria. The study assessed the quality of the included guidelines using the Appraisal of Guidelines for Research and Evaluation tool, extracted the primary recommendations in the guidelines, determined the highest level of evidence supporting the recommendations, and reclassified the evidence using the Oxford Centre for Evidence-Based Medicine grading system. Results: Seven guidelines were included in the final analysis. The quality of the guidelines varied widely. The discrepancies may have been caused by the lack of external experts, the failure to consider influencing factors in guideline application, and the lack of consideration of the public's opinion. Analysis of the heterogeneity in primary recommendations revealed differences in algorithms for managing chronic HEV infection, the dosage of ribavirin, and a low level of evidence supporting the primary recommendations. Conclusions: Guideline quality and primary recommendations vary considerably. Refinement by guideline developers and researchers would facilitate updating and applying guidelines for diagnosing and treating HEV infection.

Towards reliable healthcare Imaging: conditional contrastive generative adversarial network for handling class imbalancing in MR Images.

Background: Medical imaging datasets frequently encounter a data imbalance issue, where the majority of pixels correspond to healthy regions, and the minority belong to affected regions. This uneven distribution of pixels exacerbates the challenges associated with computer-aided diagnosis. The networks trained with imbalanced data tends to exhibit bias toward majority classes, often demonstrate high precision but low sensitivity. Method: We have designed a new network based on adversarial learning namely conditional contrastive generative adversarial network (CCGAN) to tackle the problem of class imbalancing in a highly imbalancing MRI dataset. The proposed model has three new components: (1) class-specific attention, (2) region rebalancing module (RRM) and supervised contrastive-based learning network (SCoLN). The class-specific attention focuses on more discriminative areas of the input representation, capturing more relevant features. The RRM promotes a more balanced distribution of features across various regions of the input representation, ensuring a more equitable segmentation process. The generator of the CCGAN learns pixel-level segmentation by receiving feedback from the SCoLN based on the true negative and true positive maps. This process ensures that final semantic segmentation not only addresses imbalanced data issues but also enhances classification accuracy. Results: The proposed model has shown state-of-art-performance on five highly imbalance medical image segmentation datasets. Therefore, the suggested model holds significant potential for application in medical diagnosis, in cases characterized by highly imbalanced data distributions. The CCGAN achieved the highest scores in terms of dice similarity coefficient (DSC) on various datasets: 0.965 ± 0.012 for BUS2017, 0.896 ± 0.091 for DDTI, 0.786 ± 0.046 for LiTS MICCAI 2017, 0.712 ± 1.5 for the ATLAS dataset, and 0.877 ± 1.2 for the BRATS 2015 dataset. DeepLab-V3 follows closely, securing the second-best position with DSC scores of 0.948 ± 0.010 for BUS2017, 0.895 ± 0.014 for DDTI, 0.763 ± 0.044 for LiTS MICCAI 2017, 0.696 ± 1.1 for the ATLAS dataset, and 0.846 ± 1.4 for the BRATS 2015 dataset.

CBCT-based synthetic CT image generation using a diffusion model for CBCT-guided lung radiotherapy.

BACKGROUND: Although cone beam computed tomography (CBCT) has lower resolution compared to planning CTs (pCT), its lower dose, higher high-contrast resolution, and shorter scanning time support its widespread use in clinical applications, especially in ensuring accurate patient positioning during the image-guided radiation therapy (IGRT) process. PURPOSE: While CBCT is critical to IGRT, CBCT image quality can be compromised by severe stripe and scattering artifacts. Tumor movement secondary to respiratory motion also decreases CBCT resolution. In order to improve the image quality of CBCT, we propose a Lung Diffusion Model (L-DM) framework. METHODS: Our proposed algorithm is based on a conditional diffusion model trained on pCT and deformed CBCT (dCBCT) image pairs to synthesize lung CT images from dCBCT images and benefit CBCT-based radiotherapy. dCBCT images were used as the constraint for the L-DM. The image quality and Hounsfield unit (HU) values of the synthetic CTs (sCT) images generated by the proposed L-DM were compared to three selected mainstream generation models. RESULTS: We verified our model in both an institutional lung cancer dataset and a selected public dataset. Our L-DM showed significant improvement in the four metrics of mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and structural similarity index measure (SSIM). In our institutional dataset, our proposed L-DM decreased the MAE from 101.47 to 37.87 HU and increased the PSNR from 24.97 to 29.89 dB, the NCC from 0.81 to 0.97, and the SSIM from 0.80 to 0.93. In the public dataset, our proposed L-DM decreased the MAE from 173.65 to 58.95 HU, while increasing the PSNR, NCC, and SSIM from 13.07 to 24.05 dB, 0.68 to 0.94, and 0.41 to 0.88, respectively. CONCLUSIONS: The proposed L-DM significantly improved sCT image quality compared to the pre-correction CBCT and three mainstream generative models. Our model can benefit CBCT-based IGRT and other potential clinical applications as it increases the HU accuracy and decreases the artifacts from input CBCT images.

Artificial intelligence-based motion tracking in cancer radiotherapy: A review.

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Artificial intelligence (AI) has recently demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges, including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review presents the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provides a literature summary on the topic. We will also discuss the limitations of these AI-based studies and propose potential improvements.

Blood pressure variability and prognostic significance in traumatic brain injury: analysis of the eICU-CRD database.

BACKGROUND: Preliminary evidence demonstrates that visit-to-visit systolic blood pressure (SBP) variability is a prognostic factor of TBI. However, literature regarding the impact of initial blood pressure management on the outcomes of TBI patients is limited. We aimed to further validate the clinical significance of BPV on the prognostic outcomes of patients with TBI. METHODS: We performed the analysis by using individual patient-level data acquired from the eICU-CRD, which collected 200,859 ICU admissions of 139,367 patients in 2014 and 2015 from 208 US hospitals. Adult patients with traumatic intraparenchymal hemorrhage or contusion were included. The primary outcome was in-hospital mortality and the secondary outcome was discharge-home rate. Blood pressure variability (BPV) was calculated according to standard criteria: at least six measurements were taken in the first 24 h (hyperacute group) and 36 over days 2-7 (acute group). We estimated the associations between BPV and outcomes with logistic and proportional odds regression models. The key parameter for BPV was standard deviation (SD) of SBP, categorized into quintiles. We also calculated the average real variability (ARV), as well as maximum, minimum, and mean SBP for comparison in our analysis. RESULTS: We studied 1486 patients in the hyperacute group and 857 in the acute group. SD of SBP had a significant association with the in-hospital mortality for both the hyperacute group (highest quintile adjusted OR 2.28 95% CI 1.18-4.42; ptrend<0.001) and the acute group (highest quintile adjusted OR 2.17, 95% CI 1.08-4.36; ptrend<0.001). The strongest predictors of primary outcome were SD of SBP in the hyperacute phase and minimum SBP in the acute phase. Associations were similar for the discharge-home rate (for the hyperacute group, highest quintile adjusted OR 0.58, 95% CI 0.37-0.89; ptrend<0.001; for the acute group OR 0.55, 95% CI 0.32-0.95; ptrend<0.001). CONCLUSION: Systolic BPV seems to predict a poor outcome in patients with TBI. The benefits of early treatment to maintain appropriate SBP level might be enhanced by smooth and sustained control.

TGF-ß1-Induced LINC01094 promotes epithelial-mesenchymal transition in hepatocellular carcinoma through the miR-122-5p/TGFBR2-SAMD2-SMAD3 Axis.

Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis. It has been proven that long non-coding RNAs (lncRNAs) play an essential role in regulating HCC progression. However, the involvement of LINC01094 in regulating epithelial-mesenchymal transition (EMT) in HCC remains unclear. LINC01094 expression in HCC patients was retrieved from the Cancer Genome Atlas database. Overexpressing and downregulating LINC01094 were conducted to investigate its biological functions using Hep3B, SNU-387, and HuH-7 cells. Western blotting and morphological observation were performed to study the EMT in HCC cells. Transwell assay was adopted to determine the migration and invasion of HCC cells. The underlying mechanism of competitive endogenous RNAs (ceRNAs) was investigated using bioinformatics analysis, quantitative reverse-transcription polymerase chain reaction, and rescue experiments. Elevated LINC01094 expression was observed in HCC and associated with a poor prognosis. Knockdown of LINC01094 expression in SNU-387 and HuH-7 cells could inhibit migration, invasion, and EMT markers. Overexpression of LINC01094 indicated that LINC01094 promoted EMT via the TGF-ß/SMAD signaling pathway. The bioinformatics analysis revealed that miR-122-5p was a target of LINC01094. The miRWalk database analysis showed that TGFBR2, SMAD2, and SMAD3 were downstream targets of miR-122-5p. Mechanically, LINC01094 acted as a ceRNA that facilitated HCC metastasis by sponging miR-122-5p to regulate the expression of TGFBR2, SMAD2, and SMAD3. Further, TGF-ß1 could enhance the expression of LINC01094, forming a positive feedback loop. TGF-ß1-induced LINC01094 expression promotes HCC cell migration and invasion by targeting the miR-122-5p/TGFBR2-SMAD2-SMAD3 axis. LINC01094 may be a potential prognostic biomarker and therapeutic target for HCC metastasis.

Gut microbiota and serum metabolomic alterations in modulating the impact of fecal microbiota transplantation on ciprofloxacin-induced seizure susceptibility.

Introduction: The gut microbiota and the microbiota-gut-brain axis have gained considerable attention in recent years, emerging as key players in the mechanisms that mediate the occurrence and progression of many central nervous system-related diseases, including epilepsy. In clinical practice, one of the side effects of quinolone antibiotics is a lower seizure threshold or aggravation. However, the underlying mechanism remains unclear. Methods: We aimed to unravel the intrinsic mechanisms through 16S rRNA sequencing and serum untargeted metabolomic analysis to shed light on the effects of gut microbiota in ciprofloxacin-induced seizure susceptibility and lithium pilocarpine-induced epilepsy rat models. Results: We observed that ciprofloxacin treatment increased seizure susceptibility and caused gut dysbiosis. We also found similar changes in the gut microbiota of rats with lithium pilocarpine-induced epilepsy. Notably, the levels of Akkermansia and Bacteroides significantly increased in both the ciprofloxacin-induced seizure susceptibility and lithium pilocarpine-induced epilepsy rat models. However, Marvinbryantia, Oscillibacter, and Ruminococcaceae_NK4A214_group showed a coincidental reduction. Additionally, the serum untargeted metabolomic analysis revealed decreased levels of indole-3-propionic acid, a product of tryptophan-indole metabolism, after ciprofloxacin treatment, similar to those in the plasma of lithium pilocarpine-induced epilepsy in rats. Importantly, alterations in the gut microbiota, seizure susceptibility, and indole-3-propionic acid levels can be restored by fecal microbiota transplantation. Conclusion: In summary, our findings provide evidence that ciprofloxacin-induced seizure susceptibility is partially mediated by the gut microbiota and tryptophan-indole metabolism. These associations may play a role in epileptogenesis, and impacting the development progression and treatment outcomes of epilepsy.

[Value of SOX1 and PAX1 Gene Methylation Detection in Secondary Triage of High-Grade Cervical Lesions].

Objective To evaluate the value of SOX1 and PAX1 gene methylation detection in the secondary triage of high-grade cervical lesions.Methods Exfoliated cervical cells were collected from 122 patients tested positive for human papilloma virus (HPV) and subjected to thin-prep cytologic test (TCT) and SOX1/PAX1 gene methylation tests.Results The HPV test combined with TCT showed the sensitivity of 95.24% and the specificity of 23.75% for detecting cervical intraepithelial neoplasia (CIN) grade 2 and above (CIN2+).After the addition of the SOX1/PAX1 gene methylation detection in secondary triage,the sensitivity for detecting CIN2+ was 83.33%,which had no statistically significant difference from the sensitivity of TCT combined with HPV test (P=0.078).However,the specificity reached 77.50%,which was significantly higher than that of HPV test combined with TCT (P<0.001).The SOX1/PAX1 gene methylation level in the CIN2+ group was higher than those in the normal cervical tissue and the CIN1 group(P<0.001).The cut-off values of SOX1 and PAX1 gene methylation for CIN2+ detection were -11.81 and -11.98,respectively.Conclusion Adding the detection of SOX1/PAX1 gene methylation in secondary triage significantly improves the efficiency and accuracy of CIN2+ detection.

Unsupervised Bayesian Generation of Synthetic CT from CBCT Using Patient-Specific Score-Based Prior.

BACKGROUND: Cone-beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image-guided radiotherapy (IGRT) process, thereby making it a potential imaging modality for the implementation of adaptive radiotherapy (ART) protocols. Nonetheless, significant artifacts and incorrect Hounsfield unit (HU) values hinder their application in quantitative tasks such as target and organ segmentations and dose calculation. Therefore, acquiring CT-quality images from the CBCT scans is essential to implement online ART in clinical settings. PURPOSE: This work aims to develop an unsupervised learning method using the patient-specific diffusion model for CBCT-based synthetic CT (sCT) generation to improve the image quality of CBCT. METHODS: The proposed method is in an unsupervised framework that utilizes a patient-specific score-based model as the image prior alongside a customized total variation (TV) regularization to enforce coherence across different transverse slices. The score-based model is unconditionally trained using the same patient's planning CT (pCT) images to characterize the manifold of CT-quality images and capture the unique anatomical information of the specific patient. The efficacy of the proposed method was assessed on images from anatomical sites including head and neck (H&N) cancer, pancreatic cancer, and lung cancer. The performance of the proposed CBCT correction method was evaluated using quantitative metrics including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). Additionally, the proposed algorithm was benchmarked against two other unsupervised diffusion model-based CBCT correction algorithms.

Corrigendum: CD8 T cell response and its released cytokine IFN-γ are necessary for lung alveolar epithelial repair during bacterial pneumonia.

[This corrects the article DOI: 10.3389/fimmu.2023.1268078.].

The ZcVg3 Gene Regulates the Reproduction and Lifespan of Female Zeugodacus cucurbitae (Coquillett) Mediated by Short-Term High Temperatures.

Zeugodacus cucurbitae (Coquillett) is a significant pest affecting fruit and vegetables in tropical and subtropical regions, and its development and reproduction are enhanced after exposure to short-term high-temperature stress at 45 °C. Vitellogenin (Vg) is an essential precursor of yolk protein formation in eggs and plays a vital role in the ovarian development of insects. Interfering with the Z. cucurbitae vitellogenin receptor (ZcVgR) gene in short-term high-temperature conditions decreases the fecundity of female adults, while the transcription level of the ZcVg3 gene increases. To elucidate the reproductive function of the ZcVg3 gene and the synergistic relationship among the ZcVgs genes under short-term high temperatures, this study injected siRNA to interfere with the ZcVg3 gene after subjecting Z. cucurbitae to a 1 h treatment at 45 °C and 25 °C. The expression of the ZcVg3 gene was suppressed, leading to the upregulation of the ZcVg1 and ZcVg2 genes, and the expression of the ZcVgR gene was initially decreased and then increased. Silencing the ZcVg3 gene after a 1 h treatment at 45 °C resulted in a reduction of approximately 84.7% and 75.9% in the fecundity and spawning days of female adults compared to the control. The development rate of their ovaries and the ovarian diameter significantly decreased, and their lifespan was reduced by 71%. The ZcVg3 gene plays a crucial role in the reproduction of Z. cucurbitae in short-term high-temperature conditions. The results of this study provide potential targets for the development of RNAi-based techniques for the control of Z. cucurbitae.

Physical Exercise Improves the Neuronal Function in Ischemic Stroke Via Microglial CB2R/P2Y12 Signaling.

Physical exercise (PE) may be the single most important and accessible lifestyle habit throughout life, it inhibits the neuroinflammatory response and protects the brain against damage. As the innate cells in brain, microglia undergo morphological and functional changes to communicate with neurons protecting the neurons from injury. Herein, aiming at exploring the effects of PE on the communication between microglia-neuron during acute ischemic cerebral infarction, we carried out running wheel training before the conduction of transient middle cerebral artery occlusion (tMCAO) in C57BL/6 J and Cx3cr1-GFP mice. We found that microglial P2Y12 expression in the peri-infarct area was decreased, microglial dynamics and microglia-neuron communications were impaired, using in vivo two-photon imaging. PE up-regulated the microglial P2Y12 expression, increased the microglial dynamics, and promoted the contacts of microglia with neurons. As a result, PE inhibited neuronal Ca2+ overloads and protected against damage of the neuronal mitochondria in acute tMCAO. Mechanistically, PE increased the cannabinoid receptor 2 (CB2R) in microglia, promoted the phosphorylation of Nrf2 (NF-E2-related factor 2) at ser-344, increased the transcription factor level of Mafk, and up-regulated the level of P2Y12, whereby PE increased the levels of CB2R to promote microglia-neuron contacts to monitor and protect neuronal function.

Identification of key genes associated with cervical cancer based on bioinformatics analysis.

BACKGROUND: Cervical cancer has extremely high morbidity and mortality, and its pathogenesis is still in the exploratory stage. This study aimed to screen and identify differentially expressed genes (DEGs) related to cervical cancer through bioinformatics analysis. METHODS: GSE63514 and GSE67522 were selected from the GEO database to screen DEGs. Then GO and KEGG analysis were performed on DEGs. PPI network of DEGs was constructed through STRING website, and the hub genes were found through 12 algorithms of Cytoscape software. Meanwhile, GSE30656 was selected from the GEO database to screen DEMs. Target genes of DEMs were screened through TagetScan, miRTarBase and miRDB. Next, the hub genes screened from DEGs were merged with the target genes screened from DEMs. Finally, ROC curve and nomogram analysis were performed to assess the predictive capabilities of the hub genes. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry. RESULTS: Six hub genes, TOP2A, AURKA, CCNA2, IVL, KRT1, and IGFBP5, were mined through the protein-protein interaction network. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry, and it was found that TOP2A, AURKA as well as CCNA2 were overexpressed and IGFBP5 was low expression in cervical cancer. CONCLUSIONS: This study showed that TOP2A, AURKA, CCNA2 and IGFBP5 screened through bioinformatics analysis were significantly differentially expressed in cervical cancer samples compared with normal samples, which might be biomarkers of cervical cancer.