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Arginine is an amino acid critically involved in multiple cellular processes including the syntheses of nitric oxide and polyamines, and is a direct activator of mTOR, a nutrient-sensing kinase strongly implicated in carcinogenesis. Yet, it is also considered as a non- or semi-essential amino acid, due to normal cells’ intrinsic ability to synthesize arginine from citrulline and aspartate via ASS1 (argininosuccinate synthase 1) and ASL (argininosuccinate lyase). As such, arginine can be used as a dietary supplement and its depletion as a therapeutic strategy. Strikingly, in over 70% of tumors, ASS1 transcription is suppressed, rendering the cells addicted to external arginine, forming the basis of arginine-deprivation therapy. In this review, we will discuss arginine as a signaling metabolite, arginine’s role in cancer metabolism, arginine as an epigenetic regulator, arginine as an immunomodulator, and arginine as a therapeutic target. We will also provide a comprehensive summary of ADI (arginine deiminase)-based arginine-deprivation preclinical studies and an update of clinical trials for ADI and arginase. The different cell killing mechanisms associated with various cancer types will also be described.

Arginine plays diverse roles in cellular physiology. As a semi-essential amino acid, arginine deprivation has been used to target cancers with arginine synthesis deficiency. Arginine-deprived cancer cells exhibit mitochondrial dysfunction, transcriptional reprogramming and eventual cell death. In this study, we show in prostate cancer cells that arginine acts as an epigenetic regulator to modulate histone acetylation, leading to global upregulation of nuclear-encoded oxidative phosphorylation (OXPHOS) genes. TEAD4 is retained in the nucleus by arginine, enhancing its recruitment to the promoter/enhancer regions of OXPHOS genes and mediating coordinated upregulation in a YAP1-independent but mTOR-dependent manner. Arginine also activates the expression of lysine acetyl-transferases and increases overall levels of acetylated histones and acetyl-CoA, facilitating TEAD4 recruitment. Silencing of TEAD4 suppresses OXPHOS functions and prostate cancer cell growth in vitro and in vivo. Given the strong correlation of TEAD4 expression and prostate carcinogenesis, targeting TEAD4 may be beneficially used to enhance arginine-deprivation therapy and prostate cancer therapy. Alterations in metabolism and amino acid usage are common in cancer cells. Here, the authors show in prostate cancer cells that arginine globally upregulates nuclear-encoded oxidative phosphorylation genes by altering histone acetylation and retaining TEAD4 in the nucleus to transactivate genes.

Increasing evidence suggests that tumor development requires not only oncogene/tumor suppressor mutations to drive the growth, survival, and metastasis but also metabolic adaptations to meet the increasing energy demand for rapid cellular expansion and to cope with the often nutritional and oxygen-deprived microenvironment. One well-recognized strategy is to shift the metabolic flow from oxidative phosphorylation (OXPHOS) or respiration in mitochondria to glycolysis or fermentation in cytosol, known as Warburg effects. However, not all cancer cells follow this paradigm. In the development of prostate cancer, OXPHOS actually increases as compared to normal prostate tissue. This is because normal prostate epithelial cells divert citrate in mitochondria for the TCA cycle to the cytosol for secretion into seminal fluid. The sustained level of OXPHOS in primary tumors persists in progression to an advanced stage. As such, targeting OXPHOS and mitochondrial activities in general present therapeutic opportunities. In this review, we summarize the recent findings of the key regulators of the OXPHOS pathway in prostate cancer, ranging from transcriptional regulation, metabolic regulation to genetic regulation. Moreover, we provided a comprehensive update of the current status of OXPHOS inhibitors for prostate cancer therapy. A challenge of developing OXPHOS inhibitors is to selectively target cancer mitochondria and spare normal counterparts, which is also discussed.

This study aimed to design an effective nanoparticle-based carrier for the oral delivery of fisetin (FST) with improved biopharmaceutical properties. FST-loaded nanoparticles were prepared with polyvinyl alcohol (PVA) and poly(lactic-co-glycolic acid) (PLGA) by the interfacial deposition method. A central composite design of two independent variables, the concentration of PVA and the amount of PLGA, was applied for the optimization of the preparative parameter. The responses, including average particle size, polydispersity index, encapsulation efficiency, and zeta potential, were assessed. The optimized formulation possessed a mean particle size of 187.9 nm, the polydispersity index of 0.121, encapsulation efficiency of 79.3%, and zeta potential of −29.2 mV. The morphological observation demonstrated a globular shape for particles. Differential scanning calorimetry and powder X-ray diffraction studies confirmed that the encapsulated FST was presented as the amorphous state. The dissolution test indicated a 3.06-fold increase for the accumulating concentrations, and the everted gut sac test showed a 4.9-fold gain for permeability at the duodenum region. In conclusion, the optimized FST-loaded nanoparticle formulation in this work can be developed as an efficient oral delivery system of FST to improve its biopharmaceutic properties.

Urban waste-collection centres (WCCs) routinely overflow because maintenance routes are scheduled reactively rather than on data-driven forecasts. Overspill, odour, and leachate therefore threaten public health and sustainability targets in rapidly growing smart cities. We introduce ProWaste, an end-to-end Internet-of-Things and machine-learning platform that proactively prioritises WCC servicing. Fifteen automated and manual indicators, including population density, weather, maintenance history, and weekly waste build-up, are streamed from low-cost sensors, public APIs, and a mobile app to a cloud database. Twenty-five off-the-shelf classifiers were benchmarked under repeated stratified cross-validation; a Decision Tree Classifier offered the best balance of interpretability and near-top accuracy. Binary Particle Swarm Optimisation (BPSO) removed 80% of the inputs, revealing that three features alone predict criticality with>99% accuracy on a hold-out test set. SHAP analysis confirms the interpretability of the three-feature model. The predicted class and confidence score are pushed to a Sustainable Smart Waste Management (SSWM) app that alerts field teams and dynamically reorders maintenance queues. Compared with current practice, ProWaste can eliminate missed pickups while reducing on-road inspections and data bandwidth. The proposed architecture is readily transferable to other cities and can be extended to recycling or composting streams.

The power conversion efficiencies (PCEs) of metal-oxide-based regular perovskite solar cells have been higher than 25% for more than 2 years. Up to now, the PCEs of polymer-based inverted perovskite solar cells are widely lower than 23%. PEDOT:PSS thin films, modified PTAA thin films and P3CT thin films are widely used as the hole transport layer or hole modification layer of the highlyefficient inverted perovskite solar cells. Compared with regular perovskite solar cells, polymer-based inverted perovskite solar cells can be fabricated under relatively low temperatures. However, the intrinsic characteristics of carrier transportation in the two types of solar cells are different, which limits the photovoltaic performance of inverted perovskite solar cells. Thanks to the low activation energies for the formation of high-quality perovskite crystalline thin films, it is possible to manipulate the optoelectronic properties by controlling the crystal orientation with the different polymer-modified ITO/glass substrates. To achieve the higher PCE, the effects of polymer-modified ITO/glass substrates on the optoelectronic properties and the formation of perovskite crystalline thin films have to be completely understood simultaneously.

This study presents a blockchain-based enabling autonomous nursing professional development framework, known as BCeANPDF. The framework aims to enhance transparency, security, and professional autonomy in nursing credential management. It is grounded in the principles of competency-based human resource management. Blockchain and smart contract technologies are integrated to support independent recording, verification, and management of professional and non-professional credentials by nurses. At the same time, hospital human resource administrators continue to have the authority to conduct regulatory oversight and ensure compliance. The framework employs a three-layer architecture that includes controller, service, and repository components. These components coordinate access control, data processing, and blockchain-related operations. Seven smart contracts are designed within the framework. They automate credential ownership verification, credential updates, and compliance review processes. This design strengthens data integrity and reduces administrative workload. A prototype was implemented in a private blockchain environment to evaluate system performance. The results demonstrate stable and efficient operation. The average on-chain processing time per credential was 12.3 s. Median query latency ranged from 5 to 9 ms. These findings confirm that the framework achieves scalability and responsiveness comparable to Ethereum, while preserving data privacy and immutability. By combining decentralized trust mechanisms with credential management practices, the BCeANPDF framework offers a practical approach to supporting autonomous professional development. It also facilitates flexible management of the nursing workforce. Overall, the framework contributes to the development of transparent and competency-oriented healthcare institutions without increasing operational complexity.

Antibodies have been developed as therapeutic agents for the treatment of cancer, infection, and inflammation. In addition to binding activity toward the target, antibodies also exhibit effector-mediated activities through the interaction of the Fc glycan and the Fc receptors on immune cells. To identify the optimal glycan structures for individual antibodies with desired activity, we have developed an effective method to modify the Fc-glycan structures to a homogeneous glycoform. In this study, it was found that the biantennary N-glycan structure with two terminal alpha-2,6-linked sialic acids is a common and optimized structure for the enhancement of antibody-dependent cell-mediated cytotoxicity, complement-dependent cytotoxicity, and antiinflammatory activities.

Closed-system drug transfer devices (CSTDs) are used to prevent occupational exposure to hazardous drugs in health care providers. They are considered Class II medical devices by the US FDA and are cleared but not approved before marketing. While compatibility tests are conducted by CSTD manufacturers, the procuring institution needs to consider performing its own studies before buying these devices. Herein we tested the compatibility of the components of the Needleless ® DualGuard CSTD system (vial access clips, vial access spikes, and administration adaptors) with 10 antineoplastic drugs, under simulated clinical conditions, including compounding and administration, and examined drug potency maintenance, plasticizer migration, and device functionality. All drugs maintained potency within 5%. Diisononyl phthalate leakage was observed from the administration adaptors for paclitaxel and concentrated etoposide solution. In addition, white particles were discovered in CSTDs storing busulfan solution and small cracks were observed on devices which stored melphalan. Thus, it was concluded that even in simulated clinical conditions, instead of extreme conditions, there are still concerns regarding the efficacy and safety of CSTD components. The methodology may be used to implement and detect possible interactions between antineoplastic agents and CSTD components before procurement.

Waste management handles all kinds of waste, including household, industrial, municipal, organic, biomedical, biological, and radioactive wastes. People still face challenges in proper disposal methods for different types of waste, including landfill-bound items, recyclable materials, and biodegradable waste. Inadequate waste management poses a significant and multifaceted global challenge. The conventional method of segregating waste is a time-consuming and ineffective method that wastes human power and money. To address this issue in real time, sophisticated and sustainable waste management systems need to be implemented. The latest advancements in computer vision and deep learning offer efficient solutions for effective recycling and waste management. Existing deep learning models exhibited various limitations, such as detection accuracy and computational inefficiency, particularly when dealing with objects of varying sizes and exhibiting high degrees of visual similarity. These limitations generate various challenges in effectively capturing and representing the nuanced features of visually similar objects. To address this problem, we proposed the stacking of an enhanced Swin Transformer, improved ConvNeXt, and a spatial attention mechanism. The enhanced Swin transformers incorporate two key components- hierarchical feature extraction and shifting window mechanism to extract the global features from the garbage images effectively. The shifting window mechanism extracts the most important features from various regions of the images to identify the objects. In contrast, the hierarchical feature extraction captures long-range dependencies within the image to effectively identify different types of garbage. The improved ConvNext block with optimized parameterization extracts the local features of the image. This enhanced feature extraction capability enables the model to effectively discern fine-grained details of individual garbage particles, such as shape, texture, and subtle variations in color and appearance, leading to more accurate classification results. When we evaluated the performance of the proposed model using the publicly available Garbage Classification dataset, it attained 98.97% accuracy, 98.42% Precision, and 98.61% Recall. Due to its lightweight and low computational time and power, the proposed model surpasses the existing state-of-the-art deep learning models.