Ophiopogonin D alleviates acute lung injury by regulating inflammation via the STAT3/A20/ASK1 axis.

BACKGROUND: Acute lung injury (ALI) is characterized by acute pulmonary inflammatory infiltration. Alveolar epithelial cells (AECs) release numerous pro-inflammatory cytokines, which result in the pathological changes seen in ALI. Ophiopogonin D (OD), extracted from the roots of Ophiopogon japonicus (Thunb.) Ker Gawl. (Liliaceae), reduces inflammation; however, the efficacy of OD in ALI has not been reported and the underlying molecular mechanisms remain unclear. PURPOSE: This study investigated the anti-inflammatory effects of OD, as well as the underlying mechanisms, in AECs and a mouse ALI model. METHODS: Lipopolysaccharide (LPS) and tumor necrosis factor-α (TNF-α) were used to stimulate macrophages and A549 cells, and a mouse ALI model was established by intratracheal LPS administration. The anti-inflammatory effects and mechanisms of OD in the TNF-α-induced in vitro inflammation model was evaluated using real-time quantitative polymerase chain reaction qPCR), enzyme-linked immunosorbent assay (ELISA), western blotting, nuclear and cytoplasmic protein extraction, and immunofluorescence. The in vivo anti-inflammatory activity of OD was evaluated using hematoxylin and eosin staining, qPCR, ELISA, and western blotting. RESULTS: The bronchoalveolar lavage fluid and lung tissue of LPS-induced ALI mice exhibited increased TNF-α expression. TNF-α induced a significantly greater pro-inflammatory effect in AECs than LPS. OD reduced inflammation and mitogen-activated protein kinase (MAPK) and transcription factor p65 phosphorylation in vivo and in vitro and promoted signal transducer and activator of transcription 3 (STAT3) phosphorylation and A20 expression, thereby inducing apoptosis signal-regulating kinase 1 (ASK1) proteasomal degradation. CONCLUSION: OD exerts an anti-inflammatory effect by promoting STAT3-dependent A20 expression and ASK1 degradation. OD may therefore have therapeutic value in treating ALI and other TNF-α-related inflammatory diseases.

The role of iron overload and ferroptosis in arrhythmia pathogenesis.

Ferroptosis is a newly discovered form of programmed cell death triggered by intracellular iron overload, which leads to the accumulation of lipid peroxides in various cells. It has been implicated in the pathogenesis and progression of various diseases, including tumors, neurological disorders, and cardiovascular diseases. The intricate mechanism underlying ferroptosis involves an imbalance between the oxidation and antioxidant systems, disturbances in iron metabolism, membrane lipid peroxidation, and dysregulation of amino acid metabolism. We highlight the key molecular mechanisms governing iron overload and ferroptosis, and discuss potential molecular pathways linking ferroptosis with arrhythmias.

A radiograph-based deep learning model improves radiologists' performance for classification of histological types of primary bone tumors: A multicenter study.

PURPOSE: To develop a deep learning (DL) model for classifying histological types of primary bone tumors (PBTs) using radiographs and evaluate its clinical utility in assisting radiologists. METHODS: This retrospective study included 878 patients with pathologically confirmed PBTs from two centers (638, 77, 80, and 83 for the training, validation, internal test, and external test sets, respectively). We classified PBTs into five categories by histological types: chondrogenic tumors, osteogenic tumors, osteoclastic giant cell-rich tumors, other mesenchymal tumors of bone, or other histological types of PBTs. A DL model combining radiographs and clinical features based on the EfficientNet-B3 was developed for five-category classification. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate model performance. The clinical utility of the model was evaluated in an observer study with four radiologists. RESULTS: The combined model achieved a macro average AUC of 0.904/0.873, with an accuracy of 67.5 %/68.7 %, a macro average sensitivity of 66.9 %/57.2 %, and a macro average specificity of 92.1 %/91.6 % on the internal/external test set, respectively. Model-assisted analysis improved accuracy, interpretation time, and confidence for junior (50.6 % vs. 72.3 %, 53.07[s] vs. 18.55[s] and 3.10 vs. 3.73 on a 5-point Likert scale [P < 0.05 for each], respectively) and senior radiologists (68.7 % vs. 75.3 %, 32.50[s] vs. 21.42[s] and 4.19 vs. 4.37 [P < 0.05 for each], respectively). CONCLUSION: The combined DL model effectively classified histological types of PBTs and assisted radiologists in achieving better classification results than their independent visual assessment.

A universal recombinant adenovirus type 5 vector-based COVID-19 vaccine.

A universal recombinant adenovirus type-5 (Ad5) vaccine against COVID19 (Ad-US) was constructed, and immunogenicity and broad-spectrum of Ad5-US were evaluated with both intranasal and intramuscular immunization routes. The humoral immune response of Ad5-US in serum and bronchoalveolar lavage fluid were evaluated by the enzyme-linked immunosorbent assay (ELISA), recombinant vesicular stomatitis virus based pseudovirus neutralization assay, and angiotensin-converting enzyme-2 (ACE2) -binding inhibition assay. The cellular immune response and Th1/Th2 biased immune response of Ad5-US were evaluated by the IFN-γ ELISpot assay, intracellular cytokine staining, and Meso Scale Discovery (MSD) profiling of Th1/Th2 cytokines. Intramuscular priming followed by an intranasal booster with Ad5-US elicited the broad-spectrum and high levels of IgG, IgA, pseudovirus neutralizing antibody (PNAb), and Th1-skewing of the T-cell response. Overall, the adenovirus type-5 vectored universal SARS-CoV-2 vaccine Ad5-US was successfully constructed, and Ad5-US was highly immunogenic and broad spectrum. Intramuscular priming followed by an intranasal booster with Ad5-US induced the high and broad spectrum systemic immune responses and local mucosal immune responses.

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning.

OBJECTIVE: To develop a deep learning (DL) model for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI and further develop a DL model for classifying axial spondyloarthritis (axSpA) and non-axSpA. MATERIALS AND METHODS: This study retrospectively collected 706 patients with FM who underwent SIJ MRI from center 1 (462 axSpA and 186 non-axSpA) and center 2 (37 axSpA and 21 non-axSpA). Patients from center 1 were divided into the training, validation, and internal test sets (n = 455, 64, and 129). Patients from center 2 were used as the external test set. We developed a UNet-based model to segment FM. Based on segmentation results, a classification model was built to distinguish axSpA and non-axSpA. Dice Similarity Coefficients (DSC) and area under the curve (AUC) were used for model evaluation. Radiologists' performance without and with model assistance was compared to assess the clinical utility of the models. RESULTS: Our segmentation model achieved satisfactory DSC of 81.86% ± 1.55% and 85.44% ± 6.09% on the internal cross-validation and external test sets. The classification model yielded AUCs of 0.876 (95% CI: 0.811-0.942) and 0.799 (95% CI: 0.696-0.902) on the internal and external test sets, respectively. With model assistance, segmentation performance was improved for the radiological resident (DSC, 75.70% vs. 82.87%, p < 0.05) and expert radiologist (DSC, 85.03% vs. 85.74%, p > 0.05). CONCLUSIONS: DL is a novel method for automatic and accurate segmentation of FM on SIJ MRI and can effectively increase radiologist's performance, which might assist in improving diagnosis and progression of axSpA. CRITICAL RELEVANCE STATEMENT: DL models allowed automatic and accurate segmentation of FM on sacroiliac joint MRI, which might facilitate quantitative analysis of FM and have the potential to improve diagnosis and prognosis of axSpA. KEY POINTS: • Deep learning was used for automatic segmentation of fat metaplasia on MRI. • UNet-based models achieved automatic and accurate segmentation of fat metaplasia. • Automatic segmentation facilitates quantitative analysis of fat metaplasia to improve diagnosis and prognosis of axial spondyloarthritis.

Variants in 3p24.3 predicts the risk of early neurological deterioration in large artery atherosclerotic stroke.

The rate of early neurological deterioration (END) differs in different subtypes of ischaemic stroke. Previous studies showed PLCL2 gene is a novel susceptibility locus for the occurrence of atherosclerosis and thrombotic events. The objective of this research is to examine the efficacy that PLCL2 may have on the risk of END in large artery atherosclerotic (LAA) stroke. Tagged single nucleotide polymorphisms (SNPs) were identified by a strategy of fine-mapping. The genotyping of the selected SNPs was performed by SNPscan. The impact of PLCL2 on indicating the susceptibility of END in LAA patients was evaluated by binary logistic regression. The SNP-SNP interactions of PLCL2 for END was assessed by generalized multifactor dimensionality reduction (GMDR). A total of 1527 LAA stroke patients were recruited, 582 patients (38 %) experienced END. Compared to participants without END, participants experienced END were much older (P = 0.018), more likely to suffer pre-existing diabetes mellitus (P = 0.036), higher frequent in active tobacco users (P = 0.022) and had much higher median NIHSS on admission (P < 0.001). Rs4685423 was identified to be a predictor to the risk of END: the frequency of END in AA genotype patients is lower than that in AC or CC genotype patients (multivariate-adjusted, OR 0.63; 95 % CI 0.49-0.80; P < 0.001). The SNP-SNP interactions analysis indicates rs4685423 has the greatest impacton the risk of END for LAA patients. The time from admission diagnosis to END onset in AA genotype patients is much later than that in CA or CC genotype patients (log-rank, P = 0.005). In summary, the PLCL2 rs4685423 SNP is probably associated with the END risk in LAA stroke patients.

JAK/STAT3 signaling in cardiac fibrosis: a promising therapeutic target.

Cardiac fibrosis is a serious health problem because it is a common pathological change in almost all forms of cardiovascular diseases. Cardiac fibrosis is characterized by the transdifferentiation of cardiac fibroblasts (CFs) into cardiac myofibroblasts and the excessive deposition of extracellular matrix (ECM) components produced by activated myofibroblasts, which leads to fibrotic scar formation and subsequent cardiac dysfunction. However, there are currently few effective therapeutic strategies protecting against fibrogenesis. This lack is largely because the molecular mechanisms of cardiac fibrosis remain unclear despite extensive research. The Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling cascade is an extensively present intracellular signal transduction pathway and can regulate a wide range of biological processes, including cell proliferation, migration, differentiation, apoptosis, and immune response. Various upstream mediators such as cytokines, growth factors and hormones can initiate signal transmission via this pathway and play corresponding regulatory roles. STAT3 is a crucial player of the JAK/STAT pathway and its activation is related to inflammation, malignant tumors and autoimmune illnesses. Recently, the JAK/STAT3 signaling has been in the spotlight for its role in the occurrence and development of cardiac fibrosis and its activation can promote the proliferation and activation of CFs and the production of ECM proteins, thus leading to cardiac fibrosis. In this manuscript, we discuss the structure, transactivation and regulation of the JAK/STAT3 signaling pathway and review recent progress on the role of this pathway in cardiac fibrosis. Moreover, we summarize the current challenges and opportunities of targeting the JAK/STAT3 signaling for the treatment of fibrosis. In summary, the information presented in this article is critical for comprehending the role of the JAK/STAT3 pathway in cardiac fibrosis, and will also contribute to future research aimed at the development of effective anti-fibrotic therapeutic strategies targeting the JAK/STAT3 signaling.

A deep learning fusion network trained with clinical and high-frequency ultrasound images in the multi-classification of skin diseases in comparison with dermatologists: a prospective and multicenter study.

Background: Clinical appearance and high-frequency ultrasound (HFUS) are indispensable for diagnosing skin diseases by providing internal and external information. However, their complex combination brings challenges for primary care physicians and dermatologists. Thus, we developed a deep multimodal fusion network (DMFN) model combining analysis of clinical close-up and HFUS images for binary and multiclass classification in skin diseases. Methods: Between Jan 10, 2017, and Dec 31, 2020, the DMFN model was trained and validated using 1269 close-ups and 11,852 HFUS images from 1351 skin lesions. The monomodal convolutional neural network (CNN) model was trained and validated with the same close-up images for comparison. Subsequently, we did a prospective and multicenter study in China. Both CNN models were tested prospectively on 422 cases from 4 hospitals and compared with the results from human raters (general practitioners, general dermatologists, and dermatologists specialized in HFUS). The performance of binary classification (benign vs. malignant) and multiclass classification (the specific diagnoses of 17 types of skin diseases) measured by the area under the receiver operating characteristic curve (AUC) were evaluated. This study is registered with www.chictr.org.cn (ChiCTR2300074765). Findings: The performance of the DMFN model (AUC, 0.876) was superior to that of the monomodal CNN model (AUC, 0.697) in the binary classification (P = 0.0063), which was also better than that of the general practitioner (AUC, 0.651, P = 0.0025) and general dermatologists (AUC, 0.838; P = 0.0038). By integrating close-up and HFUS images, the DMFN model attained an almost identical performance in comparison to dermatologists (AUC, 0.876 vs. AUC, 0.891; P = 0.0080). For the multiclass classification, the DMFN model (AUC, 0.707) exhibited superior prediction performance compared with general dermatologists (AUC, 0.514; P = 0.0043) and dermatologists specialized in HFUS (AUC, 0.640; P = 0.0083), respectively. Compared to dermatologists specialized in HFUS, the DMFN model showed better or comparable performance in diagnosing 9 of the 17 skin diseases. Interpretation: The DMFN model combining analysis of clinical close-up and HFUS images exhibited satisfactory performance in the binary and multiclass classification compared with the dermatologists. It may be a valuable tool for general dermatologists and primary care providers. Funding: This work was supported in part by the National Natural Science Foundation of China and the Clinical research project of Shanghai Skin Disease Hospital.

Automatic detection, segmentation, and classification of primary bone tumors and bone infections using an ensemble multi-task deep learning framework on multi-parametric MRIs: a multi-center study.

OBJECTIVES: To develop an ensemble multi-task deep learning (DL) framework for automatic and simultaneous detection, segmentation, and classification of primary bone tumors (PBTs) and bone infections based on multi-parametric MRI from multi-center. METHODS: This retrospective study divided 749 patients with PBTs or bone infections from two hospitals into a training set (N = 557), an internal validation set (N = 139), and an external validation set (N = 53). The ensemble framework was constructed using T1-weighted image (T1WI), T2-weighted image (T2WI), and clinical characteristics for binary (PBTs/bone infections) and three-category (benign/intermediate/malignant PBTs) classification. The detection and segmentation performances were evaluated using Intersection over Union (IoU) and Dice score. The classification performance was evaluated using the receiver operating characteristic (ROC) curve and compared with radiologist interpretations. RESULT: On the external validation set, the single T1WI-based and T2WI-based multi-task models obtained IoUs of 0.71 ± 0.25/0.65 ± 0.30 for detection and Dice scores of 0.75 ± 0.26/0.70 ± 0.33 for segmentation. The framework achieved AUCs of 0.959 (95%CI, 0.955-1.000)/0.900 (95%CI, 0.773-0.100) and accuracies of 90.6% (95%CI, 79.7-95.9%)/78.3% (95%CI, 58.1-90.3%) for the binary/three-category classification. Meanwhile, for the three-category classification, the performance of the framework was superior to that of three junior radiologists (accuracy: 65.2%, 69.6%, and 69.6%, respectively) and comparable to that of two senior radiologists (accuracy: 78.3% and 78.3%). CONCLUSION: The MRI-based ensemble multi-task framework shows promising performance in automatically and simultaneously detecting, segmenting, and classifying PBTs and bone infections, which was preferable to junior radiologists. CLINICAL RELEVANCE STATEMENT: Compared with junior radiologists, the ensemble multi-task deep learning framework effectively improves differential diagnosis for patients with primary bone tumors or bone infections. This finding may help physicians make treatment decisions and enable timely treatment of patients. KEY POINTS: • The ensemble framework fusing multi-parametric MRI and clinical characteristics effectively improves the classification ability of single-modality models. • The ensemble multi-task deep learning framework performed well in detecting, segmenting, and classifying primary bone tumors and bone infections. • The ensemble framework achieves an optimal classification performance superior to junior radiologists' interpretations, assisting the clinical differential diagnosis of primary bone tumors and bone infections.

Therapeutic potential of toosendanin: Novel applications of an old ascaris repellent as a drug candidate.

Toosendanin (TSN), extracted from Melia. toosendan Sieb.et Zucc. and Melia. azedarach L., has been developed into an ascaris repellent in China. However, with the improvement of public health protection, the incidence of ascariasis has been reduced considerably, resulting in limited medical application of TSN. Therefore, it is questionable whether this old ascaris repellent can develop into a drug candidate. Modern studies have shown that TSN has strong pharmacological activities, including anti-tumor, anti-botulinum, anti-viral and anti-parasitic potentials. It also can regulate fat formation and improve inflammation. These researches indicate that TSN has great potential to be developed into a corresponding medical product. In order to better development and application of TSN, the availability, pharmacodynamics, pharmacokinetics and toxicology of TSN are summarized systematically. In addition, this review discusses shortcomings in the current researches and provides useful suggestions about how TSN developed into a drug candidate. Therefore, this paper illustrates the possibility of developing TSN as a medical product, aimed to provide directions for the clinical application and further research of TSN.

Clinical-mediated discovery of pyroptosis in CD8+ T cell and NK cell reveals melanoma heterogeneity by single-cell and bulk sequence.

Histologically, melanoma tissues had fewer positive cells percentage of pyroptosis-related genes (PRGs), GZMA, GSDMB, NLRP1, IL18, and CHMP4A in epidermal than in normal skin. Pyroptosis, a new frontier in cancer, affects the tumor microenvironment and tumor immunotherapy. Nevertheless, the role of pyroptosis remains controversial, which reason is partly due to the heterogeneity of the cellular composition in melanoma. In this study, we present a comprehensive analysis of the single-cell transcriptome landscape of pyroptosis in melanoma specimens. Our findings reveal dysregulation in the expression of PRGs, particularly in immune cells, such as CD8+ cells (representing CD8+ T cells) and CD57+ cells (representing NK cells). Additionally, the immunohistochemical and multiplex immunofluorescence staining experiments results further confirmed GZMA+ cells and GSDMB+ cells were predominantly expressed in immune cells, especially in CD8 + T cells and NK cells. Melanoma specimens secreted a minimal presence of GZMA+ merged CD8+ T cells (0.11%) and GSDMB+ merged CD57+ cells (0.08%), compared to the control groups exhibiting proportions of 4.02% and 0.62%, respectively. The aforementioned findings indicate that a reduced presence of immune cells within tumors may play a role in diminishing the ability of pyroptosis, consequently posing a potential risk to the anti-melanoma properties. To quantify clinical relevance, we constructed a prognostic risk model and an individualized nomogram (C-index=0.58, P = 0.002), suggesting a potential role of PRGs in malignant melanoma prevention. In conclusion, our integrated single-cell and bulk RNA-seq analysis identified immune cell clusters and immune gene modules with experiment validation, contributing to our better understanding of pyroptosis in melanoma.

Engineered Peptides Enable Biomimetic Route for Collagen Intrafibrillar Mineralization.

Overcoming the short lifespan of current dental adhesives remains a significant clinical need. Adhesives rely on formation of the hybrid layer to adhere to dentin and penetrate within collagen fibrils. However, the ability of adhesives to achieve complete enclosure of demineralized collagen fibrils is recognized as currently unattainable. We developed a peptide-based approach enabling collagen intrafibrillar mineralization and tested our hypothesis on a type-I collagen-based platform. Peptide design incorporated collagen-binding and remineralization-mediating properties using the domain structure conservation approach. The structural changes from representative members of different peptide clusters were generated for each functional domain. Common signatures associated with secondary structure features and the related changes in the functional domain were investigated by attenuated total reflectance Fourier-transform infrared (ATR-FTIR) and circular dichroism (CD) spectroscopy, respectively. Assembly and remineralization properties of the peptides on the collagen platforms were studied using atomic force microscopy (AFM). Mechanical properties of the collagen fibrils remineralized by the peptide assemblies was studied using PeakForce-Quantitative Nanomechanics (PF-QNM)-AFM. The engineered peptide was demonstrated to offer a promising route for collagen intrafibrillar remineralization. This approach offers a collagen platform to develop multifunctional strategies that combine different bioactive peptides, polymerizable peptide monomers, and adhesive formulations as steps towards improving the long-term prospects of composite resins.

Mn3 O4 Nanoshell Coated Metal-Organic Frameworks with Microenvironment-Driven O2 Production and GSH Exhaustion Ability for Enhanced Chemodynamic and Photodynamic Cancer Therapies.

Nanomedicine exhibits emerging potentials to deliver advanced therapeutic strategies in the fight against triple-negative breast cancer (TNBC). Nevertheless, it is still difficult to develop a precise codelivery system that integrates highly effective photosensitizers, low toxicity, and hydrophobicity. In this study, PCN-224 is selected as the carrier to enable effective cancer therapy through light-activated reactive oxygen species (ROS) formation, and the PCN-224@Mn3 O4 @HA is created in a simple one-step process by coating Mn3 O4 nanoshells on the PCN-224 template, which can then be used as an "ROS activator" to exert catalase- and glutathione peroxidase-like activities to alleviate tumor hypoxia while reducing tumor reducibility, leading to improved photodynamic therapeutic (PDT) effect of PCN-224. Meanwhile, Mn2+ produced cytotoxic hydroxyl radicals (∙OH) via the Fenton-like reaction, thus producing a promising spontaneous chemodynamic therapeutic (CDT) effect. Importantly, by remodeling the tumor microenvironment (TME), Mn3 O4 nanoshells downregulated hypoxia-inducible factor 1α expression, inhibiting tumor growth and preventing tumor revival. Thus, the developed nanoshells, via light-controlled ROS formation and multimodality imaging abilities, can effectively inhibit tumor proliferation through synergistic PDT/CDT, and prevent tumor resurgence by remodeling TME.

A "Trojan Horse" Strategy: The Preparation of Bile Acid-Modifying Irinotecan Hydrochloride Nanoliposomes for Liver-Targeted Anticancer Drug Delivery System Study.

The bile acid transport system is a natural physiological cycling process between the liver and the small intestine, occurring approximately 6-15 times during the day. There are various bile acid transporter proteins on hepatocytes that specifically recognize bile acids for transport. Therefore, in this paper, a novel liposome, cholic acid-modified irinotecan hydrochloride liposomes (named CA-CPT-11-Lip), was prepared based on the "Trojan horse" strategy. The liposomes preparation process was optimized, and some important quality indicators were investigated. The distribution of irinotecan hydrochloride in mice was then analyzed by high-performance liquid chromatography (HPLC), and the toxicity of liposomes to hepatocellular carcinoma cells (HepG-2) was evaluated in vitro. As a result, CA-CPT-11-Lip was successfully prepared. It was spherical with a particle size of 154.16 ± 4.92 nm, and the drug loading and encapsulation efficiency were 3.72 ± 0.04% and 82.04 ± 1.38%, respectively. Compared with the conventional liposomes (without cholic acid modification, named CPT-11-Lip), CA-CPT-11-Lip had a smaller particle size and higher encapsulation efficiency, and the drug accumulation in the liver was more efficient, enhancing the anti-hepatocellular carcinoma activity of irinotecan hydrochloride. The novel nanoliposome modified by cholic acid may help to expand the application of irinotecan hydrochloride in the treatment of hepatocellular carcinoma and construct the drug delivery system mode of drug liver targeting.

Three-dimensional printed customized uncemented unipolar prosthesis combined with ligament reconstruction for tumorous defect of the distal femur in children.

BACKGROUND: Hemiarthroplasty following tumor resection of the distal femur in children provides a chance to preserve the proximal tibial physis for limb elongation. Based on three-dimensional (3D) printing technology, the uncemented unipolar prosthesis with joint stability reinforced structures (JSRSs) was custom-designed for our cases. This study aimed to describe the design and assess the short-term outcomes of this refined prosthetic hemiarthroplasty. METHODS: Seven patients (four females and three males) received 3D-printed customized uncemented unipolar prosthesis for hemiarthroplasty after removal of the distal femur, from September 2019 to October 2020 at our Orthopedics department. The limb function, growth of the preserved proximal tibial physis, joint stability, and limb length discrepancy (LLD) were assessed. Complications were recorded. RESULTS: Six patients survived with no evidence of metastasis or local recurrence at the last follow-up, and one patient died of lung metastasis at 19 months postoperatively. Follow-up ranged from 19 to 32 months, with an average of 26 months. Elongation of the tibia was observed in all cases. At the last follow-up, four patients exhibited equal growth length compared with the healthy contralateral tibia. LLD ranged from 0.8 to 1.6 cm with a mean of 1.3 cm. The average knee range of motion was 95.3° of flexion and 4.5° of extension. All patients achieved satisfactory postoperative limb function with a mean MSTS score of 25.8. The results of the drawer, Lachman, and pivot shift tests were negative in all patients. During follow-up, painless joint space narrowing was observed in two patients. The screw for ligament fixation loosened in one of the seven patients at 17 months postoperatively. No subluxation of the joint, angular deformity, or breakage of the implant was detected in the remaining patients. CONCLUSIONS: 3D-printed customized uncemented unipolar prosthesis with JSRS would be a good choice for reconstructing tumorous defect in the distal femur in children.

Lung Immune Prognostic Index Could Predict Metastasis in Patients With Osteosarcoma.

Background: The lung immune prognostic index (LIPI), composed of serum lactate dehydrogenase (LDH) and the derived neutrophil to lymphocyte ratio (dNLR), is a novel prognostic factor of lung cancer. The prognostic effect of the LIPI has never been verified in osteosarcoma. Methods: We retrospectively reviewed the osteosarcoma patients with metachronous metastasis from January 2016 to January 2021 in West China Hospital. We collected and analyzed the clinical data and constructed the LIPI for osteosarcoma. The correlation between the LIPI and metastasis was analyzed according to the Kaplan-Meier method and Cox regression analysis with hazard ratios (HRs) and 95% confidence intervals (CIs). Univariate analysis and multivariate analysis were conducted to clarify the independent risk factors of metastasis. The nomogram model was established by R software, version 4.1.0. Results: The area under the curve (AUC) and best cutoff value were 0.535 and 91, 0.519, and 5.02, 0.594 and 2.77, 0.569 and 227.14, 0.59 and 158, and 0.607 and 2.05 for ALP, LMR, NLR, PLR, LDH, and dNLR, respectively. The LIPI was composed of LDH and dNLR and showed a larger AUC than other hematological factors in the time-dependent operator curve (t-ROC). In total, 184 patients, 42 (22.8%), 96 (52.2%), and 46 (25.0%) patients had LIPIs of good, moderate, and poor, respectively (P < 0.0001). Univariate analysis revealed that pathological fracture, the initial CT report of suspicious nodule, and the NLR, PLR, ALP, and the LIPI were significantly associated with metastasis, and multivariate analysis showed that the initial CT report of suspicious nodule and the PLR, ALP, and LIPI were dependent risk factors for metastasis. Metastatic predictive factors were selected and incorporated into the nomogram construction, including the LIPI, ALP, PLR, initial CT report, and pathological fracture. The C-index of our model was 0.71. According to the calibration plot, this predictive nomogram could accurately predict 3- and 5-year metachronous metastasis. Based on the result of decision curve and clinical impact curve, this predictive nomogram could also help patients obtain significant net benefits. Conclusion: We first demonstrated the metastatic predictive effect of the LIPI on osteosarcoma. This LIPI-based model is useful for clinicians to predict metastasis in osteosarcoma patients and could help conduct timely intervention and facilitate personalized management of osteosarcoma patients.

Maslinic Acid Inhibits the Growth of Malignant Gliomas by Inducing Apoptosis via MAPK Signaling.

Background: Gliomas are primary malignant brain tumors. Despite recent advances in surgery and clinical neuro-oncology, the prognosis of patients with glioma is still poor. Therefore, there is an urgent need to find new therapeutic drugs. Methods: Here, we have studied the anticancer effect of maslinic acid in glioma and explored its potential molecular mechanism. CCK-8, Ki67 immunofluorescence, and colony formation tests are used to detect the proliferation of glioma cells. Transwell and migration experiments are used to detect the function of cell invasion and migration, and RNA-seq was performed to identify differentially expressed genes. Western blot analysis helps us identify important signaling pathways. Finally, the anticancer effect of maslinic acid was confirmed in vivo through tumor xenografting experiments. Results: Our experiments obtained high-throughput data on the treatment of maslinic acid in glioma. We found that maslinic acid significantly inhibits the proliferation, invasion, and migration of glioma cells and promotes the apoptosis of glioma cells via suppressing MAPK signaling. Conclusions: This is the first time to analyze the mechanism of maslinic acid against glioma based on transcription. Our experiments show that maslinic acid may be a useful natural product for the treatment of glioma.