Intelligent supervision of PIVAS drug dispensing based on image recognition technology.

Pharmacy Intravenous Admixture Services (PIVAS) are places dedicated to the centralized dispensing of intravenous drugs, usually managed and operated by professional pharmacists and pharmacy technicians, and are an integral part of modern healthcare. However, the workflow of PIVAS has some problems, such as low efficiency and error-prone. This study aims to improve the efficiency of drug dispensing, reduce the rate of manual misjudgment, and minimize drug errors by conducting an in-depth study of the entire workflow of PIVAS and applying image recognition technology to the drug checking and dispensing process. Firstly, through experimental comparison, a target detection model suitable for drug category recognition is selected in the drug-checking process of PIVAS, and it is improved to improve the recognition accuracy and speed of intravenous drug categories. Secondly, a corner detection model for drug dosage recognition was studied in the drug dispensing stage to further increase drug dispensing accuracy. Then the PIVAS drug category recognition system and PIVAS drug dosage recognition system were designed and implemented.

Andrographolide acts with dexamethasone to inhibit the growth of acute lymphoblastic leukemia CEM­C1 cells via the regulation of the autophagy­dependent PI3K/AKT/mTOR signaling pathway.

Acute lymphoblastic leukemia (ALL) is one of the most common malignant tumor types of the circulatory system. Dexamethasone (DEX) acts on the glucocorticoid (GC) receptor (GR) and is a first-line chemotherapy drug for ALL. However, long-term or high-dose applications of the drug can not only cause adverse reactions, such as osteoporosis and high blood pressure, but can also cause downregulation of GR and lead to drug resistance. In the present study, reverse transcription-quantitative PCR, western blotting and LysoTracker Red staining were used to observe the effects of DEX and andrographolide (AND; a botanical with antitumorigenic properties) combined treatment. It was found that AND enhanced the sensitivity of CEM-C1 cells, a GC-resistant cell line, to DEX, and synergistically upregulated GR both at the transcriptional and post-transcriptional level with DEX. The combination of AND with DEX synergistically alkalized lysosomal lumen and downregulated the expression of autophagy-related genes Beclin1 and microtubule-associated 1 protein light chain 3 (LC3), thereby inhibiting autophagy. Knocking down LC3 expression enhanced GR expression, suggesting that GR was regulated by autophagy. Furthermore, compared with the monotherapy group (AND or DEX in isolation), AND interacted with DEX to activate the autophagy-dependent PI3K/AKT/mTOR signaling pathway by enhancing the phosphorylation of PI3K, AKT and mTOR, thereby decreasing GR degradation and increasing the sensitivity of cells to GCs. In conclusion, the present study demonstrated that AND exhibited a synergistic anti-ALL effect with DEX via upregulation of GR, which was orchestrated by the autophagy-related PI3K/AKT/mTOR signaling pathway. The results of the present study therefore provided novel research avenues and strategies for the treatment of ALL.

Construct ceRNA Network and Risk Model of Breast Cancer Using Machine Learning Methods under the Mechanism of Cuproptosis.

Breast cancer (BRCA) has an undesirable prognosis and is the second most common cancer among women after lung cancer. A novel mechanism of programmed cell death called cuproptosis is linked to the development and spread of tumor cells. However, the function of cuproptosis in BRCA remains unknown. To this date, no studies have used machine learning methods to screen for characteristic genes to explore the role of cuproptosis-related genes (CRGs) in breast cancer. Therefore, 14 cuproptosis-related characteristic genes (CRCGs) were discovered by the feature selection of 39 differentially expressed CRGs using the three machine learning methods LASSO, SVM-RFE, and random forest. Through the PPI network and immune infiltration analysis, we found that PRNP was the key CRCG. The miRTarBase, TargetScan, and miRDB databases were then used to identify hsa-miR-192-5p and hsa-miR-215-5p as the upstream miRNA of PRNP, and the upstream lncRNA, CARMN, was identified by the StarBase database. Thus, the mRNA PRNP/miRNA hsa-miR-192-5p and hsa-miR-215-5p/lncRNA CARMN ceRNA network was constructed. This ceRNA network, which has not been studied before, is extremely innovative. Furthermore, four cuproptosis-related lncRNAs (CRLs) were screened in TCGA-BRCA by univariate Cox, LASSO, and multivariate Cox regression analysis. The risk model was constructed by using these four CRLs, and the risk score = C9orf163 * (1.8365) + PHC2-AS1 * (-2.2985) + AC087741.1 * (-0.9504) + AL109824.1 * (0.6016). The ROC curve and C-index demonstrated the superior predictive capacity of the risk model, and the ROC curve demonstrated that the AUC of 1-, 3-, and 5-year OS in all samples was 0.721, 0.695, and 0.633, respectively. Finally, 50 prospective sensitive medicines were screened with the pRRophetic R package, among which 17-AAG may be a therapeutic agent for high-risk patients, while the other 49 medicines may be suitable for the treatment of low-risk patients. In conclusion, our study constructs a new ceRNA network and a novel risk model, which offer a theoretical foundation for the treatment of BRCA and will aid in improving the prognosis of BRCA.

The construction of a novel ferroptosis-related lncRNA model to predict prognosis in colorectal cancer patients.

Colorectal cancer (CRC) is the most common gastrointestinal tumor with poor prognosis. Ferroptosis is a pivotal form of programmed iron-dependent cell death different from autophagy and apoptosis, and long noncoding RNA (lncRNA) can influence the prognosis of CRC via regulating ferroptosis. To explore the role and prognostic value of the constructed ferroptosis-related lncRNA model in CRC, a prognostic model was constructed and validated by screening ferroptosis-related lncRNAs associated with prognosis based on the transcriptome data and survival data of CRC patients in The Cancer Genome Atlas database. Regarding the established prognostic models, differences in signaling pathways and immune infiltration, as well as differences in immune function, immune checkpoints, and N6-methyladenosine-related genes were also analyzed. A total of 6 prognostic ferroptosis-related lncRNAs were obtained, including AP003555.1, AC010973.2, LINC01857, AP001469.3, ITGB1-DT and AC129492.1. Univariate independent prognostic analysis, multivariate independent prognostic analysis and receiver operating characteristic curves showed that ferroptosis-related lncRNAs could be recognized as independent prognostic factors. The Kaplan-Meier survival curves and the risk curves showed that the survival time of the high-risk group was shorter. Gene set enrichment analysis enrichment analysis showed that ATP-binding cassette transporters, taste transduction and VEGF signaling pathway were more active in high-risk groups that than in low-risk groups. However, the citrate cycle tricarboxylic acid cycle, fatty acid metabolism and peroxisome were significantly more active in the low-risk group than in the high-risk group. In addition, there were also differences in immune infiltration in the high-low-risk groups based on different methods, including antigen-presenting cell co-stimulation, chemokine receptor, parainflammation, and Type II IFN Response. Further analysis of Immune checkpoints showed that most of the Immune checkpoints such as TNFRSF18, LGALS9 and CTLA4 in the high-risk group were significantly higher than those in the low-risk group, and the expressions of N6-methyladenosine related genes METTL3, YTHDH2 and YTHDC1 were also significantly different in the high-risk group. Ferroptosis-related lncRNAs are closely related to the survival of colorectal cancer patients, which can be used as new biomarkers and potential therapeutic targets for the prognosis of colorectal cancer.

Unsupervised Domain Adaptation with Shape Constraint and Triple Attention for Joint Optic Disc and Cup Segmentation.

Currently, glaucoma has become an important cause of blindness. At present, although glaucoma cannot be cured, early treatment can prevent it from getting worse. A reliable way to detect glaucoma is to segment the optic disc and cup and then measure the cup-to-disc ratio (CDR). Many deep neural network models have been developed to autonomously segment the optic disc and the optic cup to help in diagnosis. However, their performance degrades when subjected to domain shift. While many domain-adaptation methods have been exploited to address this problem, they are apt to produce malformed segmentation results. In this study, it is suggested that the segmentation network be adjusted using a constrained formulation that embeds prior knowledge about the shape of the segmentation areas that is domain-invariant. Based on IOSUDA (i.e., Input and Output Space Unsupervised Domain Adaptation), a novel unsupervised joint optic cup-to-disc segmentation framework with shape constraints is proposed, called SCUDA (short for Shape-Constrained Unsupervised Domain Adaptation). A shape constrained loss function is novelly proposed in this paper which utilizes domain-invariant prior knowledge concerning the segmentation region of the joint optic cup-optical disc of fundus images to constrain the segmentation result during network training. In addition, a convolutional triple attention module is designed to improve the segmentation network, which captures cross-dimensional interactions and provides a rich feature representation to improve the segmentation accuracy. Experiments on the RIM-ONE_r3 and Drishti-GS datasets demonstrate that the algorithm outperforms existing approaches for segmenting optic discs and cups.

[Medical nucleus image segmentation network based on convolution and attention mechanism].

Although deep learning plays an important role in cell nucleus segmentation, it still faces problems such as difficulty in extracting subtle features and blurring of nucleus edges in pathological diagnosis. Aiming at the above problems, a nuclear segmentation network combined with attention mechanism is proposed. The network uses UNet network as the basic structure and the depth separable residual (DSRC) module as the feature encoding to avoid losing the boundary information of the cell nucleus. The feature decoding uses the coordinate attention (CA) to enhance the long-range distance in the feature space and highlights the key information of the nuclear position. Finally, the semantics information fusion (SIF) module integrates the feature of deep and shallow layers to improve the segmentation effect. The experiments were performed on the 2018 data science bowl (DSB2018) dataset and the triple negative breast cancer (TNBC) dataset. For the two datasets, the accuracy of the proposed method was 92.01% and 89.80%, the sensitivity was 90.09% and 91.10%, and the mean intersection over union was 89.01% and 89.12%, respectively. The experimental results show that the proposed method can effectively segment the subtle regions of the nucleus, improve the segmentation accuracy, and provide a reliable basis for clinical diagnosis.

Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark.

Human pose estimation has a variety of real-life applications, including human action recognition, AI-powered personal trainers, robotics, motion capture and augmented reality, gaming, and video surveillance. However, most current human pose estimation systems are based on RGB images, which do not seriously take into account personal privacy. Although identity-preserved algorithms are very desirable when human pose estimation is applied to scenarios where personal privacy does matter, developing human pose estimation algorithms based on identity-preserved modalities, such as thermal images concerned here, is very challenging due to the limited amount of training data currently available and the fact that infrared thermal images, unlike RGB images, lack rich texture cues which makes annotating training data itself impractical. In this paper, we formulate a new task with privacy protection that lies between human detection and human pose estimation by introducing a benchmark for IPHPDT (i.e., Identity-Preserved Human Posture Detection in Thermal images). This task has a threefold novel purpose: the first is to establish an identity-preserved task with thermal images; the second is to achieve more information other than the location of persons as provided by human detection for more advanced computer vision applications; the third is to avoid difficulties in collecting well-annotated data for human pose estimation in thermal images. The presented IPHPDT dataset contains four types of human postures, consisting of 75,000 images well-annotated with axis-aligned bounding boxes and postures of the persons. Based on this well-annotated IPHPDT dataset and three state-of-the-art algorithms, i.e., YOLOF (short for You Only Look One-level Feature), YOLOX (short for Exceeding YOLO Series in 2021) and TOOD (short for Task-aligned One-stage Object Detection), we establish three baseline detectors, called IPH-YOLOF, IPH-YOLOX, and IPH-TOOD. In the experiments, three baseline detectors are used to recognize four infrared human postures, and the mean average precision can reach 70.4%. The results show that the three baseline detectors can effectively perform accurate posture detection on the IPHPDT dataset. By releasing IPHPDT, we expect to encourage more future studies into human posture detection in infrared thermal images and draw more attention to this challenging task.

Natural product-derived icaritin exerts anti-glioblastoma effects by positively modulating estrogen receptor ß.

Glioblastoma is the most common malignancy of the central nervous system, and patients typically have a poor prognosis. Previous studies indicate a gender bias in the development of glioblastoma; women are at a lower risk compared with men, suggesting that estrogen may confer protective effects. Icaritin, a prenylflavonoid derivative from a Chinese herb of the Epimedium genus, selectively regulates the estrogen receptor (ER) and possesses anti-cancer properties. The aim of the present study was to investigate the protective effects of icaritin on glioblastoma and its underlying mechanisms, with a particular focus on its association with the ER. The results demonstrated that icaritin inhibited the growth of C6 and U87-MG glioblastoma cells in a dose- and time-dependent manner. At a concentration of 12.5 µM, icaritin induced apoptosis, which was characterized by the increased expression of the cleaved forms of caspases 3, 7, 8 and 9 and poly (ADP-ribose) polymerase, downregulation of BCL2 apoptosis regulator and upregulation of BCL2-associated X, apoptosis regulator expression. Additionally, icaritin inhibited the migration of C6 and U87-MG cells. The protein expression levels of matrix metalloproteinase (MMP)-2 and MMP-9 were also downregulated following icaritin treatment. Furthermore, icaritin treatment increased the expression of estrogen receptor (ER)ß and the phosphatase and tensin (PTEN) homolog oncoprotein, thus reducing the expression of downstream targets of PTEN; protein kinase B (Akt) and phosphorylated Akt. Subsequent experiments demonstrated that icaritin cooperates with 17ß-estradiol to inhibit the growth of glioblastoma cells, and the inhibition of ERß with the ERß-specific antagonist ICI 182,780, attenuated the anti-glioblastoma effects of icaritin. In conclusion, the results of the present study demonstrate that the anti-glioblastoma effects of icaritin may be mediated by its modulation of ERß.