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.

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.

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.

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.

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.

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.

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 .

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.

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.

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.

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.

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.

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.

Deep learning-assisted knee osteoarthritis automatic grading on plain radiographs: the value of multiview X-ray images and prior knowledge.

Background: Knee osteoarthritis (OA) is harmful to people's health. Effective treatment depends on accurate diagnosis and grading. This study aimed to assess the performance of a deep learning (DL) algorithm based on plain radiographs in detecting knee OA and to investigate the effect of multiview images and prior knowledge on diagnostic performance. Methods: In total, 4,200 paired knee joint X-ray images from 1,846 patients (July 2017 to July 2020) were retrospectively analyzed. Kellgren-Lawrence (K-L) grading was used as the gold standard for knee OA evaluation by expert radiologists. The DL method was used to analyze the performance of anteroposterior and lateral plain radiographs combined with prior zonal segmentation to diagnose knee OA. Four groups of DL models were established according to whether they adopted multiview images and automatic zonal segmentation as the DL prior knowledge. Receiver operating curve analysis was used to assess the diagnostic performance of 4 different DL models. Results: The DL model with multiview images and prior knowledge obtained the best classification performance among the 4 DL models in the testing cohort, with a microaverage area under the receiver operating curve (AUC) and macroaverage AUC of 0.96 and 0.95, respectively. The overall accuracy of the DL model with multiview images and prior knowledge was 0.96 compared to 0.86 for an experienced radiologist. The combined use of anteroposterior and lateral images and prior zonal segmentation affected diagnostic performance. Conclusions: The DL model accurately detected and classified the K-L grading of knee OA. Additionally, multiview X-ray images and prior knowledge improved classification efficacy.

Artificial intelligence-assisted ultrasound image analysis to discriminate early breast cancer in Chinese population: a retrospective, multicentre, cohort study.

Background: Early diagnosis of breast cancer has always been a difficult clinical challenge. We developed a deep-learning model EDL-BC to discriminate early breast cancer with ultrasound (US) benign findings. This study aimed to investigate how the EDL-BC model could help radiologists improve the detection rate of early breast cancer while reducing misdiagnosis. Methods: In this retrospective, multicentre cohort study, we developed an ensemble deep learning model called EDL-BC based on deep convolutional neural networks. The EDL-BC model was trained and internally validated on B-mode and color Doppler US image of 7955 lesions from 6795 patients between January 1, 2015 and December 31, 2021 in the First Affiliated Hospital of Army Medical University (SW), Chongqing, China. The model was assessed by internal and external validations, and outperformed radiologists. The model performance was validated in two independent external validation cohorts included 448 lesions from 391 patients between January 1 to December 31, 2021 in the Tangshan People's Hospital (TS), Chongqing, China, and 245 lesions from 235 patients between January 1 to December 31, 2021 in the Dazu People's Hospital (DZ), Chongqing, China. All lesions in the training and total validation cohort were US benign findings during screening and biopsy-confirmed malignant, benign, and benign with 3-year follow-up records. Six radiologists performed the clinical diagnostic performance of EDL-BC, and six radiologists independently reviewed the retrospective datasets on a web-based rating platform. Findings: The area under the receiver operating characteristic curve (AUC) of the internal validation cohort and two independent external validation cohorts for EDL-BC was 0.950 (95% confidence interval [CI]: 0.909-0.969), 0.956 (95% [CI]: 0.939-0.971), and 0.907 (95% [CI]: 0.877-0.938), respectively. The sensitivity values were 94.4% (95% [CI]: 72.7%-99.9%), 100% (95% [CI]: 69.2%-100%), and 80% (95% [CI]: 28.4%-99.5%), respectively, at 0.76. The AUC for accurate diagnosis of EDL-BC (0.945 [95% [CI]: 0.933-0.965]) and radiologists with artificial intelligence (AI) assistance (0.899 [95% [CI]: 0.883-0.913]) was significantly higher than that of the radiologists without AI assistance (0.716 [95% [CI]: 0.693-0.738]; p < 0.0001). Furthermore, there were no significant differences between the EDL-BC model and radiologists with AI assistance (p = 0.099). Interpretation: EDL-BC can identify subtle but informative elements on US images of breast lesions and can significantly improve radiologists' diagnostic performance for identifying patients with early breast cancer and benefiting the clinical practice. Funding: The National Key R&D Program of China.

Pharmacokinetics of tenvermectin in swine, a novel antiparasitic drug candidate-comparison with ivermectin.

Tenvermectin (TVM) is a novel 16-membered macrolide compound isolated and purified from the fermentation broth of genetically engineered Streptomyces avermitilis strain MHJ1011. TVM and ivermectin were administered at the dose of 0.3 mg/kg body weight through a single subcutaneous injection route followed by plasma collectiom and analysis at different time intervals. Plasma concentrations of TVM and IVM were determined by high-performance liquid chromatography with fluorescence detector. Pharmacokinetic analysis was completed using the non-compartmental method with WinNonlin™ 6.4 software. TVM is rapidly absorbed after administration with peak plasma concentrations (Cmax , 9.78 ± 2.34 ng/ml) obtained within 6-22 h. AUC0-last was 586.86 h·ng/ml ± 121.24 h·ng/ml. The mean elimination half-life of TVM (T1/2λz ) was 97.99 h ± 46.41 h. The T1/2λz of IVM was 146.59 h ± 22.26 h in the study. The present study showed that subcutaneous administration of TVM at 0.3 mg/kg body weight (BW) in swine is absorbed more rapidly than IVM in swine. Compared to the pharmacokinetic characteristics of IVM, there was little difference in the half-life of TVM among different individuals. The data will contribute to refining the formulation and dosage regime for TVM administration.

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.

Application of NGS molecular classification in the diagnosis of endometrial carcinoma: A supplement to traditional pathological diagnosis.

OBJECTIVE: This study aims to demonstrate the advantages of NGS molecular classification in EC diagnosis and to assess whether molecular classification could be performed on curettage specimens and its concordance with subsequent hysterectomy specimens. METHODS: 80 patients with hysterectomy specimens and 35/80 patients with paired curettage specimens were stratified as POLE mut, MSI-H, TP53 wt, or TP53 abn group by NGS panel. Histotype, tumor grade, IHC results, and other pathological details were taken from original pathological reports. RESULTS: The correlation analysis of 80 patients with hysterectomy specimens between NGS molecular classification and clinicopathological characters displayed that the POLE mut group was associated with EEC (87.5%) and TP53 abn subtype was correlated to a later stage (Stage II-IV, 47.6%), G3 (76.2%), serous histology (61.9%) and myometrial invasion ≥50% (47.6%). A favorable concordance (31/32, 96.9%) was shown in MSI analysis and MMR IHC results, and the agreement rate of p53 IHC and TP53 mutation was 81.5% (53/65). Compared with the p53 IHC abnormal group, the TP53 mutation group had a higher correlation with high-risk factors. A high level of concordance (31/35, 88.0%) of NGS molecular classification was achieved between curettage specimens and hysterectomy specimens while grade and histotype (including unclassified group) from curettage specimens and hysterectomy specimens showed only moderate levels of agreement, 54.3% (19/35) and 68.6% (24/35), respectively. CONCLUSION: NGS molecular classification achieved on curettage samples showed high concordance with the final hysterectomy specimens, demonstrating superior to the conventional pathological assessment of grade and histotype and potential utilization in clinical practice.

Upregulated YTHDF1 associates with tumor immune microenvironment in head and neck squamous cell carcinomas.

Background: The nature of the tumor immune microenvironment (TME) is essential for the head and neck squamous cell carcinomas (HNSCC) initiation, prognosis, and response to immunotherapy. However, its gene regulatory network remains to be elucidated. Methods: To identify N6-methyladenosine (m6A) regulators that are involved in regulating the HNSCC TME, a computational screen was applied to The Cancer Genome Atlas (TCGA) HNSCC patient samples. The effects of mutation, copy number variation (CNV), and transcriptional regulation on YTHDF1 expression were analyzed. We analyzed the TME infiltration, cancer-immunity cycle activities, and YTHDF1-related Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results: Among the 24 m6A regulators, 3 factors (YTHDF1, ELAVL1, and METTL3) were highly correlated with TME infiltration. As the top candidate, YTHDF1 was up-regulated and amplified in HNSCC. YTHDF1 promoter gains active histone marks and high chromatin accessibility, which might be transcriptionally activated by SOX2 and TP63. Moreover, YTHDF1 expression significantly associates with tumor malignant phenotype in HNSCC, which has a positive correlation with CD4+ T cells and a negative correlation with CD8+ T cells infiltration. Specifically, YTHDF1 expression is negatively associated with the cancer-immunity cycle and immune checkpoint inhibitors. In terms of the underlying biological mechanisms, YTHDF1 may interact with YTHDF2/3 to regulate several vital immune-related pathways. Conclusions: We identify YTHDF1 associated with TME and elucidate an underlying mechanism of immune escape in HNSCC, which might be used as a predictive marker in guiding immunotherapy.

Gut Microbiota Modulates the Protective Role of Ginsenoside Compound K Against Sodium Valproate-Induced Hepatotoxicity in Rat.

This study aimed to investigate the potential role of gut microbiota in the hepatotoxicity of sodium valproate (SVP) and the protective effect of ginsenoside compound K (G-CK) administration against SVP-induced hepatotoxicity in rats. Measurements of 16S rRNA showed that SVP supplementation led to a 140.749- and 248.900-fold increase in the relative abundance of Akkermansia muciniphila (A. muciniphila) and Bifidobacterium pseudolongum (B. pseudolongum), respectively (p < 0.05). The increase in A. muciniphila was almost completely reversed by G-CK treatment. The relative abundance of A. muciniphila was strongly positively correlated with aspartate transaminase (AST) and alanine aminotransferase (ALT) levels (r > 0.78, p < 0.05). The PICRUSt analysis showed that G-CK could inhibit the changes of seven pathways caused by SVP, of which four pathways, including the fatty acid biosynthesis, lipid biosynthesis, glycolysis/gluconeogenesis, and pyruvate metabolism, were found to be negatively correlated with AST and ALT levels (r ≥ 0.70, p < 0.01 or < 0.05). In addition, the glycolysis/gluconeogenesis and pyruvate metabolism were negatively correlated with the relative abundance of A. muciniphila (r > 0.65, p < 0.01 or < 0.05). This alteration of the gut microbiota composition that resulted in observed changes to the glycolysis/gluconeogenesis and pyruvate metabolism may be involved in both the hepatotoxicity of SVP and the protective effect of G-CK administration against SVP-induced hepatotoxicity. Our study provides new evidence linking the gut microbiota with SVP-induced hepatotoxicity.