Adsorption of organic pollutants, toxic metal ions, and removal of harmful bacteria can give us clean and pure drinkable water from wastewater resources. Respective magnetite nanoparticles (MNPs) were synthesized using a cheaper and greener way in an open-air environment with the use of crude latex of Jatropha curcas (JC) and leaf extract of Cinnamomum tamala (CT). Characterization of MNPs had been performed by dynamic light scattering (DLS), Ultraviolet-visible (UV-vis) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, powdered X-ray diffraction (XRD), and field emission scanning electron microscope (FE-SEM). The size ranges of the synthesized MNPs were observed in between 20–42 nm for JC-Fe₃O₄ and within 26–35 nm for CT-Fe₃O₄ by FE-SEM images. The effect of synthesized magnetic nanoparticles in wastewater treatment (bacterial portion), dye adsorption, toxic metal removal as well as antibacterial, antioxidant, and cytotoxic activities were studied. This purification will lead to an increase in the resources of pure drinking water in the future.

In this study, we have determined the anticancer activity of doxorubicin (Dox)-loaded DNA/gold nanoparticle (AuNP) nanocarrier (Dox-DNA-AuNP) for the treatment of ovarian cancer. The anticancer effect of Dox-DNA-AuNP was evaluated in vitro using the EZ-Cytox cell viability assay on three human ovarian cancer cell lines, SK-OV-3, HEY A8, and A2780. Dox-DNA-AuNP exhibited outstanding activity with good IC₅₀ values of 4.8, 7.4, and 7.6 nM for SK-OV-3, HEY A8, and A2780, respectively. In vivo evaluation further demonstrated the superior anticancer effects of Dox-DNA-AuNP by inhibiting tumor growth compared to free Dox in an established SK-OV-3 xenograft mice model. Dox-DNA-AuNP showed about a 2.5 times higher tumor growth inhibition rate than free Dox. Furthermore, the immunohistochemical analysis of Ki67 antigen expression showed the lowest number of proliferative cells in the ovarian tumor tissue treated with Dox-DNA-AuNP. These results suggest Dox-DNA-AuNP might be a potential effective agent in ovarian cancer chemotherapy.

The aim of the study was to develop a new diagnostic biomarker for identifying serum exosomal miRNAs specific to epithelial ovarian cancer (EOC) and to find out target gene of the miRNA for exploring the molecular mechanisms in EOC. A total of 84 cases of ovarian masses and sera were enrolled, comprising EOC (n = 71), benign ovarian neoplasms (n = 13). We detected expression of candidate miRNAs in the serum and tissue of both benign ovarian neoplasm group and EOC group using real-time polymerase chain reaction. Immunohistochemistry were constructed using formalin fixed paraffin embedded (FFPE) tissue to detect expression level of suppressor of cytokine signaling 4 (SOCS4). In the EOC group, miRNA-1290 was significantly overexpressed in serum exosomes and tissues as compared to benign ovarian neoplasm group (fold change ≥ 2, p < 0.05). We observed area under the receiver operating characteristic curve (AUC) for miR-1290, using a cut-off of 0.73, the exosomal miR-1290 from serum had AUC, sensitivity, and specificity values of 0.794, 69.2 and 87.3, respectively. In immunohistochemical study, expression of SOCS4 in EOC was lower than that in benign ovarian neoplasm. Serum exosomal miR-1290 could be considered as a biomarker for differential diagnosis of EOC from benign ovarian neoplasm and SOCS4 might be potential target gene of miR-1290 in EOC.

We developed a transparent and flexible electrochemical sensor using a platform based on a network of single-walled carbon nanotubes (SWCNTs) for the non-enzymatic detection of hydrogen peroxide (H₂O₂) released from living cells. We decorated the SWCNT network on a poly(ethylene terephthalate) (PET) substrate with platinum nanoparticles (PtNPs) using a potentiodynamic method. The PtNP/SWCNT/PET sensor synergized the advantages of a flexible PET substrate, a conducting SWCNT network, and a catalytic PtNP and demonstrated good biocompatibility and flexibility, enabling cell adhesion. The PtNP/SWCNT/PET-based sensor demonstrated enhanced electrocatalytic activity towards H₂O₂, as well as excellent selectivity, stability, and reproducibility. The sensor exhibited a wide dynamic range of 500 nM to 1 M, with a low detection limit of 228 nM. Furthermore, the PtNP/SWCNT/PET sensor remained operationally stable, even after bending at various angles (15°, 30°, 60°, and 90°), with no noticeable loss of current signal. These outstanding characteristics enabled the PtNP/SWCNT/PET sensor to be practically applied for the direct culture of HeLa cells and the real-time monitoring of H₂O₂ release by the HeLa cells under drug stimulation.

Falls represent a significant risk factor, necessitating accurate classification methods. This study aims to identify the optimal placement of wearable sensors—specifically accelerometers, gyroscopes, and magnetometers—for effective fall-direction classification. Although previous research identified optimal sensor locations for distinguishing falls from non-falls, limited attention has been given to the classification of fall direction across different body regions. This study assesses inertial measurement unit (IMU) sensors placed at 12 distinct body locations to determine the most effective positions for capturing fall-related data. The research was conducted in three phases: first, comparing classifiers across all sensor locations to identify the most effective; second, evaluating performance differences between sensors placed on the left and right sides of the body; and third, exploring the efficacy of combining sensors from the upper and lower body regions. Statistical analyses of the results for the most effective classifier model demonstrate that the support vector machine (SVM) is more effective than other classifiers across all sensor locations, with statistically significant differences in performance. At the same time, the comparison between the left and right sensor locations shows no significant performance differences within the same anatomical areas. Regarding optimal sensor placement, the findings indicate that sensors positioned on the pelvis and upper legs in the lower body, as well as on the shoulder and head in the upper body, were the most effective results for accurate fall-direction classification. The study concludes that the optimal sensor configuration for fall-direction classification involves strategically combining sensors placed on the pelvis, upper legs, and lower legs.