RTC_TongueNet: An improved tongue image segmentation model based on DeepLabV3.

Objective: Tongue segmentation as a basis for automated tongue recognition studies in Chinese medicine, which has defects such as network degradation and inability to obtain global features, which seriously affects the segmentation effect. This article proposes an improved model RTC_TongueNet based on DeepLabV3, which combines the improved residual structure and transformer and integrates the ECA (Efficient Channel Attention Module) attention mechanism of multiscale atrous convolution to improve the effect of tongue image segmentation. Methods: In this paper, we improve the backbone network based on DeepLabV3 by incorporating the transformer structure and an improved residual structure. The residual module is divided into two structures and uses different residual structures under different conditions to speed up the frequency of shallow information mapping to deep network, which can more effectively extract the underlying features of tongue image; introduces ECA attention mechanism after concat operation in ASPP (Atrous Spatial Pyramid Pooling) structure to strengthen information interaction and fusion, effectively extract local and global features, and enable the model to focus more on difficult-to-separate areas such as tongue edge, to obtain better segmentation effect. Results: The RTC_TongueNet network model was compared with FCN (Fully Convolutional Networks), UNet, LRASPP (Lite Reduced ASPP), and DeepLabV3 models on two datasets. On the two datasets, the MIOU (Mean Intersection over Union) and MPA (Mean Pixel Accuracy) values of the classic model DeepLabV3 were higher than those of FCN, UNet, and LRASPP models, and the performance was better. Compared with the DeepLabV3 model, the RTC_TongueNet network model increased MIOU value by 0.9% and MPA value by 0.3% on the first dataset; MIOU increased by 1.0% and MPA increased by 1.1% on the second dataset. RTC_TongueNet model performed best on both datasets. Conclusion: In this study, based on DeepLabV3, we apply the improved residual structure and transformer as a backbone to fully extract image features locally and globally. The ECA attention module is combined to enhance channel attention, strengthen useful information, and weaken the interference of useless information. RTC_TongueNet model can effectively segment tongue images. This study has practical application value and reference value for tongue image segmentation.

YOLOv8-ACU: improved YOLOv8-pose for facial acupoint detection.

Introduction: Acupoint localization is integral to Traditional Chinese Medicine (TCM) acupuncture diagnosis and treatment. Employing intelligent detection models for recognizing facial acupoints can substantially enhance localization accuracy. Methods: This study introduces an advancement in the YOLOv8-pose keypoint detection algorithm, tailored for facial acupoints, and named YOLOv8-ACU. This model enhances acupoint feature extraction by integrating ECA attention, replaces the original neck module with a lighter Slim-neck module, and improves the loss function for GIoU. Results: The YOLOv8-ACU model achieves impressive accuracy, with an mAP@0.5 of 97.5% and an mAP@0.5-0.95 of 76.9% on our self-constructed datasets. It also marks a reduction in model parameters by 0.44M, model size by 0.82 MB, and GFLOPs by 9.3%. Discussion: With its enhanced recognition accuracy and efficiency, along with good generalization ability, YOLOv8-ACU provides significant reference value for facial acupoint localization and detection. This is particularly beneficial for Chinese medicine practitioners engaged in facial acupoint research and intelligent detection.

Advances in imaging findings of preeclampsia-related reversible posterior leukoencephalopathy syndrome.

Preeclampsia (PE)-related reversible posterior leukoencephalopathy syndrome (RPLS) is a common complication of hypertensive disorders of pregnancy. The syndrome usually occurs after 20 weeks of gestation and can lead to brain injury. Severe headache, seizures, disturbance of consciousness, and other neurological symptoms may occur in severe cases. PE-RPLS has high morbidity and mortality rates and seriously damages maternal and fetal health. In recent years, the continuous advancement of medical imaging technology has provided an important imaging basis for the early diagnosis and prognostic evaluation of RPLS. This article mainly details the research status of the etiology and pathogenesis of PE-RPLS and describes its characteristic imaging findings, especially MRI findings, to provide new insights into its early diagnosis, early treatment, and improvement of prognosis.

T cell-related prognostic risk model and tumor immune environment modulation in lung adenocarcinoma based on single-cell and bulk RNA sequencing.

BACKGROUND: T cells are present in all stages of tumor formation and play an important role in the tumor microenvironment. We aimed to explore the expression profile of T cell marker genes, constructed a prognostic risk model based on these genes in Lung adenocarcinoma (LUAD), and investigated the link between this risk model and the immunotherapy response. METHODS: We obtained the single-cell sequencing data of LUAD from the literature, and screened out 6 tissue biopsy samples, including 32,108 cells from patients with non-small cell lung cancer, to identify T cell marker genes in LUAD. Combined with TCGA database, a prognostic risk model based on T-cell marker gene was constructed, and the data from GEO database was used for verification. We also investigated the association between this risk model and immunotherapy response. RESULTS: Based on scRNA-seq data 1839 T-cell marker genes were identified, after which a risk model consisting of 9 gene signatures for prognosis was constructed in combination with the TCGA dataset. This risk model divided patients into high-risk and low-risk groups based on overall survival. The multivariate analysis demonstrated that the risk model was an independent prognostic factor. Analysis of immune profiles showed that high-risk groups presented discriminative immune-cell infiltrations and immune-suppressive states. Risk scores of the model were closely correlated with Linoleic acid metabolism, intestinal immune network for IgA production and drug metabolism cytochrome P450. CONCLUSION: Our study proposed a novel prognostic risk model based on T cell marker genes for LUAD patients. The survival of LUAD patients as well as treatment outcomes may be accurately predicted by the prognostic risk model, and make the high-risk population present different immune cell infiltration and immunosuppression state.

Single-cell RNA-sequencing uncovers compound kushen injection synergistically improves the efficacy of chemotherapy by modulating the tumor environment of breast cancer.

Background: Due to lack of enough specific targets and the immunosuppressive tumor microenvironment (TME) of triple-negative breast cancer (TNBC), TNBC patients often cannot benefit from a single treatment option. This study aims to explore the regulatory effects of Compound kushen injection (CKI) plus chemotherapy on the TME of TNBC from a single cell level. Methods: A mouse TNBC model in BALB/c mice was established to evaluate the antitumor efficacy and toxicity of CKI combined with chemotherapy. Flow cytometry was used to observe the influence of CKI on the lymphocyte populations in the tumor bearing mice. Both bulk RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) were applied to portray the modulation of CKI combined with chemotherapy on the TME of TNBC mice. Results: CKI significantly enhanced the anticancer activity of chemotherapy in vivo with no obvious side effects. Flow cytometry results revealed a significantly higher activation of CD8+ T lymphocytes in the spleens and tumors of the mice with combination therapy. Bulk RNA-seq indicated that CKI could promote the cytotoxic immune cell infiltrating into tumor tissues. Meanwhile, scRNA-seq further revealed that CKI combined with chemotherapy could enhance the percentage of tumor-infiltrating CD8+ T cells, inhibit tumor-promoting signaling pathways, and promote T cell activation and positive regulation of immune response. In addition, CKI showed obvious anticancer activity against MDA-MB-231 breast tumor cells in vitro. Conclusions: The combination of CKI and chemotherapy might provide a higher efficiency and lower toxicity strategy than a single chemotherapy drug for TNBC. CKI potentiates the anti-TNBC effects of chemotherapy by activating anti-tumor immune response in mice.

Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels.

BACKGROUND: Precision medicine aims to address the demand for precise therapy at the gene and pathway levels. We aimed to design software to allow precise treatment of osteoporosis (OP) with Chinese medicines (CMs) at the gene and pathway levels. METHODS: PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP database), and the Wanfang database were searched to identify studies treating osteoporosis with CMs. The TCMSP was used to identify bioactive ingredients and related genes for each CM. Gene expression omnibus (GEO) database and the limma package were used to identify differentially expressed genes in osteoporosis. Perl software was used to identify the shared genes between the bioactive components in CM and osteoporosis. R packages and bioconductor packages were used to define the target relationship between shared genes and their related pathways. Third-party Python libraries were used to write program codes. Pyinstaller library was used to create an executable program file. RESULTS: Data mining: a total of 164 CMs were included, but Drynariae Rhizoma (gusuibu) was used to present this process. We obtained 44 precise relationships among the bioactive ingredients of Drynariae Rhizoma, shared genes, and pathways. Python programming: we developed the software to show the precise relationship among bioactive ingredients, shared genes, and pathways for each CM, including Drynariae Rhizoma. CONCLUSIONS: This study could increase the precision of CM, and could provide a valuable and convenient software for searching precise relationships among bioactive ingredients, shared genes, and pathways.

G-CSF promotes autophagy and reduces neural tissue damage after spinal cord injury in mice.

Granulocyte colony-stimulating factor (G-CSF) was investigated for its capacity to induce autophagy and related neuroprotective mechanisms in an acute spinal cord injury model. To accomplish this goal, we established a mouse spinal cord hemisection model to test the effects of recombinant human G-CSF. The results showed that autophagy was activated after spinal cord injury and G-CSF appears to induce a more rapid activation of autophagy within injured spinal cords as compared with that of non-treated animals. Apoptosis as induced in mechanically injured neurons with G-CSF treatment was enhanced after inhibiting autophagy by 3-methyladenine (3-MA), which partially blocked the neuroprotective effect of autophagy as induced by G-CSF. In addition, G-CSF inhibited the activity of the NF-κB signal pathway in neurons after mechanical injury. We conclude that G-CSF promotes autophagy by inhibiting the NF-κB signal pathway and protects neuronal structure after spinal cord injury. We therefore suggest that G-CSF, which rapidly induces autophagy after spinal cord injury to inhibit neuronal apoptosis, may thus provide an effective auxiliary therapeutic intervention for spinal cord injury.

Sox11 promotes endogenous neurogenesis and locomotor recovery in mice spinal cord injury.

We introduced a lentiviral vector containing the Sox11 gene into injured spinal cords of mice to evaluate the therapeutic potential of Sox11 in spinal cord injury. Sox11 markedly improved locomotor recovery after spinal cord injury and this recovery was accompanied by an up-regulation of Nestin/Doublecortin expression in the injured spinal cord. Sox11 was mainly located in endogenous neural stem cells lining the central canal and in newly-generated neurons in the spinal cord. In addition, Sox 11 significantly induced expressions of BDNF in the spinal cords of LV-Sox11-treated mice. We concluded that Sox11 induced activation of endogenous neural stem cells into neuronal determination and migration within the injured spinal cord. The resultant increase of BDNF at the injured site might form a distinct neurogenic niche which induces a final neuronal differentiation of these neural stem cells. Enhancing Sox11 expression to induce neurogenic differentiation of endogenous neural stem cells after injury may be a promising strategy in restorative therapy after SCI in mammals.