Neural stem cells (NSCs), capable of ischemia‐homing, regeneration, and differentiation, exert strong therapeutic potentials in treating ischemic stroke, but the curative effect is limited in the ...harsh microenvironment of ischemic regions rich in reactive oxygen species (ROS). Gene transfection to make NSCs overexpress brain‐derived neurotrophic factor (BDNF) can enhance their therapeutic efficacy; however, viral vectors must be used because current nonviral vectors are unable to efficiently transfect NSCs. The first polymeric vector, ROS‐responsive charge‐reversal poly(2‐acryloyl)ethyl(p‐boronic acid benzyl)diethylammonium bromide (B‐PDEA), is shown here, that mediates efficient gene transfection of NSCs and greatly enhances their therapeutics in ischemic stroke treatment. The cationic B‐PDEA/DNA polyplexes can effectively transfect NSCs; in the cytosol, the B‐PDEA is oxidized by intracellular ROS into negatively charged polyacrylic acid, quickly releasing the BDNF plasmids for efficient transcription and secreting a high level of BDNF. After i.v. injection in ischemic stroke mice, the transfected NSCs (BDNF‐NSCs) can home to ischemic regions as efficiently as the pristine NSCs but more efficiently produce BDNF, leading to significantly augmented BDNF levels, which in turn enhances the mouse survival rate to 60%, from 0% (nontreated mice) or ≈20% (NSC‐treated mice), and enables more rapid and superior functional reconstruction.
The first nonviral gene carrier, reactive‐oxygen‐species‐responsive charge‐reversal poly(2‐acryloyl)‐ethyl(p‐boronic acid benzyl)diethylammonium bromide (B‐PDEA), is shown to mediate efficient gene transfection to neural stem cells (NSCs). When BDNF gene plasmids are used, the transfected NSCs homing to the ischemic regions increase animal survival and reconstruct functions.
Current urban traffic congestion costs are increasing on account of the population growth of cities and increasing numbers of vehicles. Many cities are adopting intelligent transportation systems ...(ITSs) to improve traffic efficiency. ITSs can be used for monitoring traffic congestion using detectors, such as calculating an estimated time of arrival or suggesting a detour route. In this paper, we propose an urban traffic flow prediction system using a multifactor pattern recognition model, which combines Gaussian mixture model clustering with an artificial neural network. This system forecasts traffic flow by combining road geographical factors and environmental factors with traffic flow properties from ITS detectors. Experimental results demonstrate that the proposed model produces more reliable predictions compared with existing methods.
Background:
Early-stage diagnosis and treatment can improve survival rates of liver cancer patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for differential ...diagnosis of liver tumors. However, MRI diagnosis is affected by subjective experience, so deep learning may supply a new diagnostic strategy. We used convolutional neural networks (CNNs) to develop a deep learning system (DLS) to classify liver tumors based on enhanced MR images, unenhanced MR images, and clinical data including text and laboratory test results.
Methods:
Using data from 1,210 patients with liver tumors (
N
= 31,608 images), we trained CNNs to get seven-way classifiers, binary classifiers, and three-way malignancy-classifiers (Model A-Model G). Models were validated in an external independent extended cohort of 201 patients (
N
= 6,816 images). The area under receiver operating characteristic (ROC) curve (AUC) were compared across different models. We also compared the sensitivity and specificity of models with the performance of three experienced radiologists.
Results:
Deep learning achieves a performance on par with three experienced radiologists on classifying liver tumors in seven categories. Using only unenhanced images, CNN performs well in distinguishing malignant from benign liver tumors (AUC, 0.946; 95% CI 0.914–0.979 vs. 0.951; 0.919–0.982,
P
= 0.664). New CNN combining unenhanced images with clinical data greatly improved the performance of classifying malignancies as hepatocellular carcinoma (AUC, 0.985; 95% CI 0.960–1.000), metastatic tumors (0.998; 0.989–1.000), and other primary malignancies (0.963; 0.896–1.000), and the agreement with pathology was 91.9%.These models mined diagnostic information in unenhanced images and clinical data by deep-neural-network, which were different to previous methods that utilized enhanced images. The sensitivity and specificity of almost every category in these models reached the same high level compared to three experienced radiologists.
Conclusion:
Trained with data in various acquisition conditions, DLS that integrated these models could be used as an accurate and time-saving assisted-diagnostic strategy for liver tumors in clinical settings, even in the absence of contrast agents. DLS therefore has the potential to avoid contrast-related side effects and reduce economic costs associated with current standard MRI inspection practices for liver tumor patients.
Background
The surgical approach and prognosis for invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA) of the lung differ. However, they both manifest as identical ground‐glass ...nodules (GGNs) in computed tomography images, and no effective method exists to discriminate them.
Methods
We developed and validated a three‐dimensional (3D) deep transfer learning model to discriminate IAC from MIA based on CT images of GGNs. This model uses a 3D medical image pre‐training model (MedicalNet) and a fusion model to build a classification network. Transfer learning was utilized for end‐to‐end predictive modeling of the cohort data of the first center, and the cohort data of the other two centers were used as independent external validation data. This study included 999 lung GGN images of 921 patients pathologically diagnosed with IAC or MIA at three cohort centers.
Results
The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). The model had high diagnostic efficacy for the training and validation groups (accuracy: 89%, sensitivity: 95%, specificity: 84%, and AUC: 95% in the training group; accuracy: 88%, sensitivity: 84%, specificity: 93%, and AUC: 92% in the internal validation group; accuracy: 83%, sensitivity: 83%, specificity: 83%, and AUC: 89% in one external validation group; accuracy: 78%, sensitivity: 80%, specificity: 77%, and AUC: 82% in the other external validation group).
Conclusions
Our 3D deep transfer learning model provides a noninvasive, low‐cost, rapid, and reproducible method for preoperative prediction of IAC and MIA in lung cancer patients with GGNs. It can help clinicians to choose the optimal surgical strategy and improve the prognosis of patients.
This paper reports the development and validation of a three‐dimensional (3D) deep transfer learning model for the discrimination of two ground‐glass nodule (GGN) cancer subtypes, invasive adenocarcinoma (IAC), and minimally invasive adenocarcinoma (MIA), from computed tomography (CT) images.
Modifications at polypeptide side chains have been found to affect conformational and properties, which is a vital strategy to prepare functional polypeptides. This contribution describes the ...synthesis of homo and co-polypeptides containing unsaturated bonds and thioether moieties, poly(
S
-allylcysteine) (PAC). The pendant vinyl groups provide an active binding site which are able to be further modified with cysteine by thiol-ene click reaction. The sulfoxide from thioether oxidation bestows the polypeptide water solubility and enzymatic catalytic sites. The self-assembly micelles of PEG-
b
-PAC are ruptured along with oxidation to release the Nile red payloads. Moreover, poly(
S
-allyl-cysteine sulfoxide),
aka.
polyalliin, is the oxidation product of PAC which undergoes side chain cleavage catalyzed by alliinase enzyme. The polypeptides are biocompatible as confirmed by cell viability assays and may find applications in drug delivery, biosensing and nanoreactor.
Single-walled carbon nanotubes (SWCNTs) are broadly used for various biomedical applications such as drug delivery, in vivo imaging, and cancer photothermal therapy due to their unique physiochemical ...properties. However, once they enter the cells, the effects of SWCNTs on the intracellular organelles and macromolecules are not comprehensively understood. Cytochrome c (Cyt c), as a key component of the electron transport chain in mitochondria, plays an essential role in cellular energy consumption, growth, and differentiation. In this study, we found the mitochondrial membrane potential and mitochondrial oxygen uptake were greatly decreased in human epithelial KB cells treated with SWCNTs, which accompanies the reduction of Cyt c. SWCNTs deoxidized Cyt c in a pH-dependent manner, as evidenced by the appearance of a 550 nm characteristic absorption peak, the intensity of which increased as the pH increased. Circular dichroism measurement confirmed the pH-dependent conformational change, which facilitated closer association of SWCNTs with the heme pocket of Cyt c and thus expedited the reduction of Cyt c. The electron transfer of Cyt c is also disturbed by SWCNTs, as measured with electron spin resonance spectroscopy. In conclusion, the redox activity of Cyt c was affected by SWCNTs treatment due to attenuated electron transfer and conformational change of Cyt c, which consequently changed mitochondrial respiration of SWCNTs-treated cells. This work is significant to SWCNTs research because it provides a novel understanding of SWCNTs' disruption of mitochondria function and has important implications for biomedical applications of SWCNTs.
Rectal cancer (RC) is the third most commonly diagnosed cancer and has a high risk of mortality, although overall survival rates have improved. Preoperative assessments and predictions, including ...risk stratification, responses to therapy, long-term clinical outcomes, and gene mutation status, are crucial to guide the optimization of personalized treatment strategies. Radiomics is a novel approach that enables the evaluation of the heterogeneity and biological behavior of tumors by quantitative extraction of features from medical imaging. As these extracted features cannot be captured by visual inspection, the field holds significant promise. Recent studies have proved the rapid development of radiomics and validated its diagnostic and predictive efficacy. Nonetheless, existing radiomics research on RC is highly heterogeneous due to challenges in workflow standardization and limitations of objective cohort conditions. Here, we present a summary of existing research based on computed tomography and magnetic resonance imaging. We highlight the most salient issues in the field of radiomics and analyze the most urgent problems that require resolution. Our review provides a cutting-edge view of the use of radiomics to detect and evaluate RC, and will benefit researchers dedicated to using this state-of-the-art technology in the era of precision medicine.
Four porous coordination networks (PCNs), {Zn3O(H2O)3(adc)3·2(C2H6NH2)·2(DMF)·3(H2O)} n (PCN-131), Zn2(DMA)2(adc)2·2(DMA)} n (PCN-132), {Zn3O(DMF)(adc)3(4,4′-bpy)·2(C2H6NH2)·S} n (PCN-131′), and ...{Zn(adc)(4,4′-bpy)0.5·S} n (PCN-132′), have been synthesized by the assembly of anthrancene-9,10-dicarboxylic acid (H2adc) with Zn(II) under different reaction conditions, including modifications of reactant ratio, acidity variations, and the use of a secondary ligand. Single-crystal X-ray diffraction studies reveal that PCN-131, obtained from the dimethylformamide (DMF) solution under acid condition, has a three-dimentional (3D) framework structure with one-dimensional (1D) honeycomb channels. PCN-132 isolated from dimethylacetamide (DMA) solution without adding acid in synthesis is a two-dimensional (2D) layer compound. By employing 4,4′-bipyridyl (4,4′-bpy) as a secondary ligand, PCN-131′ and PCN-132′ were synchronously synthesized as a mixture outcome with more PCN-131′ than PCN-132′. In PCN-131′, 4,4′-bpy acting as a secondary ligand is arranged inside the honeycomb channel of the 3D PCN-131, resulting in an effective improvement of thermal stability of the network, while in PCN-132′, 4,4′-bpy ligands link 2D layers of PCN-132 to form a pillared-layer 3D framework. Gas adsorption has been performed for selected materials. The results show that the framework of PCN-131 is thermally unstable after removing the solvent molecules coordinated to their metal sites. While PCN-131′ is stable for gas uptake, with an evaluated Langmuir surface area of 199.04 m2 g–1, it shows a selective adsorption of CO2 over CH4.
Ten antifouling 14-membered resorcylic acid lactones 1-10 were isolated previously with low or trace natural abundance from the zoanthid-derived Cochliobolus lunatus fungus. Further optimization of ...fermentation conditions led to the isolation of two major natural compounds 7 and 8 with multi-gram quantities. By one or two steps, we semisynthesized the six trace natural compounds 1-6 and a series of derivatives 11-27 of compounds 7 and 8 with high yields (65-95%). Compounds 11-13 showed strong antiplasmodial activity against Plasmodium falciparum with IC
values of 1.84, 8.36, and 6.95 μM, respectively. Very importantly, 11 and 12 were non-toxic with very safety and high therapeutic indices (CC
/IC
> 180), and thus representing potential promising leads for antiplasmodial drug discovery. Furthermore, 11 was the only compound showed obvious antileishmanial activity against Leishmania donovani with an IC
value of 9.22 μM. Compounds 11 and 12 showed the values of IC
at 11.9 and 17.2 μM against neglected Chagas' disease causing Trypanosoma cruzi, respectively.
To establish and verify the ability of a radiomics prediction model to distinguish invasive adenocarcinoma (IAC) and minimal invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs).
...We retrospectively analyzed 118 lung GGN images and clinical data from 106 patients in our hospital from March 2016 to April 2019. All pathological classifications of lung GGN were confirmed as IAC or MIA by two pathologists. R language software (version 3.5.1) was used for the statistical analysis of the general clinical data. ITK-SNAP (version 3.6) and A.K. software (Analysis Kit, American GE Company) were used to manually outline the regions of interest of lung GGNs and collect three-dimensional radiomics features. Patients were randomly divided into training and verification groups (ratio, 7:3). Random forest combined with hyperparameter tuning was used for feature selection and prediction modeling. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate model prediction efficacy. The calibration curve was used to evaluate the calibration effect.
There was no significant difference between IAC and MIA in terms of age, gender, smoking history, tumor history, and lung GGN location in both the training and verification groups (P>0.05). For each lung GGN, the collected data included 396 three-dimensional radiomics features in six categories. Based on the training cohort, nine optimal radiomics features in three categories were finally screened out, and a prediction model was established. We found that the training group had a high diagnostic efficacy accuracy, sensitivity, specificity, and AUC of the training group were 0.89 (95%CI, 0.73 - 0.99), 0.98 (95%CI, 0.78 - 1.00), 0.81 (95%CI, 0.59 - 1.00), and 0.97 (95%CI, 0.92-1.00), respectively; those of the validation group were 0.80 (95%CI, 0.58 - 0.93), 0.82 (95%CI, 0.55 - 1.00), 0.78 (95%CI, 0.57 - 1.00), and 0.92 (95%CI, 0.83 - 1.00), respectively. The model calibration curve showed good consistency between the predicted and actual probabilities.
The radiomics prediction model established by combining random forest with hyperparameter tuning effectively distinguished IAC from MIA presenting as GGNs and represents a noninvasive, low-cost, rapid, and reproducible preoperative prediction method for clinical application.
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