Targeted Delivery of Geraniol via Hyaluronic Acid-Conjugation Enhances Its Anti-Tumor Activity Against Prostate Cancer.

Background: Targeted delivery systems have been developed to improve cancer treatment by reducing side effects and enhancing drug efficacy. Geraniol, a natural product, has demonstrated promising anti-cancer effects in various cancer types, including prostate cancer, which is the most commonly diagnosed cancer in men. Hyaluronic acid (HA), a natural carrier targeting CD44-positive prostate cancer cells, can be utilized in a targeted delivery system. Purpose: This study investigated the efficacy of a conjugate of HA and geraniol linked via a disulfide bond linker (HA-SS-Geraniol) in prostate cancer. Materials and Methods: The cytotoxicity of HA-SS-Geraniol was evaluated on human PC-3 prostate cancer cells. Flow cytometry was used to assess its effects on mitochondrial membrane potential, apoptosis, and cell cycle arrest. Additionally, proteomic analysis was conducted to explore the underlying mechanism of action induced by HA-SS-Geraniol treatment. A subcutaneous xenograft tumor model was established in nude mice to evaluate the toxicity and efficacy of HA-SS-Geraniol in vivo. Results: The results demonstrated that HA-SS-Geraniol exhibited potent cytotoxicity against PC-3 prostate cancer cells by inducing mitochondrial membrane potential loss and apoptosis in vitro. The proteomic analysis further supported the hypothesis that HA-SS-Geraniol induces cell death through mitochondria-mediated apoptosis, as evidenced by differential protein expression. The in vivo mouse model confirmed the safety of HA-SS-Geraniol and its ability to inhibit tumor growth. Conclusion: HA-SS-Geraniol holds promise as a biologically safe and potentially effective therapeutic agent for prostate cancer treatment. Its targeted delivery system utilizing HA as a carrier shows potential for improving the efficacy of geraniol in cancer therapy.

A Triple-Targeted Rutin-Based Self-Assembled Delivery Vector for Treating Ischemic Stroke by Vascular Normalization and Anti-Inflammation via ACE2/Ang1-7 Signaling.

Changes in the cerebral microenvironment caused by acute ischemic stroke-reperfusion are the main obstacle to the recovery of neurological function and an important cause of stroke recurrence after thrombolytic therapy. The intracerebral microenvironment after ischemia-reperfusion reduces the neuroplasticity of the penumbra and ultimately leads to permanent neurological damage. To overcome this challenge, we developed a triple-targeted self-assembled nanodelivery system, which combines the neuroprotective drug rutin with hyaluronic acid through esterification to form a conjugate, and then connected SS-31, a small peptide that can penetrate the blood brain barrier and target mitochondria. Brain targeting, CD44-mediated endocytosis, hyaluronidase 1-mediated degradation, and the acidic environment synergistically promoted the enrichment of nanoparticles and drug release in the injured area. Results demonstrate that rutin has a high affinity for ACE2 receptors on the cell membrane and can directly activate ACE2/Ang1-7 signaling, maintain neuroinflammation, and promote penumbra angiogenesis and normal neovascularization. Importantly, this delivery system enhanced the overall plasticity of the injured area and significantly reduced neurological damage after stroke. The relevant mechanism was expounded from the aspects of behavior, histology, and molecular cytology. All results suggest that our delivery system may be an effective and safe strategy for the treatment of acute ischemic stroke-reperfusion injury.

An AI-Based Image Quality Control Framework for Knee Radiographs.

Image quality control (QC) is crucial for the accurate diagnosis of knee diseases using radiographs. However, the manual QC process is subjective, labor intensive, and time-consuming. In this study, we aimed to develop an artificial intelligence (AI) model to automate the QC procedure typically performed by clinicians. We proposed an AI-based fully automatic QC model for knee radiographs using high-resolution net (HR-Net) to identify predefined key points in images. We then performed geometric calculations to transform the identified key points into three QC criteria, namely, anteroposterior (AP)/lateral (LAT) overlap ratios and LAT flexion angle. The proposed model was trained and validated using 2212 knee plain radiographs from 1208 patients and an additional 1572 knee radiographs from 753 patients collected from six external centers for further external validation. For the internal validation cohort, the proposed AI model and clinicians showed high intraclass consistency coefficients (ICCs) for AP/LAT fibular head overlap and LAT knee flexion angle of 0.952, 0.895, and 0.993, respectively. For the external validation cohort, the ICCs were also high, with values of 0.934, 0.856, and 0.991, respectively. There were no significant differences between the AI model and clinicians in any of the three QC criteria, and the AI model required significantly less measurement time than clinicians. The experimental results demonstrated that the AI model performed comparably to clinicians and required less time. Therefore, the proposed AI-based model has great potential as a convenient tool for clinical practice by automating the QC procedure for knee radiographs.

The Anti-Cancer Effects of Mitochondrial-Targeted Triphenylphosphonium-Resveratrol Conjugate on Breast Cancer Cells.

Breast cancer is the most commonly diagnosed cancer in women. Resveratrol, a naturally occurring phytochemical, shows great promise in developing novel anti-cancer therapies. This study hypothesized that the mitochondria-targeted delivery of resveratrol would increase its potency and induce mitochondria-mediated apoptosis. The targeted delivery of resveratrol was achieved by conjugating resveratrol to triphenylphosphonium (TPP). The anti-cancer effects of TPP-resveratrol were studied in the murine breast cancer 4T1 and the human breast cancer MDA-MB-231 cell lines. Flow cytometry was used to study apoptosis induction, cell cycle arrest, and mitochondrial membrane potential loss. The morphological changes in the mitochondria in MDA-MB-231 cells after TPP-resveratrol treatments were examined using transmission electron microscopy. Moreover, the changes in MDA-MB-231 cell metabolism after resveratrol and TPP-resveratrol treatments were studied using metabolomic analysis. We demonstrate that TPP-resveratrol significantly improved cytotoxicity in 4T1 cells and MDA-MB-231 cells by inducing apoptosis and mitochondrial membrane potential loss. Swollen and vacuolated mitochondria were observed after the TPP-resveratrol treatment. Meanwhile, TPP-resveratrol treatment down-regulated amino acid and energy metabolism and caused the dysfunction of purine and pyrimidine metabolism. Our results provide evidence supporting the targeted delivery of resveratrol to mitochondria and suggest that TPP-resveratrol may be an effective agent for breast cancer treatment.

Advances in Deep Learning-Based Medical Image Analysis.

Importance. With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia. This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications. It also discussed the existing problems in the field and provided possible solutions and future directions.Highlights. This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications. More specifically, state-of-the-art clinical applications include four major human body systems: the nervous system, the cardiovascular system, the digestive system, and the skeletal system. Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability. Future direction could include federated learning, benchmark dataset collection, and utilizing domain subject knowledge as priors.Conclusion. Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy, efficiency, stability, and scalability. Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.

3,3-Dimethyl-1-[5-(1H-1,2,4-triazol-1-yl-meth-yl)-1,3,4-thia-diazol-2-ylsulfan-yl]butan-2-one.

In the mol-ecule of the title compound, C(11)H(15)N(5)OS(2), the thia-diazole and triazole rings are not coplanar, the dihedral angle formed by their mean planes being 59.9 (2)°. The exocyclic S atom, and the methyl-ene, carbonyl, tert-butyl and one methyl carbon form an approximately planar zigzag chain, which makes a dihedral angle of 74.6 (1)° with the thia-diazole ring.

[Application of multiparameter flow cytometry in diagnosis of non-Hodgkin lymphoma].

OBJECTIVE: To evaluate the role and application of flow cytometry in the diagnosis of non-Hodgkin lymphoma (NHL). METHODS: Fresh cell samples from 40 cases of lymphoproliferative disorders were obtained by fine needle aspiration or excisional biopsies. Multiparameter flow cytometry was used to study the surface antigens of lymphoid cells. The immunophenotyping results were also correlated with morphologic features seen in the cytology preparations. RESULTS: Of the 40 cases with histologic diagnosis of NHL, 37 cases (92.5%) had the lymphoma diagnosis confirmed by this method. The concordance rate for the 20 cases of B-cell NHL was 100%. As for the 17 cases with histologic diagnosis of T-cell NHL, 12 cases (66.7%) were correctly diagnosed as T-cell NHL using flow cytometry, while 2 cases (11.8%) were interpreted as B-cell NHL and the remaining 3 cases (17.6%) were undiagnosed. CONCLUSION: Immunophenotyping by flow cytometry can serve as an ancillary technique in diagnosis and subclassification of NHL.

[The inhibiting effect of genistein on the growth of human breast cancer cells in vitro].

OBJECTIVE: To study the inhibitig effects of genistein on the growth of human breast cancer cell lines in vitro and its mechanisms. METHORD: Human breast cancer cell lines both MCF-7(positive estrogen receptor, ER+) and MDA-MB-231 (negative estrogen receptor, ER-) were cultured in vitro. The proliferation of cells was measured with MTT methord and the growth curve was drawn with cell count. The estrogen receptor in cells was show with immunohistochemistry. RESULT: Genistein inhibited proliferation of MCF-7 and MDA-MB-231 cell lines and inhibition was dependent on dose within some concentration range, IC50 being 32.5 mumol.L-1 and 46.8 mumol.L-1 respectively. Genisteins antiproliferation of MCF-7 was stimulated by ectogenesis estrogen but proliferation of MDA-MB-231 inhibited by geinstein was not related to estrogen. The positive signs of ER in cellular nuclei of MCF-7 cell line fed with genistein at concentration of 30 mumol.L-1 were significantly weaker than these not fed with genistein. CONCLUSION: Genistein obviously inhibits proliferation of both cell lines of MCF-7 and MDA-MB-231 in vitro and is dependent on dose. Genisteins antiproliferous effect on MCF-7 cell lines is stimulated by estrogen and this effect is related with ER, but genisteins inhibiting proliferation of MDA-MB-231 cell line is not through ER.