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.

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.

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.

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.

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.

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.

Caspase-4/11 promotes hyperlipidemia and chronic kidney disease-accelerated vascular inflammation by enhancing trained immunity.

To determine whether hyperlipidemia and chronic kidney disease (CKD) have a synergy in accelerating vascular inflammation via trained immunity (TI), we performed aortic pathological analysis and RNA-Seq of high-fat diet-fed (HFD-fed) 5/6 nephrectomy CKD (HFD+CKD) mice. We made the following findings: (a) HFD+CKD increased aortic cytosolic LPS levels, caspase-11 (CASP11) activation, and 998 gene expressions of TI pathways in the aorta (first-tier TI mechanism); (b) CASP11-/- decreased aortic neointima hyperplasia, aortic recruitment of macrophages, and casp11-gasdermin D-mediated cytokine secretion; (c) CASP11-/- decreased N-terminal gasdermin D (N-GSDMD) membrane expression on aortic endothelial cells and aortic IL-1B levels; (d) LPS transfection into human aortic endothelial cells resulted in CASP4 (human)/CASP11 (mouse) activation and increased N-GSDMD membrane expression; and (e) IL-1B served as the second-tier mechanism underlying HFD+CKD-promoted TI. Taken together, hyperlipidemia and CKD accelerated vascular inflammation by promoting 2-tier trained immunity.

CT-based synthetic contrast-enhanced dual-energy CT generation using conditional denoising diffusion probabilistic model.

Objective.The study aimed to generate synthetic contrast-enhanced Dual-energy CT (CE-DECT) images from non-contrast single-energy CT (SECT) scans, addressing the limitations posed by the scarcity of DECT scanners and the health risks associated with iodinated contrast agents, particularly for high-risk patients.Approach.A conditional denoising diffusion probabilistic model (C-DDPM) was utilized to create synthetic images. Imaging data were collected from 130 head-and-neck (HN) cancer patients who had undergone both non-contrast SECT and CE-DECT scans.Main Results.The performance of the C-DDPM was evaluated using Mean Absolute Error (MAE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). The results showed MAE values of 27.37±3.35 Hounsfield Units (HU) for high-energy CT (H-CT) and 24.57±3.35HU for low-energy CT (L-CT), SSIM values of 0.74±0.22 for H-CT and 0.78±0.22 for L-CT, and PSNR values of 18.51±4.55 decibels (dB) for H-CT and 18.91±4.55 dB for L-CT.Significance.The study demonstrates the efficacy of the deep learning model in producing high-quality synthetic CE-DECT images, which significantly benefits radiation therapy planning. This approach provides a valuable alternative imaging solution for facilities lacking DECT scanners and for patients who are unsuitable for iodine contrast imaging, thereby enhancing the reach and effectiveness of advanced imaging in cancer treatment planning.

Oral Decoctions Based on Qi-Yin Syndrome Differentiation After Adjuvant Chemotherapy in Resected Stage ΙΙΙA Non-Small Cell Lung Cancer: A Randomized Controlled Trial.

OBJECTIVE: Powerful adjuvant strategies are required to improve the survival of patients with completely resected stage ΙΙΙA non-small cell lung cancer (NSCLC). We aimed to compare the efficacy of traditional Chinese medicine (TCM) treatment versus observation after adjuvant chemotherapy in these patients. METHODS: Eligible patients were randomized 1:1 to receive either oral decoctions based on Qi-Yin syndrome differentiation (TCM group) or observation (observation group). The intervention lasted for 12 months. The primary endpoint was 1-year disease-free survival (DFS). Secondary endpoints were DFS, quality of life, regulatory T cells (Tregs), and cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) on the surface of Tregs in peripheral blood. We used EORTC QLQ-LC43 to evaluate quality of life. RESULTS: Between Apr 29, 2019, and Nov 11, 2021, 75 patients were randomly assigned to oral decoctions based on Qi-Yin syndrome differentiation (n = 38) or observation (n = 37). The full analysis set included 35 patients in the TCM group and 35 in the observation group. After a median follow-up of 24.2 months, oral decoctions based on Qi-Yin syndrome differentiation improved DFS compared with observation (HR 0.378, 95% CI: 0.157-0.912; P = .03). One-year DFS was 82.1% in the TCM group and 61.9% in the observation group (P = .06). Three months after randomization, scores of total health, role function, emotional function, and social function in the TCM group were higher than those in the observation group (P < .01 for all), scores of fatigue, pain, insomnia, appetite loss, constipation, cough, and chest pain were lower than those in the observation group (P < .05 for all); there was no significant difference in the proportion of Tregs between the TCM group and the observation group (P = .58); the proportion of CTLA-4+Tregs in the TCM group was lower than that in the observation group (P = .046). There were no adverse events that occurred in both groups. CONCLUSIONS: Oral decoctions based on Qi-Yin syndrome differentiation after adjuvant chemotherapy prolonged DFS, reduced the risk of disease recurrence and metastasis, improved quality of life, and down-regulated the proportion of CTLA-4+Tregs in completely resected stage ΙΙΙA NSCLC patients. TRIAL REGISTRATION: Chinese Clinical Trial Register, No. ChiCTR1800019396. Date of registration: 9 November 2018.

Advances in stress-tolerance elements for microbial cell factories.

Microorganisms, particularly extremophiles, have evolved multiple adaptation mechanisms to address diverse stress conditions during survival in unique environments. Their responses to environmental coercion decide not only survival in severe conditions but are also an essential factor determining bioproduction performance. The design of robust cell factories should take the balance of their growing and bioproduction into account. Thus, mining and redesigning stress-tolerance elements to optimize the performance of cell factories under various extreme conditions is necessary. Here, we reviewed several stress-tolerance elements, including acid-tolerant elements, saline-alkali-resistant elements, thermotolerant elements, antioxidant elements, and so on, providing potential materials for the construction of cell factories and the development of synthetic biology. Strategies for mining and redesigning stress-tolerance elements were also discussed. Moreover, several applications of stress-tolerance elements were provided, and perspectives and discussions for potential strategies for screening stress-tolerance elements were made.

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.

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.

[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.

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.].

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.

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.

Epigenetically associated IGF2BP3 upregulation promotes cell proliferation by regulating E2F1 expression in hepatocellular carcinoma.

RNA-binding proteins (RBPs) are a class of proteins that primarily function by interacting with different types of RNAs and play a critical role in regulating the transcription and translation of cancer-related genes. However, their role in the progression of hepatocellular carcinoma (HCC) remains unclear. In this study, we analyzed RNA sequencing data and the corresponding clinical information of patients with HCC to screen for prognostic RBPs. Insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) was identified as an independent prognostic factor for liver cancer. It is upregulated in HCC and is associated with a poor prognosis. Elevated IGF2BP3 expression was validated via immunohistochemical analysis using a tissue microarray of patients with HCC. IGF2BP3 knockdown inhibited the proliferation of Hep3B and HepG2 cells, whereas IGF2BP3 overexpression promoted the expansion of HuH-7 and MHCC97H cells. Mechanistically, IGF2BP3 modulates cell proliferation by regulating E2F1 expression. DNA hypomethylation of the IGF2BP3 gene may increase the expression of IGF2BP3, thereby enhancing cell proliferation in HCC. Therefore, IGF2BP3 may act as a novel prognostic biomarker and a potential therapeutic target for HCC.

A preliminary investigation of precise visualization, localization, and resection of pelvic lymph nodes in bladder cancer by using indocyanine green fluorescence-guided approach through intracutaneous dye injection into the lower limbs and perineum.

Objective: This study aimed to investigate the feasibility and effectiveness of using indocyanine green (ICG) injected intracutaneously through the lower limbs and perineum for visualized tracking, localization, and qualitative assessment of pelvic lymph nodes (LNs) in bladder cancer to achieve their accurate resection. Methods: First, ICG was injected into the LN metastasis model mice lower limbs, and real-time and dynamic in vivo and ex vivo imaging was conducted by using a near-infrared fluorescence imaging system. Additionally, 26 patients with bladder cancer were enrolled and divided into intracutaneous group and transurethral group. A near-infrared fluorescence imaging device with internal and external imaging probes was used to perform real-time tracking, localization, and resection of the pelvic LNs. Results: The mice normal LNs and the metastatic LNs exhibited fluorescence. The metastatic LNs showed a significantly higher signal-to-background ratio than the normal LNs (3.9 ± 0.2 vs. 2.0 ± 0.1, p < 0.05). In the intracutaneous group, the accuracy rate of fluorescent-labeled LNs was 97.6%, with an average of 11.3 ± 2.4 LNs resected per patient. Six positive LNs were detected in three patients (18.8%). In the transurethral group, the accuracy rate of fluorescent-labeled LNs was 84.4%, with an average of 8.6 ± 2.3 LNs resected per patient. Two positive LNs were detected in one patient (12.5%). Conclusion: Following the intracutaneous injection of ICG into the lower limbs and perineum, the dye accumulates in pelvic LNs through lymphatic reflux. By using near-infrared fluorescence laparoscopic fusion imaging, physicians can perform real-time tracking, localization, and precise resection of pelvic LNs.