Quantum dots (QDs), nanoscale semiconductor crystals, have emerged as a revolutionary class of nanomaterials with unique optical and electrochemical properties, making them highly promising for applications in disease diagnosis and treatment. Their tunable emission spectra, long-term photostability, high quantum yield, and excellent charge carrier mobility enable precise control over light emission and efficient charge utilization, which are critical for biomedical applications. This article provides a comprehensive review of recent advancements in the use of quantum dots for disease diagnosis and therapy, highlighting their potential and the challenges involved in clinical translation. Quantum dots can be classified based on their elemental composition and structural configuration. For instance, IB-IIIA-VIA group quantum dots and core-shell structured quantum dots are among the most widely studied types. These classifications are essential for understanding their diverse functionalities and applications. In disease diagnosis, quantum dots have demonstrated remarkable potential due to their high brightness, photostability, and ability to provide precise biomarker detection. They are extensively used in bioimaging technologies, enabling high-resolution imaging of cells, tissues, and even individual biomolecules. As fluorescent markers, quantum dots facilitate cell tracking, biosensing, and the detection of diseases such as cancer, bacterial and viral infections, and immune-related disorders. Their ability to provide real-time, in vivo tracking of cellular processes has opened new avenues for early and accurate disease detection. In the realm of disease treatment, quantum dots serve as versatile nanocarriers for targeted drug delivery. Their nanoscale size and surface modifiability allow them to transport therapeutic agents to specific sites, improving drug bioavailability and reducing off-target effects. Additionally, quantum dots have shown promise as photosensitizers in photodynamic therapy (PDT). When exposed to specific wavelengths of light, quantum dots interact with oxygen molecules to generate reactive oxygen species (ROS), which can selectively destroy malignant cells, vascular lesions, and microbial infections. This targeted approach minimizes damage to healthy tissues, making PDT a promising strategy for treating complex diseases. Despite these advancements, the translation of quantum dots from research to clinical application faces significant challenges. Issues such as toxicity, stability, and scalability in industrial production remain major obstacles. The potential toxicity of quantum dots, particularly to vital organs, has raised concerns about their long-term safety. Researchers are actively exploring strategies to mitigate these risks, including surface modification, coating, and encapsulation techniques, which can enhance biocompatibility and reduce toxicity. Furthermore, improving the stability of quantum dots under physiological conditions is crucial for their effective use in biomedical applications. Advances in surface engineering and the development of novel encapsulation methods have shown promise in addressing these stability concerns. Industrial production of quantum dots also presents challenges, particularly in achieving consistent quality and scalability. Recent innovations in synthesis techniques and manufacturing processes are paving the way for large-scale production, which is essential for their widespread adoption in clinical settings. This article provides an in-depth analysis of the latest research progress in quantum dot applications, including drug delivery, bioimaging, biosensing, photodynamic therapy, and pathogen detection. It also discusses the multiple barriers hindering their clinical use and explores potential solutions to overcome these challenges. The review concludes with a forward-looking perspective on the future directions of quantum dot research, emphasizing the need for further studies on toxicity mitigation, stability enhancement, and scalable production. By addressing these critical issues, quantum dots can realize their full potential as transformative tools in disease diagnosis and treatment, ultimately improving patient outcomes and advancing biomedical science.

Eight compounds were isolated and purified from the ethyl acetate part of 70% acetone extract of Rehmannia glutinosa by various chromatographic techniques such as silica gel, MCI gel CHP-20, ODS, Toyopearl HW-40C, combined with TLC and semi-preparative HPLC. Their structures were elucidated by modern spectroscopy techniques (NMR, MS, UV, IR), and identified as neomartynoside A (1), osmanthuside B₆ (2), martynoside (3), isomartynoside (4), (E)-p-hydroxycinnamic acid (5), caffeic acid (6), ferulic acid (7), and methyl caffeate (8). Compound 1 is a new phenylethanol glycoside, which was identified as neomartynoside A. Compound 2 was isolated from Rehmannia glutinosa for the first time. In addition, compounds 2, 6 and 7 significantly increased relative glucose consumption, showing potential hypoglycemic activity.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

OBJECTIVE: To investigate the effects of asperuloside on cervical cancer based on endoplasmic reticulum (ER) stress and mitochondrial pathway. METHODS: Different doses (12.5-800 µg/mL) of asperuloside were used to treat cervical cancer cell lines Hela and CaSki to calculate the half maximal inhibitory concentration (IC₅₀) of asperuloside. The cell proliferation was analyzed by clone formation assay. Cell apoptosis, intracellular reactive oxygen species (ROS) and mitochondrial membrane potential were determined by flow cytometry. The protein expressions of cleaved-caspase-3, Bcl-2, Bax, Cyt-c, cleaved-caspase-4 and glucose-regulated protein 78 (GRP78) were analyzed by Western blot. And the inhibitor of ER stress, 4-phenyl butyric acid (4-PBA) was used to treat cervical cancer cells to further verify the role of ER stress in the apoptosis of cervical cancer cells induced by asperuloside. RESULTS: Asperuloside of 325, 650, and 1300 µg/mL significantly inhibited the proliferation and promoted apoptosis of Hela and CaSki cells (P<0.01). All doses of asperuloside significantly increased intracellular ROS levels, reduced mitochondrial membrane potential, significantly reduced Bcl-2 protein expression level, and increased Bax, Cyt-c, GRP78 and cleaved-caspase-4 expressions (P<0.01). In addition, 10 mmol/L 4-PBA treatment significantly promoted cell proliferation and reduced apoptosis (P<0.05), and 650 µg/mL asperuloside could reverse 4-PBA-induced increased cell proliferation, decreased apoptosis and cleaved-caspase-3, -4 and GRP78 protein expressions (P<0.05). CONCLUSION Our study revealed the role of asperuloside in cervical cancer, suggesting that asperuloside promotes apoptosis of cervical cancer cells through ER stress-mitochondrial pathway.
Female ; Humans ; Uterine Cervical Neoplasms/metabolism* ; Caspase 3/metabolism* ; bcl-2-Associated X Protein/metabolism* ; Reactive Oxygen Species/metabolism* ; Endoplasmic Reticulum Chaperone BiP ; HeLa Cells ; Proto-Oncogene Proteins c-bcl-2/metabolism* ; Apoptosis ; Endoplasmic Reticulum Stress ; Cell Line, Tumor

Objective To investigate the association between body mass index(BMI),sex hormone and single nucleotide polymorphisms(SNPs)of follicle-stimulating hormone receptor(FSHR)gene rs2268361 and rs2349415 and its correlation with the risk of polycystic ovary syndrome(PCOS).Methods Peripheral blood was collected from 213 PCOS patients and 207 healthy controls,attending the Department of Reproductive Medicine at the First Hospital of Shanxi Medical University,and 32 follicular fluids were randomly collected from each of the PCOS and control groups from March to August 2021.Calculation of BMI of the PCOS and control groups;The levels of follicle-stimulating hormone(FSH),luteinizing hormone(LH),estradiol(E2),testosterone(T),progesterone(P)and prolactin(PRL)in peripheral blood of the two groups were detected by immunochemiluminescence method.Polymerase chain reaction(PCR)and high-resolution melting curve(HRM)were used to analyze the polymorphisms of rs2268361 and rs2349415 in FSHR of the two groups.Quantitative real-time PCR was used to detect the expression of FSHR gene mRNA in peripheral blood and ovarian granulosa cells.Results There was a strong positive correlation between LH and LH/FSH(r=0.88,P<0.05);The levels of BMI,E2,LH,LH/FSH and T in PCOS group were significantly higher than those in control group(P<0.05);FSH level was significantly lower than that of control group(P<0.001).HRM analysis showed the frequencies of CC,CT and TT genotypes at rs2349415 were 55.9%,34.3%and 9.8%in PCOS group and 68.6%,23.2%and 8.2%in control group,respectively.The frequencies of C and T alleles were 73.0%and 27.0%in PCOS group and 80.2%and 19.8%in control group,respectively.There were significant differences in genotype frequencies and allele frequencies between the two groups(P<0.05);The expression level of FSHR mRNA was higher in ovarian granulosa cells in PCOS group than in control group(P=0.004),the expression level of FSHR mRNA in rs2349415 TT genotype was higher than that in CC(P=0.002)and CT(P=0.035)genotype.Conclusion High levels of BMI, LH, E2 and T allele of rs2349415 increased the risk of PCOS.

Tricuspid regurgitation(TR)is a common heart valve disease.According to the pathogenesis,TR can be divided into primary(organic)and secondary(functional)regurgitation,of which functional TR accounts for more than 90%.Patients with severe TR have poor prognosis and poor drug treatment,and surgery(valvuloplasty)is the main treatment.At present,transcatheter edge-to-edge tricuspid valve repair(T-TEER)has become an essential program of transcatheter treatment for TR,providing minimally invasive treatment for TR patients who cannot undergo surgery or are at high risk of surgery.T-TEER reduces the degree of regurgitation by clamping leaflets,and is currently in the early stage of research and development exploration and clinical validation,mainly for functional TR.T-TEER devices have also made significant progress(TriClip,PASCAL),and Chinese-made novel-designed T-TEER devices are also undergoing clinical trials(DragonFly-TTM,SQ-Kyrin-TTM,NeoBlazarTM).This paper reviews the current applications and research progress of T-TEER.

Objective:To analyze the association between visceral fat area (VFA) and fatty liver based on quantitative CT (QCT) in people receiving health examination with normal body mass index (BMI).Methods:A cross-sectional study. A total of 1 305 physical examiners who underwent chest CT and QCT examination in the Department of Health Management of Henan Provincial People′s Hospital from January to December 2021 were retrospectively selected as subjects. The physical components at the central level of the lumbar two cone were measured with QCT, including subcutaneous fat area (SFA), VFA and liver fat content (LFC). And the metabolic indexes, such as blood lipids and blood glucose, were collected. The t-test and χ2 test were used to analyze the correlation between the detection rate of fatty live and LFCr and age and gender. According to level of VFA (<100 cm 2, 100-150 cm 2 and≥150 cm 2), the subjects were divided into three groups, and one-way ANOVA and χ2 test were used in comparison between groups. Multiple linear regression was used to analyze the correlation between VFA and metabolic indexes and LFC. Results:Of the 1 305 subjects, there were 634 males and 671 females. The detection rate of fatty liver in normal BMI population was 65.67%, and it was 72.71% and 59.02% respectively in men and women ( χ2=27.12, P<0.001), and the detection rate of fatty liver and LFC increased with age (both P<0.05). With the increase of VFA, the age, BMI, SFA, LFC, total cholesterol (TC), triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FBG), alanine aminotransferase (ALT), blood uric acid and prevalence of fatty liver increased (all P<0.05), and the low-density lipoprotein cholesterol (HDL-C) decreased ( P<0.001). Multiple linear regression analysis showed that after adjustment for age factors, regardless of male or female, LFC was independently positively related with VFA, BMI, and ALT (male β=0.206, 0.145, 0.174, female β=0.194, 0.150, 0.184; all P<0.05). FBG was positively correlated with male independently ( β=0.134; P<0.001). The indicators related to female independently were TC, TG, and blood uric acid ( β=-0.121, 0.145, 0.141, all P<0.05) Conclusion:In the population receiving health examination with normal BMI, the VFA measured by QCT technique is closely related to fatty liver.