Characterization and elimination efficiency of volatile organic compounds in mechanically recycled polyethylene terephthalate at various recycling stages.

The recycling of polyethylene terephthalate (PET) stands as an effective strategy for mitigating plastic pollution and reducing resource waste. The study aimed to investigate the characterization and elimination efficiency of volatile organic compounds (VOCs) present in rPET at various recycling stages using comprehensive two-dimensional gas chromatography-quadrupole-time-of-flight-mass spectrometry coupled with chemometrics. The results revealed that 52, 135, 95, 44, and 33 VOCs, mostly classified into three chemical groups, were tentatively identified in virgin - PET (v-PET), cold water washed - rPET (C-rPET), decontaminated - rPET (D-rPET), melt-extruded - rPET (M-rPET), and solid-state polycondensation - rPET (S-rPET), respectively. Regarding the VOCs with high and median detection frequencies, fatty acyls showed the highest elimination efficiency (100 % and 92 %), followed by organooxygen compounds (81 % and 99 %), others (97 % and 95 %), and benzene and substituted derivatives (82 % and 95 %) in term of HS-SPME. Following the recycling process, there was a general decrease in the concentration of almost all VOCs, as evidenced by the substantial reduction of o-Xylene, hexanoic acid, octanal, and D-limonene from 18.11, 22.43, 30.74, and 7.41 mg/kg to 0, 0, 3.97, and 0 mg/kg, respectively. However, it was noteworthy that the VOCs identified in the samples were not completely extracted, owing to the limitations of HS-SPME. Furthermore, chemometrics analysis indicated significant discrimination among VOCs from vPET, C-rPET, D-rPET, and M-rPET, while indistinct differences were observed between M-rPET and S-rPET. This study contributes to the enhancement of the recycling process and emphasizes the importance of safeguarding consumer health in terms of elimination of VOCs.

Opportunities and perspectives of small molecular phosphodiesterase inhibitors in neurodegenerative diseases.

Phosphodiesterase (PDE) is a superfamily of enzymes that are responsible for the hydrolysis of two second messengers: cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP). PDE inhibition promotes the gene transcription by activating cAMP-response element binding protein (CREB), initiating gene transcription of brain-derived neurotrophic factor (BDNF). The procedure exerts neuroprotective profile, and motor and cognitive improving efficacy. From this point of view, PDE inhibition will provide a promising therapeutic strategy for treating neurodegenerative disorders. Herein, we summarized the PDE inhibitors that have entered the clinical trials or been discovered in recent five years. Well-designed clinical or preclinical investigations have confirmed the effectiveness of PDE inhibitors, such as decreasing Aß oligomerization and tau phosphorylation, alleviating neuro-inflammation and oxidative stress, modulating neuronal plasticity and improving long-term cognitive impairment.

Co-delivery of doxorubicin and STING agonist cGAMP for enhanced antitumor immunity.

Many chemotherapeutic agents can induce immunogenic cell death (ICD), which leads to the release of danger-associated molecular patterns (DAMPs) and tumor-associated antigens. This process promotes dendritic cells (DCs) maturation and cytotoxic T lymphocyte (CTL) infiltration. However, cancer cells can employ diverse mechanisms to evade the host immune system. Recent studies have shown that stimulator of interferon genes (STING) agonists, such as cGAMP, can amplify ICD-triggered immune responses and enhance the infiltration of immune cells into the tumor microenvironment (TME). Building upon these findings, we constructed a doxorubicin (DOX) and cGAMP co-delivery system (DOX/cGAMP@NPs) for melanoma and triple-negative breast cancer (TNBC) therapy. The results demonstrated that DOX could effectively destroy tumors and induce the release of DAMPs by ICD. Furthermore, in orthotopic 4T1 tumors mice model and subcutaneous B16 tumor mice model, cGAMP could promote the maturation of DCs and CD8+ T cell activation and infiltration by inducing the secretion of type I interferons and pro-inflammation cytokine, which amplified the antitumor immune response induced by DOX. This strategy also promoted the depletion of immunosuppressive cells, potentially alleviating the immunosuppressive TME. In conclusion, our study highlights the combination of DOX-induced ICD and the immune-enhancing properties of cGAMP holds significant implications for future research and clinical applications.

Potential safety concerns of volatile constituents released from coffee-ground-blended single-use biodegradable drinking straws: A chemical space perspective.

Incorporating spent coffee grounds into single-use drinking straws for enhanced biodegradability also raises safety concerns due to increased chemical complexity. Here, volatile organic compounds (VOCs) present in coffee ground straws (CGS), polylactic acid straws (PLAS), and polypropylene straws (PPS) were characterized using headspace - solid-phase microextraction and migration assays, by which 430 and 153 VOCs of 10 chemical categories were identified by gas chromatography - mass spectrometry, respectively. Further, the VOCs were assessed for potential genetic toxicity by quantitative structure-activity relationship profiling and estimated daily intake (EDI) calculation, revealing that the VOCs identified in the CGS generally triggered the most structural alerts of genetic toxicity, and the EDIs of 37.9% of which exceeded the threshold of 0.15 µg person-1 d-1, also outnumbering that of the PLAS and PPS. Finally, 14 VOCs were prioritized due to their definite hazards, and generally higher EDIs or detection frequencies in the CGS. Meanwhile, the probability of producing safer CGS was also illustrated. Moreover, it was uncovered by chemical space that the VOCs with higher risk potentials tended to gather in the region defined by the molecular descriptor related to electronegativity or octanol/water partition coefficient. Our results provided valuable references to improve the chemical safety of the CGS, to promote consumer health, and to advance the sustainable development of food contact materials.

Review of triazole scaffolds for treatment and diagnosis of Alzheimer's disease.

Triazole scaffolds, a series of 5-membered heterocycles, are well known for their high efficacy, low toxicity, and superior pharmacokinetics. Alzheimer's disease (AD) is the first neurodegenerative disorder with complex pathological mechanisms. Triazole, as an aromatic group with three nitrogen atoms, forms polar and non-polar interactions with diverse key residues in the receptor-ligand binding procedure, and has been widely used in the molecular design in the development of anti-AD agents. Moreover, considering the simple synthesis approaches, triazole scaffolds are commonly used to link two pharmacodynamic groups in one chemical molecule, forming multi-target directed ligands (MTDLs). Furthermore, the click reaction between azide- and cyano-modified enzyme and ligand provides feasibility for the new modulator discovery, compound tissue distribution evaluation, enzyme localization, and pharmacological mechanism study, promoting the diagnosis of AD course.

High-resolution medical image reconstruction based on residual neural network for diagnosis of cerebral aneurysm.

Objective: Cerebral aneurysms are classified as severe cerebrovascular diseases due to hidden and critical onset, which seriously threaten life and health. An effective strategy to control intracranial aneurysms is the regular diagnosis and timely treatment by CT angiography (CTA) imaging technology. However, unpredictable patient movements make it challenging to capture sub-millimeter-level ultra-high resolution images in a CTA scan. In order to improve the doctor's judgment, it is necessary to improve the clarity of the cerebral aneurysm medical image algorithm. Methods: This paper mainly focuses on researching a three-dimensional medical image super-resolution algorithm applied to cerebral aneurysms. Although some scholars have proposed super-resolution reconstruction methods, there are problems such as poor effect and too much reconstruction time. Therefore, this paper designs a lightweight super-resolution network based on a residual neural network. The residual block structure removes the B.N. layer, which can effectively solve the gradient problem. Considering the high-resolution reconstruction needs to take the complete image as the research object and the fidelity of information, this paper selects the channel domain attention mechanism to improve the performance of the residual neural network. Results: The new data set of cerebral aneurysms in this paper was obtained by CTA imaging technology of patients in the Department of neurosurgery, the second affiliated of Guizhou Medical University Hospital. The proposed model was evaluated from objective evaluation, model effect, model performance, and detection comparison. On the brain aneurysm data set, we tested the PSNR and SSIM values of 2 and 4 magnification factors, and the scores of our method were 33.01, 28.39, 33.06, and 28.41, respectively, which were better than those of the traditional SRCNN, ESPCN and FSRCNN. Subsequently, the model is applied to practice in this paper, and the effect, performance index and diagnosis of auxiliary doctors are obtained. The experimental results show that the high-resolution image reconstruction model based on the residual neural network designed in this paper plays a more influential role than other image classification methods. This method has higher robustness, accuracy and intuition. Conclusion: With the wide application of CTA images in the clinical diagnosis of cerebral aneurysms and the increasing number of application samples, this method is expected to become an additional diagnostic tool that can effectively improve the diagnostic accuracy of cerebral aneurysms.

A novel heuristic target-dependent neural architecture search method with small samples.

It is well known that crop classification is essential for genetic resources and phenotype development. Compared with traditional methods, convolutional neural networks can be utilized to identify features automatically. Nevertheless, crops and scenarios are quite complex, which makes it challenging to develop a universal classification method. Furthermore, manual design demands professional knowledge and is time-consuming and labor-intensive. In contrast, auto-search can create network architectures when faced with new species. Using rapeseed images for experiments, we collected eight types to build datasets (rapeseed dataset (RSDS)). In addition, we proposed a novel target-dependent search method based on VGGNet (target-dependent neural architecture search (TD-NAS)). The result shows that test accuracy does not differ significantly between small and large samples. Therefore, the influence of the dataset size on generalization is limited. Moreover, we used two additional open datasets (Pl@ntNet and ICL-Leaf) to test and prove the effectiveness of our method due to three notable features: (a) small sample sizes, (b) stable generalization, and (c) free of unpromising detections.