The effects of sensorial and mobility frailty on the overall and domain-specific cognition performance of Chinese community-dwelling older adults.

This study aimed to investigate the different impacts of sensorial and mobility frailty on overall and domain-specific cognitive function. Further, the independent associations between other intricate capacity (IC) dimensions, including vitality and psychological dimensions, and overall and domain-specific cognitive function were investigated. A total of 429 participants (mean age, 72.91 ±â€…7.014 years; 57.30% female) underwent IC capacity assessment. Other covariates, such as demographics, health-related variables were also assessed. Overall or domain-specific cognitive impairment was used as a dependent variable in logistic regression analyses adjusted for demographic, health-related, and psychosocial confounders. After adjustment for demographic, health-related, and psychosocial confounders, individuals with sensorial frailty (odds ratio [OR] = 0.435; 95% confidence interval [CI] = 0.236-0.801; P = .008) had a significantly lower risk of mild cognitive impairment (MCI), marginally low delayed memory impairment (OR = 0.601, 95% CI = 0.347-1.040; P = .069), and language impairment (OR = 0.534, 95% CI = 0.305-0.936; OR = 0.318, P = .029; OR = 0.318,95% CI = 0.173-0.586; P < .001) by Boston naming and animal fluency tests than did those with both sensorial and mobility frailty or mobility frailty only. Depressive symptoms had a significant negative influence on executive function. Cardiovascular disease and non-skin malignancy were independent determinants of MCI, and diabetes mellitus was independently associated with processing speed, attention, and executive function. Sensorial and mobility frailty were independent risk factors for cognitive impairment. Mobility frailty had a greater negative influence on the overall cognitive function and memory and language function than did sensorial frailty. The reserve decline in the psychological dimension of IC and chronic diseases also had a significant adverse influence on overall and domain-specific cognition function.

Integrated Bioinformatic Analysis Reveals the Oncogenic, Survival, and Prognostic Characteristics of TPX2 in Hepatocellular Carcinoma.

Targeting protein for Xenopus kinesin-like protein 2 (TPX2), a well-known mitotic protein, has been linked to carcinogenesis in several cancers. This study investigated the role of TPX2 in hepatocellular carcinoma (HCC) from various aspects using bioinformatic analyses. TPX2 expression and its prognostic value in pan-cancers were analyzed using SangerBox. TPX2 expression and its association with prognosis, immune infiltration, tumor mutations, and signaling pathways in HCC were analyzed using UALCAN, BoxKaplan-Meier Plotter, GEPIA, Human Protein Atlas, TIMER 2.0, and SangerBox. Genes co-expressed with TPX2 in HCC were analyzed using the HCCDB database, followed by functional enrichment using SangerBox. Clinical predictive models were established based on TPX2 and its co-expressed genes using the ACLBI database. TPX2 expression significantly increased in pan-cancers and was associated with survival in nearly half of the cancer types. High TPX2 expression has been linked to poor survival outcomes in patients with HCC. TPX2 expression was positively correlated with abundant infiltration of immune cells (including B cells, CD4 + /CD8 + T cells, macrophages, neutrophils, and dendritic cells), TP53 mutation, and carcinogenesis-related pathways, such as the PI3K/AKT/mTOR pathway, cellular response to hypoxia, and tumor proliferation signature. Nineteen genes were found to be co-expressed with TPX2 in HCC, and these genes showed close positive correlations and were mainly implicated in cell cycle-related functions. A prognostic model established using TPX2 and its expressed genes could stratify HCC patients into high- and low-risk groups, with a significantly shorter survival time in high-risk groups. The prognostic model performed well in predicting 1-, 3-, and 5-year survival of patients with HCC, with areas under the curve of 0.801, 0.725, and 0.711, respectively. TPX2 functions as an oncogene in HCC, and its high expression is detrimental to the survival of patients with HCC. Thus, TPX2 may be a prognostic biomarker and potential therapeutic target for HCC.

TRIM33 enhances the ubiquitination of TFRC to enhance the susceptibility of liver cancer cells to ferroptosis.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common malignancy, and ferroptosis is a novel form of cell death driven by excessive lipid peroxidation. In recent years, ferroptosis has been widely utilized in cancer treatment, and the ubiquitination modification system has been recognized to play a crucial role in tumorigenesis and metastasis. Increasing evidence suggests that ubiquitin regulates ferroptosis-related substrates involved in this process. However, the precise mechanism of utilizing ubiquitination modification to regulate ferroptosis for HCC treatment remains unclear. METHODS: In this study, we detected the expression of TRIM33 in HCC using immunohistochemistry and western blotting techniques. The functional role of TRIM33 was verified through both in vitro and in vivo experiments. To evaluate the level of ferroptosis, mitochondrial superoxide levels, MDA levels, Fe2+ levels, and cell viability were assessed. Downstream substrates of TRIM33 were screened and confirmed via immunoprecipitation, immunofluorescence staining, and ubiquitination modification experiments. RESULTS: Our findings demonstrate that TRIM33 inhibits the growth and metastasis of HCC cells both in vitro and in vivo while promoting their susceptibility to ferroptosis. Mechanistically speaking, TRIM33 induces cellular ferroptosis through E3 ligase-dependent degradation of TFRC-a known inhibitor of this process-thus elucidating the specific type and site at which TFRC undergoes modification by TRIM33. CONCLUSION: In summary, our study reveals an important role for TRIM33 in HCC treatment while providing mechanistic support for its function. Additionally highlighted is the significance of ubiquitination modification leading to TFRC degradation-an insight that may prove valuable for future targeted therapies.

CT-derived Radiomics Predicts the Efficacy of Tyrosine Kinase Inhibitors in Osteosarcoma Patients with Pulmonary Metastasis.

BACKGROUND: To construct and validate the CT-based radiomics model for predicting the tyrosine kinase inhibitors (TKIs) effects in osteosarcoma (OS) patients with pulmonary metastasis. METHODS: OS patients with pulmonary metastasis treated with TKIs were randomly separated into training and testing cohorts (2:1 ratio). Radiomic features were extracted from the baseline unenhanced chest CT images. The random survival forest (RSF) and Kaplan-Meier survival analyses were performed to construct and evaluate radiomics signatures (R-model-derived). The univariant and multivariant Cox regression analyses were conducted to establish clinical (C-model) and combined models (RC-model). The discrimination abilities, goodness of fit and clinical benefits of the three models were assessed and validated in both training and testing cohorts. RESULTS: A total of 90 patients, 57 men and 33 women, with a mean age of 18 years and median progression-free survival (PFS) of 7.2 months, were enrolled. The R-model was developed with nine radiomic features and demonstrated significant predictive and prognostic values. In both training and testing cohorts, the time-dependent area under the receiver operating characteristic curves (AUC) of the R-model and RC-model exhibited obvious superiority over C-model. The calibration and decision curve analysis (DCA) curves indicated that the accuracy of the R-model was comparable to RC-model, which exhibited significantly better performance than C-model. CONCLUSIONS: The R-model showed promising potential as a predictor for TKI responses in OS patients with pulmonary metastasis. It can potentially identify pulmonary metastatic OS patients most likely to benefit from TKIs treatment and help guide optimized clinical decisions.

Dynamic nomogram for predicting hungry bone syndrome before parathyroidectomy.

PURPOSE: The objective of this study was to develop a dependable and uncomplicated prediction model utilizing clinical information readily accessible to patients before surgery. This model aimed to assess the likelihood of hungry bone syndrome occurrence in post-surgery patients with secondary hyperparathyroidism (SHPT), and to assist clinicians in adjusting treatment plans promptly. METHODS: In this study, we constructed an online nomogram utilizing independent variables determined through multiple logistic regression to predict the probability of HBS occurrence after parathyroidectomy in patients with secondary hyperparathyroidism. To evaluate the precision and dependability of the nomogram, we used receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). RESULTS: Multivariate logistic regression analyses on 136 eligible patients identified age, parathyroid hormone (PTH), and blood calcium as independent HBS risk factors, which were then integrated into the nomogram. The area under ROC curve demonstrated the nomogram's strong predictive accuracy. The calibration curve demonstrates consistency between the model's prediction probability and observed probability, reflecting high prediction accuracy of the nomogram. Dynamic nomograms were found to hold significant practical clinical value as demonstrated by clinical decision analysis. It can be accessed on https://min115.shinyapps.io/dynnomapp/ . CONCLUSION: In patients with secondary hyperparathyroidism, the dynamic nomogram based on age, parathyroid hormone, and blood calcium can more accurately predict the likelihood of HBS after parathyroidectomy, allowing doctors to make clinical decisions more quickly and adjust treatment plans in a timely manner to reduce the incidence of HBS.

Reduced recurrence rate and comparable functionality after wide resection and reverse total shoulder arthroplasty with allograft-prosthetic composite versus curettage for proximal humeral giant cell tumor: a multicenter retrospective study.

BACKGROUND: Giant cell tumors of bone (GCTBs) are rare, aggressive tumors, and the proximal humerus is a relatively rare location for GCTBs; limited evidence exists on which surgical approaches and reconstruction techniques are optimal. In the largest case series to date, we evaluated the recurrence rate of proximal humeral GCTBs and the functional outcomes of different resection and reconstruction options in this multicenter study. METHODS: All 51 patients included in this study received initial surgical treatment for proximal humeral GCTBs from January 2007 to December 2020, with a minimum 2-year follow-up period. Local recurrence and functional outcomes were statistically analyzed in relation to demographic, clinical, and primary surgical variables. Functional outcomes were reported by patients and were assessed by the Musculoskeletal Tumor Society score and QuickDASH instrument (shortened version of the Disabilities of the Arm, Shoulder and Hand instrument). RESULTS: The mean follow-up period was 81.5 months (range, 30-191 months), and the overall recurrence rate was 17.6% (9 of 51 patients). The majority of recurrences (n = 7) occurred in the first 2 years of follow-up. The intralesional curettage group (n = 23) showed a statistically significant difference in the recurrence rate compared with the en bloc resection group (n = 28) (34.8% vs. 3.6%, P = .007). Among shoulders receiving en bloc resection, 16 were reconstructed with hemiarthroplasty; 8, reverse total shoulder arthroplasty (rTSA) with allograft-prosthetic composite (APC) reconstruction; and 4, arthrodesis. On the basis of intention-to-treat analysis, the mean functional Musculoskeletal Tumor Society scores of the groups undergoing curettage, rTSA with APC, hemiarthroplasty, and arthrodesis were 26.0 ± 3.1, 26.0 ± 1.7, 20.3 ± 2.8, and 22.5 ± 1.3, respectively (P < .001 [with P < .001 for curettage vs. hemiarthroplasty and P = .004 for rTSA with APC vs. hemiarthroplasty]) and the mean QuickDASH scores were 14.0 ± 11.0, 11.6 ± 4.5, 33.1 ± 11.8, and 21.6 ± 4.7, respectively (P < .001 [with P < .001 for curettage vs. hemiarthroplasty and P = .003 for rTSA with APC vs. hemiarthroplasty]). CONCLUSIONS: On the basis of our data, en bloc resection followed by reverse shoulder arthroplasty showed a lower recurrence rate and no significant difference in functional outcome scores for proximal humeral GCTBs compared with intralesional curettage. Therefore, we believe that rTSA with APC may be reasonable for the initial treatment of proximal humeral GCTBs.

Implications of ultrasound-based deep learning model for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

OBJECTIVES: The current study developed an ultrasound-based deep learning model to make preoperative differentiation among hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular-cholangiocarcinoma (cHCC-ICC). METHODS: The B-mode ultrasound images of 465 patients with primary liver cancer were enrolled in model construction, comprising 264 HCCs, 105 ICCs, and 96 cHCC-ICCs, of which 50 cases were randomly selected to form an independent test cohort, and the rest of study population was assigned to a training and validation cohorts at the ratio of 4:1. Four deep learning models (Resnet18, MobileNet, DenseNet121, and Inception V3) were constructed, and the fivefold cross-validation was adopted to train and validate the performance of these models. The following indexes were calculated to determine the differential diagnosis performance of the models, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), F-1 score, and area under the receiver operating characteristic curve (AUC) based on images in the independent test cohort. RESULTS: Based on the fivefold cross-validation, the Resnet18 outperformed other models in terms of accuracy and robustness, with the overall training and validation accuracy as 99.73% (± 0.07%) and 99.35% (± 0.53%), respectively. Furthers validation based on the independent test cohort suggested that Resnet 18 yielded the best diagnostic performance in identifying HCC, ICC, and cHCC-ICC, with the sensitivity, specificity, accuracy, PPV, NPV, F1-score, and AUC of 84.59%, 92.65%, 86.00%, 85.82%, 92.99%, 92.37%, 85.07%, and 0.9237 (95% CI 0.8633, 0.9840). CONCLUSION: Ultrasound-based deep learning algorithm appeared a promising diagnostic method for identifying cHCC-ICC, HCC, and ICC, which might play a role in clinical decision making and evaluation of prognosis.

Potential prognostic biomarkers of hepatocellular carcinoma based on 4D label-free quantitative proteomics analysis pilot investigation.

BACKGROUND: Hepatocellular carcinoma carries a poor prognosis and poses a serious threat to global health. Currently, there are few potential prognostic biomarkers available for the prognosis of hepatocellular carcinoma. METHODS: This pilot study used 4D label-free quantitative proteomics to compare the proteomes of hepatocellular carcinoma and adjacent non-tumor tissue. A total of 66,075 peptides, 6363 identified proteins, and 772 differentially expressed proteins were identified in specimens from three hepatocellular carcinoma patients. Through functional enrichment analysis of differentially expressed proteins by Gene Ontology, KEGG pathway, and protein domain, we identified proteins with similar functions. RESULTS: Twelve differentially expressed proteins (RPL17, RPL27, RPL27A, RPS5, RPS16, RSL1D1, DDX18, RRP12, TARS2, YARS2, MARS2, and NARS1) were selected for identification and validation by parallel reaction monitoring. Subsequent Western blotting confirmed overexpression of RPL27, RPS16, and TARS2 in hepatocellular carcinoma compared to non-tumor tissue in 16 pairs of clinical samples. Analysis of The Cancer Genome Atlas datasets associated the increased expression of these proteins with poor prognosis. Tissue microarray revealed a negative association between high expression of RPL27 and TARS2 and the prognosis of hepatocellular carcinoma patients, although RPS16 was not significant. CONCLUSIONS: These data suggest that RPL27 and TARS2 play an important role in hepatocellular carcinoma progression and may be potential prognostic biomarkers of overall survival in hepatocellular carcinoma patients.

Serum-based Comprehensive N-Glycans Profiling Analysis in Different Gastric Disease Stages by Porous Graphitic Carbon Liquid Chromatography-Mass Spectrometry Associated With Potential Marker Discovery.

BACKGROUND/AIM: N-glycans are potential serum biomarkers due to their aberrant structure and abundance alteration during disease progression. Few studies have been associated with relative quantitative N-glycans profiling during different gastric disease stages. In this study, we conducted an investigation on the profiling of N-glycans in patients with gastric disease, as well as in healthy controls. MATERIALS AND METHODS: In this study, the porous graphitization carbon chromatography-high resolution Fourier transform mass spectrometry (PGC-FTMS) method was applied to assess comprehensive N-glycans profiling in patients at different stages of gastric disease, including gastritis, atrophic gastritis, gastric ulcer, gastric polyps, and gastric cancer. RESULTS: A total of 45 N-glycans (relative abundance >0.1%) were detected, and 9 N-glycans were found to be potential biomarkers for gastric disease detection. Along with the progression of gastric disease, the abundance of sialylated N-glycans increased, while that of core-fucosylated N-glycans decreased. Multivariate statistical analysis demonstrated that N-glycans profiling between gastritis and healthy controls had significant differences. The characteristic N-glycans distinguished gastric cancer from healthy controls, which had strong clinical diagnostic value. CONCLUSION: The relative quantitative profile of N-glycans in different gastric disease stages was revealed and serum N-glycans are proposed for distinguishing gastric disease stages in clinical application.

Comparison of carcinosarcoma and adenocarcinoma of the gallbladder: a study based on SEER population for propensity matching and nomogram analysis.

BACKGROUND: Carcinosarcoma of the gallbladder (CSGB) is an uncommon malignancy, and limited literature is available on its clinicopathological features, prognosis, and treatment options. METHODS: Using the SEER database, we selected 7634 gallbladder adenocarcinoma patients (diagnosed from 2004 to 2015) and 58 carcinosarcoma of the gallbladder patients (diagnosed from 1988 to 2019) based on predetermined criteria. We compared the overall survival (OS) and cancer-specific survival (CSS) before and after propensity score matching in two groups. Cox univariate and multivariate analyses were performed, and a nomogram was further generated to investigate the impact of clinical and pathological variables on the survival of patients with CSGB. Finally, we evaluated the effect of different treatment modalities on the overall survival of CSGB patients. RESULTS: Notably, CSGB patients had larger tumors and underwent surgery more frequently than gallbladder adenocarcinoma patients, with lower rates of deeper tumor infiltrates, and lymph node infiltrates. Conversely, gallbladder adenocarcinoma patients had a higher proportion of AJCC staging (III-IV). Despite these differences, no significant differences were found in OS and CSS between the two groups before and after propensity score matching. For CSGB patients, AJCC staging, surgery and tumor size were significant prognostic factors, while treatment modalities such as surgery combined with chemotherapy, or combined radiochemotherapy, as well as radical resection, did not significantly prolong patient survival. CONCLUSION: No significant difference was found in survival rates between CSGB and gallbladder adenocarcinoma patients, while radical surgery and different combined treatment modalities did not provide significant survival benefits.

SEER-based risk stratification system for patients with primary non-cirrhotic liver cancer.

BACKGROUND: Little research has been done on the factors affecting the survival of patients with non-cirrhotic hepatocellular carcinoma (HCC-NCL). Our aim was to develop and validate a nomogram and a new risk stratification system that can evaluate overall survival (OS) in HCC-NCL patients. METHODS: We retrospectively analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019 to study HCC-NCL patients. The patients were randomly split into training and validation groups at a 7:3 ratio and subjected to single-factor and multi-factor COX regression analysis. We then developed a nomogram and evaluated its accuracy and clinical validity using time-dependent ROC, DCA, and calibration curves. We compared the nomogram with the AJCC staging system by calculating C-index, NRI, and IDI. Finally, we used Kaplan-Meier curves to compare the nomogram and AJCC staging. These analyses were performed without altering the original intended meaning. RESULTS: AFP levels, surgical intervention, T-stage, tumor size, and M-stage were independent prognostic indicators for overall survival among the HCC-NCL population studied. We developed a nomogram based on these factors, and time-dependent ROC, calibration curves, DCA analyses, and C-index proved its accuracy. Compared to the AJCC staging system, the nomogram showed better prognostic accuracy through time-dependent ROC, DCA analyses, C-index, NRI, IDI, and Kaplan-Meier curves. CONCLUSION: We have developed and validated a survival nomogram applicable to HCC-NCL patients, with risk stratification. Our nomogram offers personalized treatment and management options superior to those provided by the AJCC staging system.

A Locally Weighted Linear Regression Look-Up Table-Based Iterative Reconstruction Method for Dual Spectral CT.

OBJECTIVE: Compared with traditional computed tomography (CT), dual spectral CT (DSCT) exhibits superior material distinguishability and thus has broad prospects in industrial and medical fields. In iterative DSCT algorithms, accurately modeling forward-projection functions is crucial, but it is very difficult to analytically provide accurate functions. METHODS: In this article, we propose a locally weighted linear regression look-up table-based (LWLR-LUT) iterative reconstruction method for DSCT. First, the proposed method uses LWLR to establish LUTs for the forward-projection functions through calibration phantoms, achieving good local information calibration. Second, the reconstructed images can be iteratively obtained through the established LUTs. The proposed method not only does not require knowledge of the X-ray spectra and the attenuation coefficients, but also implicitly accounts for some scattered radiation while fitting locally the forward-projection functions in the calibration space. RESULTS: Both numerical simulations and real data experiments demonstrate that the proposed method can achieve highly accurate polychromatic forward-projection functions and greatly improve the quality of the images reconstructed from scattering-free and scattering projections. CONCLUSION: The proposed method is simple and practical, and achieves good material decomposition effects for objects with different complex structures through simple calibration phantoms.

Development and validation of a prognostic model for HER2-low breast cancer to evaluate neoadjuvant therapy.

Background: Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC) accounts for 30-51% of all BCs. How to precisely assess the response to neoadjuvant therapy in this heterogenous tumor is currently unanswered. With the advance in multi-omics, refining the molecular subtyping other than the current hormone receptor (HR)-based subtyping to guide the neoadjuvant therapy for HER2-low BC is potentially feasible. Methods: The messenger RNA (mRNA), clinical, and pathological data of all HER2-low BC patients (n=368) from the Neoadjuvant I-SPY2 Trial, were retrieved. Ninety-eight patients achieved pathological complete response (pCR) were randomly divided into the training and validation sets with 8:2 ratio. The non-pCR cases were corporated into the above datasets with 1:1 ratio. The rest non-pCR cases were served as the test set. Random forest (RF), support vector machine (SVM), and fully connected neural network (FCNN) were applied to establish a 1-dimensional (1D) model based on mRNA data. The method with best prediction value among the 3 models was selected for further modeling when combining pathological features. A new classification of deep learning (CDn) was proposed based on a multi-omics model. After identifying pCR-related features by the integral gradient and unsupervised hierarchical clustering method, the responses to neoadjuvant therapy associated with these features across different subgroups were analyzed. Results: Compared with the RF and SVM models, the FCNN model achieved the best performance [area under the curve (AUC): 0.89] based on the mRNA feature. By combining mRNA and pathological features, the FCNN model proposed 2 new subtypes including CD1 and CD0 for HER2-low BC. CD1 increased the sensitivity to predict pCR by 23.5% [to 87.8%; 95% confidence interval (CI): 78% to 94%] and improved the specificity to pCR by 12.2% (to 77.4%; 95% CI: 69% to 87%) when comparing with the current HR classification for HER2-low BC. Conclusions: The new typing method (CD1 and CD0) proposed in this study achieved excellent performance for predicting the pCR to neoadjuvant therapy in HER2-low BC. The patients who were not sensitive to neoadjuvant therapy according to multi-omics models might receive surgical treatment directly.

Sorafenib inhibits doxorubicin-induced PD-L1 upregulation to improve immunosuppressive microenvironment in Osteosarcoma.

PURPOSE: Although undergoing conventional chemotherapy significantly improves the prognosis of Osteosarcoma, chemoresistance and failure of therapy is still a significant challenge. Furthermore, conventional chemotherapy, like doxorubicin, would upregulate the expression of programmed death-ligand 1 (PD-L1) which caused an immunosuppressive microenvironment and unsatisfied treatment result in Osteosarcoma. Thus, it is urgent to explore a strategy to overcome this disadvantage. METHODS: Human Osteosarcoma cell line MG63 and mouse Osteosarcoma cell line K7 were included in this study. Subcutaneous tumor model was used by injection of K7 cells in BALB/C mice to test the effect of doxorubicin and sorafenib on tumor growth. PD-L1 expression was tested in vitro (flow cytometry, western blot and PCR) and in vivo (flow cytometry and immunohistochemistry). Proportion of immune cells (CD4, CD8, Tregs, and cytotoxic T lymphocytes) in vivo was analyzed with flow cytometry. RESULTS: Combination of sorafenib and doxorubicin inhibited tumor growth significantly in vivo. Doxorubicin increased PD-L1 expression in vitro and in vivo, while sorafenib inhibited doxorubicin-induced PD-L1 upregulation in vitro and in vivo. Proportion of interferon-γ-secreting CD8 + T lymphocytes in tumor tissue was increased significantly when sorafenib was combined with doxorubicin, while proportion of CD4, CD8, and Tregs was not significantly changed. Extracellular signal-regulated kinases (ERK) pathway could be one of the key mechanisms by which doxorubicin induced upregulation of PD-L1 in Osteosarcoma cells. CONCLUSION: Combination of sorafenib and conventional chemotherapeutic reagents is a potent strategy to improve treatment effectiveness by modulating tumor microenvironment in Osteosarcoma through increasing proportion of cytotoxic T lymphocytes.

Superenhancers activate the autophagy-related genes Beclin1 and LC3B to drive metastasis and drug resistance in osteosarcoma.

Metastasis and drug resistance are the leading causes of poor prognosis in patients with osteosarcoma. Identifying the relevant factors that drive metastasis and drug resistance is the key to improving the therapeutic outcome of osteosarcoma. Here, we reported that autophagy was highly activated in metastatic osteosarcoma. We found increased autophagolysosomes in metastatic osteosarcoma cell lines by using electron microscopy, Western blot, and immunofluorescence experiments. We further examined the expression of the autophagy-related genes Beclin1 and LC3B in 82 patients through immunohistochemistry and found that Beclin1 and LC3B were highly related to unfavorable prognosis of osteosarcoma. Knockdown of Beclin1 and LC3B reduced invasion, metastasis, and proliferation in metastatic osteosarcoma cells. In vitro and in vivo studies also demonstrated that inhibiting by 3-MA inhibited cell growth and metastasis. Moreover, we demonstrated that autophagy-related genes were activated by SEs and that the inhibition of SEs by JQ-1 decreased the metastasis of osteosarcoma. Overall, our findings highlighted the association of autophagy with osteosarcoma progression and shed new light on autophagy-targeting therapy for osteosarcoma.

Discovery of a highly specific 18F-labeled PET ligand for phosphodiesterase 10A enabled by novel spirocyclic iodonium ylide radiofluorination.

As a member of cyclic nucleotide phosphodiesterase (PDE) enzyme family, PDE10A is in charge of the degradation of cyclic adenosine (cAMP) and guanosine monophosphates (cGMP). While PDE10A is primarily expressed in the medium spiny neurons of the striatum, it has been implicated in a variety of neurological disorders. Indeed, inhibition of PDE10A has proven to be of potential use for the treatment of central nervous system (CNS) pathologies caused by dysfunction of the basal ganglia-of which the striatum constitutes the largest component. A PDE10A-targeted positron emission tomography (PET) radioligand would enable a better assessment of the pathophysiologic role of PDE10A, as well as confirm the relationship between target occupancy and administrated dose of a given drug candidate, thus accelerating the development of effective PDE10A inhibitors. In this study, we designed and synthesized a novel 18F-aryl PDE10A PET radioligand, codenamed [18F]P10A-1910 ([18F]9), in high radiochemical yield and molar activity via spirocyclic iodonium ylide-mediated radiofluorination. [18F]9 possessed good in vitro binding affinity (IC50 = 2.1 nmol/L) and selectivity towards PDE10A. Further, [18F]9 exhibited reasonable lipophilicity (logD = 3.50) and brain permeability (P app > 10 × 10-6 cm/s in MDCK-MDR1 cells). PET imaging studies of [18F]9 revealed high striatal uptake and excellent in vivo specificity with reversible tracer kinetics. Preclinical studies in rodents revealed an improved plasma and brain stability of [18F]9 when compared to the current reference standard for PDE10A-targeted PET, [18F]MNI659. Further, dose-response experiments with a series of escalating doses of PDE10A inhibitor 1 in rhesus monkey brains confirmed the utility of [18F]9 for evaluating target occupancy in vivo in higher species. In conclusion, our results indicated that [18F]9 is a promising PDE10A PET radioligand for clinical translation.

DNA Base Pairing-Inspired Supramolecular Nanodrug Camouflaged by Cancer-Cell Membrane for Osteosarcoma Treatment.

Osteosarcoma (OS) is one of the most common bone malignant tumors which mainly develops in adolescents. Although neoadjuvant chemotherapy has improved the prognosis of patients, numerous chemotherapeutic challenges still limit their use. Here, inspired by the Watson-Crick base pairing in nucleic acids, hydrophobic (methotrexate) and hydrophilic (floxuridine) chemo-drugs are mixed and self-assembled into M:F nanoparticles (M:F NPs) through molecular recognition. Then, the obtained NPs are co-extruded with membranes derived from OS cells to form cancer-cell membrane-coated NPs (CCNPs). With protected membranes at the outer layer, CCNPs are highly stable in both physiological and weak acid tumor conditions and possess homologous tumor targeted capability. Furthermore, the proteomic analysis first identifies over 400 proteins reserved in CCNPs, most of them participating in tumor cell targeting and adhesion processes. In vitro studies reveal that CCNPs significantly inhibit the PI3K/AKT/mTOR pathway, which promotes cell apoptosis and cell cycle arrest. More importantly, cell membrane camouflage significantly prolongs the circulation half-life of CCNPs, elevates the drug accumulation at tumor sites, and promotes anti-tumor efficacy in vivo. As a convenient and effective strategy to construct a biomimetic NP with high drug loading ratio, the CCNPs provide new potentials for precise and synergistic antitumor treatment.

Deep Learning for Approaching Hepatocellular Carcinoma Ultrasound Screening Dilemma: Identification of α-Fetoprotein-Negative Hepatocellular Carcinoma From Focal Liver Lesion Found in High-Risk Patients.

Background: First-line surveillance on hepatitis B virus (HBV)-infected populations with B-mode ultrasound is relatively limited to identifying hepatocellular carcinoma (HCC) without elevated α-fetoprotein (AFP). To improve the present HCC surveillance strategy, the state of the art of artificial intelligence (AI), a deep learning (DL) approach, is proposed to assist in the diagnosis of a focal liver lesion (FLL) in HBV-infected liver background. Methods: Our proposed deep learning model was based on B-mode ultrasound images of surgery that proved 209 HCC and 198 focal nodular hyperplasia (FNH) cases with 413 lesions. The model cohort and test cohort were set at a ratio of 3:1, in which the test cohort was composed of AFP-negative HBV-infected cases. Four additional deep learning models (MobileNet, Resnet50, DenseNet121, and InceptionV3) were also constructed as comparative baselines. To evaluate the models in terms of diagnostic power, sensitivity, specificity, accuracy, confusion matrix, F1-score, and area under the receiver operating characteristic curve (AUC) were calculated in the test cohort. Results: The AUC of our model, Xception, achieved 93.68% in the test cohort, superior to other baselines (89.06%, 85.67%, 83.94%, and 78.13% respectively for MobileNet, Resnet50, DenseNet121, and InceptionV3). In terms of diagnostic power, our model showed sensitivity, specificity, accuracy, and F1-score of 96.08%, 76.92%, 86.41%, and 87.50%, respectively, and PPV, NPV, FPR, and FNR calculated from the confusion matrix were respectively 80.33%, 95.24%, 23.08%, and 3.92% in identifying AFP-negative HCC from HBV-infected FLL cases. Satisfactory robustness of our proposed model was shown based on 5-fold cross-validation performed among the models above. Conclusions: Our DL approach has great potential to assist B-mode ultrasound in identifying AFP-negative HCC from FLL found in surveillance of HBV-infected patients.

Construction and validation of a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis and immune infiltration.

BACKGROUND: Apoptosis played vital roles in the formation and progression of osteosarcoma. However, no studies elucidated the prognostic relationships between apoptosis-associated genes (AAGs) and osteosarcoma. METHODS: The differentially expressed genes associated with osteosarcoma metastasis and apoptosis were identified from GEO and MSigDB databases. The apoptosis-associated prognostic signature was established through univariate and multivariate cox regression analyses. The Kaplan-Meier (KM) survival curve, ROC curve and nomogram were constructed to investigate the predictive value of this signature. CIBERSORT algorithm and ssGSEA were used to explore the relationships between immune infiltration and AAG signature. The above results were validated in another GEO dataset and the expression of AAGs was also validated in osteosarcoma patient samples by immunohistochemistry. RESULTS: HSPB1 and IER3 were involved in AAG signature. In training and validation datasets, apoptosis-associated risk scores were negatively related to patient survival rates and the AAG signature was regarded as the independent prognostic factor. ROC and calibration curves demonstrated the signature and nomogram were reliable. GSEA revealed the signature related to immune-associated pathways. ssGSEA indicated that one immune cell and three immune functions were significantly dysregulated. The immunohistochemistry analyses of patients' samples revealed that AAGs were significantly differently expressed between metastasis and non-metastasis osteosarcomas. CONCLUSIONS: The present study identified and validated a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis. It could serve as the potential biomarker and therapeutic targets for osteosarcoma in the future.