Glutamine addiction in tumor cell: oncogene regulation and clinical treatment.

After undergoing metabolic reprogramming, tumor cells consume additional glutamine to produce amino acids, nucleotides, fatty acids, and other substances to facilitate their unlimited proliferation. As such, the metabolism of glutamine is intricately linked to the survival and progression of cancer cells. Consequently, targeting the glutamine metabolism presents a promising strategy to inhibit growth of tumor cell and cancer development. This review describes glutamine uptake, metabolism, and transport in tumor cells and its pivotal role in biosynthesis of amino acids, fatty acids, nucleotides, and more. Furthermore, we have also summarized the impact of oncogenes like C-MYC, KRAS, HIF, and p53 on the regulation of glutamine metabolism and the mechanisms through which glutamine triggers mTORC1 activation. In addition, role of different anti-cancer agents in targeting glutamine metabolism has been described and their prospective applications are assessed.

Long-term trends of lung cancer incidence and survival in southeastern China, 2011-2020: a population-based study.

BACKGROUND: Lung cancer is the primary cause of cancer-related deaths in China. This study analysed the incidence and survival trends of lung cancer from 2011 to 2020 in Fujian Province, southeast of China, and provided basis for formulating prevention and treatment strategies. METHODS: The population-based cancer data was used to analyse the incidence of lung cancer between 2011 and 2020, which were stratified by sex, age and histology. The change of incidence trend was analysed using Joinpoint regression. The relative survival of lung cancer with onset in 2011-2014, 2015-2017 and 2018-2020 were calculated using the cohort, complete and period methods, respectively. RESULTS: There were 23,043 patients diagnosed with lung cancer in seven registries between 2011 and 2020, with an age-standardized incidence rate (ASIR) of 37.7/100,000. The males ASIR increased from 51.1/100,000 to 60.5/100,000 with an annual percentage change (APC) of 1.5%. However, females ASIR increased faster than males, with an APC of 5.7% in 2011-2017 and 21.0% in 2017-2020. Compared with 2011, the average onset age of males and females in 2020 was 1.5 years and 5.9 years earlier, respectively. Moreover, the proportion of adenocarcinoma has increased, while squamous cell carcinoma and small cell carcinoma have decreased over the past decade. The 5-year relative survival of lung cancer increased from 13.8 to 23.7%, with a greater average increase in females than males (8.7% and 2.6%). The 5-year relative survival of adenocarcinoma, squamous cell carcinoma and small cell carcinoma reached 47.1%, 18.3% and 6.9% in 2018-2020, respectively. CONCLUSIONS: The incidence of lung cancer in Fujian Province is on the rise, with a significant rise in adenocarcinoma, a younger age of onset and the possibility of overdiagnosis. Thus, Fujian Province should strengthen the prevention and control of lung cancer, giving more attention to the prevention and treatment of lung cancer in females and young populations.

Waste Biomass-Mediated Synthesis of TiO2/P, K-Containing Grapefruit Peel Biochar Composites with Enhanced Photocatalytic Activity.

In this study, TiO2/P, K-containing grapefruit peel biochar (TiO2/P, K-PC) composites were synthesized in situ biomimetically using grapefruit peel as the bio-template and carbon source and tetrabutyl titanate as the titanium source. This was achieved using the two-step rotary impregnation-calcination method. Adjusting the calcination temperature of the sample in an air atmosphere could regulate the mass ratio of TiO2 to carbon. The prepared samples were subjected to an analysis of their compositions, structures, morphologies, and properties. It demonstrated that the prepared samples were complexes of anatase TiO2 and P, K-containing carbon, with the presence of graphitic carbon. They possessed a unique morphological structure with abundant pores and a large surface area. The grapefruit peel powder played a crucial role in the induction and assembly of TiO2/P, K-PC composites. The sample PCT-400-550 had the best photocatalytic activity, with the degradation rate of RhB, MO, and MB dye solutions reaching more than 99% within 30 min, with satisfactory cyclic stability. The outstanding photocatalytic activity can be credited to its unique morphology and the efficient collaboration between TiO2 and P, K-containing biochar.

Extracellular vesicle-cell adhesion molecules in tumours: biofunctions and clinical applications.

Cell adhesion molecule (CAM) is an umbrella term for several families of molecules, including the cadherin family, integrin family, selectin family, immunoglobulin superfamily, and some currently unclassified adhesion molecules. Extracellular vesicles (EVs) are important information mediators in cell-to-cell communication. Recent evidence has confirmed that CAMs transported by EVs interact with recipient cells to influence EV distribution in vivo and regulate multiple cellular processes. This review focuses on the loading of CAMs onto EVs, the roles of CAMs in regulating EV distribution, and the known and possible mechanisms of these actions. Moreover, herein, we summarize the impacts of CAMs transported by EVs to the tumour microenvironment (TME) on the malignant behaviour of tumour cells (proliferation, metastasis, immune escape, and so on). In addition, from the standpoint of clinical applications, the significance and challenges of using of EV-CAMs in the diagnosis and therapy of tumours are discussed. Finally, considering recent advances in the understanding of EV-CAMs, we outline significant challenges in this field that require urgent attention to advance research and promote the clinical applications of EV-CAMs. Video Abstract.

Diagnosis of lung squamous cell carcinoma based on metagenomic Next-Generation Sequencing.

BACKGROUND: The clinical treatment of patients suspected of pulmonary infections often rely on empirical antibiotics. However, preliminary diagnoses were based on clinical manifestations and conventional microbiological tests, which could later be proved wrong. In this case, we presented a patient whose initial diagnosis was lung abscess, but antibiotic treatments had no effect, and metagenomic Next-Generation Sequencing (mNGS) indicated presence of neoplasm. CASE PRESENTATION: A 62-year-old female was diagnosed with lung abscess at three different health facilities. However, mNGS of bronchoalveolar lavage fluid did not support pulmonary infections. Rather, the copy number variation analysis using host DNA sequences suggested neoplasm. Using H&E staining and immunohistochemistry of lung biopsy, the patient was eventually diagnosed with lung squamous cell carcinoma. CONCLUSIONS: mNGS not only detects pathogens and helps diagnose infectious diseases, but also has potential in detecting neoplasm via host chromosomal copy number analysis. This might be beneficial for febrile patients with unknown or complex etiology, especially when infectious diseases were initially suspected but empirical antibiotic regimen failed.

Enhanced photocatalytic activity of porous TiO2 containing C/P/K derived from grapefruit peel.

Grapefruit peel possesses a porous structure and a significant specific surface area. In this study, we introduce an innovative and eco-friendly approach for synthesizing porous TiO2. This was accomplished by employing grapefruit peel as a bio-template and tetrabutyl titanate as the precursor, utilizing a two-step rotary impregnation-calcination process. The TiO2 faithfully reproduced the structural characteristics of the grapefruit peel across different scales, simultaneously incorporating C, P, K elements from the original grapefruit peel into the final samples. The fabricated samples were analyzed using XRD, XPS, SEM, TEM, BET, and UV-vis DRS. The results showed that the TiO2 displays an anatase phase, and possesses a high specific surface area. The investigation of photocatalytic performance demonstrated that the CPK-TiO2-10 sample exhibited outstanding photocatalytic activity against Rhodamine B (RhB) solution, achieving complete degradation within 60 minutes. Additionally, the total organic carbon (TOC) removal rate reached 91.34% after 60 minutes of irradiation. The sample maintained a high degradation efficiency, even after five recycling cycles. This exceptional performance can be attributed to its porous structure, enriched with pores and a larger surface area, as well as the beneficial effects of doping with C, P, K elements in TiO2.

Crosstalk between autophagy and CSCs: molecular mechanisms and translational implications.

Cancer stem cells(CSCs) play a key role in regulating tumorigenesis, progression, as well as recurrence, and possess typical metabolic characteristics. Autophagy is a catabolic process that can aid cells to survive under stressful conditions such as nutrient deficiency and hypoxia. Although the role of autophagy in cancer cells has been extensively studied, CSCs possess unique stemness, and their potential relationship with autophagy has not been fully analyzed. This study summarizes the possible role of autophagy in the renewal, proliferation, differentiation, survival, metastasis, invasion, and treatment resistance of CSCs. It has been found that autophagy can contribute to the maintenance of CSC stemness, facilitate the tumor cells adapt to changes in the microenvironment, and promote tumor survival, whereas in some other cases autophagy acts as an important process involved in the deprivation of CSC stemness thus leading to tumor death. Mitophagy, which has emerged as another popular research area in recent years, has a great scope when explored together with stem cells. In this study, we have aimed to elaborate on the mechanism of action of autophagy in regulating the functions of CSCs to provide deeper insights for future cancer treatment.

Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning.

Prediction of kidney function and chronic kidney disease (CKD) through kidney ultrasound imaging has long been considered desirable in clinical practice because of its safety, convenience, and affordability. However, this highly desirable approach is beyond the capability of human vision. We developed a deep learning approach for automatically determining the estimated glomerular filtration rate (eGFR) and CKD status. We exploited the transfer learning technique, integrating the powerful ResNet model pretrained on an ImageNet dataset in our neural network architecture, to predict kidney function based on 4,505 kidney ultrasound images labeled using eGFRs derived from serum creatinine concentrations. To further extract the information from ultrasound images, we leveraged kidney length annotations to remove the peripheral region of the kidneys and applied various data augmentation schemes to produce additional data with variations. Bootstrap aggregation was also applied to avoid overfitting and improve the model's generalization. Moreover, the kidney function features obtained by our deep neural network were used to identify the CKD status defined by an eGFR of <60 ml/min/1.73 m2. A Pearson correlation coefficient of 0.741 indicated the strong relationship between artificial intelligence (AI)- and creatinine-based GFR estimations. Overall CKD status classification accuracy of our model was 85.6% -higher than that of experienced nephrologists (60.3%-80.1%). Our model is the first fundamental step toward realizing the potential of transforming kidney ultrasound imaging into an effective, real-time, distant screening tool. AI-GFR estimation offers the possibility of noninvasive assessment of kidney function, a key goal of AI-powered functional automation in clinical practice.

A machine learning approach for predicting urine output after fluid administration.

BACKGROUND AND OBJECTIVE: To develop a machine learning model to predict urine output (UO) in sepsis patients after fluid resuscitation. METHODS: We identified sepsis patients in the Multiparameter Intelligent Monitoring in Intensive Care-III v1.4 database according to the Sepsis-3 criteria. We focused on two outcomes: whether the UO decreased after fluid administration and whether oliguria (defined as UO less than the threshold of 0.5 mL/kg/h) developed. A gradient tree-based machine learning model implemented with an eXtreme Gradient Boosting algorithm was used to integrate relevant physiological parameters for predicting the aforementioned outcomes. A confusion matrix was computed. RESULTS: A total of 232,929 events in 19,275 patients were included. Using decreased UO as the outcome measure, the optimal model achieved an area under the curve (AUC) of 0.86; for predicting oliguria, most models achieved an AUC greater than 0.86, and the highest sensitivity was 92.2% when the model was applied to patients with baseline oliguria. CONCLUSIONS: Machine learning could help clinicians evaluate fluid status in sepsis patients after fluid administration, thus preventing fluid overload-related complications.

Reconstruction of sparse-view X-ray computed tomography using adaptive iterative algorithms.

In this paper, we propose two reconstruction algorithms for sparse-view X-ray computed tomography (CT). Treating the reconstruction problems as data fidelity constrained total variation (TV) minimization, both algorithms adapt the alternate two-stage strategy: projection onto convex sets (POCS) for data fidelity and non-negativity constraints and steepest descent for TV minimization. The novelty of this work is to determine iterative parameters automatically from data, thus avoiding tedious manual parameter tuning. In TV minimization, the step sizes of steepest descent are adaptively adjusted according to the difference from POCS update in either the projection domain or the image domain, while the step size of algebraic reconstruction technique (ART) in POCS is determined based on the data noise level. In addition, projection errors are used to compare with the error bound to decide whether to perform ART so as to reduce computational costs. The performance of the proposed methods is studied and evaluated using both simulated and physical phantom data. Our methods with automatic parameter tuning achieve similar, if not better, reconstruction performance compared to a representative two-stage algorithm.