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Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.

The interaction between Typhoon Nepartak (2016) and the upper-tropospheric cold low (UTCL) is simulated to better understand the impact of UTCL on the structural and intensity change of tropical cyclones (TCs). An experiment without UTCL is also performed to highlight the quantitative impacts of UTCL. Furthermore, idealized sensitivity experiments are carried out to further investigate the specific TC–UTCL configurations leading to different interactions. It is shown that a TC interacting with the UTCL is associated with a more axisymmetric inner-core structure and an earlier rapid intensification. Three plausible mechanisms related to the causality between a UTCL and the intensity change of TC are addressed. First, the lower energy expenditure on outflow expansion leads to higher net heat energy and intensification rate. Second, the external eddy forcing reinforces the secondary circulation and promotes further TC development. Ultimately, the shear-induced downward and radial ventilation of the low-entropy air is unexpectedly reduced despite the presence of UTCL, leading to stronger inner-core convections in the upshear quadrants. In general, the TC–UTCL interaction process of Nepartak is favorable for TC intensification owing to the additional positive effect and the reduced negative effect. In addition, results from sensitivity experiments indicate that the most favorable interaction would occur when the UTCL is located to the north or northwest of the TC at a stable and proper distance of about one Rossby radius of deformation of the UTCL.

Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software. MCMICRO is a modular and open-source computational pipeline for transforming highly multiplexed whole-slide images of tissues into single-cell data. MCMICRO is versatile and can be used with CODEX, mxIF, CyCIF, mIHC and H&E staining data.

Structural variants (SVs) have been associated with changes in gene expression, which may contribute to alterations in phenotypes and disease development. However, the precise identification and characterization of SVs remain challenging. While long-read sequencing offers superior accuracy for SV detection, short-read sequencing remains essential due to practical and cost considerations, as well as the need to analyze existing short-read datasets. Numerous algorithms for short-read SV detection exist, but none are universally optimal, each having limitations for specific SV sizes and types. In this study, we evaluated the efficacy of six advanced SV detection algorithms, including the commercial software DRAGEN, using the GIAB v0.6 Tier 1 benchmark and HGSVC2 cell lines. We employed both individual and combination strategies, with systematic assessments of recall, precision, and F1 scores. Our results demonstrate that the union combination approach enhanced detection capabilities, surpassing single algorithms in identifying deletions and insertions, and delivered comparable recall and F1 scores to the commercial software DRAGEN. Interestingly, expanding the number of algorithms from three to five in the combination did not enhance performance, highlighting the efficiency of a well-chosen ensemble over a larger algorithmic pool.

Combined PARP and immune checkpoint inhibition has yielded encouraging results in ovarian cancer, but predictive biomarkers are lacking. We performed immunogenomic profiling and highly multiplexed single-cell imaging on tumor samples from patients enrolled in a Phase I/II trial of niraparib and pembrolizumab in ovarian cancer (NCT02657889). We identify two determinants of response; mutational signature 3 reflecting defective homologous recombination DNA repair, and positive immune score as a surrogate of interferon-primed exhausted CD8 + T-cells in the tumor microenvironment. Presence of one or both features associates with an improved outcome while concurrent absence yields no responses. Single-cell spatial analysis reveals prominent interactions of exhausted CD8 + T-cells and PD-L1 + macrophages and PD-L1 + tumor cells as mechanistic determinants of response. Furthermore, spatial analysis of two extreme responders shows differential clustering of exhausted CD8 + T-cells with PD-L1 + macrophages in the first, and exhausted CD8 + T-cells with cancer cells harboring genomic PD-L1 and PD-L2 amplification in the second. A Phase I/II trial previously revealed variable anti-tumor efficacy of the PARP inhibitor niraparib in combination with the PD-1 inhibitor pembrolizumab in platinum-resistant ovarian cancer patients. Here, the authors perform an integrated genomic and immunomics analysis of tumor samples from the same patients and find potential predictive biomarkers of response to such combination therapy.

Derailed transmembrane receptor trafficking could be a hallmark of tumorigenesis and increased tumor invasiveness, but receptor dynamics have not been used to differentiate metastatic cancer cells from less invasive ones. Using single-particle tracking techniques, we  developed a phenotyping asssay named T ransmembrane Re ceptor D ynamics (TReD), studied the dynamics of epidermal growth factor receptor (EGFR) in seven breast epithelial cell lines and developed a phenotyping assay named T ransmembrane Re ceptor D ynamics (TReD). Here we show a clear evidence that increased EGFR diffusivity and enlarged EGFR confinement size in the plasma membrane (PM) are correlated with the enhanced metastatic potential in these cell lines. By comparing the TReD results with the gene expression profiles, we found a clear negative correlation between the EGFR diffusivities and the breast cancer luminal differentiation scores (r = −0.75). Upon the induction of epithelial-mesenchymal transition (EMT), EGFR diffusivity significantly increased for the non-tumorigenic MCF10A (99%) and the non-invasive MCF7 (56%) cells, but not for the highly metastatic MDA-MB-231 cell. We believe that the reorganization of actin filaments during EMT modified the PM structures, causing the receptor dynamics to change. TReD can thus serve as a new biophysical marker to probe the metastatic potential of cancer cells and even to monitor the transition of metastasis.

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

Cancer immunotherapy is a growing field. GM-CSF, a potent cytokine promoting the differentiation of myeloid cells, can also be used as an immunostimulatory adjuvant to elicit antitumor immunity. Additionally, GM-CSF is essential for the differentiation of dendritic cells, which are responsible for processing and presenting tumor antigens for the priming of antitumor cytotoxic T lymphocytes. Some strategies have been developed for GM-CSF-based cancer immunotherapy in clinical practice: GM-CSF monotherapy, GM-CSF-secreting cancer cell vaccines, GM-CSF-fused tumor-associated antigen protein-based vaccines, GM-CSF-based DNA vaccines and GM-CSF combination therapy. GM-CSF also contributes to the regulation of immunosuppression in the tumor microenvironment. This review provides recommendations regarding GM-CSF-based cancer immunotherapy.

Background Ovarian cancer (OC) is the most lethal gynecological cancer due to the recurrence of drug-resistance. Cancer initiating cells (CICs) are proposed to be responsible for the aggressiveness of OC. The rarity and difficulty of in vitro long-term cultivation of CICs challenge the development of CIC-targeting therapeutics. Reprogramming cancer cells into induced cancer initiating cell (iCICs) could be an approach to solve these. Several inducible CICs have been acquired by activating the expression of stemness genes in different cancer cells. However, few reports have demonstrated the feasibility in OC. Methods Patients with primary OC receiving surgery were enrolled. Tumor tissue were collected, and OCT4, SOX2, and NANOG expressions were assessed by immunohistochemistry (IHC) staining to investigate the association of stemness markers with overall survival (OS). An high-grade serous ovarian cancer (HGSOC) cell line, OVCAR-3 was reprogrammed by transducing Yamanaka four factors OCT4, SOX2, KLF4 and MYC ( OSKM ) to establish an iOCIC model, iOVCAR-3-OSKM. CIC characteristics of iOVCAR-3-OSKM were evaluated by RT-PCR, sphere formation assay and animal experiments. Drug-resistance and migration ability were accessed by dye-efflux activity assay, MTT assay and migration assay. Gene profile was presented through RNA-sequencing. Lineage differentiation ability and organoid culture were determined by in vitro differentiation assays. Results In OC patients, the co-expression of multiple stem-related transcription factors ( OCT4, SOX2 , and NANOG ) was associated with worse OS. iOVCAR-3-OSKM cells generated by reprogramming successfully exhibited stemness characteristics with strong sphere-forming and tumorigenesis ability. iOVCAR-3-OSKM cells also showed malignant potential with higher drug resistance to chemodrug, Paclitaxel (PTX) and migration ability. iOVCAR-3-OSKM was maintainable and expandable on feeder-dependent culture condition, it also preserved ovarian lineage differentiation abilities, which could well differentiate into OC cells with CK-7 and CA125 expressions and develop into an organoid mimic poor prognostic OC histological feature. Conclusions The establishment of iOVCAR-3-OSKM not only allows us to fill the gap in the information on induced CICs in OC but also provides a potential strategy to develop personalized CICs and organoid models for treating OC in the near future.

Epigenetic plasticity is a pivotal factor that drives metastasis. Here, we show that the promoter of the gene that encodes the ubiquitin ligase subunit FBXL7 is hypermethylated in advanced prostate and pancreatic cancers, correlating with decreased FBXL7 mRNA and protein levels. Low FBXL7 mRNA levels are predictive of poor survival in patients with pancreatic and prostatic cancers. FBXL7 mediates the ubiquitylation and proteasomal degradation of active c-SRC after its phosphorylation at Ser 104. The DNA-demethylating agent decitabine recovers FBXL7 expression and limits epithelial-to-mesenchymal transition and cell invasion in a c-SRC-dependent manner. In vivo, FBXL7-depleted cancer cells form tumours with a high metastatic burden. Silencing of c-SRC or treatment with the c-SRC inhibitor dasatinib together with FBXL7 depletion prevents metastases. Furthermore, decitabine reduces metastases derived from prostate and pancreatic cancer cells in a FBXL7-dependent manner. Collectively, this research implicates FBXL7 as a metastasis-suppressor gene and suggests therapeutic strategies to counteract metastatic dissemination of pancreatic and prostatic cancer cells.Moro et al. show that hypermethylation-induced silencing of the ubiquitin ligase FBXL7 rescues c-SRC from ubiquitin-mediated degradation and enhances epithelial-to-mesenchymal transition and metastasis.