Heat-induced SUMOylation differentially affects bacterial effectors in plant cells.

Bacterial pathogens deliver effectors into host cells to suppress immunity. How host cells target these effectors is critical in pathogen-host interactions. SUMOylation, an important type of posttranslational modification in eukaryotic cells, plays a critical role in immunity, but its effect on bacterial effectors remains unclear in plant cells. In this study, using bioinformatic and biochemical approaches, we found that at least 16 effectors from the bacterial pathogen Pseudomonas syringae pv. tomato DC3000 are SUMOylated by the enzyme cascade from Arabidopsis thaliana. Mutation of SUMOylation sites on the effector HopB1 enhances its function in the induction of plant cell death via stability attenuation of a plant receptor kinase BRASSINOSTEROID INSENSITIVE 1 (BRI1)-ASSOCIATED RECEPTOR KINASE 1. By contrast, SUMOylation is essential for the function of another effector, HopG1, in the inhibition of mitochondria activity and jasmonic acid signaling. SUMOylation of both HopB1 and HopG1 is increased by heat treatment, and this modification modulates the functions of these 2 effectors in different ways in the regulation of plant survival rates, gene expression, and bacterial infection under high temperatures. Therefore, the current work on the SUMOylation of effectors in plant cells improves our understanding of the function of dynamic protein modifications in plant-pathogen interactions in response to environmental conditions.

S-acylation of a non-secreted peptide controls plant immunity via secreted-peptide signal activation.

Small peptides modulate multiple processes in plant cells, but their regulation by post-translational modification remains unclear. ROT4 (ROTUNDIFOLIA4) belongs to a family of Arabidopsis non-secreted small peptides, but knowledge on its molecular function and how it is regulated is limited. Here, we find that ROT4 is S-acylated in plant cells. S-acylation is an important form of protein lipidation, yet so far it has not been reported to regulate small peptides in plants. We show that this modification is essential for the plasma membrane association of ROT4. Overexpression of S-acylated ROT4 results in a dramatic increase in immune gene expression. S-acylation of ROT4 enhances its interaction with BSK5 (BRASSINOSTEROID-SIGNALING KINASE 5) to block the association between BSK5 and PEPR1 (PEP RECEPTOR1), a receptor kinase for secreted plant elicitor peptides (PEPs), thereby activating immune signaling. Phenotype analysis indicates that S-acylation is necessary for ROT4 functions in pathogen resistance, PEP response, and the regulation of development. Collectively, our work reveals an important role for S-acylation in the cross-talk of non-secreted and secreted peptide signaling in plant immunity.

FAT1 as a tumor mutation burden specific gene affects the immunotherapy effect in head and neck squamous cell cancer.

BACKGROUND: Response to immunotherapy is the main challenge of head and neck squamous cancer (HNSCC) treatment. Previous studies have indicated that tumor mutational burden (TMB) is associated with prognosis, but it is not always a precise index. Hence, investigating specific genetic mutations and tumor microenvironment (TME) changes in TMB-high patients is essential for precision therapy of HNSCC. METHODS: A total of 33 HNSCC patients were enrolled in this study. We calculated the TMB score based on next-generation sequencing (NGS) sequencing and grouped these patients based on TMB score. Then, we examined the immune microenvironment of HNSCC using assessments of the bulk transcriptome and the single-cell RNA sequence (scRNA-seq) focusing on the molecular nature of TMB and mutations in HNSCC from our cohort. The association of the mutation pattern and TMB was analyzed in The Cancer Genome Atlas (TCGA) and validated by our cohort. RESULTS: 33 HNSCC patients were divided into three groups (TMB-low, -medium, and -high) based on TMB score. In the result of 520-gene panel sequencing data, we found that FAT1 and LRP1B mutations were highly prevalent in TMB-high patients. FAT1 mutations are associated with resistance to immunotherapy in HNSCC patients. This involves many metabolism-related pathways like RERE, AIRE, HOMER1, etc. In the scRNA-seq data, regulatory T cells (Tregs), monocytes, and DCs were found mainly enriched in TMB-high samples. CONCLUSION: Our analysis unraveled the FAT1 gene as an assistant predictor when we use TMB as a biomarker of drug resistance in HNSCC. Tregs, monocytes, and dendritic cells (DCs) were found mainly enriched in TMB-high samples.

Transcriptomics analyses reveal the key genes involved in stamen petaloid formation in Alcea rosea L.

Alcea rosea L. is a traditional flower with a long cultivation history. It is extensively cultivated in China and is widely planted in green belt parks or used as cut flowers and potted ornamental because of its rich colors and flower shapes. Double-petal A. rosea flowers have a higher aesthetic value compared to single-petal flowers, a phenomenon determined by stamen petaloid. However, the underlying molecular mechanism of this phenomenon is still very unclear. In this study, an RNA-based comparative transcriptomic analysis was performed between the normal petal and stamen petaloid petal of A. rosea. A total of 3,212 differential expressed genes (DEGs), including 2,620 up-regulated DEGs and 592 down-regulated DEGs, were identified from 206,188 unigenes. Numerous DEGs associated with stamen petaloid were identified through GO and KEGG enrichment analysis. Notably, there were 63 DEGs involved in the plant hormone synthesis and signal transduction, including auxin, cytokinin, gibberellin, abscisic acid, ethylene, brassinosteroid, jasmonic acid, and salicylic acid signaling pathway and 56 key transcription factors (TFs), such as MADS-box, bHLH, GRAS, and HSF. The identification of these DEGs provides an important clue for studying the regulation pathway and mechanism of stamen petaloid formation in A. rosea and provides valuable information for molecular plant breeding.

Preparation of Multifunctional Hydrogels with In Situ Dual Network Structure and Promotion of Wound Healing.

As an emerging biomedical material, wound dressings play an important therapeutic function in the process of wound healing. It can provide an ideal healing environment while protecting the wound from a complex external environment. A hydrogel wound dressing composed of tilapia skin gelatin (Tsg) and fucoidan (Fuc) was designed in this article to enhance the microenvironment of wound treatment and stimulate wound healing. By mixing horseradish peroxidase (HRP), hydrogen peroxide (H2O2), tilapia skin gelatin-tyramine (Tsg-Tyr), and carboxylated fucoidan-tyramine in agarose (Aga), using the catalytic cross-linking of HRP/H2O2 and the sol-gel transformation of Aga, a novel gelatin-fucoidan (TF) double network hydrogel wound dressing was constructed. The TF hydrogels have a fast and adjustable gelation time, and the addition of Aga further enhances the stability of the hydrogels. Moreover, Tsg and Fuc are coordinated with each other in terms of biological efficacy, and the TF hydrogel demonstrated excellent antioxidant properties and biocompatibility in vitro. Also, in vivo wound healing experiments showed that the TF hydrogel could effectively accelerate wound healing, reduce wound microbial colonization, alleviate inflammation, and promote collagen deposition and angiogenesis. In conclusion, TF hydrogel wound dressings have the potential to replace traditional dressings in wound healing.

Next-generation sequencing identified that RET variation associates with lymph node metastasis and the immune microenvironment in thyroid papillary carcinoma.

BACKGROUND: To date, although most thyroid carcinoma (THCA) achieves an excellent prognosis, some patients experience a rapid progression episode, even with differentiated THCA. Nodal metastasis is an unfavorable predictor. Exploring the underlying mechanism may bring a deep insight into THCA. METHODS: A total of 108 THCA from Chinese patients with next-generation sequencing (NGS) were recruited. It was used to explore the gene alteration spectrum of THCA and identify gene alterations related to nodal metastasis in papillary thyroid carcinoma (PTC). The Cancer Genome Atlas THCA cohort was further studied to elucidate the relationship between specific gene alterations and tumor microenvironment. A pathway enrichment analysis was used to explore the underlying mechanism. RESULTS: Gene alteration was frequent in THCA. BRAF, RET, POLE, ATM, and BRCA1 were the five most common altered genes. RET variation was positively related to nodal metastasis in PTC. RET variation is associated with immune cell infiltration levels, including CD8 naïve, CD4 T and CD8 T cells, etc. Moreover, Step 3 and Step 4 of the cancer immunity cycle (CIC) were activated, whereas Step 6 was suppressed in PTC with RET variation. A pathway enrichment analysis showed that RET variation was associated with several immune-related pathways. CONCLUSION: RET variation is positively related to nodal metastasis in Chinese PTC, and anti-tumor immune response may play a role in nodal metastasis triggered by RET variation.

A risk stratification system developed to predict contralateral incidental malignant foci in early papillary thyroid carcinoma preoperatively.

BACKGROUND: In clinical practice, contralateral incidental malignant foci (CIMFs) can be found in some early (cT1N0M0) papillary thyroid carcinomas (PTCs) on postoperative pathological examination. To screen out the patients with high risk of CIMF preoperatively would help in determining the extent of thyroid surgery. METHODS: From October 2016 to February 2021, 332 patients diagnosed with early (cT1N0M0) PTC who underwent total thyroidectomy were included and randomly allocated into a training dataset (n = 233) and a test dataset (n = 99). Demographic and clinicopathological features were recorded and analyzed using logistic regression analysis. A coefficient-based nomogram was developed and validated. RESULTS: Logistic regression analyses revealed that the predictive model including BRAF V600E mutation, multifocality and margin of the contralateral nodule achieved the best diagnostic performance. The nomogram showed good discrimination, with AUCs of 0.795 (95 % CI, 0.736-0.853) for the training set and 0.726 (95 % CI, 0.609-0.843) for the test set. The calibration curve of the nomogram presented good agreement. CONCLUSION: The risk stratification system can be used to quantify the probability of CIMF and may assist in helping the patients choose total thyroidectomy or thyroid lobectomy with early (cT1N0M0) PTC.

Development of a Time Projection Chamber Readout with Hybrid Pixel Sensors for Beam Monitoring.

To monitor the position and profile of therapeutic carbon beams in real-time, in this paper, we proposed a system called HiBeam-T. The HiBeam-T is a time projection chamber (TPC) with forty Topmetal-II- CMOS pixel sensors as its readout. Each Topmetal-II- has 72 × 72 pixels with the size of 83 µm × 83 µm. The detector consists of the charge drift region and the charge collection area. The readout electronics comprise three Readout Control Modules and one Clock Synchronization Module. This Hibeam-T has a sensitive area of 20 × 20 cm and can acquire the center of the incident beams. The test with a continuous 80.55 MeV/u 12C6+ beam shows that the measurement resolution to the beam center could reach 6.45 µm for unsaturated beam projections.

Comprehensive assessment of osteoporosis in lumbar spine using compositional MR imaging of trabecular bone.

OBJECTIVES: To comprehensively assess osteoporosis in the lumbar spine, a compositional MR imaging technique is proposed to quantify proton fractions for all the water components as well as fat in lumbar vertebrae measured by a combination of a 3D short repetition time adiabatic inversion recovery prepared ultrashort echo time (STAIR-UTE) MRI and IDEAL-IQ. METHODS: A total of 182 participants underwent MRI, quantitative CT, and DXA. Lumbar collagen-bound water proton fraction (CBWPF), free water proton fraction (FWPF), total water proton fraction (TWPF), bone mineral density (BMD), and T-score were calculated in three vertebrae (L2-L4) for each subject. The correlations of the CBWPF, FWPF, and TWPF with BMD and T-score were investigated respectively. A comprehensive diagnostic model combining all the water components and clinical characteristics was established. The performances of all the water components and the comprehensive diagnostic model to discriminate between normal, osteopenia, and osteoporosis cohorts were also evaluated using receiver operator characteristic (ROC). RESULTS: The CBWPF showed strong correlations with BMD (r = 0.85, p < 0.001) and T-score (r = 0.72, p < 0.001), while the FWPF and TWPF showed moderate correlations with BMD (r = 0.65 and 0.68, p < 0.001) and T-score (r = 0.47 and 0.49, p < 0.001). The high area under the curve values obtained from ROC analysis demonstrated that CBWPF, FWPF, and TWPF have the potential to differentiate the normal, osteopenia, and osteoporosis cohorts. At the same time, the comprehensive diagnostic model shows the best performance. CONCLUSIONS: The compositional MRI technique, which quantifies CBWPF, FWPF, and TWPF in trabecular bone, is promising in the assessment of bone quality. KEY POINTS: • Compositional MR imaging technique is able to quantify proton fractions for all the water components (i.e., collagen-bound water proton fraction (CBWPF), free water proton fraction (FWPF), and total water proton fraction (TWPF)) in the human lumbar spine. • The biomarkers derived from the compositional MR imaging technique showed moderate to high correlations with bone mineral density (BMD) and T-score and showed good performance in distinguishing people with different bone mass. • The comprehensive diagnostic model incorporating CBWPF, FWPF, TWPF, and clinical characteristics showed the highest clinical diagnostic capability for the assessment of osteoporosis.

Pam2 lipopeptides enhance the immunosuppressive activity of monocytic myeloid-derived suppressor cells by STAT3 signal in chronic inflammation.

Chronic inflammation develops when the immune system is unable to clear a persistent insult. Unresolved chronic inflammation leads to immunosuppression to maintain the internal homeostatic conditions, which is mediated primarily by myeloid-derived suppressor cells (MDSCs). Toll-like receptors 2 (TLR2) has an important role in chronic inflammation and can be activated by a vast number and diversity of TLR2 ligands, for example Pam2CSK4. However, the regulatory effect of TLR2 signaling on MDSCs in chronic inflammation remains controversial. This study demonstrated that heat-killed Mycobacterium bovis BCG-induced pathology-free chronic inflammation triggered suppressive monocytic MDSCs (M-MDSCs) that expressed TLR2. Activation of TLR2 signaling by Pam2CSK4 treatment enhanced immunosuppression of M-MDSCs by upregulating inducible nitric oxide synthase (iNOS) activity and nitric oxide (NO) production partly through signal transducer and activator of transcription 3 (STAT3) activation. Thus, TLR2 has a fundamental role in promoting the MDSC-mediated immunosuppressive environment during chronic inflammation and might represent a potentially therapeutic target in chronic inflammation disease.

Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks.

OBJECTIVE: To explore the application of deep learning in patients with primary osteoporosis, and to develop a fully automatic method based on deep convolutional neural network (DCNN) for vertebral body segmentation and bone mineral density (BMD) calculation in CT images. MATERIALS AND METHODS: A total of 1449 patients were used for experiments and analysis in this retrospective study, who underwent spinal or abdominal CT scans for other indications between March 2018 and May 2020. All data was gathered from three different CT vendors. Among them, 586 cases were used for training, and other 863 cases were used for testing. A fully convolutional neural network, called U-Net, was employed for automated vertebral body segmentation. The manually sketched region of vertebral body was used as the ground truth for comparison. A convolutional neural network, called DenseNet-121, was applied for BMD calculation. The values post-processed by quantitative computed tomography (QCT) were identified as the standards for analysis. RESULTS: Based on the diversity of CT vendors, all testing cases were split into three testing cohorts: Test set 1 (n = 463), test set 2 (n = 200), and test set 3 (n = 200). Automated segmentation correlated well with manual segmentation regarding four lumbar vertebral bodies (L1-L4): the minimum average dice coefficients for three testing sets were 0.823, 0.786, and 0.782, respectively. For testing sets from different vendors, the average BMDs calculated by automated regression showed high correlation (r > 0.98) and agreement with those derived from QCT. CONCLUSIONS: A deep learning-based method could achieve fully automatic identification of osteoporosis, osteopenia, and normal bone mineral density in CT images. KEY POINTS: • Deep learning can perform accurate fully automated segmentation of lumbar vertebral body in CT images. • The average BMDs obtained by deep learning highly correlates with ones derived from QCT. • The deep learning-based method could be helpful for clinicians in opportunistic osteoporosis screening in spinal or abdominal CT scans.

Ginsenoside compound K alleviates sodium valproate-induced hepatotoxicity in rats via antioxidant effect, regulation of peroxisome pathway and iron homeostasis.

Sodium valproate (SVP) is a first-line treatment for various forms of epilepsy; however, it can cause severe liver injury. Ginsenoside compound K (G-CK) is the main active ingredient of the traditional herbal medicine ginseng. According to our previous research, SVP-induced elevation of ALT and AST levels, as well as pathological changes of liver tissue, was believed to be significantly reversed by G-CK in LiCl-pilocarpine induced epileptic rats. Thus, we aimed to evaluate the protective effect of G-CK on hepatotoxicity caused by SVP. The rats treated with SVP showed liver injury with evident increases in hepatic index, transaminases activity, alkaline phosphatase level, hepatic triglyceride and lipid peroxidation; significant decreases in plasma albumin level and antioxidant capacity; and obvious changes in histopathological and subcellular structures. All of these changes could be mitigated by co-administration with G-CK. Proteomic analysis indicated that hepcidin, soluble epoxide hydrolase (sEH, UniProt ID P80299), and the peroxisome pathway were involved in the hepatoprotective effect of G-CK. Changes in protein expression of hepcidin and sEH were verified by ELISA and Western blot analysis, respectively. In addition, we observed that the hepatic iron rose in SVP group and decreased in the combination group. In summary, our findings demonstrate the clear hepatoprotective effect of G-CK against SVP-induced hepatotoxicity through the antioxidant effect, regulation of peroxisome pathway relying on sEH (P80299) downregulation, as well as regulation of iron homeostasis dependent on hepcidin upregulation.

Regulation of stem-like cancer cells by glutamine through ß-catenin pathway mediated by redox signaling.

BACKGROUND: Cancer stem cells (CSCs) are thought to play an important role in tumor recurrence and drug resistance, and present a major challenge in cancer therapy. The tumor microenvironment such as growth factors, nutrients and oxygen affect CSC generation and proliferation by providing the necessary energy sources and growth signals. The side population (SP) analysis has been used to detect the stem-like cancer cell populations based on their high expression of ABCG2 that exports Hoechst-33342 and certain cytotoxic drugs from the cells. The purpose of this research is to investigate the effect of a main nutrient molecule, glutamine, on SP cells and the possible underlying mechanism(s). METHODS: Biochemical assays and flow cytometric analysis were used to evaluate the effect of glutamine on stem-like side population cells in vitro. Molecular analyses including RNAi interfering, qRT-PCR, and immunoblotting were employed to investigate the molecular signaling in response to glutamine deprivation and its influence on tumor formation capacity in vivo. RESULTS: We show that glutamine supports the maintenance of the stem cell phenotype by promoting glutathione synthesis and thus maintaining redox balance for SP cells. A deprivation of glutamine in the culture medium significantly reduced the proportion of SP cells. L-asparaginase, an enzyme that catalyzes the hydrolysis of asparagine and glutamine to aspartic acid and glutamate, respectively, mimics the effect of glutamine withdrawal and also diminished the proportion of SP cells. Mechanistically, glutamine deprivation increases intracellular ROS levels, leading to down-regulation of the ß-catenin pathway. CONCLUSION: Glutamine plays a significant role in maintaining the stemness of cancer cells by a redox-mediated mechanism mediated by ß-catenin. Inhibition of glutamine metabolism or deprivation of glutamine by L-asparaginase may be a new strategy to eliminate CSCs and overcome drug resistance.

Reduced expression of p21-activated protein kinase 1 correlates with poor histological differentiation in pancreatic cancer.

BACKGROUND: P21-activated protein kinase 1 (PAK1), a main downstream effector of small Rho GTPases, is overexpressed in many malignancies. PAK1 overexpression is associated with poor prognosis in some tumor types, including breast cancer, gastric cancer, and colorectal cancer. However, the expression and clinical relevance of PAK1 expression in human pancreatic cancer remains unknown. METHODS: The present study investigated the clinical and prognostic significance of PAK1 expression in pancreatic carcinoma. We examined and scored the expression of PAK1 by immunohistochemistry in 72 primary pancreatic carcinoma samples and 20 liver metastatic samples. The relationships between PAK1 and clinicopathological parameters and prognosis in primary and metastatic pancreatic cancer were analyzed. RESULTS: Among the total 92 cases, primary pancreatic cancer samples had a significantly higher rate (38/72, 52.8%) of high PAK1 expression than liver metastatic samples (5/20, 25.0%) (P=0.028). Among the 72 primary pancreatic cancer patients, high PAK1 expression was associated with younger age (P=0.038) and moderately or well differentiated tumor (P=0.007). Moreover, a positive relationship was found between high PAK1 expression and overall survival (OS) (P<0.005). Patients with high PAK1 expression had a better OS than those with low PAK1 expression. Univariate and multivariate analysis by Cox regression including PAK1 and other prognostic pathological markers demonstrated high PAK1 immunostaining as a prognostic factor for survival in pancreatic cancer patients (P<0.005). CONCLUSIONS: We report for the first time that PAK1 is a novel prognostic marker for pathologically confirmed human pancreatic cancer. Reduced expression of PAK1 correlates with poor histological differentiation in pancreatic cancer.

Log-less metadata management on metadata server for parallel file systems.

This paper presents a novel metadata management mechanism on the metadata server (MDS) for parallel and distributed file systems. In this technique, the client file system backs up the sent metadata requests, which have been handled by the metadata server, so that the MDS does not need to log metadata changes to nonvolatile storage for achieving highly available metadata service, as well as better performance improvement in metadata processing. As the client file system backs up certain sent metadata requests in its memory, the overhead for handling these backup requests is much smaller than that brought by the metadata server, while it adopts logging or journaling to yield highly available metadata service. The experimental results show that this newly proposed mechanism can significantly improve the speed of metadata processing and render a better I/O data throughput, in contrast to conventional metadata management schemes, that is, logging or journaling on MDS. Besides, a complete metadata recovery can be achieved by replaying the backup logs cached by all involved clients, when the metadata server has crashed or gone into nonoperational state exceptionally.

Codelivery of anti-CD47 antibody and chlorin e6 using a dual pH-sensitive nanodrug for photodynamic immunotherapy of osteosarcoma.

Osteosarcoma is a malignant tumor originating from bone tissue that progresses rapidly and has a poor patient prognosis. Immunotherapy has shown great potential in the treatment of osteosarcoma. However, the immunosuppressive microenvironment severely limits the efficacy of osteosarcoma treatment. The dual pH-sensitive nanocarrier has emerged as an effective antitumor drug delivery system that can selectively release drugs into the acidic tumor microenvironment. Here, we prepared a dual pH-sensitive nanocarrier, loaded with the photosensitizer Chlorin e6 (Ce6) and CD47 monoclonal antibodies (aCD47), to deliver synergistic photodynamic and immunotherapy of osteosarcoma. On laser irradiation, Ce6 can generate reactive oxygen species (ROS) to kill cancer cells directly and induces immunogenic tumor cell death (ICD), which further facilitates the dendritic cell maturation induced by blockade of CD47 by aCD47. Moreover, both calreticulin released during ICD and CD47 blockade can accelerate phagocytosis of tumor cells by macrophages, promote antigen presentation, and eventually induce T lymphocyte-mediated antitumor immunity. Overall, the dual pH-sensitive nanodrug loaded with Ce6 and aCD47 showed excellent immune-activating and anti-tumor effects in osteosarcoma, which may lay the theoretical foundation for a novel combination model of osteosarcoma treatment.

Somatic mutations in four novel genes contribute to homologous recombination deficiency in breast cancer: a real-world clinical tumor sequencing study.

Breast cancers involving mutations in homologous recombination (HR) genes, most commonly BRCA1 and BRCA2 (BRCA1/2), respond well to PARP inhibitors and platinum-based chemotherapy. However, except for these specific HR genes, it is not clear which other mutations contribute to homologous recombination defects (HRD). Here, we performed next-generation sequencing of tumor tissues and matched blood samples from 119 breast cancer patients using the OncoScreen Plus panel. Genomic mutation characteristics and HRD scores were analyzed. In the HR genes, we found that BRCA1/2 and PLAB2 mutations were related to HRD. HRD was also detected in a subset of patients without germline or somatic mutations in BRCA1/2, PLAB2, or other HR-related genes. Notably, LRP1B, NOTCH3, GATA2, and CARD11 (abbreviated as LNGC) mutations were associated with high HRD scores in breast cancer patients. Furthermore, functional experiments demonstrated that silencing CARD11 and GATA2 impairs HR repair efficiency and enhances the sensitivity of tumor cells to olaparib treatment. In summary, in the absence of mutations in the HR genes, the sensitivity of tumor cells to PARP inhibitors and platinum-based chemotherapy may be enhanced in a subset of breast cancer patients with LNGC somatic mutations.

Automatic grading of knee osteoarthritis with a plain radiograph radiomics model: combining anteroposterior and lateral images.

OBJECTIVES: To establish a radiomics-based automatic grading model for knee osteoarthritis (OA) and evaluate the influence of different body positions on the model's effectiveness. MATERIALS AND METHODS: Plain radiographs of a total of 473 pairs of knee joints from 473 patients (May 2020 to July 2021) were retrospectively analyzed. Each knee joint included anteroposterior (AP) and lateral (LAT) images which were randomly assigned to the training cohort and the testing cohort at a ratio of 7:3. First, an assessment of knee OA severity was done by two independent radiologists with Kallgren-Lawrence grading scale. Then, another two radiologists independently delineated the region of interest for radiomic feature extraction and selection. The radiomic classification features were dimensionally reduced and a machine model was conducted using logistic regression (LR). Finally, the classification efficiency of the model was evaluated using receiver operating characteristic curves and the area under the curve (AUC). RESULTS: The AUC (macro/micro) of the model using a combination of AP and LAT (AP&LAT) images were 0.772/0.778, 0.818/0.799, and 0.864/0.879, respectively. The radiomic features from the combined images achieved better classification performance than the individual position image (p < 0.05). The overall accuracy of the radiomic model with AP&LAT images was 0.727 compared to 0.712 and 0.417 for radiologists with 4 years and 2 years of musculoskeletal diagnostic experience. CONCLUSIONS: A radiomic model constructed by combining the AP&LAT images of the knee joint can better grade knee OA and assist clinicians in accurate diagnosis and treatment. CRITICAL RELEVANCE STATEMENT: A radiomic model based on plain radiographs accurately grades knee OA severity. By utilizing the LR classifier and combining AP&LAT images, it improves accuracy and consistency in grading, aiding clinical decision-making, and treatment planning. KEY POINTS: Radiomic model performed more accurately in K/L grading of knee OA than junior radiologists. Radiomic features from the combined images achieved better classification performance than the individual position image. A radiomic model can improve the grading of knee OA and assist in diagnosis and treatment.

TP53-specific mutations serve as a potential biomarker for homologous recombination deficiency in breast cancer: a clinical next-generation sequencing study.

Background: TP53 mutations and homologous recombination deficiency (HRD) occur frequently in breast cancer. However, the characteristics of TP53 pathogenic mutations in breast cancer patients with/without HRD are not clear. Methods: Clinical next-generation sequencing (NGS) of both tumor and paired blood DNA from 119 breast cancer patients (BRCA-119 cohort) was performed with a 520-gene panel. Mutations, tumor mutation burden (TMB), and genomic HRD scores were assessed from NGS data. NGS data from 47 breast cancer patients in the HRD test cohort were analyzed for further verification. Results: All TP53 pathogenic mutations in patients had somatic origin, which was associated with the protein expression of estrogen receptor and progestogen receptor. Compared to patients without TP53 pathologic mutations, patients with TP53 pathologic mutations had higher levels of HRD scores and different genomic alterations. The frequency of TP53 pathologic mutation was higher in the HRD-high group (HRD score ≥ 42) relative to that in the HRD-low group (HRD score < 42). TP53 has different mutational characteristics between the HRD-low and HRD-high groups. TP53-specific mutation subgroups had diverse genomic features and TMB. Notably, TP53 pathogenic mutations predicted the HRD status of breast cancer patients with an area under the curve (AUC) of 0.61. TP53-specific mutations, namely HRD-low mutation, HRD-high mutation, and HRD common mutation, predicted the HRD status of breast cancer patients with AUC values of 0.32, 0.72, and 0.58, respectively. Interestingly, TP53 HRD-high mutation and HRD common mutation combinations showed the highest AUC values (0.80) in predicting HRD status. Conclusions: TP53-specific mutation combinations predict the HRD status of patients, indicating that TP53 pathogenic mutations could serve as a potential biomarker for poly-ADP-ribose polymerase (PARP) inhibitors in breast cancer patients .

AI-Based multimodal Multi-tasks analysis reveals tumor molecular heterogeneity, predicts preoperative lymph node metastasis and prognosis in papillary thyroid carcinoma: A retrospective study.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer globally, especially when lymph node metastasis (LNM) occurs. Molecular heterogeneity, driven by genetic alterations and tumor microenvironment components, contributes to the complexity of PTC. Understanding these complexities is essential for precise risk stratification and therapeutic decisions. METHODS: This study involved a comprehensive analysis of 521 patients with PTC from our hospital and 499 patients from The Cancer Genome Atlas (TCGA). The real-world cohort 1 comprised 256 patients with stage I-III PTC. Tissues from 252 patients were analyzed by DNA-based next-generation sequencing, and tissues from four patients were analyzed by single-cell RNA sequencing (scRNA-seq). Additionally, 586 PTC pathological sections were collected from TCGA, and 275 PTC pathological sections were collected from the real-world cohort 2. A deep learning multimodal model was developed using matched histopathology images, genomic, transcriptomic, and immune cell data to predict LNM and disease-free survival (DFS). RESULTS: This study included a total of 1,011 PTC patients, comprising 256 patients from cohort 1, 275 patients from cohort 2, and 499 patients from TCGA. In cohort 1, we categorized PTC into four molecular subtypes based on BRAF, RAS, RET, and other mutations. BRAF mutations were significantly associated with LNM and impacted DFS. ScRNA-seq identified distinct T cell subtypes and reduced B cell diversity in BRAF-mutated PTC with LNM. The study also explored cancer-associated fibroblasts and macrophages, highlighting their associations with LNM. The deep learning model was trained using 405 pathology slides and RNA sequences from 328 PTC patients and validated with 181 slides and RNA sequences from 140 PTC patients in the TCGA cohort. It achieved high accuracy, with an AUC of 0.86 in the training cohort, 0.84 in the validation cohort, and 0.83 in the real-world cohort 2. High-risk patients in the training cohort had significantly lower DFS rates (P<0.001). Model AUCs were 0.91 at 1 year, 0.93 at 3 years, and 0.87 at 5 years. In the validation cohort, high-risk patients also had lower DFS (P<0.001); the AUCs were 0.89, 0.87, and 0.80 at 1, 3, and 5 years. We utilized the GradCAM algorithm to generate heatmaps from pathology-based deep learning models, which visually highlighted high-risk tumor areas in PTC patients. This enhanced clinicians' understanding of the model's predictions and improved diagnostic accuracy, especially in cases with lymph node metastasis. CONCLUSION: The AI-based analysis uncovered vital insights into PTC molecular heterogeneity, emphasizing BRAF mutations' impact. The integrated deep learning model shows promise in predicting metastasis, offering valuable contributions to improved diagnostic and therapeutic strategies.