With the rapid development of biomedicine, people have a deeper understanding with the biological characteristics of malignant tumors, and begin to notice that in most tumors, there are ...over-expression of several molecules such as epidermal growth factor receptor(EGFR), vascular endothelial growth factor (VEGF) and its receptors,mammalian target of rapamycin(mTOR),programmed cell death receptor-1(PD-1),cyclin-dependent kinases(CDKs) and so on, whose levels are closely related to the prognosis of tumors. It has been found that the drugs targeting the above molecules can significantly improve the survival rate of cancer patients, and have the advantages of high selectivity, low toxicity and high therapeutic index. Targeted drugs, as new ones in the field of cancer, have achieved good efficacy in most tumor treatments. Oral cancer is an aggressive malignant tumour that is prone to relapse and metastasis. More than 90% of them are squamous cell carcinoma, and the 5-year survival rate remains at about 50%–60%.The proposing of targeted therapy opens up a new way for the treatment of oral cancer and brings dawn to patients with advanced diseases. Currently,a variety of targeted therapeutic drugs are being tested in various clinical trials in patients with oral squamous cell carcinoma(OSCC)·In this paper, we discuss the research progress of targeted therapeutic drugs in the treatment of OSCC in recent years.
•In this paper,the research progress of the most common molecule targeted agents in the treatment of OSCC was discussed.•In each section,the specific role of various targets played in OSCC was highly generalized.•Pharmacological activity,achieved clinical efficacy and defects existed currently of the drugs were described carefully.•Finally,present situation and future prospects of targeted therapeutic drugs were concluded.
Educational big data significantly impacts education, and Massive Open Online Courses (MOOCs), a crucial learning approach, have evolved to be more intelligent with these technologies. Deep neural ...networks have significantly advanced the crucial task within MOOCs, predicting student academic performance. However, most deep learning-based methods usually ignore the temporal information and interaction behaviors during the learning activities, which can effectively enhance the model’s predictive accuracy. To tackle this, we formulate the learning processes of e-learning students as dynamic temporal graphs to encode the temporal information and interaction behaviors during their studying. We propose a novel academic performance prediction model (APP-TGN) based on temporal graph neural networks. Specifically, in APP-TGN, a dynamic graph is constructed from online learning activity logs. A temporal graph network with low-high filters learns potential academic performance variations encoded in dynamic graphs. Furthermore, a global sampling module is developed to mitigate the problem of false correlations in deep learning-based models. Finally, multi-head attention is utilized for predicting academic outcomes. Extensive experiments are conducted on a well-known public dataset. The experimental results indicate that APP-TGN significantly surpasses existing methods and demonstrates excellent potential in automated feedback and personalized learning.
This study aims to elucidate the biological functions of ferroptosis-related genes in periodontitis, along with their correlation to tumor microenvironment (TME) features such as immune infiltration. ...It aims to provide potential diagnostic markers of ferroptosis for clinical management of periodontitis.
Utilizing the periodontitis-related microarray dataset GSE16134 from the Gene Expression Omnibus (GEO) and a set of 528 ferroptosis-related genes identified in prior studies, this research unveils differentially expressed ferroptosis-related genes in periodontitis. Subsequently, a protein-protein interaction network was constructed. Subtyping of periodontitis was explored, followed by validation through immune cell infiltration and gene set enrichment analyses. Two algorithms, randomForest and SVM(Support Vector Machine), were employed to reveal potential ferroptosis diagnostic markers for periodontitis. The diagnostic efficacy, immune correlation, and potential transcriptional regulatory networks of these markers were further assessed. Finally, potential targeted drugs for differentially expressed ferroptosis markers in periodontitis were predicted.
A total of 36 ferroptosis-related genes (30 upregulated, 6 downregulated) were identified from 829 differentially expressed genes between 9 periodontitis samples and the control group. Subsequent machine learning algorithm screening highlighted 4 key genes: SLC1A5(Solute Carrier Family 1 Member 5), SLC2A14(Solute Carrier Family 1 Member 14), LURAP1L(Leucine Rich Adaptor Protein 1 Like), and HERPUD1(Homocysteine Inducible ER Protein With Ubiquitin Like Domain 1). Exploration of these 4 key genes, supported by time-correlated ROC analysis, demonstrated reliability, while immune infiltration results indicated a strong correlation between key genes and immune factors. Furthermore, Gene Set Enrichment Analysis (GSEA) was conducted for the four key genes, revealing enrichment in GO/KEGG pathways that have a significant impact on periodontitis. Finally, the study predicted potential transcriptional regulatory networks and targeted drugs associated with these key genes in periodontitis.
The ferroptosis-related genes identified in this study, including SLC1A5, SLC2A14, LURAP1L, and HERPUD1, may serve as novel diagnostic and therapeutic targets for periodontitis. They are likely involved in the occurrence and development of periodontitis through mechanisms such as immune infiltration, cellular metabolism, and inflammatory chemotaxis, potentially linking the ferroptosis pathway to the progression of periodontitis. Targeted drugs such as flurofamide, L-733060, memantine, tetrabenazine, and WAY-213613 hold promise for potential therapeutic interventions in periodontitis associated with these ferroptosis-related genes.
Talaromyces amestolkiae
is an important fungal species owing to its ubiquity in soils, plants, air, and food. In this study, we identified a novel six-segmented polymycovirus,
Talaromyces amestolkiae
...polymycovirus 1 (TaPmV-1). Each of the double-stranded (ds) RNA segments of TaPmV-1 contained a single open reading frame, and the proteins encoded by dsRNA1, dsRNA2, dsRNA3, and dsRNA 5 shared significant amino acid identities of 56, 40, 47, and 43%, respectively, with the corresponding proteins of
Aspergillus fumigatus
polymycovirus-1(AfuPmV-1). DsRNA1, dsRNA3, and dsRNA5 of TaPmV-1 encoded an RNA-dependent RNA polymerase (RdRp), a viral methyltransferase, and a PAS-rich protein, respectively. The functions of the proteins encoded by dsRNA2, dsRNA4, and dsRNA6 have not been elucidated. Comparison of the virus-infected strain LSH3 with virus-cured strain LSHVF revealed that infection with TaPmV-l may reduce the production of red pigments and induce the clustering of fungal sclerotia. Furthermore, transcriptomic analyses demonstrated that infection with TaPmV-l downregulated the expression of transcripts related to metabolism, and may correlate with the reduced production of red pigments and clustering of sclerotia in
T. amestolkiae
. These results of this study provide novel insights into the mechanism of fungal gene regulation by polymycovirus infections at the transcriptome level, and this study is the first to report a novel polymycovirus of
T. amestolkiae
.
Phase retrieval and phase unwrapping are the two important problems for enabling quantitative phase imaging of cells in phase-shifting digital holography. To simultaneously cope with these two ...problems, a deep-learning phase-shifting digital holography method is proposed in this paper. The proposed method can establish the continuous mapping function of the interferogram to the ground-truth phase using the end-to-end convolutional neural network. With a well-trained deep convolutional neural network, this method can retrieve the phase from one-frame blindly phase-shifted interferogram, without phase unwrapping. The feasibility and applicability of the proposed method are verified by the simulation experiments of the microsphere and white blood cells, respectively. This method will pave the way to the quantitative phase imaging of biological cells with complex substructures.
To assess the feasibility and clinical utility of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) and macular edema (ME) by combining fundus photos and optical coherence ...tomography (OCT) images in a community hospital. Fundus photos and OCT images were taken for 600 diabetic patients in a community hospital. Ophthalmologists graded these fundus photos according to the International Clinical Diabetic Retinopathy (ICDR) Severity Scale as the ground truth. Two existing trained AI models were used to automatically classify the fundus images into DR grades according to ICDR, and to detect concomitant ME from OCT images, respectively. The criteria for referral were DR grades 2-4 and/or the presence of ME. The sensitivity and specificity of AI grading were evaluated. The number of referable DR cases confirmed by ophthalmologists and AI was calculated, respectively. DR was detected in 81 (13.5%) participants by ophthalmologists and in 94 (15.6%) by AI, and 45 (7.5%) and 53 (8.8%) participants were diagnosed with referable DR by ophthalmologists and by AI, respectively. The sensitivity, specificity and area under the curve (AUC) of AI for detecting DR were 91.67%, 96.92% and 0.944, respectively. For detecting referable DR, the sensitivity, specificity and AUC of AI were 97.78%, 98.38% and 0.981, respectively. ME was detected from OCT images in 49 (8.2%) participants by ophthalmologists and in 57 (9.5%) by AI, and the sensitivity, specificity and AUC of AI were 91.30%, 97.46% and 0.944, respectively. When combining fundus photos and OCT images, the number of referrals identified by ophthalmologists increased from 45 to 75 and from 53 to 85 by AI. AI-based DR screening has high sensitivity and specificity and may feasibly improve the referral rate of community DR.
This paper aimed to develop and validate a deep learning (DL) model for automated detection of the laterality of the eye on anterior segment photographs. Anterior segment photographs for training a ...DL model were collected with the Scheimpflug anterior segment analyzer. We applied transfer learning and fine-tuning of pre-trained deep convolutional neural networks (InceptionV3, VGG16, MobileNetV2) to develop DL models for determining the eye laterality. Testing datasets, from Scheimpflug and slit-lamp digital camera photography, were employed to test the DL model, and the results were compared with a classification performed by human experts. The performance of the DL model was evaluated by accuracy, sensitivity, specificity, operating characteristic curves, and corresponding area under the curve values. A total of 14,468 photographs were collected for the development of DL models. After training for 100 epochs, the DL models of the InceptionV3 mode achieved the area under the receiver operating characteristic curve of 0.998 (with 95% CI 0.924-0.958) for detecting eye laterality. In the external testing dataset (76 primary gaze photographs taken by a digital camera), the DL model achieves an accuracy of 96.1% (95% CI 91.7%-100%), which is better than an accuracy of 72.3% (95% CI 62.2%-82.4%), 82.8% (95% CI 78.7%-86.9%) and 86.8% (95% CI 82.5%-91.1%) achieved by human graders. Our study demonstrated that this high-performing DL model can be used for automated labeling for the laterality of eyes. Our DL model is useful for managing a large volume of the anterior segment images with a slit-lamp camera in the clinical setting.
GLP-1 receptor agonists are a class of diabetes medicines offering self-regulating glycemic efficacy and may best be administrated in long-acting forms. Among GLP-1 receptor agonists, exenatide is ...the one requiring the least dose so that controlled-release poly(d,l-lactic-co-glycolic acid) (PLGA) microspheres may best achieve this purpose. Based on this consideration, the present study extended the injection interval of exenatide microspheres from one week of the current dosage form to four weeks by simply blending Mg(OH)2 powder within the matrix of PLGA microspheres. Mg(OH)2 served as the diffusion channel creator in the earlier stage of the controlled-release period and the decelerator of the self-catalyzed degradation of PLGA (by the formed lactic and glycolic acids) in the later stage due to its pH-responsive solubility. As a result, exenatide gradually diffused from the microspheres through Mg(OH)2-created diffusion channels before degradation of the PLGA matrix, followed by a mild release due to Mg(OH)2-buffered degradation of the polymer skeleton. In addition, an extruding–settling process comprising squeezing the PLGA solution through a porous glass membrane and sedimentation-aided solidification of the PLGA droplets was used to prepare the microspheres to ensure narrow size distribution and 95% encapsulation efficiency in an aqueous continuous phase. A pharmacokinetic study using rhesus monkey model confirmed the above formulation design by showing a steady blood concentration profile of exenatide with reduced CMAX and dosage form index. Mg(OH)2.
Ocular fundus angiography is an indispensable component of the tests utilized for fundus diseases. Dynamic angiography results can provide additional information; however, many difficulties remain. ...In this study, we introduce a modified method, time-lapse angiography (TLA), to dynamically present imaging results.
TLA, combining time-lapse photography and fundus angiography (using Heidelberg retina angiography II, Germany), includes pre-photographing and post- photosynthesis and ultimately produces a video that is approximately 15 s in length.
Four typical videos in the article showed the characteristics of TLA, including a short and rapid but continuous and integral presentation, highly valid information, high definition, etc. CONCLUSIONS: TLA is beneficial for the diagnosis of diseases and the assessment of progression and is convenient for peer communication, patient interpretation, and student education. The application of time-lapse photography in ocular fundus angiography is a monumental and innovative attempt.
Virus-based tumour vaccines offer many advantages compared to other antigen-delivering systems. They generate concerted innate and adaptive immune response, and robust CD8
T cell responses. We ...engineered a non-replicating pseudotyped influenza virus (S-FLU) to deliver the well-known cancer testis antigen, NY-ESO-1 (NY-ESO-1 S-FLU). Intranasal or intramuscular immunization of NY-ESO-1 S-FLU virus in mice elicited a strong NY-ESO-1-specific CD8
T cell response in lungs and spleen that resulted in the regression of NY-ESO-1-expressing lung tumour and subcutaneous tumour, respectively. Combined administration with anti-PD-1 antibody, NY-ESO-1 S-FLU virus augmented the tumour protection by reducing the tumour metastasis. We propose that the antigen delivery through S-FLU is highly efficient in inducing antigen-specific CD8
T cell response and protection against tumour development in combination with PD-1 blockade.
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