Preoperative computed tomography semantic features in predicting lymph node metastasis of part-solid nodules in non-small cell lung cancer: a multicenter retrospective study.

Background: Lymph node metastasis (LNM) is the most common route of metastasis for lung cancer, and it is an independent risk factor for long-term survival and recurrence in patients with non-small cell lung cancer (NSCLC). The purpose of this study was to explore the value of preoperative computed tomography (CT) semantic features in the differential diagnosis of LNM in part-solid nodules (PSNs) of NSCLC. Methods: A total of 955 patients with NSCLC confirmed by postoperative pathology were retrospectively enrolled from January 2019 to March 2023. The clinical, pathological data and preoperative CT images of these patients were investigated and statistically analyzed in order to identify the risk factors for LNM. Multivariate logistic regression was used to select independent risk factors and establish different prediction models. Ten-fold cross-validation was used for model training and validation. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated, and the Delong test was used to compare the predictive performance between the models. Results: LNM occurred in 68 of 955 patients. After univariate analysis and adjustment for confounding factors, smoking history, pulmonary disease, solid component proportion, pleural contact type, and mean diameter were identified as the independent risk factors for LNM. The image predictors model established by the four independent factors of CT semantic features, except smoking history, showed a good diagnostic efficacy for LNM. The AUC in the validation group was 0.857, and the sensitivity, specificity, and accuracy of the model were all 77.6%. Conclusions: Preoperative CT semantic features have good diagnostic value for the LNM of NSCLC. The image predictors model based on pulmonary disease, solid component proportion, pleural contact type, and mean diameter demonstrated excellent diagnostic efficacy and can provide non-invasive evaluation in clinical practice.

Predicting lymphovascular invasion in non-small cell lung cancer using deep convolutional neural networks on preoperative chest CT.

RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images with deep learning technology. MATERIALS AND METHODS: This retrospective observational study included a series of consecutive patients who underwent surgical resection for non-small cell lung cancer (NSCLC) and received pathologically confirmed diagnoses. The cohort was randomly divided into a training group comprising 70 % of the patients and a validation group comprising the remaining 30 %. Four distinct deep convolutional neural network (DCNN) prediction models were developed, incorporating different combination of two-dimensional (2D) and three-dimensional (3D) CT imaging features as well as clinical-radiological data. The predictive capabilities of the models were evaluated by receiver operating characteristic curves (AUC) values and confusion matrices. The Delong test was utilized to compare the predictive performance among the different models. RESULTS: A total of 3034 patients with NSCLC were recruited in this study including 106 LVI+ patients. In the validation cohort, the Dual-head Res2Net_3D23F model achieved the highest AUC of 0.869, closely followed by the models of Dual-head Res2Net_3D3F (AUC, 0.868), Dual-head Res2Net_3D (AUC, 0.867), and EfficientNet-B0_2D (AUC, 0.857). There was no significant difference observed in the performance of the EfficientNet-B0_2D model when compared to the Dual-head Res2Net_3D3F and Dual-head Res2Net_3D23F. CONCLUSION: Findings of this study suggest that utilizing deep convolutional neural network is a feasible approach for predicting pathological LVI in patients with NSCLC.

MRI-based radiomics for preoperative prediction of recurrence and metastasis in rectal cancer.

OBJECTIVES: To explore the value of multi-parametric MRI (mp-MRI) radiomic model for preoperative prediction of recurrence and/or metastasis (RM) as well as survival benefits in patients with rectal cancer. METHODS: A retrospective analysis of 234 patients from two centers with histologically confirmed rectal adenocarcinoma was conducted. All patients were divided into three groups: training, internal validation (in-vad) and external validation (ex-vad) sets. In the training set, radiomic features were extracted from T2WI, DWI, and contrast enhancement T1WI (CE-T1) sequence. Radiomic signature (RS) score was then calculated for feature screening to construct a rad-score model. Subsequently, preoperative clinical features with statistical significance were selected to construct a clinical model. Independent predictors from clinical and RS related to RM were selected to build the combined model and nomogram. RESULTS: After feature extraction, 26 features were selected to construct the rad-score model. RS (OR = 0.007, p < 0.01), MR-detected T stage (mrT) (OR = 2.92, p = 0.03) and MR-detected circumferential resection margin (mrCRM) (OR = 4.70, p = 0.01) were identified as independent predictors of RM. Then, clinical model and combined model were constructed. ROC curve showed that the AUC, accuracy, sensitivity and specificity of the combined model were higher than that of the other two models in three sets. Kaplan-Meier curves showed that poorer disease-free survival (DFS) time was observed for patients in pT3-4 stages with low RS score (p < 0.001), similar results were also found in pCRM-positive patients (p < 0.05). CONCLUSION: The mp-MRI radiomics model can be served as a noninvasive and accurate predictors of RM in rectal cancer that may support clinical decision-making.

A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study.

BACKGROUND: To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer. METHODS: Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms. RESULTS: Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians. CONCLUSION: The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.

Deep learning-based radiomic nomograms for predicting Ki67 expression in prostate cancer.

BACKGROUND: To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa). MATERIALS: The data of 229 patients with PCa from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. Deep learning features were extracted and selected from each patient's prostate multiparametric MRI (diffusion-weighted imaging, T2-weighted imaging, and contrast-enhanced T1-weighted imaging sequences) data to establish a deep radiomic signature and construct models for the preoperative prediction of Ki67 expression. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a joint model. The predictive performance of multiple deep-learning models was then evaluated. RESULTS: Seven prediction models were constructed: one clinical model, three deep learning models (the DLRS-Resnet, DLRS-Inception, and DLRS-Densenet models), and three joint models (the Nomogram-Resnet, Nomogram-Inception, and Nomogram-Densenet models). The areas under the curve (AUCs) of the clinical model in the testing, internal validation, and external validation sets were 0.794, 0.711, and 0.75, respectively. The AUCs of the deep models and joint models ranged from 0.939 to 0.993. The DeLong test revealed that the predictive performance of the deep learning models and the joint models was superior to that of the clinical model (p < 0.01). The predictive performance of the DLRS-Resnet model was inferior to that of the Nomogram-Resnet model (p < 0.01), whereas the predictive performance of the remaining deep learning models and joint models did not differ significantly. CONCLUSION: The multiple easy-to-use deep learning-based models for predicting Ki67 expression in PCa developed in this study can help physicians obtain more detailed prognostic data before a patient undergoes surgery.

Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer.

OBJECTIVES: To investigate the feasibility and efficacy of a nomogram that combines clinical and radiomic features of magnetic resonance imaging (MRI) for preoperative perirectal fat invasion (PFI) prediction in rectal cancer. METHODS: This was a retrospective study. A total of 363 patients from two centers were included in the study. Patients in the first center were randomly divided into training cohort (n = 212) and internal validation cohort (n = 91) at the ratio of 7:3. Patients in the second center were allocated to the external validation cohort (n = 60). Among the training cohort, the numbers of patients who were PFI positive and PFI negative were 108 and 104, respectively. The radiomics features of preoperative T2-weighted images, diffusion-weighted images and enhanced T1-weighted images were extracted, and the total Radscore of each patient was obtained. We created Clinic model and Radscore model, respectively, according to clinical data or Radscore only. And that, we assembled the combined model using the clinical data and Radscore. We used DeLong's test, receiver operating characteristic, calibration and decision curve analysis to assess the models' performance. RESULTS: The three models had good performance. Clinic model and Radscore model showed equivalent performance with AUCs of 0.85, 0.82 (accuracy of 81%, 81%) in the training cohort, AUCs of 0.78, 0.86 (accuracy of 74%, 84%) in the internal cohort, and 0.84, 0.84 (accuracy of 80%, 82%) in the external cohort without statistical difference (DeLong's test, p > 0.05). AUCs and accuracy of Combined model were 0.89 and 87%, 0.90 and 88%, and 0.90 and 88% in the three cohorts, respectively, which were higher than that of Clinic model and Radscore model, but only in the training cohort with a statistical difference (DeLong's test, p < 0.05). The calibration curves of the nomogram exhibited acceptable consistency, and the decision curve analysis indicated higher net benefit in clinical practice. CONCLUSION: A nomogram combining clinical and radiomic features of MRI to compute the probability of PFI in rectal cancer was developed and validated. It has the potential to serve as a preoperative biomarker for predicting pathological PFI of rectal cancer.

Preoperative prediction of extramural venous invasion in rectal cancer by dynamic contrast-enhanced and diffusion weighted MRI: a preliminary study.

BACKGROUND: To explore the value of the quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters in assessing preoperative extramural venous invasion (EMVI) in rectal cancer. METHODS: Eighty-two rectal adenocarcinoma patients who had underwent MRI preoperatively were enrolled in this study. The differences in quantitative DCE-MRI and DWI parameters including Krans, Kep and ADC values were analyzed between MR-detected EMVI (mrEMVI)-positive and -negative groups. Multivariate logistic regression analysis was performed to build the combined prediction model for pathologic EMVI (pEMVI) with statistically significant quantitative parameters. The performance of the model for predicting pEMVI was evaluated using receiver operating characteristic (ROC) curve. RESULTS: Of the 82 patients, 24 were mrEMVI-positive and 58 were -negative. In the mrEMVI positive group, the Ktrans and Kep values were significantly higher than those in the mrEMVI negative group (P < 0.01), but the ADC values were significantly lower (P < 0.01). A negative correlation was observed between the Ktrans vs ADC values and Kep vs ADC values in patients with rectal cancer. Among the four quantitative parameters, Ktrans and ADC value were independently associated with mrEMVI by multivariate logistic regression analysis. ROC analysis showed that combined prediction model based on quantitative DCE parameters and ADC values had a good prediction efficiency for pEMVI in rectal cancer. CONCLUSION: The quantitative DCE-MRI parameters, Krans, Kep and ADC values play important role in predicting EMVI of rectal cancer, with Ktrans and ADC value being independent predictors of EMVI in rectal cancer.

An Analysis of Computed Tomography Imaging Features and Predictive Factors for Postoperative Recurrence and Metastasis of Abdominal Paragangliomas.

Methods: We studied 51 abdominal PGL patients at the First Affiliated Hospital of Bengbu Medical College, Tongde Hospital, and Sir Run Shaw Hospital, Hangzhou, Zhejiang Province, China, from June 2009 to May 2019. Thereafter, the clinical research data, tumor biomarkers, and CT features were compared between the aggressive PGLs and the nonaggressive PGLs using independent-samples t-tests and chi-square tests. Results: Of the 51 cases, 43 were benign and 8 had malignant tendencies. Postoperative recurrence and metastasis were more likely to occur when the tumor diameter was >8 cm or/and the enhancement degree was not obvious. Clinical symptoms, tumor markers, sex, age, and CT image characteristics including morphology, presence of cystic degeneration, "pointed peach" sign, calcification, hemorrhage, enlarged lymph nodes, and peritumor and intratumor blood vessels were not significantly different between the two groups (p > 0.05). Conclusion: Our findings suggest that CT features, including size >8 cm and enhancement degree, could provide important evidence to assess risk factors for aggressive PGLs.

Death Pathways of Cancer Cells Modulated by Surface Molecule Density on Gold Nanorods.

Necrosis induces strong inflammation with undesirable implications in clinics compared with apoptosis. Fortunately, the switch between necrosis and apoptosis could be realized by tailoring the appropriate structural properties of gold nano rods (GNRs) that could precisely modulate cell death pathways. Herein, the intracellular interaction between GNRs and organelles is monitored and it is found that lysosomes dominates necrosis/apoptosis evoking. Then the surface molecule density of GNRs, which is first defined as ρsurf. molecule (Nsurf. molecules /(a × π × Diameter × Length)), mediates lysosome activities as the membrane permeabilization (LMP), the Cathepsin B and D release, the cross-talk between lysosome and different organelles, which selectively evokes apoptosis or necrosis and the production of TNF-α from macrophages. GNRs with small ρsurf. molecule mainly induce apoptosis, while with large ρsurf. molecule they greatly contribute to necrosis. Interestingly, necrosis can be suppressed by GNRs with higher ρsurf. molecule due to the overexpression of key protease caspase 8, which cleaves the RIP1-RIP3 complex and activates caspase 3 followed by necrosis to apoptosis transition. This investigation indicates that the ρsurf. molecule greatly affects the utility of nanomaterials and different structural properties of nanomaterials have different implications in clinics.

An azine-based polymer derived hierarchically porous N-doped carbon for hydrophilic dyes removal.

In this study, a novel hierarchically porous N-doped carbon (HPNC) material was successfully prepared by soft-templating method. The commercial triblock copolymer of Pluronic F127 and a polyazine derived from hydrazine hydrate & glyoxal were used as soft template and precursor, respectively. The obtained materials were fully characterized and tested as a sorbent for the removal of hydrophilic dyes of Methylene blue (MB), Basic Fuchsin (BF), Eosin Y (EY) and Rhodamine B (RB) from their aqueous effluents. According to the characterization results, the synthesized material of HPNC-1000 presented thick fibrous morphology with micron size in diameter, hierarchically porous structure with surface area of 1853 m2/g, pore volume of 1.59 cm3/g and nitrogen content of 4.5 wt%. Adsorption-desorption investigation reveals that synergistic effect of hydrophobic interaction and hydrogen-bonding formation of the dye molecules with the sorbent was most pronounced in the adsorptions. The maximum adsorption capacities for MB, BF, EY and RB reached 0.83, 0.92, 1.23 and 1.83 mmol g-1, respectively. The adsorption processes well fitted by the pseudo first-order kinetic model and the Liu's isotherm. The sorbent can be regenerated by above 90% of the initial adsorption efficiency after six regeneration cycles.

A Tauopathy-Homing and Autophagy-Activating Nanoassembly for Specific Clearance of Pathogenic Tau in Alzheimer's Disease.

The hyperphosphorylated and aggregated tau accumulation represents a significant pathological hallmark of tauopathies including Alzheimer's disease (AD), which is highly associated with defective autophagy in neuronal cells. Autophagy-activating strategies demonstrate the therapeutic potential for AD in many studies; however, further development is limited by their low efficacy and serious side effects that result from a lack of selectivity for diseased cells. Herein, we report a tauopathy-homing nanoassembly (THN) with autophagy-activating capacity for AD treatment. Specifically, the THN can bind to hyperphosphorylated and/or aggregated tau and selectively accumulate in cells undergoing tauopathy. The THN further promotes the clearance of pathogenic tau accumulation by stimulating autophagic flux, consequently rescuing neuron viability and cognitive functions in AD rats. This study presents a promising nanotechnology-based strategy for tauopathy-homing and autophagy-mediated specific removal of pathogenic tau in AD.

Intrinsic brain abnormalities of irritable bowel syndrome with diarrhea: a preliminary resting-state functional magnetic resonance imaging study.

BACKGROUND: The aim of the present study was to explore the brain active characteristics of patients with irritable bowel syndrome with diarrhea (IBS-D) using resting-state functional magnetic resonance imaging technology. METHODS: Thirteen IBS-D patients and fourteen healthy controls (HC) were enrolled. All subjects underwent head MRI examination during resting state. A voxel-based analysis of fractional amplitude of low frequency fluctuation (fALFF) maps between IBS-D and HC was performed using a two-sample t-test. The relationship between the fALFF values in abnormal brain regions and the scores of Symptom Severity Scale (IBS-SSS) were analyzed using Pearson correlation analysis. RESULTS: Compared with HC, IBS-D patients had lower fALFF values in the left medial superior frontal gyrus and higher fALFF values in the left hippocampus and right precuneus. There was a positive correlation between the duration scores of IBS-SSS and fALFF values in the right precuneus. CONCLUSION: The altered fALFF values in the medial superior frontal gyri, left hippocampus and right precuneus revealed changes of intrinsic neuronal activity, further revealing the abnormality of gut-brain axis of IBS-D.

Differentiation of gastric schwannomas from gastrointestinal stromal tumors by CT using machine learning.

OBJECTIVE: To identify schwannomas from gastrointestinal stromal tumors (GISTs) by CT features using Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT). METHODS: This study enrolled 49 patients with schwannomas and 139 with GISTs proven by pathology. CT features with P < 0.1 derived from univariate analysis were inputted to four models. Five machine learning (ML) versions, multivariate analysis, and radiologists' subjective diagnostic performance were compared to evaluate diagnosis performance of all the traditional and advanced methods. RESULTS: The CT features with P < 0.1 were as follows: (1) CT attenuation value of unenhancement phase (CTU), (2) portal venous enhancement (CTV), (3) degree of enhancement in the portal venous phase (DEPP), (4) CT attenuation value of portal venous phase minus arterial phase (CTV-CTA), (5) enhanced potentiality (EP), (6) location, (7) contour, (8) growth pattern, (9) necrosis, (10) surface ulceration, (11) enlarged lymph node (LN). LR (M1), RF, DT, and GBDT models contained all of the above 11 variables, while LR (M2) was developed using six most predictive variables derived from (M1). LR (M2) model with AUC of 0.967 in test dataset was thought to be optimal model in differentiating the two tumors. Location in gastric body, exophytic and mixed growth pattern, lack of necrosis and surface ulceration, enlarged lymph nodes, and larger EP were the most important CT features suggestive of schwannomas. CONCLUSION: LR (M2) provided the optimal diagnostic potency among other ML versions, multivariate analysis, and radiologists' performance on differentiation of schwannomas from GISTs.

Clinical and CT findings of COVID-19: differences among three age groups.

BACKGROUND: The novel coronavirus pneumonia (coronavirus disease 2019, COVID-19) has spread around the world. We aimed to recapitulate the clinical and CT imaging features of COVID-19 and their differences in three age groups. METHODS: The clinical and CT data of patients with COVID-19 (n = 307) that had been divided into three groups (Group 1: < 40 years old; Group 2: 40 ≤ age < 60 years old; Group 3: ≥ 60 years old) according to age were analyzed retrospectively. RESULTS: Of all patients, 114 (37.1%) had histories of epidemiological exposure, 48 (15.6%) were severe/critical cases, 31 had hypertension (10.1%), 15 had diabetes mellitus (4.9%), 3 had chronic obstructive pulmonary disease (COPD, 1%). Among the three groups, severe/critical type, hypertension and diabetes occurred more commonly in the elderly group compared with Group 1&2 (P < 0.05, respectively). Cough and chest tightness/pain were more commonly appeared in Group 2&3 compared with Group 1 (P < 0.05, respectively). Compared with Group 1 and 2, there were more abnormal laboratory examination indexes (including CRP increase, abnormal percentage of lymphocytes, neutrophils and monocytes) in Group 3 (P < 0.05, respectively). CT images revealed that more lobes were affected and more subpleural lesions were involved in the elderly group, besides, crazy paving sign, bronchodilatation and pleural thickening were more commonly seen in the elderly group, with significant difference between Group 1&2, Group 2&3 (P < 0.05, respectively). CONCLUSIONS: COVID-19 presented representative clinical manifestations, laboratory examinations and CT findings, but three age groups possessed their own specific characteristics. Grasping the clinical and CT features stratified by age will be helpful for early definite diagnosis of COVID-19.

Self-assembly hollow manganese Prussian white nanocapsules attenuate Tau-related neuropathology and cognitive decline.

Alzheimer's disease (AD) is a prevalent chronic neurodegenerative disease. However, to date, none of the developed drug candidates targeting at a single therapeutic target of AD have achieved success in clinical trials. Herein, we proposed a hypothesis of hollow manganese Prussian white nanocapsules (HMPWCs)-mediated attenuation of Tau-related pathology and alleviation of cognitive decline via simultaneously alleviating neuroinflammation, scavenging reactive oxygen species, and reducing hyperphosphorylated Tau proteins. The HMPWCs self-assemblied with manganese Prussian white analogue and bovine serum albumin via a novel biomimetic mineralization present good biocompatibility, variable valence states, and low oxidation-reduction potential. They own the outstanding capabilities of relieving oxidative stress, inhibiting Tau neuropathology, and counteracting neuroinflammation, which could be used to treat Tau-related AD-like neurodegeneration. Importantly, they can also attenuate the cognitive impairments of Tau-related AD-like rats without significant side effects. This research takes the advantages of catalytic chemistry, nanomedicine and specific neurodegenerative microenvironment together, providing an alternative efficient treatment strategy for Tau-related neurodegeneration diseases, such as AD, Pick's disease, frontotemporal dementia, Creutzfeldt-Jakob Disease and progressive supranuclear palsy.

PET imaging of metabolic changes after neural stem cells and GABA progenitor cells transplantation in a rat model of temporal lobe epilepsy.

PURPOSE: Stem cell transplantation is promising for temporal lobe epilepsy (TLE) treatment. This study aimed to use PET imaging for the investigation of dynamic metabolic changes after transplantation of human neural stem cells (NSCs) and human GABA progenitor cells (GPCs) in a rat model of TLE. METHODS: 18F-FDG PET imaging, video-electroencephalography (EEG), whole-cell patch-clamp recordings and immunostaining were performed after transplantation of NSCs and GPCs. RESULTS: PET imaging demonstrated that glucose metabolism was gradually improved in the NSCs group, but decreased in GPCs and the control. Video-EEG manifested that seizures were suppressed after NSCs or GPCs transplantation; whole-cell patch-clamp confirmed increased inhibitory response of GPC-derived cells; immunostaining studies verified that the transplanted NSCs and GPCs could survive, migrate and differentiate into mature neuronal subtypes. CONCLUSION: 18F-FDG PET imaging could be a distinguishing approach for evaluation of dynamic glycolytic metabolic changes after transplantation of NSCs and GPCs in TLE. Whole-cell patch-clamp provides evidence for functional maturation and integration of transplanted stem cells within host circuits.

Successful in vivo hyperthermal therapy toward breast cancer by Chinese medicine shikonin-loaded thermosensitive micelle.

The Chinese traditional medicine Shikonin is an ideal drug due to its multiple targets to tumor cells. But in clinics, improving its aqueous solubility and tumor accumulation is still a challenge. Herein, a copolymer with tunable poly(N-isopropylacrymaide) and polylactic acid block lengths is designed, synthesized, and characterized in nuclear magnetic resonance. The corresponding thermosensitive nanomicelle (TN) with well-defined core-shell structure is then assembled in an aqueous solution. For promoting the therapeutic index, the physical-chemistry properties of TNs including narrow size, low critical micellar concentration, high serum stability, tunable volume phase transition temperature (VPTT), high drug-loading capacity, and temperature-controlled drug release are systematically investigated and regulated through the fine self-assembly. The shikonin is then entrapped in a degradable inner core resulting in a shikonin-loaded thermosensitive nanomicelle (STN) with a VPTT of ~40°C. Compared with small-molecular shikonin, the in vitro cellular internalization and cytotoxicity of STN against breast cancer cells (Michigan Cancer Foundation-7) are obviously enhanced. In addition, the therapeutic effect is further enhanced by the programmed cell death (PCD) specifically evoked by shikonin. Interestingly, both the proliferation inhibition and PCD are synergistically promoted as T > VPTT, namely the temperature-regulated passive targeting. Consequently, as intravenous injection is administered to the BALB/c nude mice bearing breast cancer, the intratumor accumulation of STNs is significantly increased as T > VPTT, which is regulated by the in-house developed heating device. The in vivo antitumor assays against breast cancer further confirm the synergistically enhanced therapeutic efficiency. The findings of this study indicate that STN is a potential effective nanoformulation in clinical cancer therapy.

Mastocarcinoma therapy synergistically promoted by lysosome dependent apoptosis specifically evoked by 5-Fu@nanogel system with passive targeting and pH activatable dual function.

This manuscript describes a synergistic therapy for mastocarcinoma by pH and temperature dual-sensitive nanogel, and effects of microstructure, composition and properties of nanogel on the cellular response mechanism. The extracellular internalization of nanogels was obviously enhanced, due to the passive targeting function at T>VPTT. Interestingly, the increased cytotoxicity was further synergistically enhanced by an unexpected apoptosis as evoked by the 5-fluorouracil loaded nanogel (FLNG). The systemically evaluation of the effectors generated from different sub-cellular organelles including endosome, lysosome, autophagosome confirmed that it was a lysomal dependent apoptosis. Such specific apoptosis was mainly attributed to its activatable protonated PEI at low pH, which caused lysosomal membrane destruction and lysosomal enzyme cathepsin B (Cat B) leakage. This Cat B was then translocated to the mitochondria resulting in mitochondrial membrane permeability increase and mitochondrial membrane potential (MMP) decrease, followed by cytochrome c (Cyt C) release. Cyt C was the main molecule that evoked apoptosis as reflected by overexpression of caspase 9. Additionally, such lysosome dependent, apoptosis was further enhanced by the passive cellular targeting at T>VPTT. Thus, the tumor growth inhibition was synergistically enhanced by the extracellular temperature dependent passive targeting and intracellular pH activatable lysosomal dependent apoptosis.

Lysosome-mitochondria-mediated apoptosis specifically evoked in cancer cells induced by gold nanorods.

AIM: The main aim of this article is to explain the apoptosis mechanisms of cancer cells specifically triggered by gold nanorods (GNRs). MATERIALS & METHODS: GNRs were synthesized and optimized, the lysosome damage, cathepsin D, mitochondrial membrane potential, caspase-9, cleaved caspase-9, caspase-3 and intracellular GNRs location related to apoptosis was systematically evaluated. RESULTS: GNRs specifically induce cancer cell apoptosis while posing a negligible impact on normal cells. After incubation with GNRs, the lysosomal permeability in cancer cells as indicated by cathepsin D was markedly higher than that in normal cells and resulted in an obvious decrease in mitochondrial membrane potential. Western blot analysis further confirmed that apoptosis occurred through caspase-9 and caspase-3 activation following mitochondrial damage. Transmission electron microscope images showed that GNRs did not appear in most of the damaged mitochondria but mainly accumulated in lysosomes. CONCLUSION: These findings indicated that GNR-induced apoptosis specifically in cancer cells by affecting lysosomes and mitochondria.

Synergistic anti-tumor therapy by a comb-like multifunctional antibody nanoarray with exceptionally potent activity.

Simultaneously blocking multiple mediators offers new hope for the treatment of complex diseases. However, the curative potential of current combination therapy by chronological administration of separate monoclonal antibodies (mAbs) or multi-specific mAbs is still moderate due to inconvenient manipulation, low cooperative effectors, poor pharmacokinetics and insufficient tumor accumulation. Here, we describe a facile strategy that arms distinct mAbs with cooperative effectors onto a long chain to form a multicomponent comb-like nano mAb. Unlike dissociative parental mAbs, the multifunctional mAb nanoarray (PL-RB) constructed from type I/II anti-CD20 mAbs shows good pharmacokinetics. This PL-RB simultaneously targets distinct epitopes on a single antigen (Ag) and neighboring Ags on different lymphocytes. This unique intra- and intercellular Ag cross-linking endows the multifunctional mAb nanoarray with potent apoptosis activity. The exceptional apoptosis, complement-dependent cytotoxicity (CDC), antibody-dependent cellular cytotoxicity (ADCC) that are synchronously evoked by the nano PL-RB are further synergistically promoted via enhanced permeability and retention (EPR), which resulted in high intratumor accumulation and excellent anti-lymphoma efficiency.