Lactylated Apolipoprotein C-II Induces Immunotherapy Resistance by Promoting Extracellular Lipolysis.

Mortality rates due to lung cancer are high worldwide. Although PD-1 and PD-L1 immune checkpoint inhibitors boost the survival of patients with non-small-cell lung cancer (NSCLC), resistance often arises. The Warburg Effect, which causes lactate build-up and potential lysine-lactylation (Kla), links immune dysfunction to tumor metabolism. The role of non-histone Kla in tumor immune microenvironment and immunotherapy remains to be clarified. Here, global lactylome profiling and metabolomic analyses of samples from patients with NSCLC is conducted. By combining multi-omics analysis with in vitro and in vivo validation, that intracellular lactate promotes extracellular lipolysis through lactyl-APOC2 is revealed. Mechanistically, lactate enhances APOC2 lactylation at K70, stabilizing it and resulting in FFA release, regulatory T cell accumulation, immunotherapy resistance, and metastasis. Moreover, the anti-APOC2K70-lac antibody that sensitized anti-PD-1 therapy in vivo is developed. This findings highlight the potential of anti lactyl-APOC2-K70 approach as a new combination therapy for sensitizing immunotherapeutic responses.

Segmentation model of soft tissue sarcoma based on self-supervised learning.

Introduction: Soft tissue sarcomas, similar in incidence to cervical and esophageal cancers, arise from various soft tissues like smooth muscle, fat, and fibrous tissue. Effective segmentation of sarcomas in imaging is crucial for accurate diagnosis. Methods: This study collected multi-modal MRI images from 45 patients with thigh soft tissue sarcoma, totaling 8,640 images. These images were annotated by clinicians to delineate the sarcoma regions, creating a comprehensive dataset. We developed a novel segmentation model based on the UNet framework, enhanced with residual networks and attention mechanisms for improved modality-specific information extraction. Additionally, self-supervised learning strategies were employed to optimize feature extraction capabilities of the encoders. Results: The new model demonstrated superior segmentation performance when using multi-modal MRI images compared to single-modal inputs. The effectiveness of the model in utilizing the created dataset was validated through various experimental setups, confirming the enhanced ability to characterize tumor regions across different modalities. Discussion: The integration of multi-modal MRI images and advanced machine learning techniques in our model significantly improves the segmentation of soft tissue sarcomas in thigh imaging. This advancement aids clinicians in better diagnosing and understanding the patient's condition, leveraging the strengths of different imaging modalities. Further studies could explore the application of these techniques to other types of soft tissue sarcomas and additional anatomical sites.

Magnetic anchoring device assisted-laparoscopic sleeve gastrectomy versus conventional laparoscopic sleeve gastrectomy: A retrospective cohort study.

Background: Bariatric surgeries, including the sleeve gastrectomy, have been recognized as the most effectively treatment strategy for severe obesity. Magnetic devices have been successfully used in bariatric surgeries. Here, we intended to evaluate the safety and efficiency of magnetic anchoring device assisted-laparoscopic sleeve gastrectomy (MLSG), and to make a comparison of the short-term results between conventional laparoscopic sleeve gastrectomy (CLSG) and MLSG. Methods: The retrospective cohort study was carried out by analyzing and summarizing the data from a database of routinely collected data. The cohort included the patients who underwent either CLSG (n = 120) or MLSG (n = 115) at a single center between January 2018 and December 2020 with a two-year follow-up. The effects of these two surgeries on the weight loss, resolution of comorbidities and quality of life (QOL) were analyzed. Results: The two groups were similar in gender, age, body mass index, abdominal girth, as well as the type and proportion of comorbidities. And the cases in MLSG group had a markedly shorter time of operation (MLSG, 72.59 min vs. CLSG, 76.67 min; P = 0.003). Length of stay in hospital was significantly shorter in the MLSG group than that in the CLSG group (MLSG, 5.59 days vs. CLSG, 5.96 days; P = 0.016). Neither fatal event nor conversion to open surgery happened among all cases. There were no differences in terms of the postoperative complications between the two groups. Magnetic device-related mild hepatic lacerations occurred and were handled by hemostatic treatments in 3 cases. The QOL of patients in MLSG was better at 6-month after surgery, but there was no significant difference between the two groups at 1-year or 2-year after surgery. Conclusion: Both MLSG and CLSG prove safe and effective, and the patients underwent MLSG have a shorter length of stay in hospital, and a better QOL during 6 months after surgery.

METTL3-induced lncRNA GBAP1 promotes hepatocellular carcinoma progression by activating BMP/SMAD pathway.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common and challenging cancers in the world. N6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play critical roles in the progression of HCC. However, there are few reports on genome-wide screening and functional annotations of m6A-methylated lncRNAs in HCC. METHODS: The expression levels of m6A methyltransferase METTL3 and the association with the prognosis in HCC were determined by RT-qPCR, public dataset platforms. Then, RNA-seq, Pearson correlation analysis, MeRIP-qPCR, RNA half-life assay, gene site-directed mutation, RIP assay and RT-qPCR analysis were employed to determine the downstream target of METTL3 in HCC. Subsequently, the expression levels and roles of lncRNA glucosylceramidase beta pseudogene 1 (GBAP1) in HCC were determined by Kaplan-meier curves, RT-qPCR, in vitro functional experiments and in vivo tumorigenesis and lung metastasis models. Then, the downstream target and pathway of GBAP1 were explored by GO biological process, KEGG pathway enrichment, luciferase reporter assay, RIP assay and rescue experiments and so on. RESULTS: METTL3 was upregulated in HCC and closely related to HCC prognosis. And METTL3 induced GBAP1 expression by acting as the m6A writer of GBAP1 and IGF2BP2 worked as its m6A reader. Clinically, GBAP1 expression was significantly associated with tumor size, venous infiltration, TNM stage and prognosis of HCC, Functionally, GBAP1 promoted HCC metastasis and growth both in vitro and in vivo. Furthermore, GBAP1 acted as the molecular sponge for miR-22-3p to increase the expression of bone morphogenetic protein receptor type 1A (BMPR1A), which then activated BMP/SMAD pathway in HCC cells. CONCLUSIONS: Our findings demonstrated that METTL3-induced GBAP1 promoted migration, invasion and proliferation of HCC cells via GBAP1/miR-22-3p/BMPR1A/SMAD axis. GBAP1 could be a potential prognosis indicator and therapeutic target for HCC.

Radiomics model based on intravoxel incoherent motion and diffusion kurtosis imaging for predicting histopathological grade and Ki-67 expression level of soft tissue sarcomas.

BACKGROUND: Accurate identification of the histopathological grade and the Ki-67 expression level is important in clinical cases of soft tissue sarcomas (STSs). PURPOSE: To explore the feasibility of a radiomics model based on intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) and diffusion kurtosis imaging (DKI) MRI parameter maps in predicting the histopathological grade and Ki-67 expression level of STSs. MATERIAL AND METHODS: In total, 42 patients diagnosed with STSs between May 2018 and January 2020 were selected. The MADC software in Functool of GE ADW 4.7 workstation was used to obtain standard apparent diffusion coefficient (ADC), D, D*, f, mean diffusivity, and mean kurtosis (MK). The histopathological grade and Ki-67 expression level of STSs were identified. The radiomics features of IVIM and DKI parameter maps were used as the dataset. The area under the receiver operating characteristic curve (AUC) and F1-score were calculated. RESULTS: D-SVM achieved the best diagnostic performance for histopathological grade. The AUC in the validation cohort was 0.88 (sensitivity: 0.75 [low level] and 0.83 [high level]; specificity: 0.83 [low level] and 0.75 [high level]; F1-score: 0.75 [low level] and 0.83 [high level]). MK-SVM achieved the best diagnostic performance for Ki-67 expression level. The AUC in the validation cohort was 0.83 (sensitivity: 0.83 [low level] and 0.50 [high level; specificity: 0.50 [low level] and 0.83 [high level]; F1-score: 0.77 [low level] and 0.57 [high level]). CONCLUSION: The proposed radiomics classifier could predict the pathological grade of STSs and the Ki-67 expression level in STSs.

Macrophage membrane-biomimetic adhesive polycaprolactone nanocamptothecin for improving cancer-targeting efficiency and impairing metastasis.

The recent remarkable success and safety of mRNA lipid nanoparticle technology for producing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines has stimulated intensive efforts to expand nanoparticle strategies to treat various diseases. Numerous synthetic nanoparticles have been developed for pharmaceutical delivery and cancer treatment. However, only a limited number of nanotherapies have enter clinical trials or are clinically approved. Systemically administered nanotherapies are likely to be sequestered by host mononuclear phagocyte system (MPS), resulting in suboptimal pharmacokinetics and insufficient drug concentrations in tumors. Bioinspired drug-delivery formulations have emerged as an alternative approach to evade the MPS and show potential to improve drug therapeutic efficacy. Here we developed a biodegradable polymer-conjugated camptothecin prodrug encapsulated in the plasma membrane of lipopolysaccharide-stimulated macrophages. Polymer conjugation revived the parent camptothecin agent (e.g., 7-ethyl-10-hydroxy-camptothecin), enabling lipid nanoparticle encapsulation. Furthermore, macrophage membrane cloaking transformed the nonadhesive lipid nanoparticles into bioadhesive nanocamptothecin, increasing the cellular uptake and tumor-tropic effects of this biomimetic therapy. When tested in a preclinical murine model of breast cancer, macrophage-camouflaged nanocamptothecin exhibited a higher level of tumor accumulation than uncoated nanoparticles. Furthermore, intravenous administration of the therapy effectively suppressed tumor growth and the metastatic burden without causing systematic toxicity. Our study describes a combinatorial strategy that uses polymeric prodrug design and cell membrane cloaking to achieve therapeutics with high efficacy and low toxicity. This approach might also be generally applicable to formulate other therapeutic candidates that are not compatible or miscible with biomimetic delivery carriers.

Soft tissue sarcoma: intravoxel incoherent motion and diffusion kurtosis imaging parameters correlate with the histological grade and Ki-67 expression.

BACKGROUND: Accurate prediction of the histological grade and Ki-67 expression of soft tissue sarcoma (STS) before surgery is essential for the subsequent diagnosis, treatment, and prognostic evaluation of patients. PURPOSE: To evaluate intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) in predicting the histological grade and Ki-67 expression of STS. MATERIAL AND METHODS: A total of 40 patients underwent 3-T MRI, including conventional sequences; IVIM and DKI parameters were obtained. All patients were divided into a low-grade (grade 1 and grade 2) group and a high-grade (grade 3) group through pathological analysis. Ki-67 expression of each lesion was calculated. Chi-square test, independent sample t-test, Mann-Whitney U test, Pearson, Spearman, and receiver operating characteristic curve analysis were performed. RESULTS: There were 17 patients in the low-grade group and 23 in the high-grade group. Ki-67 expression was in the range of 10%-80%. D value was inversely correlated with Ki-67 expression. MK value showed a moderate positive correlation with Ki-67 expression. Regarding histological grading, only the peritumoral enhancement was statistically different between low- and high-grade STS on conventional MRI (P=0.024). The high-grade group had significantly higher MK value and lower D and MD value than the low-grade group. MK value showed the best diagnostic performance. The combination of MK and MD yielded the highest specificity (88.24%), and the combination of D, MK, and MD yielded the best area under the curve value (0.841) and sensitivity (95.65%). CONCLUSION: IVIM and DKI parameters were correlated with Ki-67 expression and could help differentiate between low- and high-grade STS.

A radiomics-based study for differentiating parasellar cavernous hemangiomas from meningiomas.

To investigate the value of the radiomic models for differentiating parasellar cavernous hemangiomas from meningiomas and to compare the classification performance with different MR sequences and classifiers. A total of 96 patients with parasellar tumors (40 cavernous hemangiomas and 56 meningiomas) were enrolled in this retrospective multiple-center study. Univariate and multivariate analyses were performed to identify the clinical factors and semantic features of MRI scans. Radiomics features were extracted from five MRI sequences using radiomics software. Three feature selection methods and six classifiers were evaluated in the training cohort to construct favorable radiomic machine-learning classifiers. The performance of different classifiers was evaluated using the AUC and compared to neuroradiologists. The detection rates of T1WI, T2WI, and CE-T1WI for parasellar cavernous hemangiomas and meningiomas were approximately 100%. In contrast, the ADC maps had the detection rate of 18/22 and 19/25, respectively, (AUC, 0.881) with 2.25 cm as the critical value diameter. Radiomics models with the SVM and KNN classifiers based on T2WI and ADC maps had favorable predictive performances (AUC > 0.90 and F-score value > 0.80). These models outperformed MRI model (AUC 0.805) and neuroradiologists (AUC, 0.756 and 0.545, respectively). Radiomic models based on T2WI and ADC and combined with SVM and KNN classifiers have the potential to be a viable method for differentiating parasellar hemangiomas from meningiomas. T2WI is more universally applicable than ADC values due to its higher detection rate for parasellar tumors.

The Clinicopathological and Prognostic Value of NLR, PLR and MLR in Non-Muscular Invasive Bladder Cancer.

BACKGROUND: The clinicopathological and prognostic relevance of neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and monocyte/lymphocyte ratio (MLR) in non-muscular invasive bladder cancer (NMIBC) was investigated. METHODS: All patients who underwent transurethral resection of bladder tumor (TURBT) and postoperative intravesical chemotherapy had their peripheral blood levels of NLR, PLR, and MLR quantified. The preoperative peripheral blood levels of NLR, PLR, and MLR were analyzed in patients with G1, G2, and G3 NMIBC. A total of 208 patients was divided into poor prognosis (PP, with recurrence, n=51) and good prognosis (GP, no recurrence, n=157) groups, according to whether the recurrence of NMIBC was observed at 1-year follow-up after treatment. Univariate and multivariate logistic regression analyses were performed to evaluate the prognostic factors in NMIBC. In addition, receiver operating characteristic (ROC) curves were used to analyze the prognostic performance of NLR, PLR, and MLR in NMIBC. RESULTS: The preoperative peripheral blood level of PLR was significantly increased in patients with G3 NMIBC compared with that in patients with G1 (p < 0.05) and G2 NMIBC (p < 0.05). The results of univariate and multivariate logistic regression analyses showed that the tumor diameter, differentiation grade, and preoperative peripheral blood levels of NLR, PLR, and MLR were independent prognostic factors for NMIBC recurrence (p < 0.05). Compared with the NMIBC patients without recurrence, 3.490%, 177.575% and 3.175% were determined as the optimum prognostic cutoffs for NLR, PLR, and MLR, respectively. ROC curve was used to evaluate the sensitivity, specificity, and area under the curve (AUC) of NLR, PLR, MLR, and combinations. In contrast to NLR, PLR, or MLR, the combination of NLR, PLR, and MLR (AUC 0.758, sensitivity 66.70%, specificity 89.80%,Youden index 0.565) improved the prognostic performance in the discrimination of NMIBC patients with recurrence from thosewithout recurrence. CONCLUSIONS: The preoperative peripheral blood levels of NLR, PLR, and MLR, which were closely related to the grade and recurrence of NMIBC, were easy to detect and inexpensive. Moreover, these three factors showed the potential for auxiliary prognostic evaluation of NMIBC, wherein the combination than individual values exhibited better prognostic performance.

Brusatol suppresses the growth of intrahepatic cholangiocarcinoma by PI3K/Akt pathway.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a malignancy with a hidden onset, high metastasis recurrence rate, and poor prognosis. Research on effective drugs for ICC is important for improving the prognosis of patients in the clinic. Brusatol is a quassinoid extracted from the seeds of Brucea sumatrana and has been shown to have the potential to inhibit tumor metastasis and proliferation. There has been no scientific research on the therapeutic effect of brusatol on ICC. Our study offers a novel strategy for the therapy of ICC. PURPOSE: Explore effects of brusatol treatment on ICC and clarify the possible mechanism. STUDY DESIGN: Various cell functional experiments and basic experimental techniques were applied to ICC cell lines to explore the influences of brusatol on ICC cells; this conclusion was further verified in animal models. METHODS: The anti-cancer effects of the drug on the cell, protein, and RNA level were verified by cell functional experiments, WB blotting and transcriptome sequencing experiments, respectively. Finally, the experimental results were verified using subcutaneous tumor experiments in nude mice. RESULTS: The consequences exhibited that the levels of epithelial markers of ICC cells increased after brusatol treatment, and the levels of interstitial indicators decreased, suppressing the epithelial-mesenchymal transition (EMT) process. Brusatol inhibited proliferation, induced apoptosis, and suppressed the migration and invasion abilities of Hucc-T1 and RBE oncocytes via activating PI3K/Akt pathway. It also suppressed the growth of Hucc-T1 xenografts in nude mice. CONCLUSION: Brusatol inhibits the proliferation and EMT process in ICC oncocytes by the PI3K/Akt pathway and promotes apoptosis in oncocytes.

Zearalenone Induces Apoptosis and Cytoprotective Autophagy in Chicken Granulosa Cells by PI3K-AKT-mTOR and MAPK Signaling Pathways.

Zearalenone (ZEA) is a nonsteroidal estrogenic mycotoxin found in several food commodities worldwide. ZEA causes reproductive disorders, genotoxicity, and testicular toxicity in animals. However, little is known about the functions of apoptosis and autophagy after exposure to ZEA in granulosa cells. This study investigated the effects of ZEA on chicken granulosa cells. The results show that ZEA at different doses significantly inhibited the growth of chicken granulosa cells by inducing apoptosis. ZEA treatment up-regulated Bax and downregulated Bcl-2 expression, promoted cytochrome c release into the cytosol, and triggered mitochondria-mediated apoptosis. Consequently, caspase-9 and downstream effector caspase-3 were activated, resulting in chicken granulosa cells apoptosis. ZEA treatment also upregulated LC3-II and Beclin-1 expression, suggesting that ZEA induced a high level of autophagy. Pretreatment with chloroquine (an autophagy inhibitor) and rapamycin (an autophagy inducer) increased and decreased the rate of apoptosis, respectively, in contrast with other ZEA-treated groups. Autophagy delayed apoptosis in the ZEA-treated cells. Therefore, autophagy may prevent cells from undergoing apoptosis by reducing ZEA-induced cytotoxicity. In addition, our results further show that the autophagy was stimulated by ZEA through PI3K-AKT-mTOR and MAPK signaling pathways in chicken granulosa cells.

PAIP 2019: Liver cancer segmentation challenge.

Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). Additionally, the PAIP of this year attempted to address potential future problems of AI applicability in clinical settings. In the challenge, participants were asked to use analytical data and statistical metrics to evaluate the performance of automated algorithms in two different tasks. The participants were given the two different tasks: Task 1 involved investigating Liver Cancer Segmentation and Task 2 involved investigating Viable Tumor Burden Estimation. There was a strong correlation between high performance of teams on both tasks, in which teams that performed well on Task 1 also performed well on Task 2. After evaluation, we summarized the top 11 team's algorithms. We then gave pathological implications on the easily predicted images for cancer segmentation and the challenging images for viable tumor burden estimation. Out of the 231 participants of the PAIP challenge datasets, a total of 64 were submitted from 28 team participants. The submitted algorithms predicted the automatic segmentation on the liver cancer with WSIs to an accuracy of a score estimation of 0.78. The PAIP challenge was created in an effort to combat the lack of research that has been done to address Liver cancer using digital pathology. It remains unclear of how the applicability of AI algorithms created during the challenge can affect clinical diagnoses. However, the results of this dataset and evaluation metric provided has the potential to aid the development and benchmarking of cancer diagnosis and segmentation.

Inverse iron oxide/metal catalysts from galvanic replacement.

Key chemical transformations require metal and redox sites in proximity at interfaces; however, in traditional oxide-supported materials, this requirement is met only at the perimeters of metal nanoparticles. We report that galvanic replacement can produce inverse FeOx/metal nanostructures in which the concentration of oxide species adjoining metal domains is maximal. The synthesis involves reductive deposition of rhodium or platinum and oxidation of Fe2+ from magnetite (Fe3O4). We discovered a parallel dissolution and adsorption of Fe2+ onto the metal, yielding inverse FeOx-coated metal nanoparticles. This nanostructure exhibits the intrinsic activity in selective CO2 reduction that simple metal nanoparticles have only at interfaces with the support. By enabling a simple way to control the surface functionality of metal particles, our approach is not only scalable but also enables a versatile palette for catalyst design.

Invasive ductal breast cancer: preoperative predict Ki-67 index based on radiomics of ADC maps.

PURPOSE: The purpose of this study is to develop a radiomics model for predicting the Ki-67 proliferation index in patients with invasive ductal breast cancer through magnetic resonance imaging (MRI) preoperatively. MATERIALS AND METHODS: A total of 128 patients who were clinicopathologically diagnosed with invasive ductal breast cancer were recruited. This cohort included 32 negative Ki67 expression (Ki67 proliferation index < 14%) and 96 cases with positive Ki67 expression (Ki67 proliferation index ≥ 14%). All patients had undergone diffusion-weighted imaging (DWI) MRI before surgery on a 3.0T MRI scanner. Radiomics features were extracted from apparent diffusion coefficient (ADC) maps which were obtained by DWI-MRI from patients with invasive ductal breast cancer. 80% of the patients were divided into training set to build radiomics model, and the rest into test set to evaluate its performance. The least absolute shrinkage and selection operator (LASSO) was used to select radiomics features, and then, the logistic regression (LR) model was established using fivefold cross-validation to predict the Ki-67 index. The performance was evaluated by receiver-operating characteristic (ROC) analysis, accuracy, sensitivity and specificity. RESULTS: Quantitative imaging features (n = 1029) were extracted from ADC maps, and 11 features were selected to construct the LR model. Good identification ability was exhibited by the ADC-based radiomics model, with areas under the ROC (AUC) values of 0.75 ± 0.08, accuracy of 0.71 in training set and 0.72, 0.70 in test set. CONCLUSIONS: The ADC-based radiomics model is a feasible predictor for the Ki-67 index in patients with invasive ductal breast cancer. Therefore, we proposed that three-dimensional imaging features from ADC maps could be used as candidate biomarker for preoperative prediction the Ki-67 index noninvasively.

Soft Tissue Sarcomas: Preoperative Predictive Histopathological Grading Based on Radiomics of MRI.

RATIONALE AND OBJECTIVES: The purpose of this study is to develop a radiomics model for predicting the histopathological grades of soft tissue sarcomas preoperatively through magnetic resonance imaging (MRI). MATERIALS AND METHODS: Thirty-five patients who were pathologically diagnosed with soft tissue sarcomas and their histological grades were recruited. All patients had undergone MRI before surgery on a 3.0T MRI scanner. Radiomics features were extracted from fat-suppressed T2-weighted imaging. We used the least absolute shrinkage and selection operator (LASSO) regression method to select features. Then three machine learning classification methods, including random forests, k-nearest neighbor, and support vector machine algorithm were trained using the 5-fold cross validation strategy to separate the soft tissue sarcomas with low- and high-histopathological grades. RESULTS: The radiomics features were significantly associated with the histopathological grades. Quantitative imaging features (n = 1049) were extracted from fat-suppressed T2-weighted imaging, and five features were selected to construct the radiomics model. The model that used support vector machine classification method achieved the best performance among the three methods, with areas under the receiver operating characteristic curves Area Under Curve (AUC) values of 0.92 ± 0.07, accuracy of 0.88. CONCLUSION: Good accuracy and AUC could be obtained using only five radiomic features. Therefore, we proposed that three-dimensional imaging features from fat-suppressed T2-weighted imaging could be used as candidate biomarkers for preoperative prediction of histopathological grades of soft tissue sarcomas noninvasively.

Microfluidics for Cancer Nanomedicine: From Fabrication to Evaluation.

Self-assembled drug delivery systems (sDDSs), made from nanocarriers and drugs, are one of the major types of nanomedicines, many of which are in clinical use, under preclinical investigation, or in clinical trials. One of the hurdles of this type of nanomedicine in real applications is the inherent complexity of their fabrication processes, which generally lack precise control over the sDDS structures and the batch-to-batch reproducibility. Furthermore, the classic 2D in vitro cell model, monolayer cell culture, has been used to evaluate sDDSs. However, 2D cell culture cannot adequately replicate in vivo tissue-level structures and their highly complex dynamic 3D environments, nor can it simulate their functions. Thus, evaluations using 2D cell culture often cannot correctly correlate with sDDS behaviors and effects in humans. Microfluidic technology offers novel solutions to overcome these problems and facilitates studying the structure-performance relationships for sDDS developments. In this Review, recent advances in microfluidics for 1) fabrication of sDDSs with well-defined physicochemical properties, such as size, shape, rigidity, and drug-loading efficiency, and 2) fabrication of 3D-cell cultures as "tissue/organ-on-a-chip" platforms for evaluations of sDDS biological performance are in focus.

The electrically conductive function of high-molecular weight poly(ethylene oxide) in polymer gel electrolytes used for dye-sensitized solar cells.

The electrically conductive function of high-molecular weight poly(ethylene oxide) (PEO) (M(w) = 2 x 10(6) g mol(-1)) was investigated when it was used to gelate liquid electrolyte to fabricate a series of polymer gel electrolytes for dye-sensitized solar cells (DSCs). With the PEO weight ratio increasing from 2.5 to 15.0% (vs. liquid electrolyte), rheological behavior measurement showed that the viscosity of the polymer gel electrolytes increased ca 465 times. However, it was observed by steady-state voltammetry and electrochemical impedance spectra (EIS) measurements that the diffusion coefficient of I(3)(-)/I(-) decreased constantly while the conductivity of the polymer gel electrolytes increased initially and then decreased. These two inconsistent behaviours showed that the mobility of Li(+) was enhanced by PEO. EIS measurement revealed that the internal resistance of the DSCs were reduced since the enhanced mobility of Li(+) was helpful for the transport of electrons within the TiO(2) film through an ambipolar diffusion mechanism. When these polymer gel electrolytes were used to assemble DSCs, the conversion efficiency of DSCs increased continuously until it reached its maximum as the PEO weight ratio increased from 2.5 to 10.0%. By optimizing the dye adsorbing time and the thickness of the TiO(2) film, a quasi-solid DSC based on a polymer gel electrolyte with a PEO weight ratio of 10.0% showed a considerable conversion efficiency, 6.12 and 10.11% under 100 and 30 mW cm(-2) illumination, respectively. Finally, a stability test indicated that the more PEO was added into the polymer gel electrolytes, the better stability was obtained for the corresponding DSCs.