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Pharmacologically active stem of the oriental herbal adaptogen, Siberian ginseng, was employed for the ecofriendly synthesis of Siberian ginseng silver nanoparticles (Sg-AgNPs) and Siberian ginseng gold nanoparticles (Sg-AuNPs). First, for metabolic characterization of the sample, liquid chromatography-tandem mass spectrometry analysis (indicated the presence of eleutherosides A and E), total phenol content, and total reducing sugar were analyzed. Second, the water extract of the sample mediated the biological synthesis of both Sg-AgNPs and Sg-AuNPs that were crystalline face-centered cubical structures with a Z-average hydrodynamic diameter of 126 and 189 nm, respectively. Moreover, Fourier transform infrared analysis indicated that proteins and aromatic hydrocarbons play a key role in the formation and stabilization of Sg-AgNPs, whereas phenolic compounds accounted for the synthesis and stability of Sg-AuNPs. 3-(4,5-Dimethyl-2-thiazolyl)-2,5-diphenyl-2H tetrazolium bromide (MTT) assay determined that Sg-AgNPs conferred strong cytotoxicity against MCF7 (human breast cancer cell line) and was only slightly toxic to HaCaT (human keratinocyte cell line) at 10 µg⋅mL(-1). However, Sg-AuNPs did not display cytotoxic effects against both of the cell lines. The disc diffusion assay indicated a dose-dependent increase in the zone of inhibition of Staphylococcus aureus (ATCC 6538), Bacillus anthracis (NCTC 10340), Vibrio parahaemolyticus (ATCC 33844), and Escherichia coli (BL21) treated with Sg-AgNPs, whereas Sg-AuNPs did not show inhibitory activity. In addition, the 2,2-diphenyl-1-picrylhydrazyl assay demonstrated that both Sg-AgNPs and Sg-AuNPs possess strong antioxidant activity. To the best of our knowledge, this is the first report unraveling the potential of Eleutherococcus senticosus for silver and gold nanoparticle synthesis along with its biological applications, which in turn would promote widespread usage of the endemic Siberian ginseng.

There has been a growing interest in the design of environmentally affable and biocompatible nanoparticles among scientists to find novel and safe biomaterials. Meyer berries have unique phytochemical profile and exhibit beneficial pharmacological activities such as antihyperglycemic, antiobesity, antiaging, and antioxidant properties. A comprehensive study of the biologically active compounds in ginseng berry extract (GBE) and the ability of ginseng berry (GB) as novel material for the biosynthesis of gold nanoparticles (GBAuNPs) and silver nanoparticles (GBAgNPs) was conducted. In addition, the effects of GBAuNPs and GBAgNPs on skin cell lines for further potential biological applications are highlighted. GBAuNPs and GBAgNPs were synthesized using aqueous GBE as a reducing and capping agent. The synthesized nanoparticles were characterized for their size, morphology, and crystallinity. The nanoparticles were evaluated for antioxidant, anti-tyrosinase, antibacterial, and cytotoxicity activities and for morphological changes in human dermal fibroblast and murine melanoma skin cell lines. The phytochemicals contained in GBE effectively reduced and capped gold and silver ions to form GBAuNPs and GBAgNPs. The optimal synthesis conditions (ie, temperature and v/v % of GBE) and kinetics were investigated. Polysaccharides and phenolic compounds present in GBE were suggested to be responsible for stabilization and functionalization of nanoparticles. GBAuNPs and GBAgNPs showed increased scavenging activity against 2,2-diphenyl-1-picrylhydrazyl free radicals compared to GBE. GBAuNPs and GBAgNPs effectively inhibited mushroom tyrosinase, while GBAgNPs showed antibacterial activity against and . In addition, GBAuNPs were nontoxic to human dermal fibroblast and murine melanoma cell lines, and GBAgNPs showed cytotoxic effect on murine melanoma cell lines. The current results evidently suggest that GBAgNPs can act as potential agents for antioxidant, anti-tyrosinase, and antibacterial activities. In addition, GBAuNPs can be further developed into mediators in drug delivery and as antioxidant, anti-tyrosinase, and protective skin agents in cosmetic products. Consequently, the study showed the advantages of using nanotechnology and green chemistry to enhance the natural properties of GBs.

Zinc oxide nanoparticles (ZnO NPs) are used in modern cancer therapy based on their specific target, efficacy, low toxicity and biocompatibility. The photocatalytic performance of Zinc oxide (ZnO) nanocomposites with hyaluronic acid (HA) was used to study anticancer properties against various human cancer cell lines. Zinc oxide (ZnO) nanocomposites functionalized by hyaluronic acid (HA) were prepared by a co-precipitation method (HA-ZnONcs). The submicron-flower-shaped nanocomposites were further functionalized with ginsenoside Rh2 by a cleavable ester bond via carbodiimide chemistry to form Rh2HAZnO. The physicochemical behaviors of the synthesized ZnO nanocomposites were characterized by various analytical and spectroscopic techniques. We carried out 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium bromide (MTT) assay to evaluate the toxicity of Rh2HAZnO in various human cancer cells (A549, MCF-7, and HT29). Furthermore, to confirm the apoptotic effects of Rh2HAZnO and to determine the role of the Caspase-9/p38 MAPK pathways by various molecular techniques such as RT-PCR and Western blotting. Furthermore, Rh2HAZnO induced morphological changes of these cell lines, mainly intracellular reactive oxygen species (ROS) were observed by ROS staining and nucleus by Hoechst staining. We confirmed that Rh2HAZnO exhibits the anti-cancer effects on A549 lung cancer, HT29 colon cancer, and MCF7 breast cancer cells. Moreover, intracellular reactive oxygen species (ROS) were observed in three cancer cell lines. Rh2HAZnO induced apoptotic process through p53-mediated pathway by upregulating p53 and BAX and downregulating BCL2. Specifically, Rh2HAZnO induced activation of cleaved PARP (Asp214) in A549 lung cancer cells and upregulated Caspase-9/phosphorylation of p38 MAPK in other cell lines (HT29 and MCF-7). Furthermore, Rh2HAZnO induced morphological changes in the nucleus of these cell lines. These results suggest that the potential anticancer activity of novel Rh2HAZnO nanoparticles might be linked to induction of apoptosis through the generation of ROS by activation of the Caspase-9/p38 MAPK pathway.

A rapid biological synthesis of multifunctional gold nanoparticle (AuNp) and monodisperse silver nanoparticle (AgNp) was achieved by an aqueous extract of black Meyer root. The physicochemical transformation into black ginseng (BG) greatly enhanced the pharmacological activities of white ginseng and its minor ginsenoside content. The optimal temperature conditions and kinetics of bioreduction were investigated. Formation of BG-AuNps and BG-AgNps was verified by ultraviolet-visible spectrophotometry at 548 and 412 nm, respectively. The biosynthesized BG-AgNps were spherical and monodisperse with narrow distribution, while BG-AuNps were icosahedral-shaped and moderately polydisperse. Synthesized nanoparticles exhibited long-term stability in buffers of pH 7.0-8.0 and biological media (5% bovine serum albumin) at an ambient temperature and at 37°C. BG-AgNps showed effective antibacterial activity against and . BG-AuNps and BG-AgNps demonstrated increased scavenging activity against 2,2-diphenyl-1-picrylhydrazyl free radicals. In addition, BG-AuNps and BG-AgNps were nontoxic to HaCaT and MCF-7 cells; the latter showed no cytotoxicity at concentrations lower than 10 µg/mL. At higher concentrations, BG-AgNps exhibited apparent apoptotic activity in MCF-7 breast cancer cell line through reactive oxygen species generation and nuclear fragmentation.

A facile synthesis and biological applications of silver (DH-AgNps) and gold nanoparticles (DH-AuNps) mediated by the aqueous extract of Angelicae Pubescentis Radix (Du Huo) are explored. Du Huo is a medicinal root belonging to Angelica pubescens Maxim which possesses anti-inflammatory, analgesic, and antioxidant properties. The absorption spectra of nanoparticles in varying root extract and metal ion concentration, pH, reaction temperatures, and time were recorded by ultraviolet–visible (UV-Vis) spectroscopy. The presence of DH-AgNps and DH-AuNps was confirmed from the surface plasmon resonance intensified at ~414 and ~540 nm, respectively. Field emission transmission electron micrograph (FE-TEM) analysis revealed the formation of quasi-spherical DH-AgNps and spherical icosahedral DH-AuNps. These novel DH-AgNps and DH-AuNps maintained an average crystallite size of 12.48 and 7.44 nm, respectively. The biosynthesized DH-AgNps and DH-AuNps exhibited antioxidant activity against 2,2-diphenyl-1-picrylhydrzyl (DPPH) radicals and the former exhibited antimicrobial activity against clinical pathogens including Escherichia coli , Staphylococcus aureus , Pseudomonas aeruginosa , and Salmonella enterica . The expected presence of flavonoids, sesquiterpenes, and phenols on the nanoparticle surface were conjectured to grant protection against aggregation and free radical scavenging activity. DH-AgNps and DH-AuNps were further investigated for their cytotoxic properties in RAW264.7 macrophages for their potential application as drug carriers to sites of inflammation. In conclusion, this green synthesis is favorable for the advancement of plant mediated nano-carriers in drug delivery systems, cancer diagnostic, and medical imaging. Graphical abstract Schematic diagram of biosynthesis of DH-AgNps and DH-AuNps and evaluation of their bioactivities.

Gold nanoparticles (GNPs) are forecasted to provide an attractive platform in biomedicine and catalysis with their potentials of combining a variety of biophysicochemical properties into an integrated nanodevice with great therapeutic and optical functions. There are several reports of crude plant extracts mediating the conversion of metal ions into nanoparticles. However, we aimed to investigate the capability of single bioactive compounds, namely ginsenosides compound K (C-K) and Rh2, to accommodate a synergistic chemical reduction of gold salts by one-pot green chemistry. Ginsenosides C-K and Rh2 are unique triterpenoid saponins present in Panax ginseng Meyer, a perennial plant traditionally used as an oriental medicinal herbal with long history. C-K and Rh2 have demonstrated diverse pharmacological properties such as anticancer, anti-inflammation, anti-aging, and neuroprotective properties. The reduction of gold ions by these ginsenosides led to the production of nontoxic GNPs as tested in mouse macrophage (J774A.1) and human kidney epithelial (HEK-293) in vitro. The kinetics of the bioreduction and the influence of pH were examined by an ultraviolet-visible (UV-Vis) spectrophotometer. GNPs were characterized by field emission transmission electron microscopy (FE-TEM), X-ray diffraction (XRD), dynamic light scattering (DLS), and Fourier transform infrared (FTIR) spectroscopy. Ginsenoside loading efficiency of C-K-GNPs and Rh2-GNPs was determined to be approximately 62.83% and 54.91%, respectively, by thermogravimetric analysis (TGA). These results suggest that one-pot synthesis by ginsenosides C-K and Rh2 may be useful for producing ginsenoside-loaded gold nanocarriers. Graphical abstract

The role of antineuronal antibody titres in the acute and long-term diagnostic and therapeutic management of autoimmune encephalitis (AE) remains unclear. In this retrospective monocentric cohort study, we aimed to (I) identify specific characteristics in antibody testing distinguishing AE from non-AE patients, (II) evaluate the prognostic significance of antineuronal antibody findings and (III) assess outcomes and long-term immunotherapy in patients with AE. Patients with suspected autoimmune-associated neuropsychiatric conditions underwent antineuronal antibody testing between 01/2017 and 03/2023. Patients with positive antibody tests were stratified into AE and non-AE groups based on the clinical criteria proposed by Graus and colleagues. Long-term outcomes, antibody titres, and therapeutic strategies were analysed in AE patients over a three-year follow-up period. Among 2,466 patients tested, 53 met the diagnostic criteria for AE. In AE patients with paired serum and CSF samples (n = 44), antibodies were detectable in both serum and CSF in 55% of cases (n = 24), in serum only in 36% (n = 16), and in CSF only in 9% (n = 4). AE patients with poor outcomes (n=5) showed a trend toward higher median CSF titres in the acute phase and at four months post-onset compared to patients with good outcomes (n=14); however, differences were not statistically significant. Regarding long-term immunotherapy, rituximab-treated patients experienced fewer relapses than those receiving intravenous-immunoglobulins (IVIG; p-value = 0.02). These exploratory results from a small, heterogeneous cohort require confirmation in larger, prospective studies. Based on our data regarding serum and CSF antibodies, in a resource- limited setting we propose a stepwise diagnostic approach starting with serum screening; in suspected anti-NMDAR-AE, initial paired serum/CSF testing remains essential. If antibodies are detected in serum, additional CSF antibody testing may provide diagnostic confirmation and help guide treatment decisions, as high acute-phase CSF titres may suggest poorer long-term outcomes; however, this potential prognostic value requires confirmation in larger, antibody-specific studies.

Purpose Machine Learning (ML) has become an essential tool for analyzing biomedical data, facilitating the prediction of treatment outcomes and patient survival. However, the effectiveness of ML models heavily relies on both the choice of algorithms and the quality of the input data. In this study, we aimed to develop a novel predictive model to estimate individual survival for patients diagnosed with glioblastoma (GBM), focusing on key variables such as O6-Methylguanine-DNA Methyltransferase (MGMT) methylation status, age, and sex. Methods To identify the optimal approach, we utilized retrospective data from 218 patients treated at our brain tumor center. The performance of the ML models was evaluated within repeated tenfold regression. The pipeline comprised five regression estimators, including both linear and non-linear algorithms. Permutation feature importance highlighted the feature with the most significant impact on the model. Statistical significance was assessed using a permutation test procedure. Results The best machine learning algorithm achieved a mean absolute error (MAE) of 12.65 (SD = ± 2.18) and an explained variance (EV) of 7% (SD = ± 1.8%) with p  < 0.001. Linear algorithms led to more accurate predictions than non-linear estimators. Feature importance testing indicated that age and positive MGMT-methylation influenced the predictions the most. Conclusion In summary, here we provide a novel approach allowing to predict GBM patient’s survival in months solely based on key parameters such as age, sex and MGMT-methylation status and underscores MGMT-methylation status as key prognostic factor for GBM patients survival.

Artificial intelligence is gaining increasing relevance in the field of radiology. This study retrospectively evaluates how a commercially available deep learning algorithm can detect pneumonia in chest radiographs (CR) in emergency departments. The chest radiographs of 948 patients with dyspnea between 3 February and 8 May 2020, as well as 15 October and 15 December 2020, were used. A deep learning algorithm was used to identify opacifications associated with pneumonia, and the performance was evaluated by using ROC analysis, sensitivity, specificity, PPV and NPV. Two radiologists assessed all enrolled images for pulmonal infection patterns as the reference standard. If consolidations or opacifications were present, the radiologists classified the pulmonal findings regarding a possible COVID-19 infection because of the ongoing pandemic. The AUROC value of the deep learning algorithm reached 0.923 when detecting pneumonia in chest radiographs with a sensitivity of 95.4%, specificity of 66.0%, PPV of 80.2% and NPV of 90.8%. The detection of COVID-19 pneumonia in CR by radiologists was achieved with a sensitivity of 50.6% and a specificity of 73%. The deep learning algorithm proved to be an excellent tool for detecting pneumonia in chest radiographs. Thus, the assessment of suspicious chest radiographs can be purposefully supported, shortening the turnaround time for reporting relevant findings and aiding early triage.