Biochemistry and Cell Biology最新文献

Each anti-cancer drug has special effects on the target cells. One of the most important reasons to recommend an anti-cancer drug is related to the influences of it on the mechanical properties of the target cells. In this study, the effects of cetuximab and cisplatin anti-cancer drugs on the mechanical properties of A-549 and Calu-6 cells as the cancerous lung cells have been investigated. For both of the cells and anti-cancer drugs, MTT assessment has been used to define the convenient dosages for 24 and 48 h incubations based on IC50 concentration for the cell line viability. The mechanical specifications of the cells before and after treatment were obtained using nanoindentation by the JPK Instruments' NanoWizard3 atomic force microscope. The results show that cetuximab increases the stiffness of A-549 cell from 1225 to 3403 and 12 690 Pa for 24 and 48 h incubations. The influence of cetuximab on the Calu-6 shows that the elastic modulus after 24 and 48 h culture times increases about cisplatin anti-cancer drug, for A-549 cell indicates that the elastic modulus rises from 1225 to 1506 and 2375 Pa for 24 and 48 h, respectively. For Calu-6 cell, cisplatin has an important role to increase the stiffness of the cell. Applying cisplatin increases the elastic modulus from 33 to 682.8 Pa for 24 h and 1105 Pa after 48 h incubations.

GPRC5A is the first member of a new class of orphan receptors coupled to G proteins, which also includes GPRC5B, GPRC5C, and GPRC5D. Since its cloning and identification in the 1990s, substantial progress has been made in understanding the possible functions of this receptor. GPRC5A has been implicated in a variety of cellular events, such as cytoskeleton reorganization, cell proliferation, cell cycle regulation, migration, and survival. It appears to be a central player in different pathological processes, including tumorigenesis, inflammation, immune response, and tissue damage. The levels of GPRC5A expression differ depending on the type of cancer, with increased expression in colon, pancreas, and prostate cancers; decreased expression in lung cancer; and varied results in breast cancer. In this review, we discuss the early discovery of GPRC5A as a phorbol ester-induced gene and later as a retinoic acid-induced gene, its regulation, and its participation in important canonical pathways related to numerous types of tumors and inflammatory processes. GPRC5A represents a potential new target for cancer, inflammation, and immunity therapies.

For the past 6 months, there has been an ongoing revolution in Iran after the brutal death of Zhina (Mahsa) Amini in morality police custody. Iranian universities' professors and students have been on the frontline of this revolution and have been fired or sentenced. On the other hand, Iranian high schools and primary schools have been under suspected toxic gas attack. In the current article, the latest status of oppression of the university students and professors and toxic gas attack on primary and high schools in Iran has been evaluated.

Insensitivity and resistance to 5-fluorouracil (5FU) remain as major hurdles for effective and durable 5FU-based chemotherapy in colorectal cancer (CRC) patients. In this study, we identified prostaglandin E synthase (PTGES)/prostaglandin E2 (PGE2) axis as an important regulator for 5FU sensitivity in CRC cells. We found that PTGES expression and PGE2 production are elevated in CRC cells in comparison to normal colorectal epithelial cells. Depletion of PTGES significantly enhanced the inhibitory effect of 5FU on CRC cell viability that was fully reverted by exogenous supplement of PGE2. Inhibition of PTGES enzymatic function, by either inducing loss-of-function mutant or treatment with selective inhibitors, phenocopied the PTGES depletion in terms of 5FU sensitization. Mechanistically, PTGES/PGE2 axis modulates glycolysis in CRC cells, thereby regulating the 5FU sensitivity. Importantly, high PTGES expression is correlated with poor prognosis in 5FU-treated CRC patients. Thus, our study defines PTGES/PGE2 axis as a novel therapeutic target for enhancing the efficacy of 5FU-based chemotherapy in CRC.

Nootkatone (NKT) exhibits potential pharmacological activities including anti-oxidation and anti-inflammation. Nevertheless, little is known about the roles of NKT in asthmatic airway inflammation. In the study, mice were sensitized and challenged with ovalbumin (OVA) to establish experimental allergic asthma model. After treatment with NKT, lung tissues, peripheral blood, and bronchoalveolar lavage fluid (BALF) were collected to assess inflammatory cytokines, oxidative stress, and pathological alternations. The effects of NKT on regulating reactive oxygen species (ROS)-induced NLR family pyrin domain containing 3 (NLRP3) inflammasome activation was assessed in IL-13-treated BEAS-2B cell model. We found that NKT treatment decreased the production of Th2 inflammatory cytokines (IL-4, IL-5, and IL-13) in BALF and IgE levels in serum, and alleviated inflammatory cell penetration, goblet cell proliferation, collagen accumulation, and mucus hypersecretion in lung tissues. NKT treatment mitigated oxidative stress and NLRP3 inflammasome activation in asthmatic mice. IL-13 treatment induced oxidative stress and NLRP3-mediated pyroptosis in BEAS-2B bronchial epithelial cells, whereas these effects were blocked by NKT. NKT protected against airway remodeling, as indicated by decreased epithelial-mesenchymal transition. Taken together, these results demonstrate that NKT mitigates asthmatic airway inflammation by inhibiting ROS-triggered NLRP3 activation and may be a potential agent for treating asthma.

Cerebral microbleeds (CMBs) in the brain are the essential indicators of critical brain disorders such as dementia and ischemic stroke. Generally, CMBs are detected manually by experts, which is an exhaustive task with limited productivity. Since CMBs have complex morphological nature, manual detection is prone to errors. This paper presents a machine learning-based automated CMB detection technique in the brain susceptibility-weighted imaging (SWI) scans based on statistical feature extraction and classification. The proposed method consists of three steps: (1) removal of the skull and extraction of the brain; (2) thresholding for the extraction of initial candidates; and (3) extracting features and applying classification models such as random forest and naïve Bayes classifiers for the detection of true positive CMBs. The proposed technique is validated on a dataset consisting of 20 subjects. The dataset is divided into training data that consist of 14 subjects with 104 microbleeds and testing data that consist of 6 subjects with 63 microbleeds. We were able to achieve 85.7% sensitivity using the random forest classifier with 4.2 false positives per CMB, and the naïve Bayes classifier achieved 90.5% sensitivity with 5.5 false positives per CMB. The proposed technique outperformed many state-of-the-art methods proposed in previous studies.

A medical disorder known as diabetic retinopathy (DR) affects people who suffer from diabetes. Many people are visually impaired due to DR. Primary cause of DR in patients is high blood sugar, and it affects blood vessels available in the retinal cell. The recent advancement in deep learning and computer vision methods, and their automation applications can recognize the presence of DR in retinal cells and vessel images. Authors have proposed an attention-based hybrid model to recognize diabetes in early stage to prevent harmful clauses. Proposed methodology uses DenseNet121 architecture for convolution learning and then, the feature vector will be enhanced with channel and spatial attention model. The proposed architecture also simulates binary and multiclass classification to recognize the infection and the spreading of disease. Binary classification recognizes DR images either positive or negative, while multiclass classification represents an infection on a scale of 0-4. Simulation of the proposed methodology has achieved 98.57% and 99.01% accuracy for multiclass and binary classification, respectively. Simulation of the study also explored the impact of data augmentation to make the proposed model robust and generalized. Attention-based deep learning model has achieved remarkable accuracy to detect diabetic infection from retinal cellular images.

Globally, retinal disorders impact thousands of individuals. Early diagnosis and treatment of these anomalies might halt their development and prevent many people from developing preventable blindness. Iris spot segmentation is critical due to acquiring iris cellular images that suffer from the off-angle iris, noise, and specular reflection. Most currently used iris segmentation techniques are based on edge data and noncellular images. The size of the pigment patches on the surface of the iris increases with eye syndrome. In addition, iris images taken in uncooperative settings frequently have negative noise, making it difficult to segment them precisely. The traditional diagnosis processes are costly and time consuming since they require highly qualified personnel and have strict environments. This paper presents an explainable deep learning model integrated with a multiclass support vector machine to analyze iris cellular images for early pigment spot segmentation and classification. Three benchmark datasets MILE, UPOL, and Eyes SUB were used in the experiments to test the proposed methodology. The experimental results are compared on standard metrics, demonstrating that the proposed model outperformed the methods reported in the literature regarding classification errors. Additionally, it is observed that the proposed parameters are highly effective in locating the micro pigment spots on the iris surfaces.

Professor Mohammad Hashemi was a clinical biochemist and cancer genetic scientist. He has been chair and head of Department of Clinical Biochemistry at Zahedan University of Medical Sciences, Zahedan, Iran. He has played an important role in the improvement of understanding of genetics of disease in southeast Iran. He was also a part of international team for the discovery of the role of calprotectin (S100A8/A9) in cancer biology via regulation of cell fate in tumor cells. He had over 300 peer-reviewed scientific publications and trained significant numbers of high quality personals (>40) in the field of biomedical sciences. His sudden death in 2019 shocked national and international scientific society but his scientific legacy will remain alive forever.