Nano-mediated strategy: recent advance in the application of drug delivery systems in melanoma treatment and diagnosis.

Melanoma is a common malignant tumor, with a five-year mortality rate as high as 62% in cases of metastatic melanoma according to cancer statistics (2024). In recent years, the focus of melanoma research has predominantly centered on immunotherapy and targeted therapy, grappling with challenges such as resistance and immunogenicity. The discovery of nanoparticles (NPs) has brought nano-delivery systems to the forefront of melanoma diagnosis and treatment. Although certain NPs, like liposomes, have gained clinical approval, utilizing most nano-delivery systems for melanoma diagnosis and treatment remains largely exploratory. The inherent limitations of NPs present a major obstacle to their clinical translation. By selecting suitable nanocarriers and functionalizing NPs to optimize nano-delivery systems, and combining these systems with other therapies, it is possible to reduce the systemic toxicity and resistance associated with conventional therapies and the NPs themselves. This optimization could significantly improve the effectiveness of nano-delivery systems in the early detection and timely treatment of melanoma. However, there have been few reviews on the optimization of NPs and the combined application of other therapies in the treatment and diagnostic application of melanoma in the past three years. This review summarizes the latest applications of nano-delivery systems in the diagnosis and treatment of melanoma over the past three years, including innovations and achievements in both preclinical and clinical studies, offering new perspectives on their potential and future application prospects. It integrates clinical data and patent information, highlights trends in nano-delivery system development, and offers new insights into their clinical translation. Additionally, it discusses the challenges and opportunities of nano-delivery systems in melanoma treatment, providing a foundation for advancing their application in diagnosis, treatment, and clinical translation.

Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development.

Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming, and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. It does that by synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide.

Temporal Variations in Fire Impacts on Characteristics and Composition of Soil-Derived Dissolved Organic Matter at Qipan Mountain, China.

Dissolved organic matter (DOM), the most reactive fraction of forest soil organic matter, is increasingly impacted by wildfires worldwide. However, few studies have quantified the temporal changes in soil DOM quantity and quality after fire. Here, soil samples were collected after the Qipan Mountain Fire (3-36 months) from pairs of burned and unburned sites. DOM contents and characteristics were analyzed using carbon quantification and various spectroscopic and spectrometric techniques. Compared with the unburned sites, burned sites showed higher contents of bulk DOM and most DOM components 3 months after the fire but lower contents of them 6-36 months after the fire. During the sharp drop of DOM from 3 to 6 months after the fire, carboxyl-rich alicyclic molecule-like and highly unsaturated compounds had greater losses than condensed aromatics. Notably, the burned sites had consistently higher abundances of oxygen-poor dissolved black nitrogen and fluorescent DOM 3-36 months after the fire, particularly the abundance of pyrogenic C2 (excitation/emission maxima of <250/∼400 nm) that increased by 150% before gradually declining. This study advances the understanding of temporal variations in the effects of fire on different soil DOM components, which is crucial for future postfire environmental management.

Neurophysiological features of STN LFP underlying sleep fragmentation in Parkinson's disease.

BACKGROUND: Sleep fragmentation is a persistent problem throughout the course of Parkinson's disease (PD). However, the related neurophysiological patterns and the underlying mechanisms remained unclear. METHOD: We recorded subthalamic nucleus (STN) local field potentials (LFPs) using deep brain stimulation (DBS) with real-time wireless recording capacity from 13 patients with PD undergoing a one-night polysomnography recording, 1 month after DBS surgery before initial programming and when the patients were off-medication. The STN LFP features that characterised different sleep stages, correlated with arousal and sleep fragmentation index, and preceded stage transitions during N2 and REM sleep were analysed. RESULTS: Both beta and low gamma oscillations in non-rapid eye movement (NREM) sleep increased with the severity of sleep disturbance (arousal index (ArI)-betaNREM: r=0.9, p=0.0001, sleep fragmentation index (SFI)-betaNREM: r=0.6, p=0.0301; SFI-gammaNREM: r=0.6, p=0.0324). We next examined the low-to-high power ratio (LHPR), which was the power ratio of theta oscillations to beta and low gamma oscillations, and found it to be an indicator of sleep fragmentation (ArI-LHPRNREM: r=-0.8, p=0.0053; ArI-LHPRREM: r=-0.6, p=0.0373; SFI-LHPRNREM: r=-0.7, p=0.0204; SFI-LHPRREM: r=-0.6, p=0.0428). In addition, long beta bursts (>0.25 s) during NREM stage 2 were found preceding the completion of transition to stages with more cortical activities (towards Wake/N1/REM compared with towards N3 (p<0.01)) and negatively correlated with STN spindles, which were detected in STN LFPs with peak frequency distinguishable from long beta bursts (STN spindle: 11.5 Hz, STN long beta bursts: 23.8 Hz), in occupation during NREM sleep (ß=-0.24, p<0.001). CONCLUSION: Features of STN LFPs help explain neurophysiological mechanisms underlying sleep fragmentations in PD, which can inform new intervention for sleep dysfunction. TRIAL REGISTRATION NUMBER: NCT02937727.

Alterations in Synaptic Connectivity and Synaptic Transmission in Alzheimer's Disease with High Physical Activity.

Background: Alzheimer's disease (AD) is a progressive neurodegeneration disease. Physical activity is one of the most promising modifiable lifestyles that can be effective in slowing down the progression of AD at an early stage. Objective: Explore the molecular processes impaired in AD that were conversely preserved and enhanced by physical activity. Methods: Integrated transcriptomic analyses were performed in datasets that contain AD patients and elders with different degrees of physical activity. The changes of the hub genes were validated through analyzing another two datasets. The expression of the hub genes was further detected in the hippocampus and cortexes of APP/PS1 transgenic mice with or without physical activity by Quantitative polymerase chain reaction (qPCR). Results: Cross-comparison highlighted 195 DEGs displaying opposed regulation patterns between AD and high physical activity (HPA). The common DEGs were predominantly involved in synaptic vesicle recycling and synaptic transmission, largely downregulated in AD patients but upregulated in the elders with HPA. Two key modules and four hub genes that were related to synaptic vesicle turnover were obtained from the PPI network. The expression of these hub genes (SYT1, SYT4, SH3GL2, and AP2M1) was significantly decreased in AD transgenic mice and was reversed by HPA training. Conclusions: HPA may reverse AD pathology by upregulating a range of synaptic vesicle transport related proteins which might improve the efficiency of synaptic vesicle turnover and facilitate inter-neuronal information transfer. The study provides novel insights into the mechanisms underlining the protective effects of HPA on AD.

Protective effect and mechanism of Qingfei Paidu decoction on myocardial damage mediated by influenza viruses.

Introduction: Significant attention has been paid to myocardial damage mediated by the single-stranded RNA virus. Qingfei Paidu decoction (QFPDD) has been proved to protect the damage caused by the influenza virus A/PR/8/1934 (PR8), but its specific mechanism is unclear. Methods: Molecular biological methods, together with network pharmacology, were used to analyze the effects and underlying mechanism of QFPDD treatment on PR8-induced myocardial damage to obtain insights into the treatment of COVID-19-mediated myocardial damage. Results: Increased apoptosis and subcellular damage were observed in myocardial cells of mice infected by PR8. QFPDD treatment significantly inhibited the apoptosis and subcellular damage induced by the PR8 virus. The inflammatory factors IFN-ß, TNF-α, and IL-18 were statistically increased in the myocardia of the mice infected by PR8, and the increase in inflammatory factors was prevented by QFPDD treatment. Furthermore, the expression levels or phosphorylation of necroptosis-related proteins RIPK1, RIPK3, and MLKL were abnormally elevated in the group of infected mice, while QFPDD restored the levels or phosphorylation of these proteins. Our study demonstrated that HIF-1α is a key target of QFPDD in the treatment of influenza virus-mediated injury. The HIF-α level was significantly increased by PR8 infection. Both the knockdown of HIF-1α and treatment of the myocardial cell with QFPDD significantly reversed the increased inflammatory factors during infection. Overexpression of HIF-1α reversed the inhibition effects of QFPDD on cytokine expression. Meanwhile, seven compounds from QFPDD may target HIF-1α. Conclusion: QFPDD can ameliorate influenza virus-mediated myocardial damage by reducing the degree of cell necroptosis and apoptosis, inhibiting inflammatory response and the expression of HIF-1α. Thus, our results provide new insights into the treatment of respiratory virus-mediated myocardial damage.

Rhein potentiates doxorubicin in treating triple negative breast cancer by inhibiting cancer-associated fibroblasts.

Cancer-associated fibroblasts (CAFs), one of the most abundant stromal cells in the tumor microenvironment, mediate desmoplastic responses. CAFs are major drivers for the failure of triple-negative breast cancer (TNBC) chemotherapy. It is well-documented that many traditional Chinese medicines (TCMs) exhibit potent anti-fibrotic effects based on their capacity to suppress the production of ECM proteins. Therefore, the combination of TCMs exhausting CAFs with chemotherapy is a potential regimen for treating TNBC. Here, TGF-ß was used to induce the transformation of NIH/3T3 cells into CAFs for screening TCMs to inhibit tumor fibrosis. After screening 11 candidate TCMs for inhibiting CAFs using the TMS method, rhein (Rhe) was found to strongly inhibit the proliferation of CAFs. Therefore, Rhe was chosen as a representative TCM to inhibit CAFs in TNBC. A 4T1Fluc/CAFs tumor sphere resembling the TME in vivo was constructed to explore the feasibility of inhibiting CAFs to sensitize DOX in treating TNBC. It was found that CAFs apparently hindered the penetration of DOX into 4T1Fluc/CAFs tumor spheres and decreased the the sensitivity of 4T1Fluc cells to DOX, while Rhe significantly restored the sensitivity of 4T1Fluc cells to DOX by inhibiting the proliferation of CAFs. Consistent with in vitro results, Rhe reversed the abnormal activation of CAFs and diminished the accumulation of collagen in 4T1Fluc mouse xenograft models. This removal of stromal barrier facilitated the antitumor efficacy of DOX. Altogether, this study demonstrated for the first time that Rhe could inhibit tumor tissue fibrosis and synergize DOX to treat TNBC.

Predicting the transmission dynamics of novel coronavirus infection in Shanxi province after the implementation of the "Class B infectious disease Class B management" policy.

Background: China managed coronavirus disease 2019 (COVID-19) with measures against Class B infectious diseases, instead of Class A infectious diseases, in a major shift of its epidemic response policies. We aimed to generate robust information on the transmission dynamics of novel coronavirus infection in Shanxi, a province located in northern China, after the implementation of the "Class B infectious disease Class B management" policy. Methods: We consolidated infection data in Shanxi province from December 6, 2022 to January 14, 2023 through a network questionnaire survey and sentinel surveillance. A dynamics model of the SEIQHCVR was developed to track the infection curves and effective reproduction number (Rt). Results: Our model was effective in estimating the trends of novel coronavirus infection, with the coefficient of determination (R2) above 90% in infections, inpatients, and critically ill patients. The number of infections in Shanxi province as well as in urban and rural areas peaked on December 20, 2022, with the peak of inpatients and critically ill patients occurring 2 to 3 weeks after the peak of infections. By the end of January 2023, 87.72% of the Shanxi residents were predicted to be infected, and the outbreak subsequently subsided. A small wave of COVID-19 infections may re-emerge at the end of April. In less than a month, the Rt values of positive infections, inpatients and critically ill patients were all below 1.0. Conclusion: The outbreak in Shanxi province is currently at a low prevalence level. In the face of possible future waves of infection, there is a strong need to strengthen surveillance and early warning.