Galectin-3 contributes to pathogenesis of IgA nephropathy.

IgA nephropathy (IgAN) is the most common type of glomerulonephritis that frequently progresses to kidney failure. However, the molecular pathogenesis underlying IgAN remains largely unknown. Here, we investigated the role of galectin-3 (Gal-3), a galactoside-binding protein in IgAN pathogenesis, and showed that Gal-3 expression by the kidney was significantly enhanced in patients with IgAN. In both TEPC-15 hybridoma-derived IgA-induced, passive, and spontaneous "grouped" ddY IgAN models, Gal-3 expression was clearly increased with disease severity in the glomeruli, peri-glomerular regions, and some kidney tubules. Gal-3 knockout (KO) in the passive IgAN model had significantly improved proteinuria, kidney function and reduced severity of kidney pathology, including neutrophil infiltration and decreased differentiation of Th17 cells from kidney-draining lymph nodes, despite increased percentages of regulatory T cells. Gal-3 KO also inhibited the NLRP3 inflammasome, yet it enhanced autophagy and improved kidney inflammation and fibrosis. Moreover, administration of 6-de-O-sulfated, N-acetylated low-molecular-weight heparin, a competitive Gal-3 binding inhibitor, restored kidney function and improved kidney lesions in passive IgAN mice. Thus, our results suggest that Gal-3 is critically involved in IgAN pathogenesis by activating the NLRP3 inflammasome and promoting Th17 cell differentiation. Hence, targeting Gal-3 action may represent a new therapeutic strategy for treatment of this kidney disease.

CW-NET for multitype cell detection and classification in bone marrow examination and mitotic figure examination.

MOTIVATION: Bone marrow (BM) examination is one of the most important indicators in diagnosing hematologic disorders and is typically performed under the microscope via oil-immersion objective lens with a total 100× objective magnification. On the other hand, mitotic detection and identification is critical not only for accurate cancer diagnosis and grading but also for predicting therapy success and survival. Fully automated BM examination and mitotic figure examination from whole-slide images is highly demanded but challenging and poorly explored. First, the complexity and poor reproducibility of microscopic image examination are due to the cell type diversity, delicate intralineage discrepancy within the multitype cell maturation process, cells overlapping, lipid interference and stain variation. Second, manual annotation on whole-slide images is tedious, laborious and subject to intraobserver variability, which causes the supervised information restricted to limited, easily identifiable and scattered cells annotated by humans. Third, when the training data are sparsely labeled, many unlabeled objects of interest are wrongly defined as background, which severely confuses AI learners. RESULTS: This article presents an efficient and fully automatic CW-Net approach to address the three issues mentioned above and demonstrates its superior performance on both BM examination and mitotic figure examination. The experimental results demonstrate the robustness and generalizability of the proposed CW-Net on a large BM WSI dataset with 16 456 annotated cells of 19 BM cell types and a large-scale WSI dataset for mitotic figure assessment with 262 481 annotated cells of five cell types. AVAILABILITY AND IMPLEMENTATION: An online web-based system of the proposed method has been created for demonstration (see https://youtu.be/MRMR25Mls1A).

Structure-Guided Discovery of PD-1/PD-L1 Interaction Inhibitors: Peptide Design, Screening, and Optimization via Computation-Aided Phage Display Engineering.

Cancer immunotherapy harnesses the immune system to combat tumors and has emerged as a major cancer treatment modality. The PD-1/PD-L1 immune checkpoint modulates interactions between tumor cells and T cells and has been extensively targeted in cancer immunotherapy. However, the monoclonal antibodies known to target this immune checkpoint have considerable side effects, and novel PD-1/PD-L1 inhibitors are therefore required. Herein, a peptide inhibitor to disrupt PD-1/PD-L1 interactions was designed through structure-driven phage display engineering coupled to computational modification and optimization. BetaPb, a novel peptide library constructed by using the known structure of PD-1/PD-L, was used to develop inhibitors against the immune checkpoint, and specific peptides with high affinity toward PD-1 were screened through enzyme-linked immunosorbent assays, homogeneous time-resolved fluorescence, and biolayer interferometry. A potential inhibitor, B8, was preliminarily screened through biopanning. The binding affinity of B8 toward PD-1 was confirmed through computation-aided optimization. Assessment of B8 variants (B8.1, B8.2, B8.3, B8.4, and B8.5) demonstrated their attenuation of PD-1/PD-L1 interactions. B8.4 exhibited the strongest attenuation efficiency at a half-maximal effective concentration of 0.1 µM and the strongest binding affinity to PD-1 (equilibrium dissociation constant = 0.1 µM). B8.4 outperformed the known PD-1/PD-L1 interaction inhibitor PL120131 in disrupting PD-1/PD-L1 interactions, revealing that B8.4 has remarkable potential for modification to yield an antitumor agent. This study provides valuable information for the future development of peptide-based drugs, therapeutics, and immunotherapies for cancer.

Optimization of the stroke hospital selection strategy and the distribution of endovascular thrombectomy resources.

Nowadays, emergency medical technicians (EMTs) decide to send a suspected stroke patient to a primary stroke center (PSC) or to an endovascular thrombectomy (EVT)-capable hospital, based on the Cincinnati Prehospital Stroke Scale (CPSS) and the number of symptoms a patient presents at the scene. Based on existing studies, the patient is likely to have a better functional outcome after three months if the time between the onset of symptoms and receiving EVT treatment is shorter. However, if an acute ischemic stroke (AIS) patient with large vessel occlusion (LVO) is first sent to a PSC, and then needs to be transferred to an EVT-capable hospital, the time to get definitive treatment is significantly increased. For this purpose, We formulate an integer programming model to minimize the expected time to receive a definitive treatment for stroke patients. We then use real-world data to verify the validity of the model. Also, we expand our model to find the optimal redistribution and centralization of EVT resources. It will enable therapeutic teams to increase their experience and skills more efficiently within a short period of time.

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer.

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti-vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.

Single-chain fragment antibody disrupting the EphA4 function as a therapeutic drug for gastric cancer.

Studies have shown that the high expression of EphA4 in gastric cancer tissues may correlate with unfavorable clinical pathological characteristics. Therefore, EphA4 may be an effective target for treating gastric cancer in addition to HER-2/neu. In this study, generated scFv S3 can bind endogenous EphA4 of gastric cancer cells and has significant membrane staining. Additionally, scFv S3 binding to EphA4 inhibits the growth and migration of cancer cells and the growth induction that ephrinA1 generates in gastric cancer cells. We found that EphA4 molecules may degrade through antibody treatment of cells, and the increase in LAMP1 and LAMP2 indicates that lysosome is involved in the degradation. The scFv S3 administration leads to the signals pAKT, pERK, and pSTAT3 decrease in cancer cells. The xenograft model of HER-2/neu low expressing gastric cancer cell SNU-16 exhibits better therapeutic effects by scFv S3 than trastuzumab scFv. The scFv S3 administration in vivo can degrade EphA4 molecules in tumor tissues, decreasing Ki67 and increasing cleaved C3 molecule expression. Furthermore, we identified and validated that scFv S3 generates essential ionic bonding with R162 on EphA4. The antibody may provide effective treatment for patients with gastric cancer and abnormal activation or overexpression of EphA4 signaling.

An auristatin-based antibody-drug conjugate targeting EphA2 in pancreatic cancer treatment.

Pancreatic adenocarcinoma, a highly aggressive form of cancer with a poor prognosis, necessitates the development of innovative treatment strategies. Our prior research showcased the growth-inhibiting effects of the anti-EphA2 antibody drug hSD5 on pancreatic cancer tumors. This antibody targets and induces the degradation of the EphA2 receptor while also prompting the antibody's internalization. A deeper dive into the hSD5 Fab crystallographic structure and docking studies revealed that hSD5's CDRH3 drives the primary interaction between hSD5 and the EphA2 active site. In this study, we developed a novel antibody-drug conjugate (ADC)-the auristatin-based hSD5-vedotin specifically targeting EphA2 in pancreatic cancer cells. This ADC aims at the tumor-specific antigen EphA2, triggering endocytosis and releasing the conjugated payload molecule Monomethyl auristatin E (MMAE), amplifying the tumor-killing effect. Upon cellular entry, hSD5-vedotin demonstrated an impressive tumor-killing response, inhibiting tumor cell growth and promoting apoptosis even at lower antibody concentrations. In a pancreatic cancer xenograft animal model, hSD5-vedotin showcased the potential to suppress tumor growth entirely. Notably, potential immune resistance responses were also observed in recurrent pancreatic cancer tumors. Our empirical results underscore the possibility of developing hSD5-vedotin further, which we anticipate will have a broader and more potent therapeutic impact on pancreatic cancer and other EphA2-related cancers.

The Blockade of Mitogen-Activated Protein Kinase 14 Activation by Marine Natural Product Crassolide Triggers ICD in Tumor Cells and Stimulates Anti-Tumor Immunity.

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.

Effect of using G-FAST to recognize emergent large vessel occlusion: A city-wide community experience.

BACKGROUND/PURPOSE: A prehospital bypass strategy was suggested for large vessel occlusion. This study aimed to evaluate the effect of a bypass strategy using the gaze-face-arm-speech-time test (G-FAST) implemented in a metropolitan community. METHODS: Pre-notified patients with positive Cincinnati Prehospital Stroke Scale and symptom onset <3 h from July 2016 to December 2017 (pre-intervention period) and those with positive G-FAST and symptom onset <6 h from July 2019 to December 2020 (intervention period) were included. Patients aged <20 years and those with missing in-hospital data were excluded. The primary outcomes were the rates of receiving endovascular thrombectomy (EVT) and intravenous thrombolysis (IVT). The secondary outcomes were total prehospital time, door-to-computed tomography (CT) time, door-to-needle (DTN) time, and door-to-puncture (DTP) time. RESULTS: We included 802 and 695 pre-notified patients from the pre-intervention and intervention periods, respectively. The characteristics of the patients in the two periods were similar. In the primary outcomes, pre-notified patients during the intervention period showed higher rates of receiving EVT (4.49% vs. 15.25%, p < 0.001) and IVT (15.34% vs. 21.58%, p = 0.002). In the secondary outcomes, pre-notified patients during intervention period had longer total prehospital time (mean 23.38 vs 25.23 min, p < 0.001), longer door-to-CT time (median 10 vs 11 min, p < 0.001), longer DTN time (median 53 vs 54.5 min, p < 0.001) but shorter DTP time (median 141 vs 139.5 min, p < 0.001). CONCLUSION: The prehospital bypass strategy with G-FAST showed benefits for stroke patients.

Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides.

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.