A feature refinement and adaptive generative adversarial network for thermal infrared image colorization.

Colorizing thermal infrared images poses a significant challenge as current methods struggle with issues such as unrealistic color saturation and limited texture. To address these challenges, we propose the Feature Refinement and Adaptive Generative Adversarial Network (FRAGAN). Our approach enhances the detailed, semantic, and contextual capabilities of image coloring by combining multi-level interactions that integrate the lost detailed information from the encoding stage with the semantic information from the decoding stage. Additionally, we introduce the Residual Feature Refinement Module (RFRM) to improve both the accuracy and generalization ability of the model, thereby elevating the quality of colorization results. The Feature Adaptation Module (FAM) is employed to mitigate sub-region information loss during downsampling. Furthermore, we introduce the Trinity Attention Module (TAM) to accurately capture the spatial and channel-wise interaction features of local semantic information. Extensive experimentation on the KAIST dataset and the FLIR dataset demonstrates the superiority of our proposed FRAGAN methodology, surpassing both the performance metrics and visual quality of current state-of-the-art methods. The colorized images generated by our proposed FRAGAN exhibit enhanced clarity and realism. Our code and models are available at GitHub.

Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection.

Infrared pedestrian target detection is affected by factors such as the low resolution and contrast of infrared pedestrian images, as well as the complexity of the background and the presence of multiple targets occluding each other, resulting in indistinct target features. To address these issues, this paper proposes a method to enhance the accuracy of pedestrian target detection by employing contour information to guide multi-scale feature detection. This involves analyzing the shapes and edges of the targets in infrared images at different scales to more accurately identify and differentiate them from the background and other targets. First, we propose a preprocessing method to suppress background interference and extract color information from visible images. Second, we propose an information fusion residual block combining a U-shaped structure and residual connection to form a feature extraction network. Then, we propose an attention mechanism based on a contour information-guided approach to guide the network to extract the depth features of pedestrian targets. Finally, we use the clustering method of mIoU to generate anchor frame sizes applicable to the KAIST pedestrian dataset and propose a hybrid loss function to enhance the network's adaptability to pedestrian targets. The extensive experimental results show that the method proposed in this paper outperforms other comparative algorithms in pedestrian detection, proving its superiority.

Co-expression module analysis reveals high expression homogeneity for both coding and non-coding genes in sepsis.

Sepsis is a life-threatening condition characterized by a harmful host response to infection with organ dysfunction. Annually about 20 million people are dead owing to sepsis and its mortality rates is as high as 20%. However, no studies have been carried out to investigate sepsis from the system biology point of view, as previous research predominantly focused on individual genes without considering their interactions and associations. Here, we conducted a comprehensive exploration of genome-wide expression alterations in both mRNAs and long non-coding RNAs (lncRNAs) in sepsis, using six microarray datasets. Co-expression networks were conducted to identify mRNA and lncRNA modules, respectively. Comparing these sepsis modules with normal modules, we observed a homogeneous expression pattern within the mRNA/lncRNA members, with the majority of them displaying consistent expression direction. Moreover, we identified consistent modules across diverse datasets, consisting of 20 common mRNA members and two lncRNAs, namely CHRM3-AS2 and PRKCQ-AS1, which are potential regulators of sepsis. Our results reveal that the up-regulated common mRNAs are mainly involved in the processes of neutrophil mediated immunity, while the down-regulated mRNAs and lncRNAs are significantly overrepresented in T-cell mediated immunity functions. This study sheds light on the co-expression patterns of mRNAs and lncRNAs in sepsis, providing a novel perspective and insight into the sepsis transcriptome, which may facilitate the exploration of candidate therapeutic targets and molecular biomarkers for sepsis.

Improved Thermal Infrared Image Super-Resolution Reconstruction Method Base on Multimodal Sensor Fusion.

When traditional super-resolution reconstruction methods are applied to infrared thermal images, they often ignore the problem of poor image quality caused by the imaging mechanism, which makes it difficult to obtain high-quality reconstruction results even with the training of simulated degraded inverse processes. To address these issues, we proposed a thermal infrared image super-resolution reconstruction method based on multimodal sensor fusion, aiming to enhance the resolution of thermal infrared images and rely on multimodal sensor information to reconstruct high-frequency details in the images, thereby overcoming the limitations of imaging mechanisms. First, we designed a novel super-resolution reconstruction network, which consisted of primary feature encoding, super-resolution reconstruction, and high-frequency detail fusion subnetwork, to enhance the resolution of thermal infrared images and rely on multimodal sensor information to reconstruct high-frequency details in the images, thereby overcoming limitations of imaging mechanisms. We designed hierarchical dilated distillation modules and a cross-attention transformation module to extract and transmit image features, enhancing the network's ability to express complex patterns. Then, we proposed a hybrid loss function to guide the network in extracting salient features from thermal infrared images and reference images while maintaining accurate thermal information. Finally, we proposed a learning strategy to ensure the high-quality super-resolution reconstruction performance of the network, even in the absence of reference images. Extensive experimental results show that the proposed method exhibits superior reconstruction image quality compared to other contrastive methods, demonstrating its effectiveness.

Infrared and Visible Image Fusion for Highlighting Salient Targets in the Night Scene.

The goal of infrared and visible image fusion in the night scene is to generate a fused image containing salient targets and rich textural details. However, the existing image fusion methods fail to take the unevenness of nighttime luminance into account. To address the above issue, an infrared and visible image fusion method for highlighting salient targets in the night scene is proposed. First of all, a global attention module is designed, which rescales the weights of different channels after capturing global contextual information. Second, the loss function is divided into the foreground loss and the background loss, forcing the fused image to retain rich texture details while highlighting the salient targets. Finally, a luminance estimation function is introduced to obtain the trade-off control parameters of the foreground loss function based on the nighttime luminance. It can effectively highlight salient targets by retaining the foreground information from the source images. Compared with other advanced methods, the experimental results adequately demonstrate the excellent fusion performance and generalization of the proposed method.

Co-Delivery of 5-Fluorouracil and Paclitaxel in Mitochondria-Targeted KLA-Modified Liposomes to Improve Triple-Negative Breast Cancer Treatment.

In this research, KLA-modified liposomes co-loaded with 5-fluorouracil and paclitaxel (KLA-5-FU/PTX Lps) were developed, and their antitumor activity against triple-negative breast cancer (TNBC) was evaluated. KLA-5-FU/PTX Lps were prepared using the thin-film dispersion method, and their in vitro anticancer efficacy was assessed in human breast cancer cells (MDA-MB-231). An MDA-MB-231 tumor-bearing mouse model was also established to evaluate their antitumor efficacy in vivo. KLA-5-FU/PTX Lps showed enhanced cytotoxicity against MDA-MB-231 cells, improved drug delivery to mitochondria, and induced mitochondria-mediated apoptosis. The modified liposomes also showed favorable antitumor activity in vivo due to their strong ability to target tumors and mitochondria. The liposomes showed no obvious systemic toxicity. Our results suggest that KLA-5-FU/PTX Lps are a promising system with which to target the delivery of antitumor drugs to mitochondria as a treatment for TNBC.

Clinical Experience of Personalized Phage Therapy Against Carbapenem-Resistant Acinetobacter baumannii Lung Infection in a Patient With Chronic Obstructive Pulmonary Disease.

Overuse of antibiotics in clinical medicine has contributed to the global spread of multidrug-resistant bacterial pathogens, including Acinetobacter baumannii. We present a case of an 88-year-old Chinese man who developed hospital-acquired pneumonia caused by carbapenem-resistant A. baumannii (CRAB). A personalized lytic pathogen-specific single-phage preparation was nebulized to the patient continuously for 16 days in combination with tigecycline and polymyxin E. The treatment was well tolerated and resulted in clearance of the pathogen and clinical improvement of the patient's lung function.

The pH-triggered drug release and simultaneous carrier decomposition of effervescent SiO2-drug-Na2CO3 composite nanoparticles: to improve the antitumor activity of hydrophobic drugs.

To achieve a better release effect of hydrophobic drugs and spontaneous nanocarrier disintegration by dissolution as well as the CO2 production of Na2CO3 further, improving the therapeutic effect of hydrophobic drugs, and thereby avoiding the accumulation of the nanocarrier in vivo to produce organ toxicity, effervescent SiO2-drug-Na2CO3 composite nanoparticles (ESNs) were prepared in this study using a tetraethyl orthosilicate hydrolysis method. Sodium carbonate was used as the effervescent disintegrant to respond to the acidic microenvironment of the tumor. The properties of ESNs were assessed and TEM images were taken to verify the self-disintegration characteristics of nanocarrier materials. The in vitro anticancer efficacy of ESNs was evaluated in human breast cancer MCF-7 cells. ESNs loaded with hydrophobic drugs were successfully constructed, and showed high entrapment efficiency and drug loading. The nanocarrier successfully achieved self-disintegration in a PBS environment of pH value at 5.0, and showed excellent antitumor effect in vitro. ESNs can effectively load hydrophobic drugs and achieve self-disintegration, while avoiding toxicity from the accumulation of the nanocarrier. These results suggest that ESNs are a promising drug delivery system capable of maximizing the anticancer therapeutic efficacy and minimizing the systemic toxicity.

Importance of respiratory airway management as well as psychological and rehabilitative treatments to COVID-19 patients.

The clinical therapy for severe 2019 coronavirus disease (i.e., COVID-19) sufferers is relatively challenging. Herein, the processes involving salvage of a critical COVID-19 patient were retrospectively analyzed. The condition of an obese female critical COVID-19 sufferer progressively worsened in the initial period after admission. According to her symptoms and examination reports, endotracheal intubation and mechanical ventilation were timely conducted and meanwhile high-dose sedatives and analgesics were administrated. In the later therapeutic phase, however, sedative and analgesic dosages were gradually reduced, and psychological and rehabilitative therapies were conducted, concomitantly with enhancement of airway care to facilitate sputum expectoration. Eventually, the endotracheal tube was feasibly removed after intubation for 18 days and subsequently replaced with noninvasive ventilation and a high-flow nasal cannula oxygen therapy. Intensive airway care alongside psychological and rehabilitative therapies can shorten the mechanical ventilation time and improve the prognosis of COVID-19 sufferers.

Simulation-based comparison of Biopharmaceutics Classification System and drug structure.

Background: The Biopharmaceutics Classification System (BCS), which classifies bioactive molecules based on solubility and permeability, is widely used to guide new drug development and drug formulation, as well as predict pharmacokinetics. Here we performed computer simulations to study correlations between a molecule's structure and its BCS classification. Methods: A total of 411 small molecules were assigned to BCS categories based on published drug data, and their Pybel-FP4 fingerprints were extrapolated. The information gain (IG) of each fingerprint was calculated and its characteristic structure analyzed. IG was calculated using multiple thresholds, and results were verified using support vector machine prediction, while taking into account the dose coefficient (0-0.1, 0.1-1, or >1). Structural functional features common to fingerprints of compounds in each type of BCS class were determined using computer simulations. Results: BCS classes III and IV appear to share several structural and functional characteristics, including secondary aliphaticamine, Michael acceptor, isothiourea, and sulfonamide sulfonic derivatives. Conclusion: We demonstrate that our approach can correlate characteristic fingerprints of small-molecule drugs with BCS classifications, which may help guide the development and optimization of new drugs.

Decreasing acute toxicity and suppressing colorectal carcinoma using Sorafenib-loaded nanoparticles.

Objective: A polymer-based nanoparticle was constructed to target sorafenib delivery to colorectal carcinoma cells and decrease the side effects of the drug.Methods: Sorafenib-loaded nanoparticles (S-NPs) based on PEG-PLGA were prepared using a double emulsion solvent evaporation method. The properties of S-NPs were evaluated and then their effects on the viability of colorectal cancer cells and normal human cells were assessed. The mechanism of S-NP internalization was explored using cellular uptake assays and in vitro fluorescence confocal imaging. Acute toxicity of sorafenib on its own or within S-NPs was assessed in mice.Results: S-NPs showed high drug loading and entrapment efficiencies, they did not cause extensive hemolysis, and they efficiently inhibited growth of colorectal cancer cell lines and human umbilical vein endothelial cells. S-NPs showed lower acute toxicity than the free drug.Conclusions: Loading sorafenib into nanoparticles can enhance its uptake by colorectal cancer cells and decrease its acute toxicity.