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Liquid biopsies, based on cell free DNA (cfDNA) and proteins, have shown the potential to detect early stage cancers of diverse tissue types. However, most of these studies were retrospective, using individuals previously diagnosed with cancer as cases and healthy individuals as controls. Here, we developed a liquid biopsy assay, named the hepatocellular carcinoma screen (HCCscreen), to identify HCC from the surface antigen of hepatitis B virus (HBsAg) positive asymptomatic individuals in the community population. The training cohort consisted of individuals who had liver nodules and/or elevated serum α-fetoprotein (AFP) levels, and the assay robustly separated those with HCC from those who were non-HCC with a sensitivity of 85% and a specificity of 93%. We further applied this assay to 331 individuals with normal liver ultrasonography and serum AFP levels. A total of 24 positive cases were identified, and a clinical follow-up for 6–8 mo confirmed four had developed HCC. No HCC cases were diagnosed from the 307 test-negative individuals in the follow-up during the same time-scale. Thus, the assay showed 100% sensitivity, 94% specificity, and 17% positive predictive value in the validation cohort. Notably, each of the four HCC cases was at the early stage (<3 cm) when diagnosed. Our study provides evidence that the use of combined detection of cfDNA alterations and protein markers is a feasible approach to identify early stage HCC from asymptomatic community populations with unknown HCC status.

Tyrosine (Tyr) is crucial to the maintenance of the monoclonal antibody (mAb) titers and quality attributes in fed-batch cultures of recombinant Chinese hamster ovary (rCHO) cells. However, the relation between tyrosine and these aspects is not yet fully defined. In order to further elucidate such a relation, two groups of fed-batch experiments with high tyrosine (H-T) or low tyrosine (L-T) additions producing an IgG1 monoclonal antibody against CD20 were implemented to investigate the intracellular and extracellular effects of tyrosine on the culture performance. It was found that the scarcity of tyrosine led to the distinctive reduction in both viable cell density and antibody specific production rate, hence the sharply reduced titer, possibly related to the impaired translation efficiency caused by the substrate limitation of tyrosine. In addition, alterations to the critical quality attributes were detected in the L-T group, compared to those in the H-T condition. Notable decrease in the contents of intact antibody was found under the L-T condition because of the elevated reductive level in the supernatant. Moreover, the aggregate content in the L-T condition was also reduced, probably resulting from the accumulation of extracellular cystine. In particular, the lysine variant content noticeably increased with tyrosine limitation owing to the downregulation of two carboxypeptidases, i.e., CpB and CpH. Overall, understanding the role of tyrosine in these aspects is fundamental to the increase of product titers and control of critical quality attributes in the monoclonal antibody production of rCHO cell fed-batch cultures.Key points• Tyrosine is essential in the maintenance of product titers and the control of product qualities in high cell density cultivations in rCHO cell.• This study revealed the bottleneck of decreased qmAbupon the deficiency of tyrosine.• The impact of tyrosine on the critical product qualities and the underlying mechanisms were also thoroughly assessed.

The animation production process is traditionally labor-intensive, requiring extensive manual effort in character motion design, scene composition, and post-production editing. To overcome these limitations, this research introduces a robot-assisted automation system integrated with artificial intelligence (AI) to streamline and accelerate animation development. The system incorporates a motion capture interface for acquiring human movement data, a feedback-enabled robotic arm to replicate and analyze motions, and a simulation environment for virtual testing. Preprocessing includes missing-value handling and Z-score normalization, after which structured motion sequences (3D joint coordinates, robotic servo positions, and torque data) are provided as input to the Scalable Elephant Herding-tuned Conditional Generative Adversarial Network (SEH-ConGAN). The model generates refined outputs such as smooth motion trajectories, facial expression synthesis, and context-aware style transfer. Statistical analysis using a paired t-test, 95% confidence intervals, and Cohen’s d effect size was performed to confirm the significant performance improvement of SEH-ConGAN over baseline models Performance is evaluated using 5-fold cross-validation and achieves an accuracy of 0.96, precision of 0.97, recall of 0.96, and F1-score of 0.96. Comparative analysis of motion generation metrics shows that SEH-ConGAN surpasses existing models achieving the best MPJPE (16.7), FID (11.3), Smoothness (0.028), and Diversity (0.72), demonstrating superior motion accuracy, trajectory smoothness, and animation realism. . The findings demonstrate that combining robotics with SEH-ConGAN provides a scalable solution for producing high-quality animations with reduced time, cost, and manual intervention.

Background Secretory carcinoma of the breast is one of the rarest entities, accounting for less than 0.15 % of all infiltrating breast carcinomas. It has characteristic histopathological and molecular features and, in general, a more favorable prognosis. In this case report, we describe a local, advanced secretory carcinoma of the breast with aggressive course and an unfavorable outcome. Case presentation A hard, painless, and palpably bossed mass approximately 12.0 cm in diameter occupied most of the left breast of a 39-year-old woman with fixation to the overlying skin. Breast ultrasonography and magnetic resonance imaging (MRI) scans gave the same grading as BI-RADS IV. A needle biopsy was performed, and the pathological diagnosis was secretory carcinoma. Neoadjuvant chemotherapy (NAC) was then performed, after which ultrasonography and MRI scans revealed chemo-resistance of the tumor to NAC. Left breast mastectomy and axillary lymphadenectomy were subsequently performed. Tumor cells were triple-negative and positive for S-100 and periodic acid-Schiff (PAS) staining. Fluorescence in-situ hybridization (FISH) analysis indicated a fusion arrangement of the ETV6-NTRK3 gene. The patient developed multiple distant metastases in the brain and died of these metastases 19 months after initial diagnosis. Conclusions Secretory carcinomas of the breast have been described as a low-grade histologic subtype with a favorable prognosis. This case showed chemo-resistance to neoadjuvant chemotherapy, multiple distant metastases, and a final unfavorable outcome. Further research is needed to better understand the behavior and treatment of this rare tumor.

Deep learning (DL) systems could improve diagnostic accuracy and efficiency in detecting cervical atypia, but their effectiveness remains insufficiently explored. This multicentre, randomised crossover trial evaluated the clinical utility of a DL system in cervical cytopathology. A total of 1,920 women aged 18 years or older undergoing liquid-based cytology for cervical cancer screening were included, and their slides were digitized and randomly assigned (1:1) to two reading sequences. Four non-expert cytopathologists with 1–3 years of experience assessed slides using DL assistance for one group and manual microscopy for the other, and then switched roles after a four-week washout period. Each slide was evaluated twice in a randomly shuffled order. DL significantly improved sensitivity (85.7% vs 71.3%, p  < 0.001), with a difference of 14.3% (95% CI: 7.6% to 21.1%), exceeding the 5% superiority margin. Specificity was comparable (86.5% vs 85.1%, p  = 0.238), and non-inferiority was supported, as the lower limit of the 95% CI for the difference (1.4%; 95% CI: −1.0% to 3.8%) was above the pre-specified margin of −5%. Reading time was markedly reduced with DL (175 seconds vs 31 seconds, p  < 0.001). DL assistance could enhance both sensitivity and efficiency while rigorously preserving specificity in cervical cytology interpretation. Trial registration: ChiCTR2300078722.

Accurate prognosis prediction is essential for guiding cancer treatment and improving patient outcomes. While recent studies have demonstrated the potential of histopathological images in survival analysis, existing models are typically developed in a cancer-specific manner, lack extensive external validation, and often rely on molecular data that are not routinely available in clinical practice. To address these limitations, we present PROGPATH, a unified model capable of integrating histopathological image features with routinely collected clinical variables to achieve pancancer prognosis prediction. PROGPATH employs a weakly supervised deep learning architecture built upon the foundation model for image encoding. Morphological features are aggregated through an attention-guided multiple instance learning module and fused with clinical information via a cross-attention transformer. A router-based classification strategy further refines the prediction performance. PROGPATH was trained on 7999 whole-slide images (WSIs) from 6,670 patients across 15 cancer types, and extensively validated on 17 external cohorts with a total of 7374 WSIs from 4441 patients, covering 12 cancer types from 8 consortia and institutions across three continents. PROGPATH achieved consistently superior performance compared with state-of-the-art multimodal prognosis prediction models. It demonstrated strong generalizability across cancer types and robustness in stratified subgroups, including early- and advanced-stage patients, treatment cohorts (radiotherapy and pharmaceutical therapy), and biomarker-defined subsets. We further provide model interpretability by identifying pathological patterns critical to PROGPATH’s risk predictions, such as the degree of cell differentiation and extent of necrosis. Together, these results highlight the potential of PROGPATH to support pancancer outcome prediction and inform personalized cancer management strategies.

Inhibitors of cyclin-dependent kinases (Cdks) have been reported to have activities in many types of cancer cells by inhibiting Cdk7 and Cdk9, which control transcription. SNS-032 is a potent and selective inhibitor of Cdk2, Cdk7 and Cdk9 and has emerged in clinical trials. Here, we examined the viability of MCF-7 and MDA-MB-435 breast cancer cells in the presence of SNS-032 and observed a dose-dependent inhibition of cellular proliferation in both cell lines. SNS-032 had a direct apoptosis-inducing effect through both the extrinsic and intrinsic apoptotic pathways in breast cancer cells as shown by a dose-dependent increase in Annexin V-positive cells and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL)-positive cells, as well as activation of caspase-8, -9 and poly(ADP-ribose) polymerase (PARP). At the molecular level, SNS-032 induced a marked dephosphorylation of serine 2 and 5 of RNA polymerase (RNA Pol) II and blocked RNA synthesis. Consistent with the inherently rapid turnover rates of their transcripts and proteins, the anti-apoptotic proteins Mcl-1 and X-linked inhibitor of apoptosis protein (XIAP) were rapidly reduced on exposure to SNS-032. Our results also indicated that SNS-032 suppressed the growth of breast cancer xenografts in mice. These data demonstrate that the use of SNS-032 may be a rational and novel therapeutic strategy for human breast cancer and warrants further clinical investigation.

Background DNA‐Dependent Protein Kinase Catalytic Subunit (PRKDC), a key component of the DNA damage repair pathway, is associated with chemotherapy resistance and tumor progression. Methods Here we analyzed transcriptome data of ~2,000 breast cancer patients and performed functional studies in vitro to investigate the function of PRKDC in breast cancer. Results Our results revealed overexpression of PRKDC in multiple breast cancer subtypes. Consistent with patients’ data, overexpression of PRKDC was also observed in breast cancer cell lines compared to normal breast epithelial cells. Knockdown of PRKDC in MCF‐7 and T47D breast cancer cell lines resulted in proliferation inhibition, reduced colony formation and G2/M cell cycle arrest. Furthermore, we showed that PRKDC knockdown induced proliferation inhibition through activation of p38 MAPK, but not ERK MAPK, signaling pathway in breast cancer cells. Blockage of p38 MAPK signaling could largely rescue proliferation inhibition and cell cycle arrest induced by PRKDC knockdown. Moreover, we analyzed gene expression and clinical data from six independent breast cancer cohorts containing ~1,000 patients. In all cohorts, our results consistently showed that high expression of PRKDC was significantly associated with poor survival in both treated and untreated breast cancer patients. Conclusion Together, our results suggest that high expression of PRKDC facilitates breast cancer cell growth via regulation of p38 MAPK signaling, and is a prognostic marker for poor survival in breast cancer patients. High expression of PRKDC promotes breast cancer cell growth via p38 MAPK signaling.

Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task 1 , 2 . Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations 3 . Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer. A study describes the development of a generalizable foundation machine learning framework to extract pathology imaging features for cancer diagnosis and prognosis prediction.

The (1 −  x )K 0.5 Na 0.5 NbO 3  −  x (Ba 0.85 Ca 0.15 )(Zr 0.1 Ti 0.9 )O 3 (KNN–BCTZ) lead-free ceramics were fabricated by conventional solid-state sintering technique. The microstructure and electrical properties of the ceramics were investigated. The X-ray diffraction analysis revealed that the ceramics formed a single phase perovskite solid solutions with the symmetry of orthorhombic at x  < 0.03. The crystal phase of the ceramics changed from orthorhombic phase to pseudocubic phase when x  > 0.04. The coexistence of orthorhombic and pseudocubic (tetragonal) phases was observed near room temperature when 0.03 ≤  x  ≤ 0.04. The grains grew up obviously when 2 mol% BCTZ was added, but the grain size was found to reduce gradually with further increasing BCTZ content. The T C and T O-T decreased with the increasing BCTZ content. The ferroelectric and piezoelectric properties were abruptly degraded as x  ≥ 0.05. Optimum properties ( d 33  = 136 pC/N, k p  = 27 %, k t  = 26.5 %, Q m  = 25, P r  = 14.67 μC/cm 2 , E c  = 11.23 kV/cm, T C  = 347 °C, ε 33 T / ε 0 = 8 6 1.5 , tan δ  = 0.04) were obtained for the ceramica with x  = 0.03.