Influence of low-intensity anticoagulation and low-dose antiplatelet agent on coagulation-fibrinolysis system after mechanical prosthetic valve replacement

AbstractJ Thorac Cardiovasc Surg 1998;115:952-

Genome Wide Association Study of Age at Menarche in the Japanese Population

Age at menarche (AAM) is a complex trait involving both genetic and environmental factors. To identify the genetic factors associated with AAM, we conducted a large-scale meta-analysis of genome-wide association studies using more than 15,000 Japanese female samples. Here, we identified an association between SNP (single nucleotide polymorphism) rs364663 at the LIN28B locus and AAM, with a P-value of 5.49×10−7 and an effect size of 0.089 (year). We also evaluated 33 SNPs that were previously reported to be associated with AAM in women of European ancestry. Among them, two SNPs rs4452860 and rs7028916 in TMEM38B indicated significant association with AAM in the same directions as reported in previous studies (P = 0.0013 with an effect size of 0.051) even after Bonferroni correction for the 33 SNPs. In addition, six loci in or near CCDC85A, LOC100421670, CA10, ZNF483, ARNTL, and RXRG exhibited suggestive association with AAM (P<0.05). Our findings elucidated the impact of genetic variations on AAM in the Japanese population

Pyrene is highly emissive when attached to the RNA duplex but not to the DNA duplex: the structural basis of this difference

Through binding and fluorescence studies of oligonucleotides covalently attached to a pyrene group via one carbon linker at the sugar residue, we previously found that pyrene-modified RNA oligonucleotides do not emit well in the single-stranded form, yet the attached pyrene emits with a significantly high quantum yield upon binding to a complementary RNA strand. In sharp contrast, similarly modified pyrene–DNA probes exhibit very weak fluorescence both in the double-stranded and single-stranded forms. The pyrene-modified RNA oligonucleotides therefore provide a useful tool for monitoring RNA hybridization. The purpose of this paper is to present the structural basis for the different fluorescence properties of pyrene-modified RNA/RNA and pyrene-modified DNA/DNA duplexes. The results of absorption, fluorescence anisotropy and circular dichroism studies all consistently indicated that the pyrene attached to the RNA duplex is located outside of the duplex, whereas the pyrene incorporated into the DNA duplex intercalates into the double helix. (1)H NMR measurements unambiguously confirmed that the pyrene attached to the DNA duplex indeed intercalates between the base pairs of the duplex. Molecular dynamics simulations support these differences in the local structural elements around the pyrene between the pyrene–RNA/RNA and the pyrene–DNA/DNA duplexes

Genome Wide Association Study of Age at Menarche in the Japanese Population

Age at menarche (AAM) is a complex trait involving both genetic and environmental factors. To identify the genetic factors associated with AAM, we conducted a large-scale meta-analysis of genome-wide association studies using more than 15,000 Japanese female samples. Here, we identified an association between SNP (single nucleotide polymorphism) rs364663 at the LIN28B locus and AAM, with a P-value of 5.49×10−7 and an effect size of 0.089 (year). We also evaluated 33 SNPs that were previously reported to be associated with AAM in women of European ancestry. Among them, two SNPs rs4452860 and rs7028916 in TMEM38B indicated significant association with AAM in the same directions as reported in previous studies (P = 0.0013 with an effect size of 0.051) even after Bonferroni correction for the 33 SNPs. In addition, six loci in or near CCDC85A, LOC100421670, CA10, ZNF483, ARNTL, and RXRG exhibited suggestive association with AAM (P<0.05). Our findings elucidated the impact of genetic variations on AAM in the Japanese population.</p

Multi-institutional phase II study on the safety and efficacy of dynamic tumor tracking-stereotactic body radiotherapy for lung tumors

Background and purpose: This study aimed to evaluate the safety and efficacy of dynamic tumor tracking-stereotactic body radiotherapy (DTT-SBRT) for lung tumors. Materials and methods: Patients with cStage I primary lung cancer or metastatic lung cancer with an expected range of respiratory motion of ≥10 mm were eligible for the study. The prescribed dose was 50 Gy in four fractions. A gimbal-mounted linac was used for DTT-SBRT delivery. The primary endpoint was local control at 2 years. Results: Forty-eight patients from four institutions were enrolled in this study. Forty-two patients had primary non-small-cell lung cancer, and six had metastatic lung tumors. DTT-SBRT was delivered for 47 lesions in 47 patients with a median treatment time of 28 min per fraction. The median respiratory motion during the treatment was 13.7 mm (range: 4.5–28.1 mm). The motion-encompassing method was applied for the one remaining patient due to the poor correlation between the abdominal wall and tumor movement. The median follow-up period was 32.3 months, and the local control at 2 years was 95.2% (lower limit of the one-sided 85% confidence interval [CI]: 90.3%). The overall survival and progression-free survival at 2 years were 79.2% (95% CI: 64.7%–88.2%) and 75.0% (95% CI: 60.2%–85.0%), respectively. Grade 3 toxicity was observed in one patient (2.1%) with radiation pneumonitis. Grade 4 or 5 toxicity was not observed. Conclusion: DTT-SBRT achieved excellent local control with low incidences of severe toxicities in lung tumors with respiratory motion

Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy

[Background] In infrared reflective (IR) marker-based hybrid real-time tumor tracking (RTTT), the internal target position is predicted with the positions of IR markers attached on the patient’s body surface using a prediction model. In this work, we developed two artificial intelligence (AI)-driven prediction models to improve RTTT radiotherapy, namely, a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS) model. The models aim to improve the accuracy in predicting three-dimensional tumor motion. [Methods] From patients whose respiration-induced motion of the tumor, indicated by the fiducial markers, exceeded 8 mm, 1079 logfiles of IR marker-based hybrid RTTT (IR Tracking) with the gimbal-head radiotherapy system were acquired and randomly divided into two datasets. All the included patients were breathing freely with more than four external IR markers. The historical dataset for the CNN model contained 1003 logfiles, while the remaining 76 logfiles complemented the evaluation dataset. The logfiles recorded the external IR marker positions at a frequency of 60 Hz and fiducial markers as surrogates for the detected target positions every 80-640 ms for 20-40 s. For each logfile in the evaluation dataset, the prediction models were trained based on the data in the first three quarters of the recording period. In the last quarter, the performance of the patient-specific prediction models was tested and evaluated. The overall performance of the AI-driven prediction models was ranked by the percentage of predicted target position within 2 mm of the detected target position. Moreover, the performance of the AI-driven models was compared to a regression prediction model currently implemented in gimbal-head radiotherapy systems. [Results] The percentage of the predicted target position within 2 mm of the detected target position was 95.1%, 92.6% and 85.6% for the CNN, ANFIS, and regression model, respectively. In the evaluation dataset, the CNN, ANFIS, and regression model performed best in 43, 28 and 5 logfiles, respectively. [Conclusions] The proposed AI-driven prediction models outperformed the regression prediction model, and the overall performance of the CNN model was slightly better than that of the ANFIS model on the evaluation dataset

Independent calculation-based verification of volumetric-modulated arc therapy–stereotactic body radiotherapy plans for lung cancer

This study aimed to investigate the feasibility of independent calculation‐based verification of volumetric‐modulated arc therapy (VMAT)–stereotactic body radiotherapy (SBRT) for patients with lung cancer using a secondary treatment planning system (sTPS). In all, 50 patients with lung cancer who underwent VMAT‐SBRT between April 2018 and May 2019 were included in this study. VMAT‐SBRT plans were devised using the Collapsed‐Cone Convolution in RayStation (primary TPS: pTPS). DICOM files were transferred to Eclipse software (sTPS), which utilized the Eclipse software, and the dose distribution was then recalculated using Acuros XB. For the verification of dose distribution in homogeneous phantoms, the differences among pTPS, sTPS, and measurements were evaluated using passing rates of a dose difference of 5% (DD5%) and gamma index of 3%/2 mm (γ3%/2 mm). The ArcCHECK cylindrical diode array was used for measurements. For independent verification of dose‐volume parameters per the patient’s geometry, dose‐volume indices for the planning target volume (PTV) including D95% and the isocenter dose were evaluated. The mean differences (± standard deviations) between the pTPS and sTPS were then calculated. The gamma passing rates of DD5% and γ3%/2 mm criteria were 99.2 ± 2.4% and 98.6 ± 3.2% for pTPS vs. sTPS, 92.9 ± 4.0% and 94.1 ± 3.3% for pTPS vs. measurement, and 93.0 ± 4.4% and 94.3 ± 4.1% for sTPS vs. measurement, respectively. The differences between pTPS and sTPS for the PTVs of D95% and the isocenter dose were −3.1 ± 2.0% and −2.3 ± 1.8%, respectively. Our investigation of VMAT‐SBRT plans for lung cancer revealed that independent calculation‐based verification is a time‐efficient method for patient‐specific quality assurance

Three-dimensional architecture and assembly mechanism of the egg-shaped shell in testate amoeba Paulinella micropora

Unicellular euglyphid testate amoeba Paulinella micropora with filose pseudopodia secrete approximately 50 siliceous scales into the extracellular template-free space to construct a shell isomorphic to that of its mother cell. This shell-constructing behavior is analogous to building a house with bricks, and a complex mechanism is expected to be involved for a single-celled amoeba to achieve such a phenomenon; however, the three-dimensional (3D) structure of the shell and its assembly in P. micropora are still unknown. In this study, we aimed to clarify the positional relationship between the cytoplasmic and extracellular scales and the structure of the egg-shaped shell in P. micropora during shell construction using focused ion beam scanning electron microscopy (FIB-SEM). 3D reconstruction revealed an extensive invasion of the electron-dense cytoplasm between the long sides of the positioned and stacked scales, which was predicted to be mediated by actin filament extension. To investigate the architecture of the shell of P. micropora, each scale was individually segmented, and the position of its centroid was plotted. The scales were arranged in a left-handed, single-circular ellipse in a twisted arrangement. In addition, we 3D printed individual scales and assembled them, revealing new features of the shell assembly mechanism of P. micropora. Our results indicate that the shell of P. micropora forms an egg shape by the regular stacking of precisely designed scales, and that the cytoskeleton is involved in the construction process

Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma

The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A total of 339 patients who underwent lobectomy or limited resection for lung adenocarcinoma were included. The patients were randomly divided (3:2) into training and test cohorts. Two prediction models were created using the training cohort: a conventional model based on the tumor consolidation/tumor (C/T) ratio and a machine learning model based on peritumoral radiomics features. The areas under the curve for the two models in the testing cohort were 0.70 and 0.76, respectively ( = 0.045). The cumulative incidence of recurrence (CIR) was significantly higher in the STAS high-risk group when using the radiomics model than that in the low-risk group (44% vs. 4% at 5 years;  = 0.002) in patients who underwent limited resection in the testing cohort. In contrast, the 5-year CIR was not significantly different among patients who underwent lobectomy (17% vs. 11%;  = 0.469). In conclusion, the machine learning model for STAS prediction based on peritumoral radiomics features performed better than the C/T ratio model