Various ADC parameters were correlated with prognostic factors and subtype, except for ADCmin . HER2 positivity showed high ADC values and high Ki-67 index revealed low ADC values.

Cuprates, a member of high-Tc superconductors, have been on the long-debate on their superconducting mechanism, so that predicting the critical temperature of cuprates still remains elusive. Herein, using machine learning and first principle calculations, we predict the maximum superconducting transition temperature (Tc,max) of hole-doped cuprates and suggest the explicit functional form for Tc,max with the root-mean-square-error of 3.705 K and the coefficient of determination R 2 of 0.969. We employed two machine learning models; one is a parametric brute force searching method and another is a non-parametric random forest regression model. We have found that material dependent parameters such as the Bader charge of apical oxygen, the bond strength between apical atoms, and the number of superconducting layers are important features to estimate Tc,max. Furthermore, we predict the Tc,max of hypothetical cuprates generated by replacing apical cations with other elements. When Ga is an apical cation, the predicted Tc,max is the highest among the hypothetical structures with 71, 117, and 131 K for one, two, and three CuO2 layers, respectively. These findings suggest that machine learning could guide the design of new high-Tc superconductors in the future.Understanding the material dependence of superconducting temperature (Tc) has been a longstanding subject of importance in the condensed matter physics community. However, this becomes especially challenging for the high Tc cuprates, as their underlying mechanism of superconductivity, despite intensive experimental and theoretical studies, still remains elusive over 30 years since the discovery of La2CuO4 [1]. All cuprate superconductors share the common characteristics of the two-dimensional CuO2 superconducting layer and strong electronic

Bone mineral density (BMD) is a key feature in diagnosing bone diseases. Although computational tomography (CT) is a common imaging modality, it seldom provides bone mineral density information in a clinic owing to technical difficulties. Thus, a dual-energy X-ray absorptiometry (DXA) is required to measure bone mineral density at the expense of additional radiation exposure. In this study, a deep learning framework was developed to estimate the bone mineral density from an axial cut of the L1 bone on computational tomography. As a result, the correlation coefficient between bone mineral density estimates and dual-energy X-ray absorptiometry bone mineral density was .90. When the samples were categorized into abnormal and normal groups using a standard (T-score =−1.0), the maximum F1 score in the diagnostic test was .875. In addition, it was identified using explainable artificial intelligence techniques that the network intensively sees a local area spanning tissues around the vertebral foramen. This method is well suited as an auxiliary tool in clinical practice and as an automatic screener for identifying latent patients in computational tomography databases.

Background: Differences in the effects of propofol and dexmedetomidine sedation on electroencephalogram patterns have been reported previously. However, the reliability of the Bispectral Index (BIS) value for assessing the sedation caused by dexmedetomidine remains debatable. The purpose of this study is to evaluate the correlation between the BIS value and the Modified Observer’s Assessment of Alertness/Sedation (MOAA/S) scale in patients sedated with dexmedetomidine. Methods: Forty-two patients (age range, 20–80 years) who were scheduled for elective surgery under spinal anesthesia were enrolled in this study. Spinal anesthesia was performed using 0.5% bupivacaine, which was followed by dexmedetomidine infusion (loading dose, 0.5–1 μg/kg for 10 min; maintenance dose, 0.3–0.6 μg/kg/h). The MOAA/S score was used to evaluate the level of sedation, and the Vital Recorder program was used to collect data (vital signs and BIS values). Results: A total of 215082 MOAA/S scores and BIS data pairs were analyzed. The baseline variability of the BIS value was 7.024%, and the decrease in the BIS value was associated with a decrease in the MOAA/S score. The correlation coefficient and prediction probability between the two measurements were 0.566 (P < 0.0001) and 0.636, respectively. The mean ± standard deviation values of the BIS were 87.22 ± 7.06, 75.85 ± 9.81, and 68.29 ± 12.65 when the MOAA/S scores were 5, 3, and 1, respectively. Furthermore, the cut-off BIS values in the receiver operating characteristic analysis at MOAA/S scores of 5, 3, and 1 were 82, 79, and 73, respectively. Conclusion: The BIS values were significantly correlated with the MOAA/S scores. Thus, the BIS along with the clinical sedation scale might prove useful in assessing the hypnotic depth of a patient during sedation with dexmedetomidine.

Objective: We retrospectively analyzed the outcome of 28 patients over 65 years old with medically intractable Trigeminal Neuralgia (TN) to evaluate whether CyberKnife RadioSurgery (CKRS) is an effective and safe modality of therapy. Methods: We collect retrospective data on 28 patients undergoing CKRS in patients with TN since April 1, 2004. We performed CKRS to treat TN in older patients. And a Computed Tomography (CT) cisternography obtained a distortion-free localization of the trigeminal nerve. The treatment target was to designate a 3-5 mm proximal region's trigeminal nerve, excluding the REZ of the trigeminal nerve in the brain stem. We set treatment planning that the radiation targets could be included in the 80-percent isodose curve with radiosurgery doses of 60-64 Gy. The median follow-up period after CKRS was 36 months (24-48 months). Pain intensity was measured using a Visual Analog Scale (VAS), Quality of Life Scale (QOLS) were measured at baseline and after CKRS. Results: Relief of pain in 28 patients with TN after CKRS showed that pain relief was achieved in 15 patients within the first 24 hours after CKRS. Within 7 days, 25 of the 28 study patients reported early pain relief. The pain control rate was 85.7% at follow up of 24 months. Pain relapsed in four of 28 patients. Twelve patients had hyperesthesia (35.7%) with no other complication. The mean (±S.D.) VAS of baseline status was 7.7 (±0.97) before CKRS in patients with TN. VAS decreased to 3.6±1.8 within 24 hours after CKRS compared with baseline VAS. The relief of pain was sustained by 2.1±1.5 for 1 week and by 1.8±1.7 at 6 months. After 12 months and 24 months, VAS remained by 2.0±1.4 and 2.1±1.5. The dose of gabapentin (1,200±600 mg/day) before treatment. The gabapentin dose in the six month did not significantly decrease in taking gabapentin (900±300 mg/day) compared with the baseline dose. Consumption after 24 months was also significantly reduced by 600±300 mg/day. Conclusion: We conclude that CKRS is a safe and effective treatment for elderly patients with TN in this study. In most patients, the pain was relieved in seven days after CKRS, and no severe complications occurred. However, in older patients, CKRS could be considered as a treatment option for TN.