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Every year cervical cancer affects more than 300,000 people, and on average one woman is diagnosed with cervical cancer every minute. Early diagnosis and classification of cervical lesions greatly boosts up the chance of successful treatments of patients, and automated diagnosis and classification of cervical lesions from Papanicolaou (Pap) smear images have become highly demanded. To the authors’ best knowledge, this is the first study of fully automated cervical lesions analysis on whole slide images (WSIs) of conventional Pap smear samples. The presented deep learning-based cervical lesions diagnosis system is demonstrated to be able to detect high grade squamous intraepithelial lesions (HSILs) or higher (squamous cell carcinoma; SQCC), which usually immediately indicate patients must be referred to colposcopy, but also to rapidly process WSIs in seconds for practical clinical usage. We evaluate this framework at scale on a dataset of 143 whole slide images, and the proposed method achieves a high precision 0.93, recall 0.90, F-measure 0.88, and Jaccard index 0.84, showing that the proposed system is capable of segmenting HSILs or higher (SQCC) with high precision and reaches sensitivity comparable to the referenced standard produced by pathologists. Based on Fisher’s Least Significant Difference (LSD) test (P < 0.0001), the proposed method performs significantly better than the two state-of-the-art benchmark methods (U-Net and SegNet) in precision, F-Measure, Jaccard index. For the run time analysis, the proposed method takes only 210 seconds to process a WSI and is 20 times faster than U-Net and 19 times faster than SegNet, respectively. In summary, the proposed method is demonstrated to be able to both detect HSILs or higher (SQCC), which indicate patients for further treatments, including colposcopy and surgery to remove the lesion, and rapidly processing WSIs in seconds for practical clinical usages.

PurposeTikTok, a social media application (app), was originally positioned as a short music video community suitable for young users, and the app is user-generated content (UGC) short video of vertical music. Users can make their own creative videos. Following the rhythm of the music, users can shoot various video content, personal talents, life records, performances, dances, plot interpretations, etc. However, what are the profiles and preferences of TikTok users, whereby the social media app is mainly developed by UGC? What is the impact of TikTok on the development of social media? In addition, what is UGC's social media model for user interactions in social networks? The purpose of this paper is to address and study these proposed issues.Design/methodology/approachAll questionnaire items are designed as nominal and ordinal scales (not Likert scale). The obtained data from questionnaires are put into the relational database (N = 2,011). This empirical study takes Taiwan TikTok users as the research object, implements data mining analytics to generate user profiles through clustering analysis and further uses association rules’ analysis to analyze social media apps in social network interaction and social apps’ development by proposing two patterns and several meaningful rules.FindingsThis study finds that social media apps is a valuable practical research topic on online social media development. In addition, besides the TikTok, the authors eagerly await subsequent research to provide more valuable findings of social media apps in both theory and practice.Originality/valueThis study presents the research evidences that social media apps such as TikTok will be able to transcend the current development pattern of social media and make good use of the media and technology innovation of apps in social development and social informatics.

Thyroid cancer is the most common endocrine cancer. Papillary thyroid cancer (PTC) is the most prevalent form of malignancy among all thyroid cancers arising from follicular cells. Fine needle aspiration cytology (FNAC) is a non-invasive method regarded as the most cost-effective and accurate diagnostic method of choice in diagnosing PTC. Identification of BRAF (V600E) mutation in thyroid neoplasia may be beneficial because it is specific for malignancy, implies a worse prognosis, and is the target for selective BRAF inhibitors. To the authors’ best knowledge, this is the first automated precision oncology framework effectively predict BRAF (V600E) immunostaining result in thyroidectomy specimen directly from Papanicolaou-stained thyroid fine-needle aspiration cytology and ThinPrep cytological slides, which is helpful for novel targeted therapies and prognosis prediction. The proposed deep learning (DL) framework is evaluated on a dataset of 118 whole slide images. The results show that the proposed DL-based technique achieves an accuracy of 87%, a precision of 94%, a sensitivity of 91%, a specificity of 71% and a mean of sensitivity and specificity at 81% and outperformed three state-of-the-art deep learning approaches. This study demonstrates the feasibility of DL-based prediction of critical molecular features in cytological slides, which not only aid in accurate diagnosis but also provide useful information in guiding clinical decision-making in patients with thyroid cancer. With the accumulation of data and the continuous advancement of technology, the performance of DL systems is expected to be improved in the near future. Therefore, we expect that DL can provide a cost-effective and time-effective alternative tool for patients in the era of precision oncology.

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide due to metastasis. Paraspeckle component 1 (PSPC1) upregulation has been identified as an HCC pro-metastatic activator associated with poor patient prognosis, but with a lack of targeting strategy. Here, we report that PSPC1, a nuclear substrate of PTK6, sequesters PTK6 in the nucleus and loses its metastasis driving capability. Conversely, PSPC1 upregulation or PSPC1-Y523F mutation promotes epithelial-mesenchymal transition, stemness, and metastasis via cytoplasmic translocation of active PTK6 and nuclear translocation of β-catenin, which interacts with PSPC1 to augment Wnt3a autocrine signaling. The aberrant nucleocytoplasmic shuttling of active PTK6/β-catenin is reversed by expressing the PSPC1 C-terminal interacting domain (PSPC1-CT131), thereby suppressing PSPC1/PTK6/β-catenin-activated metastasis to prolong the survival of HCC orthotopic mice. Thus, PSPC1 is the contextual determinant of the oncogenic switch of PTK6/β-catenin subcellular localizations, and PSPC1-CT131 functions as a dual inhibitor of PSPC1 and PTK6 with potential for improving cancer therapy. PSPC1 has a critical role in promoting EMT and metastasis. Here, the authors demonstrate that PSPC1 is the contextual determinant of the oncogenic switch of PTK6/β-catenin subcellular localizations to drive metastasis of hepatocellular carcinoma cells via a PSPC1/PTK6/β-catenin signaling.

Dramatic advances in immune therapy have emerged as a promising strategy in cancer therapeutics. In addition to chemotherapy and radiotherapy, inhibitors targeting immune-checkpoint molecules such as cytotoxic T-lymphocyte antigen-4 (CTLA-4), programmed cell death receptor-1 (PD-1) and its ligand (PD-L1) demonstrate impressive clinical benefits in clinical trials. In this review, we present background information about therapies involving PD-1/PD-L1 blockade and provide an overview of current clinical trials. Furthermore, we present recent advances involving predictive biomarkers associated with positive therapeutic outcomes in cancer immunotherapy.

Background Full-endoscopic lumbar discectomy (FELD) is an alternative to posterior open surgery to treat a high-grade migrated herniated disc. However, because of the complexity of the surgery, success is dependent on the surgeon’s skill. Therefore, patients are frequently treated using open discectomy. Anatomical constraints and technical difficulties can lead to the incomplete removal of high-grade migrated discs. Methods We retrospectively reviewed patients who had undergone FELD performed by a single surgeon between January 2010 and January 2014 from a prospective spine registry in an institute. Perioperative records and data of the Oswestry Disability Index, visual analog scale scores (preoperatively and 2 weeks, 6 weeks, 3 months, 6 months, 1 year, 2 years, and 5 years after the operation), and MacNab criteria were collected. Results Of 58 patients with a follow-up duration of > 5 years, (41 and 17 patients had undergone transforaminal endoscopic lumbar discectomy [TELD] and interlaminar endoscopic lumbar discectomy [IELD], respectively), the satisfaction rate was 87.8% (five unsatisfactory cases) for TELD and 100% for IELD. The overall percentage of patients with good to excellent results according to modified MacNab criteria was 91.3% (53/58 patients). Two patients had residual discs. Two patients needed an open discectomy due to recurrent disc herniation. One IELD patient received spinal fusion surgery due to segmental instability after 5 years. Conclusion FELD has a high success rate for the management of high-grade migrated herniated discs. In patients with high-grade disc migration from L1 to L5, TELD is effective and safe. However, for L4–L5 and L5–S1 high-grade upward and downward disc migration, IELD is the favorable option and provides high patient satisfaction.

Spinal sarcopenia is a complex and multifactorial disorder associated with a loss of strength, increased frailty, and increased risks of fractures and falls. In addition, spinal sarcopenia has been associated with lumbar spine disorders and osteoporosis, which renders making decisions on treatment modalities difficult. Patients with spinal sarcopenia typically exhibit lower cumulative survival, a higher risk of in-hospital complications, prolonged hospital stays, higher postoperative costs, and higher rates of blood transfusion after thoracolumbar spine surgery. Several studies have focused on the relationships between spinal sarcopenia, appendicular muscle mass, and bone-related problems—such as osteoporotic fractures and low bone mineral density—and malnutrition and vitamin D deficiency. Although several techniques are available for measuring sarcopenia, each of them has its advantages and shortcomings. For treating spinal sarcopenia, nutrition, physical therapy, and medication have been proven to be effective; regenerative therapeutic options seem to be promising owing to their repair and regeneration potential. Therefore, in this narrative review, we summarize the characteristics, detection methodologies, and treatment options for spinal sarcopenia, as well as its role in spinal disorders.

Immunogenic cell death (ICD) refers to a type of cell death that stimulates immune responses. It is characterized by the surface exposure of damage-associated molecular patterns (DAMPs), which can facilitate the uptake of antigens by dendritic cells (DCs) and stimulate DC activation, resulting in T cell immunity. The activation of immune responses through ICD has been proposed as a promising approach for cancer immunotherapy. The marine natural product crassolide, a cembranolide isolated from the Formosan soft coral Lobophytum michaelae, has been shown to have cytotoxic effects on cancer cells. In this study, we investigated the effects of crassolide on the induction of ICD, the expression of immune checkpoint molecules and cell adhesion molecules, as well as tumor growth in a murine 4T1 mammary carcinoma model. Immunofluorescence staining for DAMP ectolocalization, Western blotting for protein expression and Z′-LYTE kinase assay for kinase activity were performed. The results showed that crassolide significantly increased ICD and slightly decreased the expression level of CD24 on the surface of murine mammary carcinoma cells. An orthotopic tumor engraftment of 4T1 carcinoma cells indicated that crassolide-treated tumor cell lysates stimulate anti-tumor immunity against tumor growth. Crassolide was also found to be a blocker of mitogen-activated protein kinase 14 activation. This study highlights the immunotherapeutic effects of crassolide on the activation of anticancer immune responses and suggests the potential clinical use of crassolide as a novel treatment for breast cancer.

Thyroid cancer is the most common cancer in the endocrine system, and papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, accounting for 70 to 80% of all thyroid cancer cases. In clinical practice, visual inspection of cytopathological slides is an essential initial method used by the pathologist to diagnose PTC. Manual visual assessment of the whole slide images is difficult, time consuming, and subjective, with a high inter-observer variability, which can sometimes lead to suboptimal patient management due to false-positive and false-negative. In this study, we present a fully automatic, efficient, and fast deep learning framework for fast screening of papanicolaou-stained thyroid fine needle aspiration (FNA) and ThinPrep (TP) cytological slides. To the authors’ best of knowledge, this work is the first study to build an automated deep learning framework for identification of PTC from both FNA and TP slides. The proposed deep learning framework is evaluated on a dataset of 131 WSIs, and the results show that the proposed method achieves an accuracy of 99%, precision of 85%, recall of 94% and F1-score of 87% in segmentation of PTC in FNA slides and an accuracy of 99%, precision of 97%, recall of 98%, F1-score of 98%, and Jaccard-Index of 96% in TP slides. In addition, the proposed method significantly outperforms the two state-of-the-art deep learning methods, i.e., U-Net and SegNet, in terms of accuracy, recall, F1-score, and Jaccard-Index (p<0.001). Furthermore, for run-time analysis, the proposed fast screening method takes 0.4 min to process a WSI and is 7.8 times faster than U-Net and 9.1 times faster than SegNet, respectively.

Ovarian cancer is a common malignant gynecological disease. Molecular target therapy, i.e., antiangiogenesis with bevacizumab, was found to be effective in some patients of epithelial ovarian cancer (EOC). Although careful patient selection is essential, there are currently no biomarkers available for routine therapeutic usage. To the authors’ best knowledge, this is the first automated precision oncology framework to effectively identify and select EOC and peritoneal serous papillary carcinoma (PSPC) patients with positive therapeutic effect. From March 2013 to January 2021, we have a database, containing four kinds of immunohistochemical tissue samples, including AIM2, c3, C5 and NLRP3, from patients diagnosed with EOC and PSPC and treated with bevacizumab in a hospital-based retrospective study. We developed a hybrid deep learning framework and weakly supervised deep learning models for each potential biomarker, and the experimental results show that the proposed model in combination with AIM2 achieves high accuracy 0.92, recall 0.97, F-measure 0.93 and AUC 0.97 for the first experiment (66% training and 34%testing) and high accuracy 0.86 ± 0.07, precision 0.9 ± 0.07, recall 0.85 ± 0.06, F-measure 0.87 ± 0.06 and AUC 0.91 ± 0.05 for the second experiment using five-fold cross validation, respectively. Both Kaplan-Meier PFS analysis and Cox proportional hazards model analysis further confirmed that the proposed AIM2-DL model is able to distinguish patients gaining positive therapeutic effects with low cancer recurrence from patients with disease progression after treatment (p < 0.005).