The role of ABCC10/MRP7 in anti-cancer drug resistance and beyond.

Multidrug resistance protein 7 (MRP7), also known as ATP-binding cassette (ABC) transporter subfamily C10 (ABCC10), is an ABC transporter that was first identified in 2001. ABCC10/MRP7 is a 171 kDa protein located on the basolateral membrane of cells. ABCC10/MRP7 consists of three transmembrane domains and two nucleotide binding domains. It mediates multidrug resistance of tumor cells to a variety of anticancer drugs by increasing drug efflux and results in reducing intracellular drug accumulation. The transport substrates of ABCC10/MRP7 include antineoplastic drugs such as taxanes, vinca alkaloids, and epothilone B, as well as endobiotics such as leukotriene C4 (LTC4) and estradiol 17 ß-D-glucuronide. A variety of ABCC10/MRP7 inhibitors, including cepharanthine, imatinib, erlotinib, tariquidar, and sildenafil, can reverse ABCC10/MRP7-mediated MDR. Additionally, the presence or absence of ABCC10/MRP7 is also closely related to renal tubular dysfunction, obesity, and other diseases. In this review, we discuss: 1) Structure and functions of ABCC10/MRP7; 2) Known substrates and inhibitors of ABCC10/MRP7 and their potential therapeutic applications in cancer; and 3) Role of ABCC10/MRP7 in non-cancerous diseases.

In-Depth Comparison of Matrigel Dissolving Methods on Proteomic Profiling of Organoids.

Patient-derived organoids recently emerged as promising ex vivo 3D culture models recapitulating histological and molecular characteristics of original tissues, thus proteomic profiling of organoids could be valuable for function investigation and clinical translation. However, organoids are usually cultured in murine Matrigel (served as scaffolds and matrix), which brings an issue to separate organoids from Matrigel. Because of the complex compositions of Matrigel and thousands of identical peptides shared between Matrigel and organoids, insufficiently dissolved Matrigel could influence proteomic analysis of organoids in multiple ways. Thus, how to dissolve Matrigel matrix and recovery organoid cells efficiently is vital for sample preparation. Here, we comprehensively compared three popular Matrigel dissolving methods (cell recovery solution, dispase, and PBS-EDTA buffer) and investigated the effect of undissolved Matrigel proteins on proteomic profiles of organoids. By integrative analysis of label-free proteomes of Matrigel and stable isotope labeling by amino acids in cell culture proteomes of organoids collected by three methods, respectively, we found that dispase showed an optimal efficiency, with the highest peptide yield and the highest incorporation ratio of stable isotope labeling by amino acids in cell culture labels (97.1%), as well as with the least potential Matrigel contaminants. To help analysis of proteomic profiles of organoids collected by the other two methods, we identified 312 high-confidence Matrigel contaminants, which could be filtered out to attenuate Matrigel interference with minimal loss of biological information. Together, our study identifies bioinformatics and experimental approaches to eliminate interference of Matrigel contaminants efficiently, which will be valuable for basic and translational proteomic research using organoid models.

Resistance-proof antimicrobial drug discovery to combat global antimicrobial resistance threat.

Drug resistance is well-defined as a serious problem in our living world. To survive, microbes develop defense strategies against antimicrobial drugs. Drugs exhibit less or no effective results against microbes after the emergence of resistance because they are unable to cross the microbial membrane, in order to alter enzymatic systems, and/or upregulate efflux pumps, etc. Drug resistance issues can be addressed effectively if a "Resistance-Proof" or "Resistance-Resistant" antimicrobial agent is developed. This article discusses first the need for resistance-proof drugs, the imminent properties of resistance-proof drugs, current and future research progress in the discovery of resistance-proof antimicrobials, the inherent challenges, and opportunities. A molecule having imminent resistance-proof properties could target microbes efficiently, increase potency, and rule out the possibility of early resistance. This review triggers the scientific community to think about how an upsurge in drug resistance can be averted and emphasizes the discussion on the development of next-generation antimicrobials that will provide a novel effective solution to combat the global problem of drug resistance. Hence, resistance-proof drug development is not just a requirement but rather a compulsion in the drug discovery field so that resistance can be battled effectively. We discuss several properties of resistance-proof drugs which could initiate new ways of thinking about next-generation antimicrobials to resolve the drug resistance problem. This article sheds light on the issues of drug resistance and discusses solutions in terms of the resistance-proof properties of a molecule. In summary, the article is a foundation to break new ground in the development of resistance-proof therapeutics in the field of infection biology.

Sunitinib resistance in renal cell carcinoma: From molecular mechanisms to predictive biomarkers.

Currently, renal cell carcinoma (RCC) is the most prevalent type of kidney cancer. Targeted therapy has replaced radiation therapy and chemotherapy as the main treatment option for RCC due to the lack of significant efficacy with these conventional therapeutic regimens. Sunitinib, a drug used to treat gastrointestinal tumors and renal cell carcinoma, inhibits the tyrosine kinase activity of a number of receptor tyrosine kinases, including vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), c-Kit, rearranged during transfection (RET) and fms-related receptor tyrosine kinase 3 (Flt3). Although sunitinib has been shown to be efficacious in the treatment of patients with advanced RCC, a significant number of patients have primary resistance to sunitinib or acquired drug resistance within the 6-15 months of therapy. Thus, in order to develop more efficacious and long-lasting treatment strategies for patients with advanced RCC, it will be crucial to ascertain how to overcome sunitinib resistance that is produced by various drug resistance mechanisms. In this review, we discuss: 1) molecular mechanisms of sunitinib resistance; 2) strategies to overcome sunitinib resistance and 3) potential predictive biomarkers of sunitinib resistance.

Raman spectral classification algorithm of cephalosporin based on VGGNeXt.

In recent years, deep learning has been widely used in the field of Raman spectral classification. However, the majority of the training and test sets are generated by the same device (generally a portable Raman spectrometer), with little difference between them, and the trained model may not be directly applicable to other devices. In this study, we established a database of six cephalosporin Raman spectra and proposed a classification algorithm VGGNeXt for cephalosporin Raman spectra. VGGNeXt takes inspiration from ConvNeXt, borrows some tricks from Swin-T, and re-improves VGG. Training data were high-resolution spectra from a benchtop Raman spectrometer, and test data were low-resolution spectra from a portable Raman spectrometer. The impact of preprocessing and dataset size on algorithm accuracy was explored. The results show that our network outperforms other comparative algorithms in all cases. After preprocessing, the VGGNeXt model achieves 100% accuracy on both full and halved data sets, and 99.9% accuracy when there are only 10 data for each cephalosporin class. The results show that the experimental ideas and processing methods in this paper solve the problems of model transfer and instrument standardization to a certain extent, and the model has good robustness.

Enhancing substance identification by Raman spectroscopy using deep neural convolutional networks with an attention mechanism.

Raman spectroscopy is widely used for substance identification, providing molecular information from various components along with noise and instrument interference. Consequently, identifying components based on Raman spectra remains challenging. In this study, we collected Raman spectral data of 474 hazardous chemical substances using a portable Raman spectrometer, resulting in a dataset of 59 468 spectra. Our research employed a deep neural convolutional network based on the ResNet architecture, incorporating an attention mechanism called the SE module. By enhancing the weighting of certain spectral features, the performance of the model was significantly improved. We also investigated the classification predictive performance of the model under small-sample conditions, facilitating the addition of new hazardous chemical categories for future deployment on mobile devices. Additionally, we explored the features extracted by the convolutional neural network from Raman spectra, considering both Raman intensity and Raman shift aspects. We discovered that the neural network did not solely rely on intensity or shift for substance classification, but rather effectively combined both aspects. This research contributes to the advancement of Raman spectroscopy applications for hazardous chemical identification, particularly in scenarios with limited data availability. The findings shed light on the significance of spectral features in the model's decision-making process and have implications for broader applications of deep learning techniques in Raman spectroscopy-based substance identification.

Therapeutic evolution in HR+/HER2- breast cancer: from targeted therapy to endocrine therapy.

Breast cancer, a complex and varied disease, has four distinct subtypes based on estrogen receptor and human epidermal growth factor receptor 2 (HER2) levels, among which a significant subtype known as HR+/HER2-breast cancer that has spurred numerous research. The prevalence of breast cancer and breast cancer-related death are the most serious threats to women's health worldwide. Current progress in treatment strategies for HR+/HER2-breast cancer encompasses targeted therapy, endocrine therapy, genomic immunotherapy, and supplementing traditional methods like surgical resection and radiotherapy. This review article summarizes the current epidemiology of HR+/HER2-breast cancer, introduces the classification of HR+/HER2-breast cancer and the commonly used treatment methods. The mechanisms of action of various drugs, including targeted therapy drugs and endocrine hormone therapy drugs, and their potential synergistic effects are deeply discussed. In addition, clinical trials of these drugs that have been completed or are still in progress are included.

Hepatocellular carcinoma and lipid metabolism: Novel targets and therapeutic strategies.

Hepatocellular carcinoma (HCC) is an increasingly prevalent disease that is associated with high and continually rising mortality rates. Lipid metabolism holds a crucial role in the pathogenesis of HCC, in which abnormalities pertaining to the delicate balance of lipid synthesis, breakdown, and storage, predispose for the pathogenesis of the nonalcoholic fatty liver disease (NAFLD), a disease precursor to HCC. If caught early enough, HCC treatment may be curative. In later stages, treatment is only halting the inevitable outcome of death, boldly prompting for novel drug discovery to provide a fighting chance for this patient population. In this review, we begin by providing a summary of current local and systemic treatments against HCC. From such we discuss hepatic lipid metabolism and highlight novel targets that are ripe for anti-cancer drug discovery. Lastly, we provide a targeted summary of current known risk factors for HCC pathogenesis, providing key insights that will be essential for rationalizing future development of anti-HCC therapeutics.

A novel method for quantitative determination of multiple substances using Raman spectroscopy combined with CWT.

The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis. We compare six traditional machine learning algorithms and two deep learning models to evaluate the performance of our new method. The experimental results show that our model has achieved good prediction results when predicting the concentration of methanol and ethanol, and the coefficient of determination R2 is greater than 0.999. At different concentrations, both MAPE and RSD outperform other models, which demonstrates that our workflow has outstanding analytical capabilities. Importantly, we have solved the problem that current quantitative analysis algorithms for Raman spectroscopy are almost unable to accurately predict the concentration of multiple substances simultaneously. In conclusion, it is foreseeable that this non-destructive, automated, and highly accurate workflow can further advance Raman spectroscopy.

Actin-related protein 2/3 complex subunit 1B promotes ovarian cancer progression by regulating the AKT/PI3K/mTOR signaling pathway.

Background and Objectives: Actin-related protein 2/3 complex subunit 1B (ARPC1B) is an essential subunit of the actin-related protein 2/3 (Arp2/3) complex. While there have been numerous research reports on Arp2/3 in relation to tumors, there needs to be more research on ARPC1B and its role in tumors, particularly at the pan-cancer level. Methods: Utilizing data from the cancer genome atlas (TCGA) and genotype-tissue expression (GTEx) databases, we analyzed ARPC1B expression differences in normal, tumor, and adjacent tissues, investigating its correlation with prognosis and clinical stages in various cancers. We conducted gene enrichment analysis and explored ARPC1B's connection to the tumor immune microenvironment and its impact on anti-tumor drug resistance. In addition, in vivo and in vitro experiments have also been carried out to find the mechanism of ARPC1B on ovarian cancer (OV) proliferation and invasion. Results: ARPC1B was highly expressed in 33 tumor types, suggesting its role as a tumor-promoting factor. Its expression correlated with poor prognosis and served as a clinical staging marker in over 10 tumor types. ARPC1B is implicated in various biological processes and signaling pathways, uniquely associated with tumor immunity, indicating immunosuppressive conditions in high-expression cases. High ARPC1B expression was linked to resistance to six anti-tumor drugs. Further experiments showed that ARPC1B can affect the proliferation, apoptosis, migration, and invasion of OV cells through the AKT/PI3K/mTOR pathway. Conclusion: ARPC1B is a biomarker for immune suppression, prognosis, clinical staging, and drug resistance, providing new insights for cancer therapeutics.

Tebentafusp: a novel drug for the treatment of metastatic uveal melanoma.

On January 25, 2022, the U.S. Food and Drug Administration (FDA) approved the use of tebentafusp, a bispecific glycoprotein 100 (gp100) peptide-human leukocyte antigen (HLA)-directed CD3 T-cell activator, for the treatment of HLA-A*02:01-positive adult patients with unresectable or metastatic uveal melanoma (mUM). Pharmacodynamic data indicate that tebentafusp targets a specific HLA-A*02:01/gp100 complex, activating both CD4+/CD8+ effector and memory T cells that induce tumor cell death. Tebentafusp is administered to patients via intravenous infusion daily or weekly, depending on the indication. Phase III trials have documented a 1-year overall survival of 73%, overall response rate of 9%, progression-free survival of 31% and disease control rate of 46%. Common adverse events reported are cytokine release syndrome, rash, pyrexia, pruritus, fatigue, nausea, chills, abdominal pain, edema, hypotension, dry skin, headache and vomiting. Compared to other types of melanomas, mUM presents with a distinct profile of genetic mutations, which phenotypically results in limited survival efficacy when using traditional melanoma treatments. The low current treatment efficacy for mUM, alongside a poor long-term prognosis and high mortality rates, gives precedence for the approval of tebentafusp to be groundbreaking in its clinical impact. This review will discuss the pharmacodynamic and pharmacokinetic profile, and the clinical trials used to evaluate the safety and efficacy of tebentafusp.

Towards Chinese text and DNA shift encoding scheme based on biomass plasmid storage.

DNA, as the storage medium in organisms, can address the shortcomings of existing electromagnetic storage media, such as low information density, high maintenance power consumption, and short storage time. Current research on DNA storage mainly focuses on designing corresponding encoders to convert binary data into DNA base data that meets biological constraints. We have created a new Chinese character code table that enables exceptionally high information storage density for storing Chinese characters (compared to traditional UTF-8 encoding). To meet biological constraints, we have devised a DNA shift coding scheme with low algorithmic complexity, which can encode any strand of DNA even has excessively long homopolymer. The designed DNA sequence will be stored in a double-stranded plasmid of 744bp, ensuring high reliability during storage. Additionally, the plasmid's resistance to environmental interference ensuring long-term stable information storage. Moreover, it can be replicated at a lower cost.

Nivolumab and relatlimab for the treatment of melanoma.

Melanoma is a highly lethal type of skin cancer. Although an early diagnosis, in combination with surgery for nonmetastatic melanomas, significantly increases the probability of survival, there are no efficacious treatments for metastatic melanoma. Nivolumab and relatlimab are monoclonal antibodies that selectively interact with and block the proteins programmed cell death protein 1 (PD-1) and lymphocyte activation protein 3 (LAG-3), respectively, and thus, their activation by their cognate ligands. The combination of these immunotherapy drugs was approved in 2022 by the United States Food and Drug Administration (FDA) for the treatment of melanoma. Data from clinical trials indicated that, compared to nivolumab monotherapy, nivolumab and relatlimab produced more than a 2-fold median increase in progression-free survival (PFS) and a higher response rate in melanoma patients. This is an important finding as the response of patients to immunotherapies is limited due to dose-limiting toxicities and secondary drug resistance. This review article will discuss the pathogenesis of melanoma and the pharmacology of nivolumab and relatlimab. In addition, we will provide i) a summary of the anticancer drugs that inhibit LAG-3 and PD-1 in cancer patients and ii) our perspective about the use of nivolumab in combination with relatlimab to treat melanoma.

Amivantamab: a monoclonal EGFR-MET bispecific antibody for EGFR exon 20 insertion in non-small cell lung cancer.

The U.S. Food and Drug Administration (FDA) first approved amivantamab, a monoclonal epidermal growth factor receptor (EGFR)-mesenchymal--epithelial transition factor (MET) bispecific antibody, in May 2021, to treat adult patients with locally advanced or metastatic non-small cell lung cancer (NSCLC) with an insertion mutation in exon 20 of EGFR. The approval of amivantamab represents a targeted therapy for this subtype of advanced NSCLC. In contrast to other drugs that inhibit the tyrosine kinase activity in the protein, EGFR, amivantamab has efficacy in inhibiting EGFR and MET. In this article, we summarize the development of therapeutic drugs for NSCLC, discuss the mechanism of action of amivantamab, review data from clinical trials with amivantamab and suggest future lines of research.

Fine-Grained Butterfly Recognition via Peer Learning Network with Distribution-Aware Penalty Mechanism.

Automatic species recognition plays a key role in intelligent agricultural production management and the study of species diversity. However, fine-grained species recognition is a challenging task due to the quite diverse and subtle interclass differences among species and the long-tailed distribution of sample data. In this work, a peer learning network with a distribution-aware penalty mechanism is proposed to address these challenges. Specifically, the proposed method employs the two-stream ResNeSt-50 as the backbone to obtain the initial predicted results. Then, the samples, which are selected from the instances with the same predicted labels by knowledge exchange strategy, are utilized to update the model parameters via the distribution-aware penalty mechanism to mitigate the bias and variance problems in the long-tailed distribution. By performing such adaptive interactive learning, the proposed method can effectively achieve improved recognition accuracy for head classes in long-tailed data and alleviate the adverse effect of many head samples relative to a few samples of the tail classes. To evaluate the proposed method, we construct a large-scale butterfly dataset (named Butterfly-914) that contains approximately 72,152 images belonging to 914 species and at least 20 images for each category. Exhaustive experiments are conducted to validate the efficiency of the proposed method from several perspectives. Moreover, the superior Top-1 accuracy rate (86.2%) achieved on the butterfly dataset demonstrates that the proposed method can be widely used for agricultural species identification and insect monitoring.

Signaling pathways and therapeutic interventions in gastric cancer.

Gastric cancer (GC) ranks fifth in global cancer diagnosis and fourth in cancer-related death. Despite tremendous progress in diagnosis and therapeutic strategies and significant improvements in patient survival, the low malignancy stage is relatively asymptomatic and many GC cases are diagnosed at advanced stages, which leads to unsatisfactory prognosis and high recurrence rates. With the recent advances in genome analysis, biomarkers have been identified that have clinical importance for GC diagnosis, treatment, and prognosis. Modern molecular classifications have uncovered the vital roles that signaling pathways, including EGFR/HER2, p53, PI3K, immune checkpoint pathways, and cell adhesion signaling molecules, play in GC tumorigenesis, progression, metastasis, and therapeutic responsiveness. These biomarkers and molecular classifications open the way for more precise diagnoses and treatments for GC patients. Nevertheless, the relative significance, temporal activation, interaction with GC risk factors, and crosstalk between these signaling pathways in GC are not well understood. Here, we review the regulatory roles of signaling pathways in GC potential biomarkers, and therapeutic targets with an emphasis on recent discoveries. Current therapies, including signaling-based and immunotherapies exploited in the past decade, and the development of treatment for GC, particularly the challenges in developing precision medications, are discussed. These advances provide a direction for the integration of clinical, molecular, and genomic profiles to improve GC diagnosis and treatments.

LncMyoD Promotes Skeletal Myogenesis and Regulates Skeletal Muscle Fiber-Type Composition by Sponging miR-370-3p.

The development of skeletal muscle is a highly ordered and complex biological process. Increasing evidence has shown that noncoding RNAs, especially long-noncoding RNAs (lncRNAs) and microRNAs, play a vital role in the development of myogenic processes. In this study, we observed that lncMyoD regulates myogenesis and changes myofiber-type composition. miR-370-3p, which is directly targeted by lncMyoD, promoted myoblast proliferation and inhibited myoblast differentiation in the C2C12 cell line, which serves as a valuable model for studying muscle development. In addition, the inhibition of miR-370-3p promoted fast-twitch fiber transition. Further analysis indicated that acyl-Coenzyme A dehydrogenase, short/branched chain (ACADSB) is a target gene of miR-370-3p, which is also involved in myoblast differentiation and fiber-type transition. Furthermore, our data suggested that miR-370-3p was sponged by lncMyoD. In contrast with miR-370-3p, lncMyoD promoted fast-twitch fiber transition. Taken together, our results suggest that miR-370-3p regulates myoblast differentiation and muscle fiber transition and is sponged by lncMyoD.

Investigation of Antibiotic Resistance, Serotype Distribution, and Genetic Characteristics of 164 Invasive Streptococcus pneumoniae from North China Between April 2016 and October 2017.

BACKGROUND: Streptococcus pneumoniae infections are the major cause of global morbidity and mortality among children and patients aged more than 65 years. This study aimed to investigate the antimicrobial resistance, bacterial serotype distribution, and genetic characteristics of invasive S. pneumoniae from different cities in North China. MATERIALS AND METHODS: A total of 164 invasive S. pneumoniae strains were collected from 8 hospitals in 5 regions of North China between April 2016 and October 2017. Minimum inhibitory concentrations (MICs) were determined using the agar dilution method. Capsular serotypes were identified using the Quellung reaction test. Molecular epidemiology was investigated using multilocus sequence typing. RESULTS: S. pneumoniae isolates were highly resistant to macrolides, clindamycin, and tetracycline in all age groups. The overall rate of resistance to penicillin was 56.7%. However, fluoroquinolones and vancomycin maintained excellent antimicrobial activities. The rate of resistance to ß-lactam in strains isolated from children aged less than 18 years was significantly higher than that in strains from other age groups. The most prevalent serotypes were 14 (22.6%), 19F (16.5%), non-vaccine types (14.0%), 19A (9.8%), and 23F (9.1%). The coverage for PCV10 and PCV13 was 59.8% and 75.6%, respectively. The vaccine coverage rate was the highest among children aged less than 5 years. The proportion of penicillin-resistant isolates was higher among vaccine-covered strains compared with non-covered strains. S. pneumoniae showed considerable clonal dissemination, and ST876 (28, 17.1%), ST271 (22, 13.4%), ST81 (17, 10.4%) and ST320 (14, 8.5%) were the major STs. CONCLUSION: All the 164 invasive S. pneumoniae isolates demonstrated high resistance to antibiotics. The coverage of S. pneumoniae vaccine was higher in children than in adults.