Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.

PURPOSE: To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. MATERIALS AND METHODS: This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. RESULTS: We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms' performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. CONCLUSION: DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms' diagnostic effectiveness in lung cancer. CLINICAL RELEVANCE STATEMENT: DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. KEY POINTS: Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes.

Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation.

BACKGROUND: Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable. METHODS: We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined. RESULTS: Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions, and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42. CONCLUSION: This meta-analysis demonstrates DL algorithms' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.

Deep Learning for Nasopharyngeal Carcinoma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

Nasopharyngeal carcinoma is a significant health challenge that is particularly prevalent in Southeast Asia and North Africa. MRI is the preferred diagnostic tool for NPC due to its superior soft tissue contrast. The accurate segmentation of NPC in MRI is crucial for effective treatment planning and prognosis. We conducted a search across PubMed, Embase, and Web of Science from inception up to 20 March 2024, adhering to the PRISMA 2020 guidelines. Eligibility criteria focused on studies utilizing DL for NPC segmentation in adults via MRI. Data extraction and meta-analysis were conducted to evaluate the performance of DL models, primarily measured by Dice scores. We assessed methodological quality using the CLAIM and QUADAS-2 tools, and statistical analysis was performed using random effects models. The analysis incorporated 17 studies, demonstrating a pooled Dice score of 78% for DL models (95% confidence interval: 74% to 83%), indicating a moderate to high segmentation accuracy by DL models. Significant heterogeneity and publication bias were observed among the included studies. Our findings reveal that DL models, particularly convolutional neural networks, offer moderately accurate NPC segmentation in MRI. This advancement holds the potential for enhancing NPC management, necessitating further research toward integration into clinical practice.

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.

Glutamine deficiency drives transforming growth factor-ß signaling activation that gives rise to myofibroblastic carcinoma-associated fibroblasts.

Tumor-promoting carcinoma-associated fibroblasts (CAFs), abundant in the mammary tumor microenvironment (TME), maintain transforming growth factor-ß (TGF-ß)-Smad2/3 signaling activation and the myofibroblastic state, the hallmark of activated fibroblasts. How myofibroblastic CAFs (myCAFs) arise in the TME and which epigenetic and metabolic alterations underlie activated fibroblastic phenotypes remain, however, poorly understood. We herein show global histone deacetylation in myCAFs present in tumors to be significantly associated with poorer outcomes in breast cancer patients. As the TME is subject to glutamine (Gln) deficiency, human mammary fibroblasts (HMFs) were cultured in Gln-starved medium. Global histone deacetylation and TGF-ß-Smad2/3 signaling activation are induced in these cells, largely mediated by class I histone deacetylase (HDAC) activity. Additionally, mechanistic/mammalian target of rapamycin complex 1 (mTORC1) signaling is attenuated in Gln-starved HMFs, and mTORC1 inhibition in Gln-supplemented HMFs with rapamycin treatment boosts TGF-ß-Smad2/3 signaling activation. These data indicate that mTORC1 suppression mediates TGF-ß-Smad2/3 signaling activation in Gln-starved HMFs. Global histone deacetylation, class I HDAC activation, and mTORC1 suppression are also observed in cultured human breast CAFs. Class I HDAC inhibition or mTORC1 activation by high-dose Gln supplementation significantly attenuates TGF-ß-Smad2/3 signaling and the myofibroblastic state in these cells. These data indicate class I HDAC activation and mTORC1 suppression to be required for maintenance of myCAF traits. Taken together, these findings indicate that Gln starvation triggers TGF-ß signaling activation in HMFs through class I HDAC activity and mTORC1 suppression, presumably inducing myCAF conversion.

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.

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.

How parental smartphone addiction affects adolescent smartphone addiction: The effect of the parent-child relationship and parental bonding.

BACKGROUND: Adolescent smartphone addiction (ASA) has fueled concerns worldwide regarding the negative health effects. This study aimed to examine whether parental smartphone addiction (PSA) affected ASA, and evaluated the mediating role of the parent-child relationship and the moderating role of parental bonding in the effect from PSA to ASA, among a Chinese sample of parent-child pairs. METHODS: A large-scale cross-sectional survey was conducted among 10- to 15-year-old students and their parents. ASA and PSA were assessed by Mobile Phone Addiction Index (MPAI). The parent-child relationship was evaluated by Child-Parent Relationship Scale-Short Form (CPRS-SF), and parental bonding was estimated by Parental Bonding Instrument (PBI). Conditional process model was used to examine the relationship between PSA and ASA, as well as the mediating effect of parent-child relationship and the moderating effect of parental bonding. RESULTS: A total of 9515 adolescents and their parents completed the online survey. PSA significantly positively predicted ASA (B = 0.488, p < 0.001). The parent-child relationship negatively mediated the association from PSA to ASA (B = -0.321, p < 0.001). Parental overprotection moderated the indirect path from PSA to ASA through the parent-child relationship (B = -0.016, p < 0.001), but parental care had not any moderation (B = -0.005, p > 0.05). Specifically, parental overprotection had a positive moderating effect on the second half mediation path. The indirect effect of PSA on ASA through parent-child relationship was greater in higher overprotection than in lower. LIMITATIONS: Cross-sectional study of self-administrated questionnaires. CONCLUSIONS: Adolescents had a higher tendency toward smartphone addiction when their parents excessively used smartphones. The findings highlighted the essential role of parent-child relationship and parental bonding in the association from PSA to ASA.

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.

Involvement of METTL3/m6Adenosine and TGFß/Smad3 signaling on Tenon's fibroblasts and in a rabbit model of glaucoma surgery.

Glaucoma filtration surgery (GFS) is a classic operation for the treatment of glaucoma, which is the second leading cause of blindness, and scar formation caused by excessive human Tenon's capsule fibroblasts (HTFs) activation is responsible for surgery failure. However, the mechanism underlying excessive HTFs activation is largely unknown. Studies have revealed that N6-methyladenosine (m6A), which is one of the most common posttranscriptional modifications, plays an important role in multiple types of cellular processes. First, we isolated and identified primary HTFs and found that transforming growth factor-ß1 (TGF-ß1) enhanced cell viability and promoted cell proliferation and extracellular matrix (ECM) deposition in HTFs. We subsequently found that TGF-ß1 elevated the quantity of m6A and promoted the expression of m6A "writers", in the process from DNA to RNA, adenylate was methylated at the sixth N position by methylases methyltransferase-like 3 (METTL3). Furthermore, we demonstrated that METTL3 repression inhibited the promotion of cell viability, proliferation and ECM deposition in HTFs treated with TGF-ß1. We then illustrated that increased METTL3 played a role by promoting Smad3 in TGF-ß1-induced HTFs. We subsequently demonstrated that the METTL3/Smad3 regulatory axis was aberrantly expressed in the rabbit model of GFS. Thus, our study reveals that METTL3 indeed plays a role in modulating Smad3 in TGF-ß1-induced HTFs and further provides novel theoretical strategies based on METTL3 for the inhibition of scar formation after GFS.

Untargeted metabolomics analysis by gas chromatography/time-of-flight mass spectrometry of human serum from methamphetamine abusers.

Methamphetamine (METH) abuse has become a global public health problem. However, the potential mechanisms involving METH-induced metabolic disorders have thus far remained poorly understood. Metabolomics can provide a clue for the cause of apparent changes and consequently be used to investigate the METH-induced dysregulation of metabolite expression and the mechanism of metabolic disorder mechanism. This laboratory investigation included 80 METH abusers and 80 healthy people. The serum metabolites were detected and analysed by gas chromatography/time-of-flight mass spectrometry. Raw data were processed with the software MS DIAL, which includes deconvolution, peak alignment and compound identification. The data matrix was processed by univariate and multivariate analyses for significant metabolite screening with the criteria of variable importance in projection values > 1, fold change > 1.5 and the t test (p value < 0.05). Significant differences in 16 metabolites (deoxycholic acid, cholic acid, hydroxylamine, etc.) in serum were found between the METH abuse group and the control group. Energy metabolic pathways and several amino acid metabolic pathways (alanine, aspartic acid and glutamate metabolism and tryptophan metabolism) were primarily involved. Further analysis indicated that the area under the receiver operating characteristic curve (AUC) was 0.998 for these 16 metabolites. Among the metabolites, three carbohydrates (d-ribose, cellobiose and maltotriose) had an AUC of 0.975, which were determined as potential markers of abuse. We observed metabolic disturbances in METH abusers, particularly perturbation in energy metabolism and amino acid metabolism, which can provide new insights into the search for biomarkers and the mechanisms underlying the adverse effects of METH on human health.

Improved Self-Supporting and Ceramifiable Properties of Ceramifiable EPDM Composites by Adding Aramid Fiber.

Ceramifiable ethylene propylene diene monomer (EPDM) composites with fiber network structures were prepared by using aramid fiber (AF), ammonium polyphosphate (APP), and silicate glass frits (SGF). The effect of AF on the curing characteristic of the ceramifiable EPDM composites was studied. The morphology of AF in the composites system was observed by optical microscopy (OM) and scanning electron microscope (SEM). The effects of the observed AF network structures on the solvent resistance, mechanical properties, ablative resistance, self-supporting property, and ceramifiable properties of the composites were investigated. Results suggested that the existence of the AF network structure improved the vulcanization properties, solvent resistance, thermal stability, and ablative resistance of the EPDM composites. An excellent self-supporting property of the EPDM composites was obtained by combining the formation of the AF network and the formation of crystalline phases at higher temperature (above 600 °C). The thermal shrinkage performance of AF and the increased thermal stability of the EPDM composites improved the ceramifiable properties of the EPDM composites.

Nanosized carbon black exposure induces neural injury: effects on nicotinamide adenine dinucleotide phosphate oxidases and endoplasmic reticulum stress.

Carbon black in ambient air is believed to be the cause of many diseases; however, its potential neural toxicity and the underlying mechanisms remain poorly understood. The present study is to evaluate the toxic effects of carbon black nanoparticles, Printex 90, on the neural cell line PC-12. The study revealed that Printex 90 treatment significantly decreased cell viability, accompanied by an enormous increase in reactive oxygen species generation and a decrease in ATP. Additionally, NOX2 and NOX4, 4-hydroxynonenal, endoplasmic reticulum (ER) stress marker proteins (IRE-1α, ATF-6, GRP78, PERK and the downstream target protein CHOP) and antioxidative enzymes (glutathione and superoxide dismutase) were evaluated. It showed that Printex 90 significantly upregulated 4-hydroxynonenal, NOX2 and NOX4 expression, and the levels, or activity, of glutathione and superoxide dismutase, were markedly reduced. For the ER stress-associated proteins, Printex 90 induced a significant increase of IRE-1α, ATF-6, GRP78, p-PERK and CHOP expression. Collectively, these results demonstrate that NOX and ER stress are involved in Printex 90-mediated neural damage. Therefore, decreased ER stress and NOX-derived reactive oxygen species generation may provide compensatory protective effects and attenuate Printex 90-induced neural injury.

Tubular Micro/Nanomotors: Propulsion Mechanisms, Fabrication Techniques and Applications.

Micro/nanomotors are self-propelled machines that can convert various energy sources into autonomous movement. With the great advances of nanotechnology, Micro/Nanomotors of various geometries have been designed and fabricated over the past few decades. Among them, the tubular Micro/Nanomotors have a unique morphology of hollow structures, which enable them to possess a strong driving force and easy surface functionalization. They are promising for environmental and biomedical applications, ranging from water remediation, sensing to active drug delivery and precise surgery. This article gives a comprehensive and clear review of tubular Micro/Nanomotors, including propulsion mechanisms, fabrication techniques and applications. In the end, we also put forward some realistic problems and speculate about corresponding methods to improve existing tubular Micro/Nanomotors.

Investigation into Cellular Glycolysis for the Mechanism Study of Energy Metabolism Disorder Triggered by Lipopolysaccharide.

Lipopolysaccharide (LPS) is the main virulence factor of Gram-negative bacteria, which can incite inflammation in tissues by inducing cells to secrete a variety of proinflammatory mediators, including cytokines, chemokines, interleukins, and prostaglandins. Herein, we chose LPS as an inducer to establish an inflammatory model of HeLa cells, and explored the effects of LPS on energy metabolism. We treated HeLa cells with different concentrations (0, 0.4, 1.0, 2.0, 4.0, and 6.0 µg/mL) of LPS for 24 h, and explored its effects on intercellular adenosine triphosphate (ATP) levels, intercellular nitrous oxide (NO) content, mitochondrial functions, and enzyme activities related to energy metabolism. Furthermore, we used metabonomics to study the metabolites that participated in energy metabolism. We found a positive correlation between LPS concentrations and intracellular ATP levels. In addition, LPS increased intracellular NO production, altered mitochondrial functions, strengthened glycolytic enzyme activities, and changed metabolites related to energy metabolism. Hence, in this study, we showed that LPS can strengthen energy metabolism by enhancing glycolysis, which could be used as an early diagnostic biomarker or a novel therapeutic target for inflammation-associated cancers.

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.