Neuroprotective Effects of Human-Induced Pluripotent Stem Cell-Derived Mesenchymal Stem Cell Extracellular Vesicles in Ischemic Stroke Models.

BACKGROUND: Stroke represents the second leading cause of death and the primary cause of long-term disability in humans. The transplantation of mesenchymal stem cells (MSC) reportedly improves functional outcomes in animal models of cerebral ischemia. Here, we evaluate the neuroprotective potential of extracellular vesicles secreted from human-induced pluripotent stem cell-derived mesenchymal stem cells (hiPS-MSC-EV) using preclinical cell-based and animal-based models of ischemic strokes. METHODS: hiPS-MSC-EV were isolated using an ultrafiltration method. HT22 cells were subjected to oxygen-glucose deprivation/reoxygenation (OGD/R) injury for 2 h, followed by treatment with hiPS-MSC-EV (100 µg/mL). Male C57BL/6 mice were subjected to middle cerebral artery occlusion (MCAO) followed by an intravenous injection of hiPS-MSC-EV (100 µg) at three distinct time points. RESULTS: Our experimental approach revealed hiPS-MSC-EV promoted HT22 cell proliferation, reduced apoptosis, and altered cellular morphology following OGD/R. In addition, hiPS-MSC-EV reduced the volume of infarcts, improved spontaneous movement abilities, and enhanced angiogenesis by expressing the VEGF and CXCR4 proteins in the infarcted hemisphere of the MCAO-treated mouse model. CONCLUSION: Our findings provide evidence of the potential neuroprotective effects of hiPS-MSC-derived extracellular vesicles (hiPS-MSC-EVs) in both in vitro and in vivo mouse models of ischemic stroke. These results suggest that hiPS-MSC-EVs may play a role in neurorestoration and offer insights into potential cell-free strategies for addressing cerebral ischemia.

PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI.

Detecting neurological abnormalities such as brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.

Clinical usefulness of abbreviated mri protocol in breast cancer detection.

BACKGROUND: The use of breast MRI for screening has increased over the past decade, mostly in women with a high risk of breast cancer. Abbreviated breast MRI (AB-MR) is introduced to make MRI a more accessible screening modality. AB-MR decreases scanning and reporting time and the overall cost of MRI. OBJECTIVE: This study aims to evaluate the diagnostic efficacy of abbreviated MRI protocol in detecting breast cancer in screening and diagnostic populations, using histopathology as the reference standard. MATERIALS AND METHODS: This is a single-centre retrospective cross-sectional study of 134 patients with 198 histologically proven breast lesions who underwent full diagnostic protocol contrast-enhanced breast MRI (FDP-MR) at the University Malaya Medical Centre (UMMC) from 1st January 2018 to 31st December 2019. AB-MR was pre-determined and evaluated with regard to the potential to detect and exclude malignancy from 3 readers of varying radiological experiences. The sensitivity of both AB-MR and FDP-MR were compared using the McNemar test, where both protocols' diagnostic performances were assessed via the receiver operating characteristic (ROC) curve. Inter-observer agreement was analysed using Fleiss Kappa. RESULT: There were 134 patients with 198 lesions. The average age was 50.9 years old (range 27 - 80). A total of 121 (90%) MRIs were performed for diagnostic purposes. Screening accounted for 9.4% of the cases, 55.6% (n=110) lesions were benign, and 44.4% (n=88) were malignant. The commonest benign and malignant lesions were fibrocystic change (27.3%) and invasive ductal carcinoma (78.4%). The mean sensitivity, specificity, positive predictive value, and negative predictive value for AB-MR were 0.96, 0.57, 0.68 and 0.94, respectively. Both AB-MR and FDP-MR showed excellent diagnostic performance with AUC of 0.88 and 0.96, respectively. The general inter-observer agreement of all three readers for AB-MR was substantial (k=0.69), with fair agreement demonstrated between AB-MR and FDP-MR (k=0.36). CONCLUSION: The study shows no evidence that the diagnostic efficacy of AB-MR is inferior to FDP-MR. AB-MR, with high sensitivity, has proven its capability in cancer detection and exclusion, especially for biologically aggressive cancers.

Alleviation of Limosilactobacillus reuteri in polycystic ovary syndrome protects against circadian dysrhythmia-induced dyslipidemia via capric acid and GALR1 signaling.

Knowledge gaps that limit the development of therapies for polycystic ovary syndrome (PCOS) concern various environmental factors that impact clinical characteristics. Circadian dysrhythmia contributes to glycometabolic and reproductive hallmarks of PCOS. Here, we illustrated the amelioration of Limosilactobacillus reuteri (L. reuteri) on biorhythm disorder-ignited dyslipidemia of PCOS via a microbiota-metabolite-liver axis. A rat model of long-term (8 weeks) darkness treatment was used to mimic circadian dysrhythmia-induced PCOS. Hepatic transcriptomics certified by in vitro experiments demonstrated that increased hepatic galanin receptor 1 (GALR1) due to darkness exposure functioned as a critical upstream factor in the phosphoinositide 3-kinase (PI3K)/protein kinase B pathway to suppress nuclear receptors subfamily 1, group D, member 1 (NR1D1) and promoted sterol regulatory element binding protein 1 (SREBP1), inducing lipid accumulation in the liver. Further investigations figured out a restructured microbiome-metabolome network following L. reuteri administration to protect darkness rats against dyslipidemia. Notably, L. reuteri intervention resulted in the decrease of Clostridium sensu stricto 1 and Ruminococcaceae UCG-010 as well as gut microbiota-derived metabolite capric acid, which could further inhibit GALR1-NR1D1-SREBP1 pathway in the liver. In addition, GALR antagonist M40 reproduced similar ameliorative effects as L. reuteri to protect against dyslipidemia. While exogenous treatment of capric acid restrained the protective effects of L. reuteri in circadian disruption-induced PCOS through inhibiting GALR1-dependent hepatic lipid metabolism. These findings purport that L. reuteri could serve for circadian disruption-associated dyslipidemia. Manipulation of L. reuteri-capric acid-GALR1 axis paves way for clinical therapeutic strategies to prevent biorhythm disorder-ignited dyslipidemia in PCOS women.

Lung Cancer Screening in Asia: An Expert Consensus Report.

INTRODUCTION: The incidence and mortality of lung cancer are highest in Asia compared with Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 respectively in East Asia. Diagnosing lung cancer at early stages makes the disease amenable to curative treatment and reduces mortality. In some areas in Asia, limited availability of robust diagnostic tools and treatment modalities, along with variations in specific health care investment and policies, make it necessary to have a more specific approach for screening, early detection, diagnosis, and treatment of patients with lung cancer in Asia compared with the West. METHOD: A group of 19 advisors across different specialties from 11 Asian countries, met on a virtual Steering Committee meeting, to discuss and recommend the most affordable and accessible lung cancer screening modalities and their implementation, for the Asian population. RESULTS: Significant risk factors identified for lung cancer in smokers in Asia include age 50 to 75 years and smoking history of more than or equal to 20 pack-years. Family history is the most common risk factor for nonsmokers. Low-dose computed tomography screening is recommended once a year for patients with screening-detected abnormality and persistent exposure to risk factors. However, for high-risk heavy smokers and nonsmokers with risk factors, reassessment scans are recommended at an initial interval of 6 to 12 months with subsequent lengthening of reassessment intervals, and it should be stopped in patients more than 80 years of age or are unable or unwilling to undergo curative treatment. CONCLUSIONS: Asian countries face several challenges in implementing low-dose computed tomography screening, such as economic limitations, lack of efforts for early detection, and lack of specific government programs. Various strategies are suggested to overcome these challenges in Asia.

A Radiomics Study: Classification of Breast Lesions by Textural Features from Mammography Images.

This study investigates the feasibility of using texture radiomics features extracted from mammography images to distinguish between benign and malignant breast lesions and to classify benign lesions into different categories and determine the best machine learning (ML) model to perform the tasks. Six hundred and twenty-two breast lesions from 200 retrospective patient data were segmented and analysed. Three hundred fifty radiomics features were extracted using the Standardized Environment for Radiomics Analysis (SERA) library, one of the radiomics implementations endorsed by the Image Biomarker Standardisation Initiative (IBSI). The radiomics features and selected patient characteristics were used to train selected machine learning models to classify the breast lesions. A fivefold cross-validation was used to evaluate the performance of the ML models and the top 10 most important features were identified. The random forest (RF) ensemble gave the highest accuracy (89.3%) and positive predictive value (66%) and likelihood ratio of 13.5 in categorising benign and malignant lesions. For the classification of benign lesions, the RF model again gave the highest likelihood ratio of 3.4 compared to the other models. Morphological and textural radiomics features were identified as the top 10 most important features from the random forest models. Patient age was also identified as one of the significant features in the RF model. We concluded that machine learning models trained against texture-based radiomics features and patient features give reasonable performance in differentiating benign versus malignant breast lesions. Our study also demonstrated that the radiomics-based machine learning models were able to emulate the visual assessment of mammography lesions, typically used by radiologists, leading to a better understanding of how the machine learning model arrive at their decision.

Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients.

OBJECTIVES: To elucidate a novel radiogenomics approach using three-dimensional (3D) topologically invariant Betti numbers (BNs) for topological characterization of epidermal growth factor receptor (EGFR) Del19 and L858R mutation subtypes. METHODS: In total, 154 patients (wild-type EGFR, 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation [M] classification) as well as between the Del19 and L858R subtypes (subtype [S] classification) were trained using 3DBN features. These features were computed from 3DBN maps by using histogram and texture analyses. The 3DBN maps were generated using computed tomography (CT) images based on the Cech complex constructed on sets of points in the images. These points were defined by coordinates of voxels with CT values higher than several threshold values. The M classification model was built using image features and demographic parameters of sex and smoking status. The SVM models were evaluated by determining their classification accuracies. The feasibility of the 3DBN model was compared with those of conventional radiomic models based on pseudo-3D BN (p3DBN), two-dimensional BN (2DBN), and CT and wavelet-decomposition (WD) images. The validation of the model was repeated with 100 times random sampling. RESULTS: The mean test accuracies for M classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.810, 0.733, 0.838, 0.782, and 0.799, respectively. The mean test accuracies for S classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.773, 0.694, 0.657, 0.581, and 0.696, respectively. CONCLUSION: 3DBN features, which showed a radiogenomic association with the characteristics of the EGFR Del19/L858R mutation subtypes, yielded higher accuracy for subtype classifications in comparison with conventional features.

Modification of maternally defined H3K4me3 regulates the inviability of interspecific Xenopus hybrids.

Increasing evidence suggests that interspecific hybridization is crucial to speciation. However, chromatin incompatibility during interspecific hybridization often renders this process. Genomic imbalances such as chromosomal DNA loss and rearrangements leading to infertility have been commonly noted in hybrids. The mechanism underlying reproductive isolation of interspecific hybridization remains elusive. Here, we identified that modification of maternally defined H3K4me3 in Xenopus laevis and Xenopus tropicalis hybrids determines the different fates of the two types of hybrids as te×ls with developmental arrest and viable le×ts. Transcriptomics highlighted that the P53 pathway was overactivated, and the Wnt signaling pathway was suppressed in te×ls hybrids. Moreover, the lack of maternal H3K4me3 in te×ls disturbed the balance of gene expression between the L and S subgenomes in this hybrid. Attenuation of p53 can postpone the arrested development of te×ls. Our study suggests an additional model of reproductive isolation based on modifications of maternally defined H3K4me3.

Extracellular vesicles from iPSC-MSCs alleviate chemotherapy-induced mouse ovarian damage via the ILK-PI3K/AKT pathway.

Chemotherapy can significantly reduce follicle counts in ovarian tissues and damage ovarian stroma, causing endocrine disorder, reproductive dysfunction, and primary ovarian insufficiency (POI). Recent studies have suggested that extracellular vesicles (EVs) secreted from mesenchymal stem cells (MSCs) exert therapeutic effects in various degenerative diseases. In this study, transplantation of EVs from human induced pluripotent stem cell-derived MSCs (iPSC-MSC-EVs) resulted in significant restoration of ovarian follicle numbers, improved granulosa cell proliferation, and inhibition of apoptosis in chemotherapy-damaged granulosa cells, cultured ovaries, and in vivo ovaries in mice. Mechanistically, treatment with iPSC-MSC-EVs resulted in up-regulation of the integrin-linked kinase (ILK) -PI3K/AKT pathway, which is suppressed during chemotherapy, most likely through the transfer of regulatory microRNAs (miRNAs) targeting ILK pathway genes. This work provides a framework for the development of advanced therapeutics to ameliorate ovarian damage and POI in female chemotherapy patients.

Impact of preoperative magnetic resonance imaging on surgery and eligibility for intraoperative radiotherapy in early breast cancer.

We looked at the usefulness of magnetic resonance imaging (MRI) in decision-making and surgical management of patients selected for intraoperative radiotherapy (IORT). We also compared lesion size measurements in different modalities (ultrasound (US), mammogram (MMG), MRI) against pathological size as the gold standard. 63 patients eligible for IORT based on clinical and imaging criteria over a 34-month period were enrolled. All had MMG and US, while 42 had additional preoperative MRI for locoregional preoperative staging. Imaging findings and pathological size concordances were analysed across the three modalities. MRI changed the surgical management of 5 patients (11.9%) whereby breast-conserving surgery (BCS) and IORT was cancelled due to detection of satellite lesion, tumor size exceeding 30mm and detection of axillary nodal metastases. Ten of 42 patients (23.8%) who underwent preoperative MRI were subjected to additional external beam radiotherapy (EBRT); 7 due to lymphovascular invasion (LVI), 2 due to involved margins, and 1 due to axillary lymph node metastatic carcinoma detected in the surgical specimen. Five of 21 (23.8%) patients without prior MRI were subjected to additional EBRT post-surgery; 3 had LVI and 2 had involved margins. The rest underwent BCS and IORT as planned. MRI and MMG show better imaging-pathological size correlation. Significant increase in the mean 'waiting time' were seen in the MRI group (34.1 days) compared to the conventional imaging group (24.4 days). MRI is a useful adjunct to conventional imaging and impacts decision making in IORT. It is also the best imaging modality to determine the actual tumour size.

Automated BI-RADS classification of lesions using pyramid triple deep feature generator technique on breast ultrasound images.

Ultrasound (US) is an important imaging modality used to assess breast lesions for malignant features. In the past decade, many machine learning models have been developed for automated discrimination of breast cancer versus normal on US images, but few have classified the images based on the Breast Imaging Reporting and Data System (BI-RADS) classes. This work aimed to develop a model for classifying US breast lesions using a BI-RADS classification framework with a new multi-class US image dataset. We proposed a deep model that combined a novel pyramid triple deep feature generator (PTDFG) with transfer learning based on three pre-trained networks for creating deep features. Bilinear interpolation was applied to decompose the input image into four images of successively smaller dimensions, constituting a four-level pyramid for downstream feature generation with the pre-trained networks. Neighborhood component analysis was applied to the generated features to select each network's 1,000 most informative features, which were fed to support vector machine classifier for automated classification using a ten-fold cross-validation strategy. Our proposed model was validated using a new US image dataset containing 1,038 images divided into eight BI-RADS classes and histopathological results. We defined three classification schemes: Case 1 involved the classification of all images into eight categories; Case 2, classification of breast US images into five BI-RADS classes; and Case 3, classification of BI-RADS 4 lesions into benign versus malignant classes. Our PTDFG-based transfer learning model attained accuracy rates of 79.29%, 80.42%, and 88.67% for Case 1, Case 2, and Case 3, respectively.

Machine Learning-Devised Immune-Related lncRNA Signature Panel Predicts the Prognosis and Immune Landscape in Breast Cancer Novel IRLP Signature in BRCA.

Long noncoding RNAs (lncRNAs) actively participate in breast cancer (BRCA) tumorigenesis via epigenetic mechanisms. Our study identified immune-related lncRNA (irlncRNA) pairs and compiled them into a set of noncoding gene signatures able to stratify subtypes of BRCA associated with variable degrees of survival and immune cell infiltration. A 40 immune-related lncRNA pair (IRLP) signature including 43 irlncRNAs was built, with high sensitivity and specificity for the prediction of survival in different molecular subtypes of BRCA. Results demonstrated that the low-risk group showed a significantly longer survival rate, and this novel IRLP signature was highly associated with survival status, T stage, metastatic disease, and overall stage in BRCA. Immune infiltrating analyses found that the low-risk group has a lower expression level of macrophage M2 and a higher expression level of immunosuppressed biomarkers than the high-risk group. DEirlncRNAs were further proven to be significantly related to the MAPK signaling, Jak-STAT signaling, and ErbB signaling pathways in BRCA. In conclusion, the 40 IRLP signature showed a promising clinical prediction value in the prognosis of different molecular subtypes and immunotherapy response in BRCA, and the underlying mechanism for these IRLPs warrants further investigations.

LRRC46 Accumulates at the Midpiece of Sperm Flagella and Is Essential for Spermiogenesis and Male Fertility in Mouse.

The sperm flagellum is essential for male fertility. Multiple morphological abnormalities of the sperm flagella (MMAF) is a severe form of asthenoteratozoospermia. MMAF phenotypes are understood to result from pathogenic variants of genes from multiple families including AKAP, DANI, DNAH, RSPH, CCDC, CFAP, TTC, and LRRC, among others. The Leucine-rich repeat protein (LRRC) family includes two members reported to cause MMAF phenotypes: Lrrc6 and Lrrc50. Despite vigorous research towards understanding the pathogenesis of MMAF-related diseases, many genes remain unknown underlying the flagellum biogenesis. Here, we found that Leucine-rich repeat containing 46 (LRRC46) is specifically expressed in the testes of adult mice, and show that LRRC46 is essential for sperm flagellum biogenesis. Both scanning electron microscopy (SEM) and Papanicolaou staining (PS) presents that the knockout of Lrrc46 in mice resulted in typical MMAF phenotypes, including sperm with short, coiled, and irregular flagella. The male KO mice had reduced total sperm counts, impaired sperm motility, and were completely infertile. No reproductive phenotypes were detected in Lrrc46-/- female mice. Immunofluorescence (IF) assays showed that LRRC46 was present throughout the entire flagella of control sperm, albeit with evident concentration at the mid-piece. Transmission electron microscopy (TEM) demonstrated striking flagellar defects with axonemal and mitochondrial sheath malformations. About the important part of the Materials and Methods, SEM and PS were used to observe the typical MMAF-related irregular flagella morphological phenotypes, TEM was used to further inspect the sperm flagellum defects in ultrastructure, and IF was chosen to confirm the location of protein. Our study suggests that LRRC46 is an essential protein for sperm flagellum biogenesis, and its mutations might be associated with MMAF that causes male infertility. Thus, our study provides insights for understanding developmental processes underlying sperm flagellum formation and contribute to further observe the pathogenic genes that cause male infertility.

IGF2 reduces meiotic defects in oocytes from obese mice and improves embryonic developmental competency.

BACKGROUND: Maternal obesity is a global issue that has devastating effects across the reproductive spectrum such as meiotic defects in oocytes, consequently worsening pregnancy outcomes. Different studies have shown that such types of meiotic defects originated from the oocytes of obese mothers. Thus, there is an urgent need to develop strategies to reduce the incidence of obesity-related oocyte defects that adversely affect pregnancy outcomes. Multiple growth factors have been identified as directly associated with female reproduction; however, the impact of various growth factors on female fertility in response to obesity remains poorly understood. METHODS: The immature GV-stage oocytes from HFD female mice were collected and cultured in vitro in two different groups (HFD oocytes with and without 50 nM IGF2), however; the oocytes from ND mice were used as a positive control. HFD oocytes treated with or without IGF2 were further used to observe the meiotic structure using different analysis including, the spindle and chromosomal analysis, reactive oxygen species levels, mitochondrial functional activities, and early apoptotic index using immunofluorescence. Additionally, the embryonic developmental competency and embryos quality of IGF2-treated zygotes were also determined. RESULTS: In our findings, we observed significantly reduced contents of insulin-like growth factor 2 (IGF2) in the serum and oocytes of obese mice. Our data indicated supplementation of IGF2 in a culture medium improves the blastocyst formation: from 46% in the HFD group to 61% in the HFD + IGF2-treatment group (50 nM IGF2). Moreover, adding IGF2 to the culture medium reduces the reactive oxygen species index and alleviates the frequency of spindle/chromosome defects. We found increased mitochondrial functional activity in oocytes from obese mice after treating the oocytes with IGF2: observed elevated level of adenosine triphosphate, increased mitochondrial distribution, higher mitochondrial membrane potentials, and reduced mitochondrial ultrastructure defects. Furthermore, IGF2 administration also increases the overall protein synthesis and decreases the apoptotic index in oocytes from obese mice. CONCLUSIONS: Collectively, our findings are strongly in favor of adding IGF2 in culture medium to overcome obesity-related meiotic structural-developmental defects by helping ameliorate the known sub-optimal culturing conditions that are currently standard with assisted reproduction technologies.

lncRNA DDGC participates in premature ovarian insufficiency through regulating RAD51 and WT1.

The list of long non-coding RNAs (lncRNAs) that participate in the function of ovarian granulosa cells (GCs) is rapidly expanding, but the mechanisms through which lncRNAs regulate GC function are not yet fully understood. Here, we recognized a minimally expressed lncRNA RP4-545C24.1 (which we named DDGC) in GCs from patients with biochemical premature ovarian insufficiency (bPOI). We further explored the role of lncRNA DDGC in GC function and its contribution to the development of bPOI. Mechanistically, silencing DDGC downregulated RAD51 by competitively binding with miR-589-5p, and this resulted in significant inhibition of DNA damage repair capacity. In addition, decreased expression of DDGC promoted ubiquitin-mediated degradation of Wilms tumor 1 (WT1) protein through interactions with heat shock protein 90 (HSP90), which led to aberrant differentiation of GCs. Moreover, DDGC was able to ameliorate the etoposide-induced DNA damage and apoptosis in vivo. Taken together, these findings provide new insights into the contribution of lncRNAs in POI pathogenesis.

Single-Oocyte Gene Expression Suggests That Curcumin Can Protect the Ovarian Reserve by Regulating the PTEN-AKT-FOXO3a Pathway.

A better understanding of the mechanism of primordial follicle activation will help us better understand the causes of premature ovarian insufficiency (POI), and will help us identify new drugs that can be applied to the clinical treatment of infertility. In this study, single oocytes were isolated from primordial and primary follicles, and were used for gene profiling with TaqMan array cards. Bioinformatics analysis was performed on the gene expression data, and Ingenuity Pathway Analysis was used to analyze and predict drugs that affect follicle activation. An ovarian in vitro culture system was used to verify the function of the drug candidates, and we found that curcumin maintains the ovarian reserve. Long-term treatment with 100 mg/kg curcumin improved the ovarian reserve indicators of AMH, FSH, and estradiol in aging mice. Mechanistic studies show that curcumin can affect the translocation of FOXO3, thereby inhibiting the PTEN-AKT-FOXO3a pathway and protecting primordial follicles from overactivation. These results suggest that curcumin is a potential drug for the treatment of POI patients and for fertility preservation.

Pten Regulates Cardiomyocyte Differentiation by Modulating Non-CG Methylation via Dnmt3.

The regulation of cardiomyocyte differentiation is a fundamental aspect of cardiac development and regenerative medicine. PTEN plays important roles during embryonic development. However, its role in cardiomyocyte differentiation remains unknown. In this study, a low-cost protocol for cardiomyocyte differentiation from mouse embryonic stem cells (ESCs) is presented and it is shown that Pten deletion potently suppresses cardiomyocyte differentiation. Transcriptome analysis shows that the expression of a series of cardiomyocyte marker genes is downregulated in Pten-/- cardiomyocytes. Pten ablation induces Dnmt3b expression via the AKT/FoxO3a pathway and regulates the expression of a series of imprinted genes, including Igf2. Double knockout of Dnmt3l and Dnmt3b rescues the deficiency of cardiomyocyte differentiation of Pten-/- ESCs. The DNA methylomes from wild-type and Pten-/- embryoid bodies and cardiomyocytes are analyzed by whole-genome bisulfite sequencing. Pten deletion significantly promotes the non-CG (CHG and CHH) methylation levels of genomic DNA during cardiomyocyte differentiation, and the non-CG methylation levels of cardiomyocyte genes and Igf2 are increased in Pten-/- cardiomyocytes. Igf2 or Igf1r deletion also suppresses cardiomyocyte differentiation through the MAPK/ERK signaling pathway, and IGF2 supplementation partially rescues the cardiomyocyte differentiation. Finally, Pten conditional knockout mice are generated and the role of PTEN in cardiomyocyte differentiation is verified in vivo.

Diagnostic accuracy of tomosynthesis-guided vacuum assisted breast biopsy of ultrasound occult lesions.

This study aims to evaluate the diagnostic accuracy of digital breast tomosynthesis-guided vacuum assisted breast biopsy (DBT-VABB) of screening detected suspicious mammographic abnormalities comprising of calcifications, asymmetric densities, architectural distortions and spiculated masses. In this institutionally approved study, a total of 170 (n = 170) DBT-VABB were performed, 153 (90%) were for calcifications, 8 (4.7%) for spiculated mass, 5 (2.9%) for asymmetric density and 4 (2.4%) for architectural distortion. All these lesions were not detected on the corresponding ultrasound. Histopathology results revealed 140 (82.4%) benign, 9 (5.3%) borderline and 21 (12.4%) malignant lesions. The total upgrade rate at surgery was 40% for atypical ductal hyperplasia and 5.9% for ductal carcinoma in-situ. 3.6% discordant benign lesions showed no upgrade. DBT-VABB showed 100% specificity, 91.3% sensitivity and 100% positive predictive value (PPV) for detecting malignant lesions. The negative predictive value (NPV) was 80%. 2 (1.2%) patients had mild complications and 1 (0.6%) had severe pain. Our study showed that DBT-VABB was a safe and reliable method, with high sensitivity, specificity, PPV and NPV in the diagnosis of non-palpable benign and malignant breast lesions. Our data also confirmed the accuracy of DBT-VABB in detecting malignant lesions and we suggest further surgical excision in borderline lesions for a more accurate diagnostic evaluation.

Comparison of two-dimensional synthesized mammograms versus original digital mammograms: a quantitative assessment.

This study objectively evaluates the similarity between standard full-field digital mammograms and two-dimensional synthesized digital mammograms (2DSM) in a cohort of women undergoing mammography. Under an institutional review board-approved data collection protocol, we retrospectively analyzed 407 women with digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) examinations performed from September 1, 2014, through February 29, 2016. Both FFDM and 2DSM images were used for the analysis, and 3216 available craniocaudal (CC) and mediolateral oblique (MLO) view mammograms altogether were included in the dataset. We analyzed the mammograms using a fully automated algorithm that computes 152 structural similarity, texture, and mammographic density-based features. We trained and developed two different global mammographic image feature analysis-based breast cancer detection schemes for 2DSM and FFDM images, respectively. The highest structural similarity features were obtained on the coarse Weber Local Descriptor differential excitation texture feature component computed on the CC view images (0.8770) and MLO view images (0.8889). Although the coarse structures are similar, the global mammographic image feature-based cancer detection scheme trained on 2DSM images outperformed the corresponding scheme trained on FFDM images, with area under a receiver operating characteristic curve (AUC) = 0.878 ± 0.034 and 0.756 ± 0.052, respectively. Consequently, further investigation is required to examine whether DBT can replace FFDM as a standalone technique, especially for the development of automated objective-based methods.

Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers.

OBJECTIVES: To propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs). MATERIALS AND METHODS: Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers' scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test. RESULTS: The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29). CONCLUSION: The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.