Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.

This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.

Progress in Serial Imaging for Prognostic Stratification of Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.

Immunotherapy, particularly with checkpoint inhibitors, has revolutionized non-small cell lung cancer treatment. Enhancing the selection of potential responders is crucial, and researchers are exploring predictive biomarkers. Delta radiomics, a derivative of radiomics, holds promise in this regard. For this study, a meta-analysis was conducted that adhered to PRISMA guidelines, searching PubMed, Embase, Web of Science, and the Cochrane Library for studies on the use of delta radiomics in stratifying lung cancer patients receiving immunotherapy. Out of 223 initially collected studies, 10 were included for qualitative synthesis. Stratifying patients using radiomic models, the pooled analysis reveals a predictive power with an area under the curve of 0.81 (95% CI 0.76-0.86, p < 0.001) for 6-month response, a pooled hazard ratio of 4.77 (95% CI 2.70-8.43, p < 0.001) for progression-free survival, and 2.15 (95% CI 1.73-2.66, p < 0.001) for overall survival at 6 months. Radiomics emerges as a potential prognostic predictor for lung cancer, but further research is needed to compare traditional radiomics and deep-learning radiomics.

Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis.

BACKGROUND: Manual detection of brain metastases is both laborious and inconsistent, driving the need for more efficient solutions. Accordingly, our systematic review and meta-analysis assessed the efficacy of deep learning algorithms in detecting and segmenting brain metastases from various primary origins in MRI images. METHODS: We conducted a comprehensive search of PubMed, Embase, and Web of Science up to May 24, 2023, which yielded 42 relevant studies for our analysis. We assessed the quality of these studies using the QUADAS-2 and CLAIM tools. Using a random-effect model, we calculated the pooled lesion-wise dice score as well as patient-wise and lesion-wise sensitivity. We performed subgroup analyses to investigate the influence of factors such as publication year, study design, training center of the model, validation methods, slice thickness, model input dimensions, MRI sequences fed to the model, and the specific deep learning algorithms employed. Additionally, meta-regression analyses were carried out considering the number of patients in the studies, count of MRI manufacturers, count of MRI models, training sample size, and lesion number. RESULTS: Our analysis highlighted that deep learning models, particularly the U-Net and its variants, demonstrated superior segmentation accuracy. Enhanced detection sensitivity was observed with an increased diversity in MRI hardware, both in terms of manufacturer and model variety. Furthermore, slice thickness was identified as a significant factor influencing lesion-wise detection sensitivity. Overall, the pooled results indicated a lesion-wise dice score of 79%, with patient-wise and lesion-wise sensitivities at 86% and 87%, respectively. CONCLUSIONS: The study underscores the potential of deep learning in improving brain metastasis diagnostics and treatment planning. Still, more extensive cohorts and larger meta-analysis are needed for more practical and generalizable algorithms. Future research should prioritize these areas to advance the field. This study was funded by the Gen. & Mrs. M.C. Peng Fellowship and registered under PROSPERO (CRD42023427776).

Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients.

Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.

Optimization of performance and sensitivity: preparation of two Ag(I)-based ECPs by using isomeric ligands.

For energetic compounds, their structure determines their performance, and even minor variations in their structure can have a significant impact on their performance. The application scenarios for energetic materials are diverse, and their performance requirements vary as well. To investigate the influence of different substituent positions on the performance of primary explosives, we prepared two Ag(I)-based complexes, [Ag(2-IZCA)ClO4]n (ECPs-1) and [Ag(4-IZCA)ClO4]n (ECPs-2), using structurally isomeric ligands, 1H-imidazole-2-carbohydrazide (2-IZCA) and 1H-imidazole-4-carbohydrazide (4-IZCA). The structures were confirmed using infrared, elemental analysis, and single-crystal X-ray diffraction. Experimental results demonstrate that both ECPs exhibit good thermal stability. However, compared to ECPs-1, ECPs-2 exhibits a lower thermal initial decomposition temperature (Td = 210 °C), lower mechanical sensitivity (IS = 27 J, FS = 84 N), and more concentrated energy output. Although theoretical predictions suggest similar detonation velocities and pressures for both compounds, actual detonation performance tests indicate that ECPs-2 has stronger explosive power and initiating capability, with potential for use as a laser initiator (E = 126 mJ). The simple preparation method and inexpensive starting materials enrich the research on primary explosives.

Differences in Non-suicidal Self-injury Behaviors between Unipolar Depression and Bipolar Depression in Adolescent Outpatients.

OBJECTIVE: Non-suicidal self-injury (NSSI) has a higher prevalence in adolescents with depressive disorders than in community adolescents. This study examined the differences in NSSI behaviors between adolescents with unipolar depression (UD) and those with bipolar depression (BD). METHODS: Adolescents with UD or BD were recruited from 20 general or psychiatric hospitals across China. The methods, frequency, and function of NSSI were assessed by Functional Assessment of Self-Mutilation. The Beck Suicide Ideation Scale was used to evaluate adolescents' suicidal ideation, and the 10-item Kessler Psychological Distress Scale to estimate the anxiety and depression symptoms. RESULTS: The UD group had higher levels of depression (19.16 vs.17.37, F=15.23, P<0.001) and anxiety symptoms (17.73 vs.16.70, F=5.00, P=0.026) than the BD group. Adolescents with BD had a longer course of NSSI than those with UD (2.00 vs.1.00 year, Z=-3.39, P=0.001). There were no statistical differences in the frequency and the number of methods of NSSI between the UD and BD groups. Depression (r=0.408, P<0.01) and anxiety (r=0.391, P<0.01) were significantly and positively related to NSSI frequency. CONCLUSION: Adolescents with BD had a longer course of NSSI than those with UD. More importantly, NSSI frequency were positively and strongly correlated with depression and anxiety symptoms, indicating the importance of adequate treatment of depression and anxiety in preventing and intervening adolescents' NSSI behaviors.

Application of Radiomics in Prognosing Lung Cancer Treated with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors: A Systematic Review and Meta-Analysis.

In the context of non-small cell lung cancer (NSCLC) patients treated with EGFR tyrosine kinase inhibitors (TKIs), this research evaluated the prognostic value of CT-based radiomics. A comprehensive systematic review and meta-analysis of studies up to April 2023, which included 3111 patients, was conducted. We utilized the Quality in Prognosis Studies (QUIPS) tool and radiomics quality scoring (RQS) system to assess the quality of the included studies. Our analysis revealed a pooled hazard ratio for progression-free survival of 2.80 (95% confidence interval: 1.87-4.19), suggesting that patients with certain radiomics features had a significantly higher risk of disease progression. Additionally, we calculated the pooled Harrell's concordance index and area under the curve (AUC) values of 0.71 and 0.73, respectively, indicating good predictive performance of radiomics. Despite these promising results, further studies with consistent and robust protocols are needed to confirm the prognostic role of radiomics in NSCLC.

Structural, vibrational and electronic properties of nitrogen-rich 2,4,6-triazide-1,3,5-triazine under high pressure.

CONTEXT AND RESULTS: 2,4,6-triazide-1,3,5-triazine (TAT) has received widespread attention for its great potential to synthesize or convert to nitrogen-rich high energy density materials (HEDMs). The TAT structure alteration in the compression process up to 30 GPa has characteristics as follows: (a) [N3] groups straighten; (b) [N3] groups gather toward the six-membered C-N heterocycles. At about 5 GPa, Raman peak split at 700 cm-1 was observed both in calculation and in-situ Raman experiment, which is caused by pressure-induced intramolecular stress. Besides, the broad band of the amorphous two-dimensional C=N network (centered at 1630 cm-1) occurred at about 12 GPa. Meantime, the study on electronic features suggests the pressure-induced deformation in TAT molecular structure cause the discontinuous change of band gap at about 4.5 GPa and 8.0 GPa, respectively. COMPUTATIONAL AND THEORETICAL TECHNIQUES: The static compression process of TAT was explored in the range of 0-30 GPa by using dispersion corrected density functional theory (DFT-D) calculations combined with in-situ Raman experiment. The GGA/PBE+G06 method that has less errors than other calculation methods was used to predict the geometry structure, vibrational properties and electronic structure of TAT under pressure.

Preparation of Primary Explosive by Self-assembly of Combustible and Oxidizing Agents under Acid.

In order to further explore the effect of ligands on the performance of primary explosives and gain a deeper understanding of the coordination mechanism, we designed furan-2-carbohydrazide (FRCA), a ligand, by using oxygen-containing heterocycles and carbohydrazide. Then, FRCA and Cu(ClO4)2 were used to synthesize coordination compounds [Cu(FRCA)2(H2O)(ClO4)2]·CH3OH (ECCs-1·CH3OH) and Cu(FRCA)2(H2O)(ClO4)2 (ECCs-1). The structure of the ECCs-1 was confirmed by single-crystal X-ray diffraction, IR and EA characterization. Further experiments on ECCs-1 show that ECCs-1 has good thermal stability, but is sensitive to mechanical stimuli (impact sensitivity = IS = 8 J, friction sensitivity = FS = 20 N). The predicted value of the detonation parameter is DEXPLO 5 = 6.6 km s-1, PEXPLO 5 = 18.8 GPa, but the ignition test, laser test, and lead plate detonation experiment show that ECCs-1 has excellent detonation performance, which is very worthy of attention.

Preparation of High-Energy ECPs by Retaining the Coordination Ability of Carbohydrazide Groups.

In order to preserve the coordinating ability of the hydrazide group, we used retrosynthetic analysis to design and synthesize ligand furan-2,5-dicarbohydrazide and its complex [Cu(FDCA)(H2O)ClO4]n(ClO4)n·nH2O (ECPs-1·H2O). The structure of the product was confirmed by single-crystal X-ray diffraction, infrared spectroscopy, and elemental analysis. The solvent-free target material ECPs-1 exhibited good thermal stability, sensitivity to mechanical stimuli, and excellent explosive properties. Furthermore, it had good potential for laser ignition and comparable detonation power to LA. The simple preparation method and inexpensive starting materials enriched the research on primary explosives.

A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer.

BACKGROUND: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSCLC) with EGFR mutations. Approximately half of the patients with EGFR-mutated NSCLC are treated with EGFR-TKIs and develop disease progression within 1 year. Therefore, the early prediction of tumor progression in patients who receive EGFR-TKIs can facilitate patient management and development of treatment strategies. We proposed a deep learning approach based on both quantitative computed tomography (CT) characteristics and clinical data to predict progression-free survival (PFS) in patients with advanced NSCLC after EGFR-TKI treatment. METHODS: A total of 593 radiomic features were extracted from pretreatment chest CT images. The DeepSurv models for the progression risk stratification of EGFR-TKI treatment were proposed based on CT radiomic and clinical features from 270 stage IIIB-IV EGFR-mutant NSCLC patients. Time-dependent PFS predictions at 3, 12, 18, and 24 months and estimated personalized PFS curves were calculated using the DeepSurv models. RESULTS: The model combining clinical and radiomic features demonstrated better prediction performance than the clinical model. The model achieving areas under the curve of 0.76, 0.77, 0.76, and 0.86 can predict PFS at 3, 12, 18, and 24 months, respectively. The personalized PFS curves showed significant differences (p < 0.003) between groups with good (PFS > median) and poor (PFS < median) tumor control. CONCLUSIONS: The DeepSurv models provided reliable multi-time-point PFS predictions for EGFR-TKI treatment. The personalized PFS curves can help make accurate and individualized predictions of tumor progression. The proposed deep learning approach holds promise for improving the pre-TKI personalized management of patients with EGFR-mutated NSCLC.

Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images.

Early detection increases overall survival among patients with lung cancer. This study formulated a machine learning method that processes chest X-rays (CXRs) to detect lung cancer early. After we preprocessed our dataset using monochrome and brightness correction, we used different kinds of preprocessing methods to enhance image contrast and then used U-net to perform lung segmentation. We used 559 CXRs with a single lung nodule labeled by experts to train a You Only Look Once version 4 (YOLOv4) deep-learning architecture to detect lung nodules. In a testing dataset of 100 CXRs from patients at Taipei Veterans General Hospital and 154 CXRs from the Japanese Society of Radiological Technology dataset, the sensitivity of the AI model using a combination of different preprocessing methods performed the best at 79%, with 3.04 false positives per image. We then tested the AI by using 383 sets of CXRs obtained in the past 5 years prior to lung cancer diagnoses. The median time from detection to diagnosis for radiologists assisted with AI was 46 (3-523) days, longer than that for radiologists (8 (0-263) days). The AI model can assist radiologists in the early detection of lung nodules.

Intelligent Bio-Impedance System for Personalized Continuous Blood Pressure Measurement.

Continuous blood pressure (BP) measurement is crucial for long-term cardiovascular monitoring, especially for prompt hypertension detection. However, most of the continuous BP measurements rely on the pulse transit time (PTT) from multiple-channel physiological acquisition systems that impede wearable applications. Recently, wearable and smart health electronics have become significant for next-generation personalized healthcare progress. This study proposes an intelligent single-channel bio-impedance system for personalized BP monitoring. Compared to the PTT-based methods, the proposed sensing configuration greatly reduces the hardware complexity, which is beneficial for wearable applications. Most of all, the proposed system can extract the significant BP features hidden from the measured bio-impedance signals by an ultra-lightweight AI algorithm, implemented to further establish a tailored BP model for personalized healthcare. In the human trial, the proposed system demonstrates the BP accuracy in terms of the mean error (ME) and the mean absolute error (MAE) within 1.7 ± 3.4 mmHg and 2.7 ± 2.6 mmHg, respectively, which agrees with the criteria of the Association for the Advancement of Medical Instrumentation (AAMI). In conclusion, this work presents a proof-of-concept for an AI-based single-channel bio-impedance BP system. The new wearable smart system is expected to accelerate the artificial intelligence of things (AIoT) technology for personalized BP healthcare in the future.

A High-Voltage TENG-Based Droplet Energy Generator With Ultralow Liquid Consumption.

A solid-liquid triboelectric nanogenerator (TENG) has attracted increasing research interest in relation to the development of regeneration energy based on water resources. The output of solid-liquid TENG remains unsolved, however, because of the low voltage output that impedes wide applications. To this end, in this work we developed a miniaturized microfluidic channel-based TENG device for highly efficient conversion of energy from the transport of a water droplet to an electrical output. We investigated an optimized design in a triboelectric material, the droplet transport and the electrostatic induction layer to provide a high voltage output and stable energy harvesting. The optimized device demonstrated maximum voltage amplitude 102 mV with an ultralow liquid consumption, 0.36 [Formula: see text], resulting in sample-energy conversion 283.33 mV/ [Formula: see text]. This novel device is expected potentially to address the limitations imposed by sample consumption in energy harvesting in the future.

Nickel clusters accelerating hierarchical zinc indium sulfide nanoflowers for unprecedented visible-light hydrogen production.

As a typical two-dimensional (2D) metal chalcogenides and visible-light responsive semiconductor, zinc indium sulfide (ZnIn2S4) has attracted much attention in photocatalysis. However, the high recombination rate of photogenerated electrons and holes seriously limits its performance for hydrogen production. In this work, we report in-situ photodeposition of Ni clusters in hierarchical ZnIn2S4 nanoflowers (Ni/ZnIn2S4) to achieve unprecedented photocatalytic hydrogen production. The Ni clusters not only provide plenty of active sites for reactions as evidenced by in-situ photoluminescence measurement, but also effectively accelerate the separation and migration of the photogenerated electrons and holes in ZnIn2S4. Consequently, the Ni/ZnIn2S4 composites exhibit good stability and reusability with highly enhanced visible-light hydrogen production. In particular, the best Ni/ZnIn2S4 photocatalyst exhibits an unprecedented hydrogen production rate of 22.2 mmol·h-1·g-1, 10.6 times that of the pure ZnIn2S4 (2.1 mmol·h-1·g-1). And its apparent quantum yield (AQY) is as high as 56.14% under 450 nm monochromatic light. Our work here suggests that depositing non-precious Ni clusters in ZnIn2S4 is quite promising for the potential practical photocatalysis in solar energy conversion.

Bio-Impedance Measurement Optimization for High-Resolution Carotid Pulse Sensing.

Continuous hemodynamic monitoring is important for long-term cardiovascular healthcare, especially in hypertension. The impedance plethysmography (IPG) based carotid pulse sensing is a non-invasive diagnosis technique for measuring pulse signals and further evaluating the arterial conditions of the patient such as continuous blood pressure (BP) monitoring. To reach the high-resolution IPG-based carotid pulse detection for cardiovascular applications, this study provides an optimized measurement parameter in response to obvious pulsation from the carotid artery. The influence of the frequency of excitation current, electrode cross-sectional area, electrode arrangements, and physiological site of carotid arteries on IPG measurement resolution was thoroughly investigated for optimized parameters. In this study, the IPG system was implemented and installed on the subject's neck above the carotid artery to evaluate the measurement parameters. The measurement results within 6 subjects obtained the arterial impedance variation of 2137 mΩ using the optimized measurement conditions, including excitation frequency of 50 kHz, a smaller area of 2 cm2, electrode spacing of 4 cm and 1.7 cm for excitation and sensing functions, and location on the left side of the neck. The significance of this study demonstrates an optimized measurement methodology of IPG-based carotid pulse sensing that greatly improves the measurement quality in cardiovascular monitoring.

IPG-based field potential measurement of cultured cardiomyocytes for optogenetic applications.

BACKGROUND: Electrophysiological sensing of cardiomyocytes (CMs) in optogenetic preparations applies various techniques, such as patch-clamp, microelectrode array, and optical mapping. However, challenges remain in decreasing the cost, system dimensions, and operating skills required for these technologies. OBJECTIVE: This study developed a low-cost, portable impedance plethysmography (IPG)-based electrophysiological measurement of cultured CMs for optogenetic applications. METHODS: To validate the efficacy of the proposed sensor, optogenetic stimulation with different pacing cycle lengths (PCL) was performed to evaluate whether the channelrhodopsin-2 (ChR2)-expressing CM beating rhythm measured by the IPG sensor was consistent with biological responses. RESULTS: The experimental results show that the CM field potential was synchronized with external optical pacing with PCLs ranging from 250 ms to 1000 ms. Moreover, irregular fibrillating waveforms induced by CM arrhythmia were detected after overdrive optical pacing. Through the combined evidence of the theoretical model and experimental results, this study confirmed the feasibility of long-term electrophysiological sensing for optogenetic CMs. CONCLUSION: This study proposes an IPG-based sensor that is low-cost, portable, and requires low-operating skills to perform real-time CM field potential measurement in response to optogenetic stimulation. SIGNIFICANCE: This study demonstrates a new methodology for convenient electrophysiological sensing of CMs in optogenetic applications.

Wearable Piezoelectric-Based System for Continuous Beat-to-Beat Blood Pressure Measurement.

Non-invasive continuous blood pressure measurement is an emerging issue that potentially can be applied to cardiovascular disease monitoring and prediction. Recently, many groups have proposed the pulse transition time (PTT) method to estimate blood pressure for long-term monitoring. However, the PTT-based methods for blood pressure estimation are limited by non-specific estimation models and require multiple calibrations. This study aims to develop a low-cost wearable piezoelectric-based system for continuous beat-to-beat blood pressure measurement. The pressure change in the radial artery was extracted by systolic and diastolic feature points in pressure pulse wave (PPW) and the pressure sensitivity of the sensor. The proposed system showed a reliable accuracy of systolic blood pressure (SBP) (mean absolute error (MAE) ± standard deviation (SD) 1.52 ± 0.30 mmHg) and diastolic blood pressure (DBP, MAE ± SD 1.83 ± 0.50), and its performance agreed with standard criteria of MAE within 5 mmHg and SD within ±8 mmHg. In conclusion, this study successfully developed a low-cost, high-accuracy piezoelectric-based system for continuous beat-to-beat SBP and DBP measurement without multiple calibrations and complex regression analysis. The system is potentially suitable for continuous, long-term blood pressure-monitoring applications.

Near-infrared light-activated red-emitting upconverting nanoplatform for T1-weighted magnetic resonance imaging and photodynamic therapy.

Photodynamic therapy (PDT) has increasingly become an efficient and attractive cancer treatment modality based on reactive oxygen species (ROS) that can induce tumor death after irradiation with ultraviolet or visible light. Herein, to overcome the limited tissue penetration in traditional PDT, a novel near-infrared (NIR) light-activated NaScF4: 40% Yb, 2% Er@CaF2 upconversion nanoparticle (rUCNP) is successfully designed and synthesized. Chlorin e6, a photosensitizer and a chelating agent for Mn2+, is loaded into human serum albumin (HSA) that further conjugates onto rUCNPs. To increase the ability to target glioma tumor, an acyclic Arg-Gly-Asp peptide (cRGDyK) is linked to rUCNPs@HSA(Ce6-Mn). This nanoplatform enables efficient adsorption and conversion of NIR light (980 nm) into bright red emission (660 nm), which can trigger the photosensitizer Ce6-Mn complex for PDT and T1-weighted magnetic resonance imaging (T1-weighted MRI) for glioma diagnosis. Our in vitro and in vivo experiments demonstrate that NIR light-activated and glioma tumor-targeted PDT can generate large amounts of intracellular ROS that induce U87 cell apoptosis and suppress glioma tumor growth owing to the deep tissue penetration of irradiated light and excellent tumor-targeting ability. Thus, this nanoplatform holds potential for applications in T1-weighted MRI diagnosis and PDT of glioma for antitumor therapy. STATEMENT OF SIGNIFICANCE: A near-infrared (NIR) light-activated nanoplatform for photodynamic therapy (PDT) was designed and synthesized. The Red-to-Green (R/G) ratio of NaScF4: 40% Yb, 2% Er almost reached 9, a value that was much higher than that of a traditional Yb/Er-codoped upconversion nanoparticle (rUCNP). By depositing a CaF2 shell, the red-emission intensities of the rUCNPs were seven times strong as that of NaScF4: 40% Yb, 2% Er. The enhanced red-emitting rUCNPs could be applied in many fields such as bioimaging, controlled release, and real-time diagnosis. The nanoplatform had a strong active glioma-targeting ability, and all results achieved on subcutaneous glioma demonstrated that our NIR light-activated red-emitting upconverting nanoplatform was efficient for PDT. By loading Ce6-Mn complex into rUCNPs@HSA-RGD, the nanoplatform could be used as a T1-weighted magnetic resonance imaging agent for tumor diagnosis.

pH-Responsive Magnetic Mesoporous Silica-Based Nanoplatform for Synergistic Photodynamic Therapy/Chemotherapy.

By overcoming drug resistance and subsequently enhancing the treatment, the combination therapy of photodynamic therapy (PDT) and chemotherapy has promising potential for cancer treatment. However, the major challenge is how to establish an advanced nanoplatform that can be efficiently guided to tumor sites and can then stably release both chemotherapy drugs and a photosensitizer simultaneously and precisely. In this study, which considered the possibility and targeting efficiency of a magnetic targeting strategy, a novel Fe3O4@mSiO2(DOX)@HSA(Ce6) nanoplatform was successfully built; this platform could be employed as an efficient synergistic antitumor nanoplatform with magnetic guidance for highly specific targeting and retention. Doxorubicin (DOX) molecules were loaded into mesoporous silica with high loading capability, and the mesoporous channels were blocked by a polydopamine coating. Human serum albumin (HSA) was conjugated to the outer surface to increase the biocompatibility and blood circulation time, as well as to provide a vehicle for loading photosensitizer chlorin e6 (Ce6). The sustained release of DOX under acidic conditions and the PDT induced by red light exerted a synergistic inhibitory effect on glioma cells. Our experiments demonstrated that the pH-responsive Fe3O4@mSiO2(DOX)@HSA(Ce6) nanoplatform was guided to the tumor region by magnetic targeting and that the nanoplatform suppressed glioma tumor growth efficiently, implying that the system is a highly promising photodynamic therapy/chemotherapy combination nanoplatform with synergistic effects for cancer treatment.