Interference of the gas chromatography- mass spectrometry instrumental background on the determination of trace cyclic volatile methylsiloxanes and exclusion of it by delayed injection.

Cyclic volatile methylsiloxanes (cVMS) have been widely found in various types of environmental media and attracted increasing attention as new pollutants. However, there is still a great challenge in the accurate quantification of trace cVMS, due to their volatility, and the high background originating from GC/MS accessories and surroundings. In this work, the main sources of the high background were investigated in detail for octamethylcyclotetrasiloxane (D4), decmethylcyclopentasiloxane (D5) and dodecmethylcyclohexosiloxane (D6). Several effective measures were employed to minimize these backgrounds, including the delayed injection method to minimize the interference from the injection septum. Then, a GC-MS method was developed for the accurate determination of D4, D5 and D6, with a linear range of 2 - 200 µg/L. The coefficient of determination was 0.9982-0.9986, the limit of detection (LOD) was 0.40-0.52 µg/L, and the quantitative range was 1.88-190 µg/L. Good reproducibility and recovery were obtained, indicating the reliability of the established analytical method.

Prevalence of unhealthy behaviors and their associations with non-suicidal self-injury, suicidal ideation and suicide attempt among Chinese adolescents.

BACKGROUND: Unhealthy lifestyle behaviors among adolescents have emerged as a significant public health concern worldwide, however, there is little investigation on the impact of unhealthy behaviors on non-suicidal self-injury (NSSI), suicidal ideation (SI) and suicide attempt (SA). This study aimed to investigate the prevalence of seven unhealthy behaviors as well as their associations with NSSI, SI and SA, and to explore whether the aforementioned associations differ across sex. METHODS: A total of 74,152 adolescents were included in this study via a multi-stage, stratified cluster, random sampling method in 2021. Information about unhealthy behaviors (insufficient physical activity, current smoking, current drinking, excessive screen time, long homework time, insufficient sleep and unhealthy BMI), NSSI, SI, SA and other demographics was collected. Sampling weights were used to estimate the prevalence, and the weighted logistic regression models were performed. Stratified analyses by sex and sensitive analyses were conducted. RESULTS: Overview, the weighted prevalence of adolescents had more than five unhealthy behaviors were 5.2%, with boys showing a higher prevalence than girls (6.5% vs.3.8%). Current smoking, current drinking, excessive screen use, long homework time, insufficient sleep, and unhealthy BMI were significantly associated with NSSI, SI and SA. Moreover, adolescents with high lifestyle risk scores were associated with an increased risk of NSSI (5-7 vs. 0: OR 6.38, 95% CI 5.24-7.77), SI (5-7 vs. 0: OR 7.67, 95% CI 6.35-9.25), and SA (5-7 vs. 0: OR 9.57, 95% CI 6.95-13.17). Significant sex differences were found in the associations of unhealthy behaviors with NSSI, SI and SA. CONCLUSION: Unhealthy behaviors are quite common among Chinese adolescents. Adolescents with multiple unhealthy behaviors are associated with increased risks of NSSI, SI, and SA. The implementation of school and family-based interventions to promote healthy lifestyles is recommended as a preventive measure against self-injurious behavior and suicidality in adolescents.

Molecular subtypes of breast cancer identified by dynamically enhanced MRI radiomics: the delayed phase cannot be ignored.

OBJECTIVES: To compare the diagnostic performance of intratumoral and peritumoral features from different contrast phases of breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building radiomics models for differentiating molecular subtypes of breast cancer. METHODS: This retrospective study included 377 patients with pathologically confirmed breast cancer. Patients were divided into training set (n = 202), validation set (n = 87) and test set (n = 88). The intratumoral volume of interest (VOI) and peritumoral VOI were delineated on primary breast cancers at three different DCE-MRI contrast phases: early, peak, and delayed. Radiomics features were extracted from each phase. After feature standardization, the training set was filtered by variance analysis, correlation analysis, and least absolute shrinkage and selection (LASSO). Using the extracted features, a logistic regression model based on each tumor subtype (Luminal A, Luminal B, HER2-enriched, triple-negative) was established. Ten models based on intratumoral or/plus peritumoral features from three different phases were developed for each differentiation. RESULTS: Radiomics features extracted from delayed phase DCE-MRI demonstrated dominant diagnostic performance over features from other phases. However, the differences were not statistically significant. In the full fusion model for differentiating different molecular subtypes, the most frequently screened features were those from the delayed phase. According to the Shapley additive explanation (SHAP) method, the most important features were also identified from the delayed phase. CONCLUSIONS: The intratumoral and peritumoral radiomics features from the delayed phase of DCE-MRI can provide additional information for preoperative molecular typing. The delayed phase of DCE-MRI cannot be ignored. CRITICAL RELEVANCE STATEMENT: Radiomics features extracted and radiomics models constructed from the delayed phase of DCE-MRI played a crucial role in molecular subtype classification, although no significant difference was observed in the test cohort. KEY POINTS: The molecular subtype of breast cancer provides a basis for setting treatment strategy and prognosis. The delayed-phase radiomics model outperformed that of early-/peak-phases, but no differently than other phases or combinations. Both intra- and peritumoral radiomics features offer valuable insights for molecular typing.

Integrating TCGA and Single-Cell Sequencing Data for Hepatocellular Carcinoma: A Novel Glycosylation (GLY)/Tumor Microenvironment (TME) Classifier to Predict Prognosis and Immunotherapy Response.

The major liver cancer subtype is hepatocellular carcinoma (HCC). Studies have indicated that a better prognosis is related to the presence of tumor-infiltrating lymphocytes (TILs) in HCC. However, the molecular pathways that drive immune cell variation in the tumor microenvironment (TME) remain poorly understood. Glycosylation (GLY)-related genes have a vital function in the pathogenesis of numerous tumors, including HCC. This study aimed to develop a GLY/TME classifier based on glycosylation-related gene scores and tumor microenvironment scores to provide a novel prognostic model to improve the prediction of clinical outcomes. The reliability of the signatures was assessed using receiver operating characteristic (ROC) and survival analyses and was verified with external datasets. Furthermore, the correlation between glycosylation-related genes and other cells in the immune environment, the immune signature of the GLY/TME classifier, and the efficacy of immunotherapy were also investigated. The GLY score low/TME score high subgroup showed a favorable prognosis and therapeutic response based on significant differences in immune-related molecules and cancer cell signaling mechanisms. We evaluated the prognostic role of the GLY/TME classifier that demonstrated overall prognostic significance for prognosis and therapeutic response before treatment, which may provide new options for creating the best possible therapeutic approaches for patients.

Essential contribution of the JAK/STAT pathway to carcinogenesis, lytic infection of herpesviruses and pathogenesis of COVID­19 (Review).

The intracellular pathway of Janus kinase/signal transducer and activator of transcription (JAK/STAT) and modification of nucleosome histone marks regulate the expression of proinflammatory mediators, playing an essential role in carcinogenesis, antiviral immunity and the interaction of host proteins with Herpesviral particles. The pathway has also been suggested to play a vital role in the clinical course of the acute infection caused by severe acute respiratory syndrome coronavirus type 2 (SARS­CoV­2; known as coronavirus infection­2019), a novel human coronavirus initially identified in the central Chinese city Wuhan towards the end of 2019, which evolved into a pandemic affecting nearly two million people worldwide. The infection mainly manifests as fever, cough, myalgia and pulmonary involvement, while it also attacks multiple viscera, such as the liver. The pathogenesis is characterized by a cytokine storm, with an overproduction of proinflammatory mediators. Innate and adaptive host immunity against the viral pathogen is exerted by various effectors and is regulated by different signaling pathways notably the JAK/STAT. The elucidation of the underlying mechanism of the regulation of mediating factors expressed in the viral infection would assist diagnosis and antiviral targeting therapy, which will help overcome the infection caused by SARS­CoV­2.

Glutathione-Responsive Organosilica Hybrid Nanosystems for Targeted Dual-Starvation Therapy in Luminal Breast Cancer.

Starvation therapy is an innovative approach in cancer treatment aimed at depriving cancer cells of necessary resources by impeding tumor angiogenesis or blocking the energy supply. In addition to the commonly observed anaerobic glycolysis energy supply mode, adipocyte-rich tumor tissue triggers the fatty acid energy supply pathway, which fuels the proliferation and metastasis of cancer cells. To completely disrupt these dual-energy-supply pathways, we developed an exceptional nanoreactor. This nanoreactor consisted of yolk-shell mesoporous organosilica nanoparticles (YSMONs) loaded with a fatty acid transport inhibitor (Dox), conjugated with a luminal breast-cancer-specific targeting aptamer, and integrated with a glucose oxidation catalyst (GOx). Upon reaching cancer cells with the assistance of the aptamer, the nanoreactor underwent a structural collapse of the shell triggered by the high concentration of glutathione within cancer cells. This collapse led to the release of GOx and Dox, achieving targeted delivery and exhibiting significant efficacy in starving therapy. Additionally, the byproducts of glucose metabolism, gluconic acid and H2O2, enhanced the acidity and reactive oxygen species levels of the intracellular microenvironment, inducing oxidative damage to cancer cells. Simultaneously, released Dox acted as a potent broad-spectrum anticancer drug, inhibiting the activity of carnitine palmitoyltransferase 1A and exerting marked effects. Combining these effects ensures high anticancer efficiency, and the "dual-starvation" nanoreactor has the potential to establish a novel synergistic therapy paradigm with considerable clinical significance. Furthermore, this approach minimizes damage to normal organs, making it highly valuable in the field of cancer treatment.

A microfluidic-chip-based system with loop-mediated isothermal amplification for rapid and parallel detection of Trichomonas vaginalis and human papillomavirus.

Cervical cancer is a significant global health issue primarily caused by high-risk types of human papillomavirus (HPV). Recent studies have reported an association between Trichomonas vaginalis (T. vaginalis) infections and HPV infections, highlighting the importance of simultaneously detecting these pathogens for effective cervical cancer risk management. However, current methods for detecting both T. vaginalis and HPV are limited. In this study, we present a novel approach using a microfluidic-chip-based system with loop-mediated isothermal amplification (LAMP) for the rapid and parallel detection of T. vaginalis, HPV16, HPV18, and HPV52 in a reagent-efficient and user-friendly manner. Compared to conventional LAMP assays in tubes, our system exhibits enhanced sensitivity with values of 2.43 × 101, 3.00 × 102, 3.57 × 101, and 3.60 × 102 copies per reaction for T. vaginalis, HPV16, HPV18, and HPV52, respectively. Additionally, we validated the performance of our chip by testing 47 clinical samples, yielding results consistent with the diagnostic methods used by the hospital. Therefore, our system not only offers a promising solution for concurrent diagnosis of T. vaginalis and HPV infections, particularly in resource-limited areas, due to its cost-effectiveness, ease of use, and rapid and accurate detection performance, but can also contribute to future research on the co-infection of these two pathogens. Moreover, the system possesses the capability to simultaneously detect up to 22 different types of pathogens, making it applicable across a wide range of domains such as diagnostics, food safety, and water monitoring.

Diagnostic and Prognostic Value of Protein Post-translational Modifications in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a common malignant tumor with high incidence and cancer mortality worldwide. Post-translational modifications (PTMs) of proteins have a great impact on protein function. Almost all proteins can undergo PTMs, including phosphorylation, acetylation, methylation, glycosylation, ubiquitination, and so on. Many studies have shown that PTMs are related to the occurrence and development of cancers. The findings provide novel therapeutic targets for cancers, such as glypican-3 and mucin-1. Other clinical implications are also found in the studies of PTMs. Diagnostic or prognostic value, and response to therapy have been identified. In HCC, it has been shown that glycosylated alpha-fetoprotein (AFP) has a higher detection rate for early liver cancer than conventional AFP. In this review, we mainly focused on the diagnostic and prognostic value of PTM, in order to provide new insights into the clinical implication of PTM in HCC.

Potential Antihuman Epidermal Growth Factor Receptor 2 Target Therapy Beneficiaries: The Role of MRI-Based Radiomics in Distinguishing Human Epidermal Growth Factor Receptor 2-Low Status of Breast Cancer.

BACKGROUND: Multiparametric MRI radiomics could distinguish human epidermal growth factor receptor 2 (HER2)-positive from HER2-negative breast cancers. However, its value for further distinguishing HER2-low from HER2-negative breast cancers has not been investigated. PURPOSE: To investigate whether multiparametric MRI-based radiomics can distinguish HER2-positive from HER2-negative breast cancers (task 1) and HER2-low from HER2-negative breast cancers (task 2). STUDY TYPE: Retrospective. POPULATION: Task 1: 310 operable breast cancer patients from center 1 (97 HER2-positive and 213 HER2-negative); task 2: 213 HER2-negative patients (108 HER2-low and 105 HER2-zero); 59 patients from center 2 (16 HER2-positive, 27 HER2-low and 16 HER2-zero) for external validation. FIELD STRENGTH/SEQUENCE: A 3.0 T/T1-weighted contrast-enhanced imaging (T1CE), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC). ASSESSMENT: Patients in center 1 were assigned to a training and internal validation cohort at a 2:1 ratio. Intratumoral and peritumoral features were extracted from T1CE and ADC. After dimensionality reduction, the radiomics signatures (RS) of two tasks were developed using features from T1CE (RS-T1CE), ADC (RS-ADC) alone and T1CE + ADC combination (RS-Com). STATISTICAL TESTS: Mann-Whitney U tests, the least absolute shrinkage and selection operator, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). RESULTS: For task 1, RS-ADC yielded higher area under the ROC curve (AUC) in the training, internal, and external validation of 0.767/0.725/0.746 than RS-T1CE (AUC = 0.733/0.674/0.641). For task 2, RS-T1CE yielded higher AUC of 0.765/0.755/0.678 than RS-ADC (AUC = 0.706/0.608/0.630). For both of task 1 and task 2, RS-Com achieved the best performance with AUC of 0.793/0.778/0.760 and 0.820/0.776/0.711, respectively, and obtained higher clinical benefit in DCA compared with RS-T1CE and RS-ADC. The calibration curves of all RS demonstrated a good fitness. DATA CONCLUSION: Multiparametric MRI radiomics could noninvasively and robustly distinguish HER2-positive from HER2-negative breast cancers and further distinguish HER2-low from HER2-negative breast cancers. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Machine Learning System for Lung Neoplasms Distinguished Based on Scleral Data.

Lung cancer remains the most commonly diagnosed cancer and the leading cause of death from cancer. Recent research shows that the human eye can provide useful information about one's health status, but few studies have revealed that the eye's features are associated with the risk of cancer. The aims of this paper are to explore the association between scleral features and lung neoplasms and develop a non-invasive artificial intelligence (AI) method for detecting lung neoplasms based on scleral images. A novel instrument was specially developed to take the reflection-free scleral images. Then, various algorithms and different strategies were applied to find the most effective deep learning algorithm. Ultimately, the detection method based on scleral images and the multi-instance learning (MIL) model was developed to predict benign or malignant lung neoplasms. From March 2017 to January 2019, 3923 subjects were recruited for the experiment. Using the pathological diagnosis of bronchoscopy as the gold standard, 95 participants were enrolled to take scleral image screens, and 950 scleral images were fed to AI analysis. Our non-invasive AI method had an AUC of 0.897 ± 0.041(95% CI), a sensitivity of 0.836 ± 0.048 (95% CI), and a specificity of 0.828 ± 0.095 (95% CI) for distinguishing between benign and malignant lung nodules. This study suggested that scleral features such as blood vessels may be associated with lung cancer, and the non-invasive AI method based on scleral images can assist in lung neoplasm detection. This technique may hold promise for evaluating the risk of lung cancer in an asymptomatic population in areas with a shortage of medical resources and as a cost-effective adjunctive tool for LDCT screening at hospitals.

Sustainable 3D Printing of Recyclable Biocomposite Empowered by Flash Graphene.

Sustainability of 3D printing can be reflected in three main aspects: deployment of renewable inks, recycling of printed products, and applications for energy- and materials- savings. In this work, we demonstrated sustainable vat-photopolymerization (VPP)-based 3D printing in a whole life-cycle process by developing a renewable ink made of soybean oil and natural polyphenols and recycling the ink for reprinting or converting printed biocomposite to flash graphene (FG) as reinforcing nanofillers in the biocomposite. We also realized its applications in fabricating lightweight, materials-saving 3D structures, acoustic metamaterials, and disposable microreactors for time-saving and efficiency-improving synthesis of metal-organic framework nanostructures. In addition to enhancing the tensile strength and Young's modulus of the biopolymers by 42% and 232% with only 0.6 wt % FG nanofillers, respectively, FG improved the printability of the ink in forming 3D tubular structures, which are usually very hard to be achieved in transparent resin. Success of this work will inspire further development for sustainability in 3D printing.

Miniaturized microfluidic-based nucleic acid analyzer to identify new biomarkers of biopsy lung cancer samples for subtyping.

Identifying new biomarkers is necessary and important to diagnose and treat malignant lung cancer. However, existing protein marker detection methods usually require complex operation steps, leading to a lag time for diagnosis. Herein, we developed a rapid, minimally invasive, and convenient nucleic acid biomarker recognition method, which enabled the combined specific detection of 11 lung cancer typing markers in a microliter reaction system after only one sampling. The primers for the combined specific detection of 11 lung cancer typing markers were designed and screened, and the microfluidic chip for parallel detection of the multiple markers was designed and developed. Furthermore, a miniaturized microfluidic-based analyzer was also constructed. By developing a microfluidic chip and a miniaturized nucleic acid analyzer, we enabled the detection of the mRNA expression levels of multiple biomarkers in rice-sized tissue samples. The miniaturized nucleic acid analyzer could detect ≥10 copies of nucleic acids. The cell volume of the typing reaction on the microfluidic chip was only 0.94 µL, less than 1/25 of that of the conventional 25-µL Eppendorf tube PCR method, which significantly reduced the testing cost and significantly simplified the analysis of multiple biomarkers in parallel. With a simple injection operation and reverse transcription loop-mediated isothermal amplification (RT-LAMP), real-time detection of 11 lung cancer nucleic acid biomarkers was performed within 45 min. Given these compelling features, 86 clinical samples were tested using the miniaturized nucleic acid analyzer and classified according to the cutoff values of the 11 biomarkers. Furthermore, multi-biomarker analysis was conducted by a machine learning model to classify different subtypes of lung cancer, with an average area under the curve (AUC) of 0.934. This method shows great potential for the identification of new nucleic acid biomarkers and the accurate diagnosis of lung cancer.

Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy.

OBJECTIVE: To investigate the value of delta-radiomics after the first cycle of neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced (DCE) MRI for early prediction of pathological complete response (pCR) in patients with breast cancer. METHODS: From September 2018 to May 2021, a total of 140 consecutive patients (training, n = 98: validation, n = 42), newly diagnosed with breast cancer who received NAC before surgery, were prospectively enrolled. All patients underwent DCE-MRI at pre-NAC (pre-) and after the first cycle (1st-) of NAC. Radiomic features were extracted from the postcontrast early, peak, and delay phases. Delta-radiomics features were computed in each contrast phases. Least absolute shrinkage and selection operator (LASSO) and a logistic regression model were used to select features and build models. The model performance was assessed by receiver operating characteristic (ROC) analysis and compared by DeLong test. RESULTS: The delta-radiomics model based on the early phases of DCE-MRI showed a highest AUC (0.917/0.842 for training/validation cohort) compared with that using the peak and delay phases images. The delta-radiomics model outperformed the pre-radiomics model (AUC = 0.759/0.617, p = 0.011/0.047 for training/validation cohort) in early phase. Based on the optimal model, longitudinal fusion radiomic models achieved an AUC of 0.871/0.869 in training/validation cohort. Clinical-radiomics model generated good calibration and discrimination capacity with AUC 0.934 (95%CI: 0.882, 0.986)/0.864 (95%CI: 0.746, 0.982) for training and validation cohort. Delta-radiomics based on early contrast phases of DCE-MRI combined clinicopathology information could predict pCR after one cycle of NAC in patients with breast cancer.

Fluorescence Detection of Cancer Stem Cell Markers Using a Sensitive Nano-Aptamer Sensor.

Antigen CD133 is a glycoprotein present on the surface of cancer stem cells (CSCs), which is a key molecule to regulate the fate of stem cells and a functional marker of stem cells. Herein, a novel fluorescence "turn-on" nano-aptamer sensor for quantifying CD133 was designed using hybridization between CD133-targeted aptamers and partially complementary paired RNA (ssRNA), which were modified on the surface of quantum dots (QDs) and gold nanoparticles (AuNPs), respectively. Owing to the hybridization of aptamers and ssRNA, the distance between QDs and AuNPs was shortened, which caused fluorescence resonance energy transfer (FRET) between them, and the florescence of QDs was quenched by AuNPs. When CD133 competitively replaced ssRNA and was bound to aptamers, AuNPs-ssRNA could be released, which led to a recovery of fluorescent signals of QDs. The increase in the relative value of fluorescence intensity was investigated to linearly correlate with the CD133 concentration in the range of 0-1.539 µM, and the detection limit was 6.99 nM. In confocal images of A549 cells, the CD133 aptamer sensor was further proved applicable in lung cancer cell samples with specificity, precision, and accuracy. Compared with complicated methods, this study provided a fresh approach to develop a highly sensitive and selective detection sensor for CSC markers.

Phosphorylated ATF1 at Thr184 promotes metastasis and regulates MMP2 expression in gastric cancer.

BACKGROUND: Studies have revealed an important role of activating transcription factor 1 (ATF1) and phosphorylated ATF1 at Ser63 in tumors. Our previous study identified Thr184 as a novel phosphorylation site of ATF1. However, the role of phosphorylated ATF1 at Thr184 (p-ATF1-T184) in tumor is unclear. This study figured out the role of p-ATF1-T184 in the metastasis of gastric cancer (GC) and in the regulation of Matrix metallopeptidase 2 (MMP2). METHODS: Immunohistochemical analysis (IHC) was performed to analyze the level of p-ATF1-T184 and its relationship with clinicopathological characteristics. Wound scratch test, Transwell assay were used to observe the role of p-ATF1-T184 in the invasion and metastasis of GC. The regulation of MMP2 by p-ATF1-T184 was investigated by a series of experiments including quantitative RT-PCR, western blot, gelatin zymography assay, Chromatin immunoprecipitation (ChIP), luciferase reporter assay and cycloheximide experiment. The Cancer Genome Atlas (TCGA) data were used to analyze the expression and prognostic role of ATF1 and MMP2 in GC. Mass spectrometry (MS) following co-immunoprecipitation (co-IP) assay was performed to identify potential upstream kinases that would phosphorylate ATF1 at Thr184. RESULTS: High expression level of p-ATF1-T184 was found and significantly associated with lymph node metastasis and poor survival in a GC cohort of 126 patients. P-ATF1-T184 promoted migration and invasion of gastric cancer cells. Phosphorylation of ATF1-T184 could regulate the mRNA, protein expression and extracellular activity of MMP2. P-ATF1-T184 further increased the DNA binding ability, transcription activity, and stabilized the protein expression of ATF1. Moreover, TCGA data and IHC results suggested that the mRNA level of ATF1 and MMP2, and protein level of p-ATF1-T184 and MMP2 could be prognosis markers of GC. Two protein kinase related genes, LRBA and S100A8, were identified to be correlated with the expression ATF1 in GC. CONCLUSION: Our results indicated that p-ATF1-T184 promoted metastasis of GC by regulating MMP2.

Label-Free and Quantitative Dry Mass Monitoring for Single Cells during In Situ Culture.

Quantitative measurement of single cells can provide in-depth information about cell morphology and metabolism. However, current live-cell imaging techniques have a lack of quantitative detection ability. Herein, we proposed a label-free and quantitative multichannel wide-field interferometric imaging (MWII) technique with femtogram dry mass sensitivity to monitor single-cell metabolism long-term in situ culture. We demonstrated that MWII could reveal the intrinsic status of cells despite fluctuating culture conditions with 3.48 nm optical path difference sensitivity, 0.97 fg dry mass sensitivity and 2.4% average maximum relative change (maximum change/average) in dry mass. Utilizing the MWII system, different intrinsic cell growth characteristics of dry mass between HeLa cells and Human Cervical Epithelial Cells (HCerEpiC) were studied. The dry mass of HeLa cells consistently increased before the M phase, whereas that of HCerEpiC increased and then decreased. The maximum growth rate of HeLa cells was 11.7% higher than that of HCerEpiC. Furthermore, HeLa cells were treated with Gemcitabine to reveal the relationship between single-cell heterogeneity and chemotherapeutic efficacy. The results show that cells with higher nuclear dry mass and nuclear density standard deviations were more likely to survive the chemotherapy. In conclusion, MWII was presented as a technique for single-cell dry mass quantitative measurement, which had significant potential applications for cell growth dynamics research, cell subtype analysis, cell health characterization, medication guidance and adjuvant drug development.

Ent-11α-hydroxy-15-oxo-kaur-16-en-19-oic acid loaded onto fluorescent mesoporous silica nanoparticles for the location and therapy of nasopharyngeal carcinoma.

Ent-11α-hydroxy-15-oxo-kaur-16-en-19-oic acid (5F) is a diterpenoid that is isolated and purified from the Chinese herbal medicine Pteris semipinnata L., and is known to exert antitumour activity in several kinds of malignant cancer cells by leading cancer cells to apoptosis. However, the antitumour effect of 5F in vivo is rarely reported due to the complexity of the physiological environment and limitations of 5F as a small anticancer drug. In the present study, we utilized FITC-doped nanoparticles for the accumulation and delivery of 5F in nasopharyngeal carcinoma CNE2 tumours transplanted in nude mice by the enhanced permeation and retention (EPR) effect. In vivo studies demonstrated that nanoparticles could efficiently deliver 5F in CNE2 transplanted tumours, and the tumour growth was effectively inhibited by the drug-loaded nanoparticles with minimal side effects. The study indicated the benefits of combining well-studied nanoparticles with traditional herbal medicine treatment and establishes a delivery platform for 5F chemotherapy.

Deep Learning Algorithm for Automated Detection of Polycystic Ovary Syndrome Using Scleral Images.

The high prevalence of polycystic ovary syndrome (PCOS) among reproductive-aged women has attracted more and more attention. As a common disorder that is likely to threaten women's health physically and mentally, the detection of PCOS is a growing public health concern worldwide. In this paper, we proposed an automated deep learning algorithm for the auxiliary detection of PCOS, which explores the potential of scleral changes in PCOS detection. The algorithm was applied to the dataset that contains the full-eye images of 721 Chinese women, among which 388 are PCOS patients. Inputs of the proposed algorithm are scleral images segmented from full-eye images using an improved U-Net, and then a Resnet model was applied to extract deep features from scleral images. Finally, a multi-instance model was developed to achieve classification. Various performance indices such as AUC, classification accuracy, precision, recall, precision, and F1-score were adopted to assess the performance of our algorithm. Results show that our method achieves an average AUC of 0.979 and a classification accuracy of 0.929, which indicates the great potential of deep learning in the detection of PCOS.

Biomimetic Upconversion Nanoparticles and Gold Nanoparticles for Novel Simultaneous Dual-Modal Imaging-Guided Photothermal Therapy of Cancer.

Multimodal imaging-guided near-infrared (NIR) photothermal therapy (PTT) is an interesting and promising cancer theranostic method. However, most of the multimodal imaging systems provide structural and functional information used for imaging guidance separately by directly combining independent imaging systems with different detectors, and many problems arise when trying to fuse different modal images that are serially taken by inviting extra markers or image fusion algorithms. Further, most imaging and therapeutic agents passively target tumors through the enhanced permeability and retention (EPR) effect, which leads to low utilization efficiency. To address these problems and systematically improve the performance of the imaging-guided PTT methodology, we report a novel simultaneous dual-modal imaging system combined with cancer cell membrane-coated nanoparticles as a platform for PTT-based cancer theranostics. A novel detector with the ability to detect both high-energy X-ray and low-energy visible light at the same time, as well as a dual-modal imaging system based on the detector, was developed for simultaneous dual-modal imaging. Cancer cell membrane-coated upconversion nanoparticles (CC-UCNPs) and gold nanoparticles (CC-AuNPs) with the capacity for immune evasion and active tumor targeting were engineered for highly specific imaging and high-efficiency PTT therapy. In vitro and in vivo evaluation of macrophage escape and active homologous tumor targeting were performed. Cancer cell membrane-coated nanoparticles (CC-NPs) displayed excellent immune evasion ability, longer blood circulation time, and higher tumor targeting specificity compared to normal PEGylated nanoparticles, which led to highly specific upconversion luminescence (UCL) imaging and PTT-based anti-tumor efficacy. The anti-cancer efficacy of the dual-modal imaging-guided PTT was also evaluated both in vitro and in vivo. Dual-modal imaging yielded precise anatomical and functional information for the PTT process, and complete tumor ablation was achieved with CC-AuNPs. Our biomimetic UCNP/AuNP and novel simultaneous dual-modal imaging combination could be a promising platform and methodology for cancer theranostics.