Deep Learning-Based Image Classification and Segmentation on Digital Histopathology for Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.

BACKGROUND: Artificial intelligence (AI)-based tools have shown promise in histopathology image analysis in improving the accuracy of oral squamous cell carcinoma (OSCC) detection with intent to reduce human error. OBJECTIVES: This systematic review and meta-analysis evaluated deep learning (DL) models for OSCC detection on histopathology images by assessing common diagnostic performance evaluation metrics for AI-based medical image analysis studies. METHODS: Diagnostic accuracy studies that used DL models for the analysis of histopathological images of OSCC compared to the reference standard were analyzed. Six databases (PubMed, Google Scholar, Scopus, Embase, ArXiv, and IEEE) were screened for publications without any time limitation. The QUADAS-2 tool was utilized to assess quality. The meta-analyses included only studies that reported true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) in their test sets. RESULTS: Of 1267 screened studies, 17 studies met the final inclusion criteria. DL methods such as image classification (n = 11) and segmentation (n = 3) were used, and some studies used combined methods (n = 3). On QUADAS-2 assessment, only three studies had a low risk of bias across all applicability domains. For segmentation studies, 0.97 was reported for accuracy, 0.97 for sensitivity, 0.98 for specificity, and 0.92 for Dice. For classification studies, accuracy was reported as 0.99, sensitivity 0.99, specificity 1.0, Dice 0.95, F1 score 0.98, and AUC 0.99. Meta-analysis showed pooled estimates of 0.98 sensitivity and 0.93 specificity. CONCLUSION: Application of AI-based classification and segmentation methods on image analysis represents a fundamental shift in digital pathology. DL approaches demonstrated significantly high accuracy for OSCC detection on histopathology, comparable to that of human experts in some studies. Although AI-based models cannot replace a well-trained pathologist, they can assist through improving the objectivity and repeatability of the diagnosis while reducing variability and human error as a consequence of pathologist burnout.

Validity and reliability of artificial intelligence chatbots as public sources of information on endodontics.

AIM: This study aimed to evaluate and compare the validity and reliability of responses provided by GPT-3.5, Google Bard, and Bing to frequently asked questions (FAQs) in the field of endodontics. METHODOLOGY: FAQs were formulated by expert endodontists (n = 10) and collected through GPT-3.5 queries (n = 10), with every question posed to each chatbot three times. Responses (N = 180) were independently evaluated by two board-certified endodontists using a modified Global Quality Score (GQS) on a 5-point Likert scale (5: strongly agree; 4: agree; 3: neutral; 2: disagree; 1: strongly disagree). Disagreements on scoring were resolved through evidence-based discussions. The validity of responses was analysed by categorizing scores into valid or invalid at two thresholds: The low threshold was set at score ≥4 for all three responses whilst the high threshold was set at score 5 for all three responses. Fisher's exact test was conducted to compare the validity of responses between chatbots. Cronbach's alpha was calculated to assess the reliability by assessing the consistency of repeated responses for each chatbot. RESULTS: All three chatbots provided answers to all questions. Using the low-threshold validity test (GPT-3.5: 95%; Google Bard: 85%; Bing: 75%), there was no significant difference between the platforms (p > .05). When using the high-threshold validity test, the chatbot scores were substantially lower (GPT-3.5: 60%; Google Bard: 15%; Bing: 15%). The validity of GPT-3.5 responses was significantly higher than Google Bard and Bing (p = .008). All three chatbots achieved an acceptable level of reliability (Cronbach's alpha >0.7). CONCLUSIONS: GPT-3.5 provided more credible information on topics related to endodontics compared to Google Bard and Bing.

Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks.

The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field. This narrative review aims to: (A) elaborate on the basic principles of machine learning and deep learning and present the basics of neural network architectures; (B) explain the workflow for developing AI solutions, from data collection through clinical integration; (C) discuss specific AI tasks and applications relevant to endodontic diagnosis and treatment. The article shows that AI offers diverse practical applications in endodontics. Computer vision methods help analyse images while natural language processing extracts insights from text. With robust validation, these techniques can enhance diagnosis, treatment planning, education, and patient care. In conclusion, AI holds significant potential to benefit endodontic research, practice, and education. Successful integration requires an evolving partnership between clinicians, computer scientists, and industry.

Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions.

Artificial intelligence (AI) is emerging as a transformative technology in healthcare, including endodontics. A gap in knowledge exists in understanding AI's applications and limitations among endodontic experts. This comprehensive review aims to (A) elaborate on technical and ethical aspects of using data to implement AI models in endodontics; (B) elaborate on evaluation metrics; (C) review the current applications of AI in endodontics; and (D) review the limitations and barriers to real-world implementation of AI in the field of endodontics and its future potentials/directions. The article shows that AI techniques have been applied in endodontics for critical tasks such as detection of radiolucent lesions, analysis of root canal morphology, prediction of treatment outcome and post-operative pain and more. Deep learning models like convolutional neural networks demonstrate high accuracy in these applications. However, challenges remain regarding model interpretability, generalizability, and adoption into clinical practice. When thoughtfully implemented, AI has great potential to aid with diagnostics, treatment planning, clinical interventions, and education in the field of endodontics. However, concerted efforts are still needed to address limitations and to facilitate integration into clinical workflows.

Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs. MATERIALS AND METHOD: Inclusion criteria were (1) studies employing AI to (2) classify, detect, or segment oral mucosa lesions, (3) on oral photographs of human subjects. Included studies were assessed for risk of bias using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A PubMed, Scopus, Embase, Web of Science, IEEE, arXiv, medRxiv, and grey literature (Google Scholar) search was conducted until June 2023, without language limitation. RESULTS: After initial searching, 36 eligible studies (from 8734 identified records) were included. Based on QUADAS-2, only 7% of studies were at low risk of bias for all domains. Studies employed different AI models and reported a wide range of outcomes and metrics. The accuracy of AI for detecting oral mucosal lesions ranged from 74 to 100%, while that for clinicians un-aided by AI ranged from 61 to 98%. Pooled diagnostic odds ratio for studies which evaluated AI for diagnosing or discriminating potentially malignant lesions was 155 (95% confidence interval 23-1019), while that for cancerous lesions was 114 (59-221). CONCLUSIONS: AI may assist in oral mucosa lesion screening while the expected accuracy gains or further health benefits remain unclear so far. CLINICAL RELEVANCE: Artificial intelligence assists oral mucosa lesion screening and may foster more targeted testing and referral in the hands of non-specialist providers, for example. So far, it remains unclear if accuracy gains compared with specialized can be realized.

Deep learning for temporomandibular joint arthropathies: A systematic review and meta-analysis.

BACKGROUND AND OBJECTIVE: The accurate diagnosis of temporomandibular disorders continues to be a challenge, despite the existence of internationally agreed-upon diagnostic criteria. The purpose of this study is to review applications of deep learning models in the diagnosis of temporomandibular joint arthropathies. MATERIALS AND METHODS: An electronic search was conducted on PubMed, Scopus, Embase, Google Scholar, IEEE, arXiv, and medRxiv up to June 2023. Studies that reported the efficacy (outcome) of prediction, object detection or classification of TMJ arthropathies by deep learning models (intervention) of human joint-based or arthrogenous TMDs (population) in comparison to reference standard (comparison) were included. To evaluate the risk of bias, included studies were critically analysed using the quality assessment of diagnostic accuracy studies (QUADAS-2). Diagnostic odds ratios (DOR) were calculated. Forrest plot and funnel plot were created using STATA 17 and MetaDiSc. RESULTS: Full text review was performed on 46 out of the 1056 identified studies and 21 studies met the eligibility criteria and were included in the systematic review. Four studies were graded as having a low risk of bias for all domains of QUADAS-2. The accuracy of all included studies ranged from 74% to 100%. Sensitivity ranged from 54% to 100%, specificity: 85%-100%, Dice coefficient: 85%-98%, and AUC: 77%-99%. The datasets were then pooled based on the sensitivity, specificity, and dataset size of seven studies that qualified for meta-analysis. The pooled sensitivity was 95% (85%-99%), specificity: 92% (86%-96%), and AUC: 97% (96%-98%). DORs were 232 (74-729). According to Deek's funnel plot and statistical evaluation (p =.49), publication bias was not present. CONCLUSION: Deep learning models can detect TMJ arthropathies high sensitivity and specificity. Clinicians, and especially those not specialized in orofacial pain, may benefit from this methodology for assessing TMD as it facilitates a rigorous and evidence-based framework, objective measurements, and advanced analysis techniques, ultimately enhancing diagnostic accuracy.

Deep learning for determining the difficulty of endodontic treatment: a pilot study.

BACKGROUND: To develop and validate a deep learning model for automated assessment of endodontic case difficulty from periapical radiographs. METHODS: A dataset of 1,386 periapical radiographs was compiled from two clinical sites. Two dentists and two endodontists annotated the radiographs for difficulty using the "simple assessment" criteria from the American Association of Endodontists' case difficulty assessment form in the Endocase application. A classification task labeled cases as "easy" or "hard", while regression predicted overall difficulty scores. Convolutional neural networks (i.e. VGG16, ResNet18, ResNet50, ResNext50, and Inception v2) were used, with a baseline model trained via transfer learning from ImageNet weights. Other models was pre-trained using self-supervised contrastive learning (i.e. BYOL, SimCLR, MoCo, and DINO) on 20,295 unlabeled dental radiographs to learn representation without manual labels. Both models were evaluated using 10-fold cross-validation, with performance compared to seven human examiners (three general dentists and four endodontists) on a hold-out test set. RESULTS: The baseline VGG16 model attained 87.62% accuracy in classifying difficulty. Self-supervised pretraining did not improve performance. Regression predicted scores with ± 3.21 score error. All models outperformed human raters, with poor inter-examiner reliability. CONCLUSION: This pilot study demonstrated the feasibility of automated endodontic difficulty assessment via deep learning models.

A hierarchical deep learning approach for diagnosing impacted canine-induced root resorption via cone-beam computed tomography.

OBJECTIVES: Canine-induced root resorption (CIRR) is caused by impacted canines and CBCT images have shown to be more accurate in diagnosing CIRR than panoramic and periapical radiographs with the reported AUCs being 0.95, 0.49, and 0.57, respectively. The aim of this study was to use deep learning to automatically evaluate the diagnosis of CIRR in maxillary incisors using CBCT images. METHODS: A total of 50 cone beam computed tomography (CBCT) images and 176 incisors were selected for the present study. The maxillary incisors were manually segmented and labeled from the CBCT images by two independent radiologists as either healthy or affected by root resorption induced by the impacted canines. We used five different strategies for training the model: (A) classification using 3D ResNet50 (Baseline), (B) classification of the segmented masks using the outcome of a 3D U-Net pretrained on the 3D MNIST, (C) training a 3D U-Net for the segmentation task and use its outputs for classification, (D) pretraining a 3D U-Net for the segmentation and transfer of the model, and (E) pretraining a 3D U-Net for the segmentation and fine-tuning the model with only the model encoder. The segmentation models were evaluated using the mean intersection over union (mIoU) and Dice coefficient (DSC). The classification models were evaluated in terms of classification accuracy, precision, recall, and F1 score. RESULTS: The segmentation model achieved a mean intersection over union (mIoU) of 0.641 and a DSC of 0.901, indicating good performance in segmenting the tooth structures from the CBCT images. For the main classification task of detecting CIRR, Model C (classification of the segmented masks using 3D ResNet) and Model E (pretraining on segmentation followed by fine-tuning for classification) performed the best, both achieving 82% classification accuracy and 0.62 F1-scores on the test set. These results demonstrate the effectiveness of the proposed hierarchical, data-efficient deep learning approaches in improving the accuracy of automated CIRR diagnosis from limited CBCT data compared to the 3D ResNet baseline model. CONCLUSION: The proposed approaches are effective at improving the accuracy of classification tasks and are helpful when the diagnosis is based on the volume and boundaries of an object. While the study demonstrated promising results, future studies with larger sample size are required to validate the effectiveness of the proposed method in enhancing the medical image classification tasks.

Gene expression study of host-human T-cell leukaemia virus type 1 (HTLV-1) interactions: adult T-cell leukaemia/lymphoma (ATLL).

BACKGROUND: In HTLV-1-associated malignant disease, adult T-cell leukaemia/lymphoma (ATLL), the interaction of virus and host was evaluated at the chemokines gene expression level. Also, IL-1ß and Caspase-1 expressions were evaluated to investigate the importance of pyroptosis in disease development and progression. METHODS AND RESULTS: The expression of host CCR6 and CXCR-3 and the HTLV-1 proviral load (PVL), Tax, and HBZ were assessed in 17 HTLV-1 asymptomatic carriers (ACs) and 12 ATLL patients using the reverse transcription-quantitative polymerase chain reaction (RT-qPCR), TaqMan method. Moreover, RT-qPCR, SYBR Green assay were performed to measure Caspase-1 and IL-1ß expression. HTLV-1-Tax did not express in 91.5% of the ATLLs, while HBZ was expressed in all ATLLs. The expression of CXCR3 dramatically decreased in ATLLs compared to ACs (p = 0.001). The expression of CCR6 was lower in ATLLs than ACs (p = 0.04). The mean of PVL in ATLL patients was statistically higher than ACs (p = 0.001). Furthermore, the expression of the IL-1ß between ATLLs and ACs was not statistically significant (p = 0.4). In contrast, there was a meaningful difference between Caspase-1 in ATLLs and ACs (p = 0.02). CONCLUSIONS: The present study indicated that in the first stage of ATLL malignancy toward acute lymphomatous, CXCR3 and its progression phase may target the pyroptosis process. Mainly, HBZ expression could be a novel therapeutic target.

Deep learning in periodontology and oral implantology: A scoping review.

Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta-analysis was performed. The risk of bias was assessed using the QUADAS-2 tool. We included 47 studies: focusing on imaging data (n = 20) and non-imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi-layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.

The Effect of Heparin and Its Preparations on Disseminated Intravascular Coagulation Mortality and Hospitalization: A Systematic Review.

Methods: The databases of PubMed, Scopus, Embase, and Web of Science were searched systematically up to November 2021. The quality of RCTs was assessed by Cochrane Collaboration's tool and the risk of bias was assessed for cohort studies through NOS score. Results: Out of 3288 articles, eight studies were eligible to be included in this study. Our review retrieved six RCTs and two retrospective cohort studies consisting of 950 participants diagnosed by DIC. A significant effect of heparin on DIC mortality was identified in four studies. Furthermore, heparin was used as a control group in three studies. Conclusions: We concluded that administration of heparin and its preparations in DIC patients could reduce the mortality rate and duration of hospitalization, especially in the earlier stages of DIC.

Effect of mesenchymal stem cells with platelet-rich plasma carriers on bone formation after rapid maxillary expansion: An Animal Study.

OBJECTIVE: To evaluate the effect of bone mesenchymal stem cells (BMSCs) with or without platelet-rich plasma (PRP) carriers on sutural new bone formation after rapid palatal expansion (RPE). SETTINGS AND SAMPLE POPULATION: Sixty male Wistar rats were used in this study. MATERIAL AND METHODS: All samples were subjected to 50cN of palatal expansion force for 7 days followed by 3 weeks of the retention period. The experimental groups received a single-dose injection of the specified solution at the time of retainer placement (BMSCs, PRP, BMSCs+PRP, normal saline). BMSCs used in this study were marked with the green fluorescent protein (GFP). New bone formation (NBF) in the sutural area was evaluated by µCT and occlusal radiography. In addition, semi-quantitative analyses were performed on histology images to analyse the quality of sutural bone, connective tissue and vascularization. Immunohistochemistry analyses were conducted for osteocalcin and collagen type I proteins. RESULTS: After the 21-day retention period, limited GFP marked cells were detected around the sutural area. Samples treated with BMSCs + PRP had the highest NBF and showed higher expression of collagen type I and osteocalcin. CONCLUSION: Injecting BMSCs + PRP may increase sutural bone density significantly. However, injecting BMSCs or PRP carriers alone did not affect sutural bone density.

A Post-Marketing Surveillance Study to Evaluate the Safety Profile of AlvotereⓇ (Docetaxel) in Iranian Patients Diagnosed with Different Types of Cancers Receiving Chemotherapy.

BACKGROUND: Docetaxel is a clinically well established antimitotic chemotherapy medication. Labeled docetaxel indications are breast cancer, gastric cancer, head and neck cancer, non-small cell lung cancer, and prostate cancer. OBJECTIVE: This is a Phase IV study to evaluate the safety profile of docetaxel (Alvotere; NanoAlvand, Iran) in Iranian patients diagnosed with different types of cancers receiving chemotherapy regimens with docetaxel. METHODS: Patients who received Alvotere as a part of their chemotherapy regimen were enrolled in this Phase IV, observational, multicenter, open-label study. Alvotere was administrated as a single agent or in combination with other chemotherapy agents. Safety parameters in each cycle were assessed, and the related data were recorded in booklets. FINDINGS: A total of 411 patients with different types of cancers were enrolled from 25 centers in Iran. The most common malignancies among participants were breast cancer (49.88%), followed by gastric cancer (22.63%). Participants' mean age was 53.33 years, and the mean total dose used in each cycle was 132 mg. According to the results, 341 patients experienced at least 1 adverse event, that the most common was alopecia (41.12%). In total, 92 (22.38%) patients had at least 1 adverse event of grade 3 or 4, and 25 (6.08%) patients showed 54 serious adverse events, which the causality assessment for all was possibly related to Alvotere. There was a significant difference between men and women in the incidence of skin and subcutaneous tissue disorders (55.63% in women vs 41.73% in men; P = 0.009). Also, the incidence of gastrointestinal disorders, nervous system disorders, skin and subcutaneous tissue disorders, hepatic enzymes increase, and fluid retention was significantly higher (P < 0.05) in patients receiving anthracyclines in their chemotherapy regimens. CONCLUSIONS: The findings of this open-label, observational, multicenter, postmarketing surveillance showed that Alvotere appears to have an acceptable safety profile in Iranian cancer patients receiving chemotherapeutic regimens. (Curr Ther Res Clin Exp. 2022; 82:XXX-XXX) © 2022 Elsevier HS Journals, Inc.

Evaluating mRNA expression of tax, B chain of PDGF and PDGF-ß receptors as well as HTLV-I proviral load in ATL patients and healthy carriers.

Adult T-cell leukemia (ATL) is a life-threatening malignant neoplasm of CD4+ T cells resulted from human T-cell leukemia virus type I (HTLV-I). Tax1 protein of HTLV-I can induce malignant proliferation of T-cells by modulating the expression of growth factors such as platelet-derived growth factor (PDGF). Here, we aimed to investigate the proviral load (PVL) of HTLV-I in ATL and also to evaluate the mRNA expression of B chain of PDGF and PDGF-ß receptors in ATL patients and HTLV-I-infected healthy carriers. To this end, peripheral blood mononuclear cells (PBMCs) were isolated by using Ficoll-Histophaque density centrifugation. The mean of HTLV-I PVL in ATL patients (42,759 ± 15,737 copies/104 cells [95% CI, 9557-75962]) was significantly (p = .01) higher than that in healthy carriers (650 ± 107 copies/104 cells [95% CI, 422-879], respectively. The HTLV-I PVL in ATL patients exhibited a significant correlation with PBMC count (R = .495, p = .001). The mRNA expression of Tax, B chain of PDGF, and PDGF-ß receptor genes was significantly higher in healthy carriers than in patients with ATL. In conclusion, the expression of the canonical PDGFß and its receptor, and their correlation with Tax expression cannot be a suitable indicator and/or prognostic factor for progression of ATL in HTLV-I carriers.

Investigating the association between rs6983267 polymorphism and susceptibility to gastrointestinal cancers in Iranian population.

Genome-wide association studies have revealed that some single nucleotide polymorphisms at 8q24, such as rs6983267, might be effective in susceptibility to various cancers in different populations. Therefore, rs6983267 might be useful as a marker for multiple cancers. In this study, we considered a population, including 478 gastrointestinal cancer cases from the Iranian population, to investigate the association between rs6983267 and susceptibility to gastrointestinal cancers. The samples were genotyped using the TaqMan real-time PCR method while 10% of them were also confirmed by sequencing. Higher frequency of G allele was associated with higher grades of tumors in esophageal cancer and the tumors located in the lower portion of the esophagus (OR 3.56; 95% CI 1.13-11.24; P = 0.03) and cardia (OR 5.24; 95% CI 1.26-21.83; P = 0.02), which both locations are involved in esophageal adenocarcinomas with poor prognosis. The results indicated that in the male subgroup, the rs6983267 GG genotype significantly enhanced the gastric cancer susceptibility (OR 4.76; 95% CI 1.57-14.45; P = 0.01). GG genotype also increased the risk of intestinal-type gastric cancer, located in non-cardia (OR 4.62; 95% CI 1.25-17.04; P = 0.02). Moreover, gastric cancer cases and controls with a family history of gastrointestinal tumors were mostly genotyped with the G allele (OR 3.61; 95% CI = 1.09-12.01; P = 0.04). There were no remarkable associations between rs6983267 and susceptibility to esophageal and colon cancers in the Iranian population. However, different genotypes of rs6983267 had significant correlations with tumor grade, cancer type, and family history of gastrointestinal cancers. Further investigations in a larger population and other ethnicities are required to confirm these results.

Machine learning and orthodontics, current trends and the future opportunities: A scoping review.

INTRODUCTION: In recent years, artificial intelligence (AI) has been applied in various ways in medicine and dentistry. Advancements in AI technology show promising results in the practice of orthodontics. This scoping review aimed to investigate the effectiveness of AI-based models employed in orthodontic landmark detection, diagnosis, and treatment planning. METHODS: A precise search of electronic databases was conducted, including PubMed, Google Scholar, Scopus, and Embase (English publications from January 2010 to July 2020). Quality Assessment and Diagnostic Accuracy Tool 2 (QUADAS-2) was used to assess the quality of the articles included in this review. RESULTS: After applying inclusion and exclusion criteria, 49 articles were included in the final review. AI technology has achieved state-of-the-art results in various orthodontic applications, including automated landmark detection on lateral cephalograms and photography images, cervical vertebra maturation degree determination, skeletal classification, orthodontic tooth extraction decisions, predicting the need for orthodontic treatment or orthognathic surgery, and facial attractiveness. Most of the AI models used in these applications are based on artificial neural networks. CONCLUSIONS: AI can help orthodontists save time and provide accuracy comparable to the trained dentists in diagnostic assessments and prognostic predictions. These systems aim to boost performance and enhance the quality of care in orthodontics. However, based on current studies, the most promising application was cephalometry landmark detection, skeletal classification, and decision making on tooth extractions.

Epigenetics evaluation of the oncogenic mechanisms of two closely related bovine and human deltaretroviruses: A system biology study.

Human T-cell lymphotropic virus (HTLV-1) and bovine leukemia virus (BLV) are oncogenic deltaretroviruses, which are the cause of adult T cell leukemia/lymphoma (ATLL) and enzootic bovine leukosis (EBL), respectively. In this study, to evaluate the virus-host interactions in the manifestation of the associated malignancy, four pooled RNA samples of each host (three RNAs in each sample) were applied to RNA-seq. Differential expression analyses were conducted separately between ATLL and EBL groups, in comparison with the healthy group, to identify functional Gene Ontology (GO) terms and hub genes, using DAVID database and MCODE plugin in Cytoscape software, respectively. A broad range of effective genes, involved in the ATLL and EBL, was up- and downregulated. In the virus side, in both malignancy, Tax was expressed very low, but the HTLV-1-HBZ and BVL-As2 transcripts were highly expressed. Some upregulated hub genes, IL2, TOP2A, MKI67, TP73, MYC, and downregulated FOS gene family (FOS, FOSB, and FOSL2), are similarly activated in both human and bovine hosts, in related cell cycle and growth factors. Taken together, it seems that in preventing the infections and cell transformations, Tax must be targeted as a viral factor, and shared peptide in virological and immunological synapses as host factors. Therefore, in the malignant stages, HBZ and As2 transcripts along with growth factors, particularly IL-2R-γ and T-bet or TOP2A, and MKI67 should be targeted in both hosts. Additional studies at the protein level are necessary to elucidate the more useful targets for the therapy of these life-threatening diseases.

Expression and function of long non-coding RNAs in head and neck squamous cell carcinoma.

No longer regarded as junk DNA, long non-coding RNAs (lncRNAs) are considered as master regulators of cancer development and metastasis nowadays. Among the recently appreciated roles of these transcripts is their fundamental contribution in the pathogenesis of head and neck squamous cell carcinoma (HNSCC). Notably, lncRNAs may have interactions with some environmental risk factors for this type of cancer. Moreover, a number of studies have verified diagnostic and prognostic values of lncRNAs in HNSCC. Emerging evidences from functional studies point to the possibility of design of lncRNA-targeted therapies in HNSCC. In the current review, we discuss the molecular mechanisms for participation of lncRNAs in the pathogenesis of HNSCC, their potential application in cancer diagnosis and most importantly in the development of personalized methods for treatment of HNSCC.

The role of long non-coding RNAs in the pathogenesis of thyroid cancer.

Long noncoding RNAs (lncRNAs) as prominent regulators of gene expression are involved in different layers of expression regulation. These transcripts participate in carcinogenesis of several human malignancies including thyroid cancer. Availability of high throughput techniques such as RNA sequencing and microarray has facilitated identification of lncRNAs whose dysregulation affect tumorigenesis process. Moreover, assessment of differentially expressed lncRNAs between resistant and sensitive cells has led to recognition of biomarkers for therapeutic response. One elucidated aspect of lncRNAs functions is their role in sponging miRNAs. Several miRNA-lncRNA-mRNA triplets have been recognized till now. Any of these triplets is a putative target of interfering with the evolution of cancer. In the current study, we have summarized recent data in the fields of biology of lncRNAs, their role in thyroid cancer and their potential as biomarker or treatment target.

Study of the mechanisms of crocetin-induced differentiation and apoptosis in human acute promyelocytic leukemia cells.

Crocetin, the major carotenoid in saffron, exhibits potent anticancer effects. However, the antileukemic effects of crocetin are still unclear, especially in primary acute promyelocytic leukemia (APL) cells. In the current study, the potential antipromyelocytic leukemia activity of crocetin and the underlying molecular mechanisms were investigated. Crocetin (100 µM), like standard anti-APL drugs, all-trans retinoic acid (ATRA, 10 µM) and As2 O 3 (arsenic trioxide, 50 µM), significantly inhibited proliferation and induced apoptosis in primary APL cells, as well as NB4 and HL60 cells. The effect was associated with the decreased expressions of prosurvival genes Akt and BCL2, the multidrug resistance (MDR) proteins, ABCB1 and ABCC1 and the inhibition of tyrosyl-DNA phosphodiesterase 1 (TDP1), while the expressions of proapoptotic genes CASP3, CASP9, and BAX/BCL2 ratio were significantly increased. In contrast, crocetin at relatively low concentration (10 µM), like ATRA (1 µM) and As 2 O 3 (0.5 µM), induced differentiation of leukemic cells toward granulocytic pattern, and increased the number of differentiated cells expressing CD11b and CD14, while the number of the immature cells expressing CD34 or CD33 was decreased. Furthermore, crocetin suppressed the expression of clinical marker promyelocytic leukemia/retinoic acid receptor-α ( PML/RARα) in NB4 and primary APL cells, and reduced the expression of histone deacetylase 1 ( HDAC1) in all leukemic cells. The results suggested that crocetin can be considered as a candidate for future preclinical and clinical trials of complementary APL treatment.