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Receptor tyrosine kinases (RTKs) play a crucial role in cellular signaling and oncogenic progression. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) have become the standard treatment for advanced non-small cell lung cancer (NSCLC) patients with EGFR-sensitizing mutations, but resistance frequently emerges between 10 to 14 months. A significant factor in this resistance is the role of human EGFR 3 (HER3), an EGFR family member. Despite its significance, effective targeting of HER3 is still developing. This review aims to bridge this gap by deeply examining HER3’s pivotal contribution to EGFR TKI resistance and spotlighting emerging HER3-centered therapeutic avenues, including monoclonal antibodies (mAbs), TKIs, and antibody-drug conjugates (ADCs). Preliminary results indicate combining HER3-specific treatments with EGFR TKIs enhances antitumor effects, leading to an increased objective response rate (ORR) and prolonged overall survival (OS) in resistant cases. Embracing HER3-targeting therapies represents a transformative approach against EGFR TKI resistance and emphasizes the importance of further research to optimize patient stratification and understand resistance mechanisms.

[This corrects the article DOI: 10.3389/fimmu.2023.1332057.].

Abstract Background The control of schistosomiasis is particularly difficult in sub-Saharan Africa, which currently harbours 95% of this disease. The target population for preventive chemotherapy (PC) is expanded to all age group at risk of infection, thus increasing the demands of praziquantel (PZQ) tablets according to the new released guideline by World Health Organization. Due to the gap between available PZQ for PC and requirements, alternative approaches to assess endemicity of schistosomiasis in a community, are urgently needed for more quick and precise methods. We aimed to find out to which degree the infection status of snails can be used to guide chemotherapy against schistosomiasis. Methods We searched literature published from January 1991 to December 2022, that reported on the prevalence rates of Schistosoma mansoni, S. haematobium in the intermediate snails Biomphalaria spp. and Bulinus spp., respectively, and in humans. A random effect model for meta-analyses was used to calculate the pooled prevalence estimate (PPE), with heterogeneity assessed using I-squared statistic (I 2 ), with correlation and regression analysis for the exploration of the relationship between human S. mansoni and S. haematobium infections and that in their specific intermediate hosts. Results Forty-seven publications comprising 59 field investigations were included. The pooled PPE of schistosomiasis, schistosomiasis mansoni and schistosomiasis haematobium in humans were 27.5% [95% confidence interval (CI): 24.0–31.1%], 25.6% (95% CI: 19.9–31.3%), and 28.8% (95% CI: 23.4–34.3%), respectively. The snails showed an overall infection rate of 8.6% (95% CI: 7.7–9.4%), with 12.1% (95% CI: 9.9–14.2%) in the Biomphalaria spp. snails and 6.9% (95% CI: 5.7–8.1%) in the Bulinus spp. snails. The correlation coefficient was 0.3 (95% CI: 0.01–0.5%, P

Neoadjuvant chemotherapy (NACT) was developed with the aims of shrinking tumors or stopping cancer cells from spreading before surgery. Unfortunately, not all breast cancer patients will benefit from NACT, and thus, patients must weigh the risks and benefits of treatment prior to the initiation of therapy. Currently, the data for predicting the efficacy of NACT is limited. Molecular testing, such as Oncotype DX, MammaPrint, and Curebest 95GC, have been developed to assist which breast cancer patients will benefit from the treatment. Patients with an increased level of Human Leukocyte Antigen-DR isotype, tumor-infiltrating lymphocytes, Fizzy-related protein homolog, and a decreased level of tumor-associated macrophages appear to benefit most from NACT.

Motion recognition provides movement information for people with physical dysfunction, the elderly and motion-sensing games production, and is important for accurate recognition of human motion. We employed three classical machine learning algorithms and three deep learning algorithm models for motion recognition, namely Random Forests (RF), K-Nearest Neighbors (KNN) and Decision Tree (DT) and Dynamic Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Compared with the Inertial Measurement Unit (IMU) worn on seven parts of body. Overall, the difference in performance among the three classical machine learning algorithms in this study was insignificant. The RF algorithm model performed best, having achieved a recognition rate of 96.67%, followed by the KNN algorithm model with an optimal recognition rate of 95.31% and the DT algorithm with an optimal recognition rate of 94.85%. The performance difference among deep learning algorithm models was significant. The DNN algorithm model performed best, having achieved a recognition rate of 97.71%. Our study validated the feasibility of using multidimensional data for motion recognition and demonstrated that the optimal wearing part for distinguishing daily activities based on multidimensional sensing data was the waist. In terms of algorithms, deep learning algorithms based on multi-dimensional sensors performed better, and tree-structured models still have better performance in traditional machine learning algorithms. The results indicated that IMU combined with deep learning algorithms can effectively recognize actions and provided a promising basis for a wider range of applications in the field of motion recognition.

The identification of novel biomarkers and therapeutic targets in advanced cancer is critical for improving cancer diagnosis and therapeutics. Survivin (SV) is highly expressed predominantly in most cancer cells and tissues but is absent or undetectable in terminally differentiated normal adult tissues. Therefore, it functions as an almost universal tumor antigen. Peptides are short chains of amino acids linked by peptide bonds. To obtain novel SV decamers that are able to induce SV-specific cytotoxic T lymphocytes (CTLs) with a higher cytotoxic efficiency against cancer cells, major histocompatibility complex (MHC) peptide binding algorithms were conducted to predict nine modified SV95 decamers (from SV95-2 to SV95-10) based on the natural SV95-104 peptide sequence of ELTLGEFLKL (here defined as SV95-1). The fluorescent density of each SV95 peptide was determined by a MHC stability assay, followed by the generation of SV95-specific CTLs with each SV95 peptide (from SV95-1 to SV95-10) and human dendritic cells (DCs) loaded with Poly(lactic-co-glycolic) acid (PLGA) nanoparticles encapsulated with SV95 peptide. Finally, IFN-γ ELISpot and CytoTox 96® Non-Radioactive Cytotoxicity Assays were employed to verify their cytotoxic efficiency of the SV95-specific CTLs generated with the corresponding artificial antigen presenting cells (aAPCs) containing SV95 (SV95-1 to SV95-10) peptide. Furthermore, the cytotoxicity of the SV95 specific CTLs generated with nine mutated SV95 peptides was compared to the one generated with natural SV95-1 peptide and TIL2080 cells. The results indicated that the HLA-A2-restricted mutated SV95 epitope decamers (SV95-6 and SV95-7) showed significant higher binding ability compared to natural peptide SV95-1 in MHC stability assay. More importantly, SV95-specific CTLs with higher cytotoxicity were successfully induced with both SV95-6 and SV95-7 peptides, which significantly eliminated target cells (not only SV95-1 peptide pulsed T2 cells, but also both HLA-A2 and SV positive cancer cells) when compared to those generated with natural SV95-1 peptide and TIL2080 cells. These findings suggest that the SV95-6 and SV95-7 peptides are two novel HLA-A2-restricted CTL epitopes and may be useful for the immunotherapy for patients with survivin expressing cancer.

Objective: To study the inhibitory effects on colorectal cancer (CRC) and the underlying mechanism of the petroleum ether extract of Aegiceras corniculatum leaves (PACL). Materials and Methods: The effect of PACL on the proliferation of CRC cell lines DLD-1, HT-29, and SW480 was measured by 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium assay and colony-forming assay. And then, a wound-healing assay was used to measure the migration ability of three CRC cells. The cell cycle and apoptosis of three CRC cells were measured by PI/RNase staining and annexin V-FITC/double staining, respectively, and the intrinsic apoptosis pathway was studied by the Western blot. The anti-CRC effect of PACL in vivo was evaluated by HT-29 xenograft zebrafish embryos. Results: PACL inhibited cell viability and proliferation in DLD-1, HT-29, and SW480 cells in a dose- and time-dependent manner. PACL can inhibit cell migration in DLD-1 and SW480 cells but not in the less mobile phenotype cell HT-29. PACL treatment resulted in cell cycle arrest of DLD-1 and HT-29 cells in the G2/M phase. Moreover, PACL can induce apoptosis in all three CRC cells, which may be achieved by regulating the intrinsic apoptosis pathway mediated by mitochondria and the endoplasmic reticulum. Interestingly, the tumor sizes were decreased after treatment with PACL and PACL combined with fluorouracil in HT-29 xenograft zebrafish embryos. Conclusions: These findings suggested that PACL may exert its anti-CRC effect by inducing apoptosis through the intrinsic apoptosis pathway and show a significant anti-CRC effect in vitro and in vivo, so it might be potentially developed as an anti-CRC agent.

ObjectiveTo develop a deep learning (DL) model for predicting axillary lymph node (ALN) metastasis using dynamic ultrasound (US) videos in breast cancer patients.MethodsA total of 271 US videos from 271 early breast cancer patients collected from Xiang’an Hospital of Xiamen University andShantou Central Hospitabetween September 2019 and June 2021 were used as the training, validation, and internal testing set (testing set A). Additionally, an independent dataset of 49 US videos from 49 patients with breast cancer, collected from Shanghai 10th Hospital of Tongji University from July 2021 to May 2022, was used as an external testing set (testing set B). All ALN metastases were confirmed using pathological examination. Three different convolutional neural networks (CNNs) with R2 + 1D, TIN, and ResNet-3D architectures were used to build the models. The performance of the US video DL models was compared with that of US static image DL models and axillary US examination performed by ultra-sonographers. The performances of the DL models and ultra-sonographers were evaluated based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, gradient class activation mapping (Grad-CAM) technology was also used to enhance the interpretability of the models.ResultsAmong the three US video DL models, TIN showed the best performance, achieving an AUC of 0.914 (95% CI: 0.843-0.985) in predicting ALN metastasis in testing set A. The model achieved an accuracy of 85.25% (52/61), with a sensitivity of 76.19% (16/21) and a specificity of 90.00% (36/40). The AUC of the US video DL model was superior to that of the US static image DL model (0.856, 95% CI: 0.753-0.959, P

Background: Neurofilament light chain (NfL) is a biomarker of neuronal injury in hereditary ATTR (ATTRv) amyloidosis. However, the correlation between NfL and nerve conduction study (NCS), the standard test for ATTRv neuropathy, has not been investigated. Objective: Elucidate the correlation between NfL and NCS parameters. Methods: 227 serum NfL measurements were performed in 45 ATTRv patients, 5 asymptomatic carriers, and 12 controls. Among them, 177 simultaneous analyses of NCS and NfL were conducted in 45 ATTRv patients. Results: NfL levels of symptomatic patients were significantly higher than those of asymptomatic carriers and controls. Serum NfL levels were correlated with NCS parameters, especially compound muscle action potential (CMAP) and sensory nerve action potential (SNAP) amplitudes, indicators of axonal damage. CMAP and/or SNAP amplitudes were undetectable in 9 patients (no-amplitude group) due to advanced neuropathy. NfL levels in the no-amplitude group were significantly higher than those in patients with detectable CMAP/SNAP. NfL levels significantly decreased with patisiran, although no significant changes were observed in CMAP and SNAP. Conclusions: NfL levels are found to be correlated with CMAP/SNAP amplitudes. Compared with NCS, NfL can be a more sensitive biomarker for detecting treatment response and active nerve damage even in patients with advanced neuropathy. [ABSTRACT FROM AUTHOR]