Earticle

A metalloporphyrin-based coordination framework material was synthesized by the reaction of Sn(OH)2(TPyP) complex (TPyP = 5,10,15,20-tetrakis(4-pyridyl)porphyrinato dianion) with Ag(OTs) (OTs = p -toluenesulfonato monoanion), and thoroughly characterized using various analytic techniques including X-ray crystallography. The X-ray crystal structural analysis revealed that peripheral coordination of pyridyl nitrogen with Ag(I) ions creates a two-dimensional network. This framework contains two different types of pore channels, with approximate edge dimensions of 21.6 × 15.2 Å and 13.7 × 7.11 Å, respectively.

Changes in DNA methylation patterns in human genes can not only impair gene activation but also contributes to epigenetic age. To measure these changes machine learning models are used which measures the degree of changes in DNA methylation pattern. Parkinson's disease is the second most common neurodegenerative disease which affects motor function. Even though it may develop before the age of 50 due to genetic factors, the gene that causes Parkinson's disease is linked to aging, so the incidence increases with age, and most people get affected by Parkinson’s disease are over the age of 50. It is a geriatric disease that occurs in older aged people. Changes in DNA methylation patterns have been observed in patients with Parkinson's disease. Therefore, the epigenetic age of patients with Parkinson's disease was measured using Epi clock, which is one of the epigenetic clock models which was trained using 6761 CpG probes from pan tissue. Additionally, the acceleration of age was measured and changes in DNA methylation patterns were confirmed. Through this, it was confirmed that although the epigenetic age of Parkinson's disease patients accelerates, the difference is small which is approximately 1 to 5 years. Even though Hypo methylation of CpG probes increased, it was to a small extent.

The female infertility condition can be induced by various factors. Not only due to the cellular senescence of reproductive system, but also reproductive disorders can suppress the ovary activity. Unfortunately, many female reproductive disorders do not have effective treatment options or not effective to the underlying cause of this condition. To overcome those limitations in current treatment options, many researchers trying to find novel treatment strategies using stem cells. Mesenchymal stem cells are promising source based on their regenerative potential. In this study, we summarized recent published studies and introduce many experimental and pre-clinical data showing the therapeutic effect of stem cells in female reproductive disorder such as primary ovarian insufficiency(POI), polycystic ovary syndrome(PCOS), endometriosis. In addition, we also present the potential of stem cells for anti-ovarian aging treatment through published literature. We also introduce specialized cell culture conditions to generate improved stem cells, which have enhanced therapeutic potential for future clinical applications. Through this review study, we suggest stem cell-based therapy as a novel and fundamental treatment option for various female reproductive disorders to restore fertility in patients.

Wildfires have become increasingly frequent and severe in recent years, driven by rising global temperatures, prolonged droughts, and shifting precipitation patterns associated with climate change. These fires not only cause substantial ecological damage but also threaten human lives and infrastructure. As a result, the ability to accurately predict the scale of wildfire damage shortly after ignition is becoming a critical component of disaster preparedness and forest management. This study proposes a machine learning-based approach to predict the magnitude of wildfire damage using post-ignition environmental and geographic variables. The research utilizes wildfire incident data collected in South Korea between 2020 and 2024. Wildfires were classified into three categories—small, medium, and large—based on area burned and fire duration, following criteria adapted from national wildfire response manuals. To build predictive models, a diverse set of variables was used, including meteorological factors, drought indices, vegetation characteristics, and spatiotemporal information such as season and administrative region.Three classification algorithms —Random Forest, XGBoost, and Support Vector Machine (SVM) were applied. Due to the imbalance in class distribution, particularly the scarcity of large wildfire cases, data resampling techniques were employed to enhance model robustness. Among the models, XGBoost demonstrated the highest accuracy of 0.96 and achieved a recall of 0.89 for large wildfires, outperforming the other methods. These results suggest that combining real-time weather data with historical environmental information can help improve early predictions of the scale of the wildfire. The proposed model may assist in supporting faster response decisions and minimizing damage in high-risk areas.