Immunostimulatory CKb11 gene combined with immune checkpoint PD-1/PD-L1 blockade activates immune response and simultaneously overcomes the immunosuppression of cancer.

Immunosuppression tumor microenvironment (TME) seriously impedes anti-tumor immune response, resulting in poor immunotherapy effect of cancer. This study develops a folate-modified delivery system to transport the plasmids encoding immune stimulatory chemokine CKb11 and PD-L1 inhibitors to tumor cells, resulting in high CKb11 secretion from tumor cells, successfully activating immune cells and increasing cytokine secretion to reshape the TME, and ultimately delaying tumor progression. The chemokine CKb11 enhances the effectiveness of tumor immunotherapy by increasing the infiltration of immune cells in TME. It can cause high expression of IFN-γ, which is a double-edged sword that inhibits tumor growth while causing an increase in the expression of PD-L1 on tumor cells. Therefore, combining CKb11 with PD-L1 inhibitors can counterbalance the suppressive impact of PD-L1 on anti-cancer defense, leading to a collaborative anti-tumor outcome. Thus, utilizing nanotechnology to achieve targeted delivery of immune stimulatory chemokines and immune checkpoint inhibitors to tumor sites, thereby reshaping immunosuppressive TME for cancer treatment, has great potential as an immunogene therapy in clinical applications.

SAHIS-Net: a spectral attention and feature enhancement network for microscopic hyperspectral cholangiocarcinoma image segmentation.

Cholangiocarcinoma (CCA) poses a significant clinical challenge due to its aggressive nature and poor prognosis. While traditional diagnosis relies on color-based histopathology, hyperspectral imaging (HSI) offers rich, high-dimensional data holding potential for more accurate diagnosis. However, extracting meaningful insights from this data remains challenging. This work investigates the application of deep learning for CCA segmentation in microscopic HSI images, and introduces two novel neural networks: (1) Histogram Matching U-Net (HM-UNet) for efficient image pre-processing, and (2) Spectral Attention based Hyperspectral Image Segmentation Net (SAHIS-Net) for CCA segmentation. SAHIS-Net integrates a novel Spectral Attention (SA) module for adaptively weighing spectral information, an improved attention-aware feature enhancement (AFE) mechanism for better providing the model with more discriminative features, and a multi-loss training strategy for effective early stage feature extraction. We compare SAHIS-Net against several general and CCA-specific models, demonstrating its superior performance in segmenting CCA regions. These results highlight the potential of our approach for segmenting medical HSI images.

Segmentation of hyphae and yeast in fungi-infected tissue slice images and its application in analyzing antifungal blue light therapy.

Candida albicans is a pathogenic fungus that undergoes morphological transitions between hyphal and yeast forms, adapting to diverse environmental stimuli and exhibiting distinct virulence. Existing research works on antifungal blue light (ABL) therapy have either focused solely on hyphae or neglected to differentiate between morphologies, obscuring potential differential effects. To address this gap, we established a novel dataset of 150 C. albicans-infected mouse skin tissue slice images with meticulously annotated hyphae and yeast. Eleven representative convolutional neural networks were trained and evaluated on this dataset using seven metrics to identify the optimal model for segmenting hyphae and yeast in original high pixel size images. Leveraging the segmentation results, we analyzed the differential impact of blue light on the invasion depth and density of both morphologies within the skin tissue. U-Net-BN outperformed other models in segmentation accuracy, achieving the best overall performance. While both hyphae and yeast exhibited significant reductions in invasion depth and density at the highest ABL dose (180 J/cm2), only yeast was significantly inhibited at the lower dose (135 J/cm2). This novel finding emphasizes the importance of developing more effective treatment strategies for both morphologies.

We studied the effects of blue light therapy on hyphal and yeast forms of Candida albicans. Through image segmentation techniques, we discovered that the changes in invasion depth and density differed between these two forms after exposure to blue light.

Immunostimulatory gene therapy combined with checkpoint blockade reshapes tumor microenvironment and enhances ovarian cancer immunotherapy.

Immune evasion has made ovarian cancer notorious for its refractory features, making the development of immunotherapy highly appealing to ovarian cancer treatment. The immune-stimulating cytokine IL-12 exhibits excellent antitumor activities. However, IL-12 can induce IFN-γ release and subsequently upregulate PDL-1 expression on tumor cells. Therefore, the tumor-targeting folate-modified delivery system F-DPC is constructed for concurrent delivery of IL-12 encoding gene and small molecular PDL-1 inhibitor (iPDL-1) to reduce immune escape and boost anti-tumor immunity. The physicochemical characteristics, gene transfection efficiency of the F-DPC nanoparticles in ovarian cancer cells are analyzed. The immune-modulation effects of combination therapy on different immune cells are also studied. Results show that compared with non-folate-modified vector, folate-modified F-DPC can improve the targeting of ovarian cancer and enhance the transfection efficiency of pIL-12. The underlying anti-tumor mechanisms include the regulation of T cells proliferation and activation, NK activation, macrophage polarization and DC maturation. The F-DPC/pIL-12/iPDL-1 complexes have shown outstanding antitumor effects and low toxicity in peritoneal model of ovarian cancer in mice. Taken together, our work provides new insights into ovarian cancer immunotherapy. Novel F-DPC/pIL-12/iPDL-1 complexes are revealed to exert prominent anti-tumor effect by modulating tumor immune microenvironment and preventing immune escape and might be a promising treatment option for ovarian cancer treatment.

Tumor-Microenvironment-Activatable Nanoparticle Mediating Immunogene Therapy and M2 Macrophage-Targeted Inhibitor for Synergistic Cancer Immunotherapy.

Immunotherapy has achieved prominent clinical efficacy in combating cancer and has recently become a mainstream treatment strategy. However, achieving broad efficacy with a single modality is challenging, and the heterogeneity of the tumor microenvironment (TME) restricts the accuracy and effectiveness of immunotherapy strategies for tumors. Herein, a TME-responsive targeted nanoparticle to enhance antitumor immunity and reverse immune escape by codelivering interleukin-12 (IL-12) expressing gene and colony-stimulating factor-1 receptor (CSF-1R) inhibitor PLX3397 (PLX) is presented. The introduction of disulfide bonds and cyclo(Arg-Gly-Asp-d-Phe-Lys) (cRGD) peptides conferred reduction reactivity and tumor targeting to the nanoparticles, respectively. It is hypothesized that activating host immunity by the local expression of IL-12, while modulating the tumor-associated macrophages (TAM) function through blocking CSF-1/CSF-1R signaling, could constitute a feasible approach for cancer immunotherapy. The fabricated functional nanoparticle successfully ameliorated the TME by stimulating the proliferation and activation of T lymphocytes, promoting the repolarization of TAMs, reducing myeloid-derived suppressor cells (MDSCs), and promoting the maturation of dendritic cells (DC) as well as the secretion of antitumor cytokines, which efficiently suppressed tumor growth and metastasis. Finally, substantial changes in the TME were deciphered by single-cell analysis including infiltration of different cells, transcriptional states, secretory signaling and cell-cell communications. These findings provide a promising combinatorial immunotherapy strategy through immunomodulatory nanoparticles.

MAEF-Net: MLP Attention for Feature Enhancement in U-Net based Medical Image Segmentation Networks.

Medical image segmentation plays an important role in diagnosis. Since the introduction of U-Net, numerous advancements have been implemented to enhance its performance and expand its applicability. The advent of Transformers in computer vision has led to the integration of self-attention mechanisms into U-Net, resulting in significant breakthroughs. However, the inherent complexity of Transformers renders these networks computationally demanding and parameter-heavy. Recent studies have demonstrated that multilayer perceptrons (MLPs), with their simpler architecture, can achieve comparable performance to Transformers in natural language processing and computer vision tasks. Building upon these findings, we have enhanced the previously proposed "Enhanced-Feature-Four-Fold-Net" (EF 3-Net) by introducing an MLP-attention block to learn long-range dependencies and expand the receptive field. This enhanced network is termed "MLP-Attention Enhanced-Feature-four-fold-Net", abbreviated as "MAEF-Net". To further enhance accuracy while reducing computational complexity, the proposed network incorporates additional efficient design elements. MAEF-Net was evaluated against several general and specialized medical image segmentation networks using four challenging medical image datasets. The results demonstrate that the proposed network exhibits high computational efficiency and comparable or superior performance to EF 3-Net and several state-of-the-art methods, particularly in segmenting blurry objects.

Pd-Catalyzed Asymmetric 5-exo-trig Cyclization/Cyclopropanation/Carbonylation of 1,6-Enynes for the Construction of Chiral 3-Azabicyclo[3.1.0]hexanes.

We herein disclose a mild and efficient access to chiral 3-azabicyclo[3.1.0]hexanes via a Pd-catalyzed asymmetric 5-exo-trig cyclization/cyclopropanation/carbonylation of 1,6-enynes. Various nucleophiles, such as alcohols, phenols, amines and water, are well compatible with the reaction system. This reaction forms three C-C bonds, two rings, two adjacent quaternary carbon stereocenters as well as one C-O/C-N bond with excellent regio- and enantioselectivities. The products could be further functionalized to generate a library of 3-azabicyclo[3.1.0]hexane frameworks.

Automatic detection of indoor occupancy based on improved YOLOv5 model.

Indoor occupancy detection is essential for energy efficiency control and Coronavirus Disease 2019 traceability. The number and location of people can be accurately identified and determined through classroom surveillance video analysis. This information is used to manage environmental equipment such as HVAC and lighting systems to reduce energy use. However, the mainstream one-stage YOLO algorithm still uses an anchor-based mechanism and couples detection heads to predict. This results in slow model convergence and poor detection performance for densely occluded targets. Therefore, this paper proposed a novel decoupled anchor-free VariFocal loss convolutional network algorithm DFV-YOLOv5 for occupancy detection to tackle these problems. The proposed method uses the YOLOv5 algorithm as a baseline. It uses the anchor-free mechanism to reduce the number of design parameters needing heuristic tuning. Afterwards, to reduce the coupling of the model, speed up the model's convergence ability, and improve the model detection performance, the detection head is decoupled based on the YOLOv5 model. It can resolve the conflict between classification and regression tasks. In addition, we use the VariFocal loss to assign more weights to difficult data points to optimize the class imbalance problem and use the training target q to measure positive samples, treating positive and negative samples asymmetrically. The total loss function is redesigned, the L 1 loss is increased, and the ablation experiment verifies the effect of the improved loss. By applying a hybrid activation function of the sigmoid linear unit and rectified linear unit, we improved the model's nonlinear representation and reduced the model's inference time. Finally, a classroom dataset was constructed to validate the occupancy detection performance of the model. The proposed model was compared with mainstream target detection models regarding average mean precision, memory allocation, execution time, and the number of parameters on the VOC2012, CrowdHuman and self-built datasets. The experimental results show that the method significantly improves the detection accuracy and robustness, shortens the inference time, and proves the practicality of the algorithm in occupancy detection compared with the mainstream target detection model and related variants of the model.

Palladium-Catalyzed Asymmetric Dearomative Carbonylation of Indoles.

Herein, we disclose a strategy for the asymmetric dearomatization of N-arylacyl indoles via a palladium-catalyzed tandem Heck/carbonylation, leading to an array of indoline-3-carboxylates bearing vicinal C2-aza-quaternary and C3 tertiary stereocenters in high yields and excellent enantio- and diastereoselectivities. This study is an important advance in the field of asymmetric carbonylation and enantioselective dearomatization reactions.

Use of ATR-FTIR Spectroscopy and Chemometrics for the Variation of Active Components in Different Harvesting Periods of Lonicera japonica.

Lonicera japonica Thunb is a commonly used Chinese herbal medicine, which belongs to the family Caprifoliaceae. The active components varied greatly during bud development. Research on the variation of the main active components is significant for the timely harvesting and quality control of Lonicera japonica. In this study, the attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) combined with the chemometric method was performed to investigate the variability of different harvesting periods of Lonicera japonica. The preliminary characterization from ATR-FTIR fingerprints showed various characteristic absorption peaks of the main active components from the different harvesting times, such as flavonoids, organic acids, iridoids, and volatile oils. Additionally, principal component analysis (PCA) scatter plots showed that there was a clear clustering trend in the samples of the same harvesting period, and the samples of the different harvesting periods could be well distinguished. Finally, further analysis by the orthogonal partial least-squares discriminant analysis (OPLS-DA) showed that there were regular changes in flavonoids, phenolic acids, iridoids, and volatile oils in different harvesting periods. Therefore, ATR-FTIR, as a novel and convenient analytical method, could be applied to evaluate the quality of Lonicera japonica.

Evaluating the efficacy of anti-fungal blue light therapies via analyzing tissue section images.

Anti-fungal blue light (ABL) therapies have been studied and applied in treating various diseases caused by fungal infection. The existing work has been mainly devoted to study the effect of various light dosages on the fungal viability and on the induced cytotoxic reactive oxygen species (ROS) in the pathogens. While in vivo experimental studies have also been reported, there is still no work targeted on quantifying the effect of light on prohibiting the pathogens from invading into the deeper sites in the skin of their host. This can be attributed to the lack of methods to analyze the tissue section images, which are the main means of examining infected tissues. This work has been devoted to solve such problems, so as to improve dosimetric analyses of ABL therapies on treating fungal infections. Specifically, the invasion depth of the fungi and their ratios to the tissue in four bins at different depths inside the skin were extracted from the tissue section images. The significance of the treatment with different dosages on inhibiting the fungi was also tested by each of these depth-related metrics. The ABL experiments using 415-nm-wavelength LED light were performed on BALB/c mice, whose skin was infected by Candida albicans (C. albicans). The proposed methods were applied to the tissue sections of the experimental animals. The results clearly verified that the fluence up to 180J/cm2 can significantly prohibit the fungal infection into the skin in terms of almost all the newly proposed metrics.

High-resolution ultrasonography for the diagnosis of brachial plexus root lesions.

The aim of this study was to investigate the feasibility of using high-resolution ultrasonography in the diagnosis of brachial plexus (BP) root lesions. A prospective study of ultrasonographic evaluation of BP nerve roots was performed in 37 patients with BP root lesions (29 with root injuries, 8 with tumors). The pre-operative ultrasonographic findings were compared with the surgical and pathohistological findings. All C5-7 roots were detected by ultrasonography in all patients, whereas 92% (68/74) of C8 and 51% (38/74) of T1 nerve roots were visualized. Among 29 patients with BP root avulsion, partial injuries or totally interrupted BP roots were detected in all patients. Cystic masses and neuromas were detected in 16 and 23 patients, respectively. In 8 patients with BP root tumors, 8 hypo-echoic masses were detected inside or partly outside of intervertebral foramina connecting to nerve roots. Surgical exploration revealed that there were 57 BP root avulsions in 29 patients. However, 2 T1 nerve root avulsions had been missed by pre-operative ultrasonography. Pathohistology revealed that all 8 BP root tumors pre-operatively diagnosed by ultrasonography were schwannomas. High-resolution ultrasonography can provide a convenient and accurate imaging modality for quick diagnosis and location of BP root lesions.