Enhanced tumor targeting and penetration of fluorophores via iRGD peptide conjugation: a strategy for the precision targeting of lung cancer.

Background: Accurate real-time tumor delineation is essential for achieving curative resection (R0 resection) during non-small cell lung cancer (NSCLC) surgery. The unique characteristics of lung tissue structure significantly challenge the use of video-assisted thoracoscopic surgery in the identification of lung nodules. This difficulty often results in an inability to discern the margins of lung nodules, necessitating either an expansion of the resection scope, or a transition to open surgery. Due to its high spatial resolution, ease of operation, and capacity for real-time observation, near-infrared fluorescence (NIRF) navigation in oncological surgery has emerged as a focal point of clinical research. Targeted NIRF probes, which accumulate preferentially in tumor tissues and are rapidly cleared from normal tissues, enhance diagnostic sensitivity and surgical outcomes. The imaging effect of the clinically approved NIRF probe indocyanine green (ICG) varies significantly from person to person. Therefore, we hope to develop a new generation of targeted NIRF probes targeting lung tumor-specific targets. Methods: First, the peptide iRGD (sequence: CRGDKGPDC) fluorescent tracer was synthesized, and characterized through mass spectrometry (MS), proton nuclear magnetic resonance (1H NMR), and high-performance liquid chromatography (HPLC). Fluorescence properties were tested subsequently. Safety was performed in vitro using both human normal liver cells and human normal breast cells. Second, Metabolism and optimal imaging time were determined by tail vein injection of iRGD fluorescent tracer. Finally, Orthotopic and metastatic lung tumor models were used to evaluate the targeting properties of the iRGD fluorescent tracer. Results: We successfully synthesized an iRGD fluorescent tracer specifically designed to target NSCLC. The molecular docking analyses indicated that this tracer has receptor affinity comparable to that of iRGD for αvß3 integrin, with a purity ≥98%. Additionally, the tracer is highly soluble in water, and its excitation and emission wavelengths are 767 and 799 nm, respectively, positioning it within the near-infrared spectrum. The cellular assays confirmed the tracer's minimal cytotoxicity, underscoring its excellent biosafety profile. In vivo studies further validated the tracer's capacity for specific NSCLC detection at the cellular level, alongside a prolonged imaging window of 6 days or more. Notably, the tracer demonstrated superior specificity in localizing very small lung nodules, which are otherwise clinically indiscernible, outperforming non-targeted ICG. Fluorescence intensity analyses across various organs revealed that the tracer is predominantly metabolized by the liver and kidneys, with excretion via bile and urine, and exhibits minimal toxicity to these organs as well as the lungs. Conclusions: The iRGD fluorescent tracer selectively accumulates in NSCLC tissues by specifically targeting αvß3 receptors, which are overexpressed on the surface of tumor cells. This targeted approach facilitates the real-time intraoperative localization of NSCLC, presenting an improved strategy for intraoperative tumor identification with significant potential for clinical application.

Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation.

Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, resulting in either models that generate molecules with poor performance or models that are overfit and produce close analogs of known molecules. In this paper, we reduce this data dependency for the generation of new chemotypes by incorporating docking scores of known and de novo molecules to expand the applicability domain of the reward function and diversify the compounds generated during reinforcement learning. Our approach employs a deep generative model initially trained using a combination of limited known drug activity and an approximate docking score provided by a second machine learned Bayes regression model, with final evaluation of high scoring compounds by a full docking simulation. This strategy results in molecules with docking scores improved by 10-20% compared to molecules of similar size, while being 130 × faster than a docking only approach on a typical GPU workstation. We also show that the increased docking scores correlate with (1) docking poses with interactions similar to known inhibitors and (2) result in higher MM-GBSA binding energies comparable to the energies of known DDR1 inhibitors, demonstrating that the Bayesian model contains sufficient information for the network to learn to efficiently interact with the binding pocket during reinforcement learning. This outcome shows that the combination of the learned latent molecular representation along with the feature-based docking regression is sufficient for reinforcement learning to infer the relationship between the molecules and the receptor binding site, which suggest that our method can be a powerful tool for the discovery of new chemotypes with potential therapeutic applications.

Influencing factors of the treatment level of elderly care workers and their career development prospects.

BACKGROUND: The increasingly aging population in current China has encouraged the emergence of the diversified and multi-level elderly care service industry, and the demand for high-quality elderly life with the help of elderly caregivers continues to grow. METHODS: Based on the existing questionnaire data, this article explores the influencing factors of the treatment level of care staff, and explores their future development prospects. RESULTS: The results show that whether they have participated in relevant vocational skills competitions, whether they have worked overtime, whether they have overtime wages, and their monthly income have significant effects on their satisfaction of treatment levels. Elderly care workers who have participated in skills competitions are more satisfied about their salary. In addition, workers who rarely and occasionally work overtime are more satisfied compared with those who have never worked overtime; Caregivers with a monthly income of 5,000-6,999 yuan are more satisfied with their salary and treatment than those with below 3,000 yuan. CONCLUSION: Therefore, in order to better match the supply and demand of care workers, we should provide formal training and skill competitions for them, appropriately increase their salary level and reasonably arrange their working hours, so as to attract more professional talents into elderly care industry.