Super-resolution reconstruction for early cervical cancer magnetic resonance imaging based on deep learning.

This study aims to develop a super-resolution (SR) algorithm tailored specifically for enhancing the image quality and resolution of early cervical cancer (CC) magnetic resonance imaging (MRI) images. The proposed method is subjected to both qualitative and quantitative analyses, thoroughly investigating its performance across various upscaling factors and assessing its impact on medical image segmentation tasks. The innovative SR algorithm employed for reconstructing early CC MRI images integrates complex architectures and deep convolutional kernels. Training is conducted on matched pairs of input images through a multi-input model. The research findings highlight the significant advantages of the proposed SR method on two distinct datasets at different upscaling factors. Specifically, at a 2× upscaling factor, the sagittal test set outperforms the state-of-the-art methods in the PSNR index evaluation, second only to the hybrid attention transformer, while the axial test set outperforms the state-of-the-art methods in both PSNR and SSIM index evaluation. At a 4× upscaling factor, both the sagittal test set and the axial test set achieve the best results in the evaluation of PNSR and SSIM indicators. This method not only effectively enhances image quality, but also exhibits superior performance in medical segmentation tasks, thereby providing a more reliable foundation for clinical diagnosis and image analysis.

Research progress on novel antibody drug conjugates in cancer therapy. / ç™Œç—‡æ²»ç–—ä¸­æ–°åž‹æŠ—ä½“å¶è”è¯ç‰©çš„ç ”ç©¶è¿›å±•.

Traditional antibody drug conjugates (ADC) combine monoclonal antibodies with cytotoxic drugs to accurately strike cancer cells, but there are still many shortcomings in stability, targeting, efficacy, and safety. Novel ADC, such as bi-specific, site-specific, dual-payload, and pro-drug type ADC, can be optimized by simultaneously binding 2 different antigens or epitopes, selecting more stable linkers, coupling with specific amino acid sites of antibodies, carrying different drug payloads, and adopting prodrug strategies, while retaining the characteristics of traditional ADC. Significantly improving the stability, targeting, efficacy and safety of drugs can better meet the needs of clinical treatment. Novel ADC will play a more important role in cancer treatment in the future. Discussing the progress of novel ADC in cancer treatment and analyzing their advantages and challenges can provide theoretical support for the development of anti-cancer strategies and provide directions for drug research and development.

Molecular characterization and expression analysis of thyroid hormone receptors in protogynous rice field eel, Monopterus albus.

Thyroid hormones (THs) play important roles in growth, development, morphogenesis, reproduction, and so on. They are mainly meditated by binding to thyroid hormone receptors (TRs) in vertebrates. As important members of the nuclear receptor superfamily, TRs and their ligands are involved in many biological processes. To investigate the potential roles of TRs in the gonadal differentiation and sex change, we cloned and characterized the TRs genes in protogynous rice field eel (Monopterus albus). In this study, three types of TRs were obtained, which were TRαA, TRαB and TRß, encoding preproproteins of 336-, 409- and 415-amino acids, respectively. Multiple alignments of the three putative TRs protein sequences showed they had a higher similarity. Tissue expression analysis showed that TRαA mainly expressed in the gonad, while TRαB and TRß in the brain. During female-to-male sex reversal, the expression levels of all the three TRs showed a similar trend of increase followed by a decrease in the gonad. Intraperitoneal injection of triiodothyronine (T3) stimulated the expression of TRαA and TRαB, while it had no significant change on the expression of TRß in the ovary. Gonadotropin-releasing hormone analogue (GnRHa) injection also significantly upregulated the expression levels of TRαA and TRαB after 6 h, while it had no significant effect on TRß. These results demonstrated that TRs were involved in the gonadal differentiation and sex reversal, and TRα may play more important roles than TRß in reproduction by the regulation of GnRHa in rice field eel.

Surgical margins and prognosis of borderline and malignant phyllodes tumors.

PURPOSE: To investigate the optimal surgical margin and prognostic risk factors for borderline and malignant phyllodes tumors (PTs). METHODS: A retrospective analysis was conducted on patients with borderline and malignant PTs at our hospital from 2011 to 2022. Univariate and multivariate Cox proportional hazard models were employed to analyze the effects of various variables on local recurrence-free survival (LRFS) and disease-free survival (DFS). RESULTS: This study comprised 150 patients, 85 classified as borderline and 65 as malignant. During a median follow-up of 66 months (range: 3-146 months), 34 cases (22.7%) experienced local recurrence, 9 cases (6.0%) exhibited distant metastasis, and 7 cases (4.7%) resulted in death. Irrespective of the histological subtypes, patients with surgical margins ≥ 1 cm exhibit significantly higher 5-year LRFS and 5-year DFS rates compared to those with margins < 1 cm. Among patients with initial margins < 1 cm, LRFS (P = 0.004) and DFS (P = 0.003) were improved in patients reoperated to achieve margins ≥ 1 cm. Surgical margin < 1 cm (HR = 2.567, 95%CI 1.137-5.793, P = 0.023) and age < 45 years (HR = 2.079, 95%CI 1.033-4.184, P = 0.040) were identified as independent risk factors for LRFS. Additionally, surgical margin < 1 cm (HR = 3.074, 95%CI 1.622-5.826, P = 0.001) and tumor size > 5 cm (HR = 2.719, 95%CI 1.307-5.656, P = 0.007) were determined to be independent risk factors for DFS. CONCLUSIONS: A negative surgical margin of at least 1 cm (with secondary resection if necessary) should be achieved for borderline and malignant PTs. Tumor size > 5 cm and age < 45 years were predictive of recurrence, suggesting multiple therapy modalities may be considered for these high-risk patients.

A deep learning-based automatic image quality assessment method for respiratory phase on computed tomography chest images.

Background: Computed tomography (CT) chest scans have become commonly used in clinical diagnosis. Image quality assessment (IQA) for CT images plays an important role in CT examination. It is worth noting that IQA is still a manual and subjective process, and even experienced radiologists make mistakes due to human limitations (fatigue, perceptual biases, and cognitive biases). There are also kinds of biases because of poor consensus among radiologists. Excellent IQA methods can reliably give an objective evaluation result and also reduce the workload of radiologists. This study proposes a deep learning (DL)-based automatic IQA method, to assess whether the image quality of respiratory phase on CT chest images are optimal or not, so that the CT chest images can be used in the patient's physical condition assessment. Methods: This retrospective study analysed 212 patients' chest CT images, with 188 patients allocated to a training set (150 patients), validation set (18 patients), and a test set (20 patients). The remaining 24 patients were used for the observer study. Data augmentation methods were applied to address the problem of insufficient data. The DL-based IQA method combines image selection, tracheal carina segmentation, and bronchial beam detection. To automatically select the CT image containing the tracheal carina, an image selection model was employed. Afterward, the area-based approach and score-based approach were proposed and used to further optimize the tracheal carina segmentation and bronchial beam detection results, respectively. Finally, the score about the image quality of the patient's respiratory phase images given by the DL-based automatic IQA method was compared with the mean opinion score (MOS) given in the observer study, in which four blinded experienced radiologists took part. Results: The DL-based automatic IQA method achieved good performance in assessing the image quality of the respiratory phase images. For the CT sequence of the same patient, the DL-based IQA method had an accuracy of 92% in the assessment score, while the radiologists had an accuracy of 88%. The Kappa value of the assessment score between the DL-based IQA method and radiologists was 0.75, with a sensitivity of 85%, specificity of 91%, positive predictive value (PPV) of 92%, negative predictive value (NPV) of 93%, and accuracy of 88%. Conclusions: This study develops and validates a DL-based automatic IQA method for the respiratory phase on CT chest images. The performance of this method surpassed that of the experienced radiologists on the independent test set used in this study. In clinical practice, it is possible to reduce the workload of radiologists and minimize errors caused by human limitations.

Clinical significance and biological function of interferon regulatory factor 1 in non-small cell lung cancer.

The clinical application and biological function of interferon regulatory factor 1 (IRF1) in non-small cell lung cancer (NSCLC) patients undergoing chemoimmunotherapy remain elusive. The aim of this study was to investigate the predictive and prognostic significance of IRF1 in NSCLC patients. We employed the cBioPortal database to predict frequency changes in IRF1 and explore its target genes. Bioinformatic methods were utilized to analyze the relationship between IRF1 and immune regulatory factors. Retrospective analysis of clinical samples was conducted to assess the predictive and prognostic value of IRF1 in chemoimmunotherapy. Additionally, A549 cells with varying IRF1 expression levels were constructed to investigate its effects on NSCLC cells, while animal experiments were performed to study the role of IRF1 in vivo. Our findings revealed that the primary mutation of IRF1 is deep deletion and it exhibits a close association with immune regulatory factors. KRAS and TP53 are among the target genes of IRF1, with interferon and IL-2 being the predominantly affected pathways. Clinically, IRF1 levels significantly correlate with the efficacy of chemoimmunotherapy. Patients with high IRF1 levels exhibited a median progression-free survival (mPFS) of 9.5 months, whereas those with low IRF1 levels had a shorter mPFS of 5.8 months. IRF1 levels positively correlate with PD-L1 distribution and circulating IL-2 levels. IL-2 enhances the biological function of IRF1 and recapitulates its role in vivo in the knockdown group. Therefore, IRF1 may possess predictive and prognostic value for chemoimmunotherapy in NSCLC patients through the regulation of the IL-2 inflammatory pathway.

Machine learning based on metabolomics unveils neutrophil extracellular trap-related metabolic signatures in non-small cell lung cancer patients undergoing chemoimmunotherapy.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the primary form of lung cancer, and the combination of chemotherapy with immunotherapy offers promising treatment options for patients suffering from this disease. However, the emergence of drug resistance significantly limits the effectiveness of these therapeutic strategies. Consequently, it is imperative to devise methods for accurately detecting and evaluating the efficacy of these treatments. AIM: To identify the metabolic signatures associated with neutrophil extracellular traps (NETs) and chemoimmunotherapy efficacy in NSCLC patients. METHODS: In total, 159 NSCLC patients undergoing first-line chemoimmunotherapy were enrolled. We first investigated the characteristics influencing clinical efficacy. Circulating levels of NETs and cytokines were measured by commercial kits. Liquid chromatography tandem mass spectrometry quantified plasma metabolites, and differential metabolites were identified. Least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest algorithms were employed. By using plasma metabolic profiles and machine learning algorithms, predictive metabolic signatures were established. RESULTS: First, the levels of circulating interleukin-8, neutrophil-to-lymphocyte ratio, and NETs were closely related to poor efficacy of first-line chemoimmunotherapy. Patients were classed into a low NET group or a high NET group. A total of 54 differential plasma metabolites were identified. These metabolites were primarily involved in arachidonic acid and purine metabolism. Three key metabolites were identified as crucial variables, including 8,9-epoxyeicosatrienoic acid, L-malate, and bis(monoacylglycerol)phosphate (18:1/16:0). Using metabolomic sequencing data and machine learning methods, key metabolic signatures were screened to predict NET level as well as chemoimmunotherapy efficacy. CONCLUSION: The identified metabolic signatures may effectively distinguish NET levels and predict clinical benefit from chemoimmunotherapy in NSCLC patients.