Reliable multilevel resistive switching in nanoscale cells is desirable for the wide adoption of resistive random access memory as the next-generation nonvolatile memory. We designed NiO-based cells in arrays of multilayered NiO/Pt nanowires to explore multilevel memory effects. Nonpolar resistive switching reproducibly occurs with significantly reduced switching voltages, narrow switching voltage distributions and a robust multilevel memory effect. A high resistance ratio (B10 5 ) between the highand low-resistance states in nanoscale cells enables stable multilevels that can be induced easily by a series of pulsed voltage. The existence of intermediate resistance states in NiO/Pt nanowire arrays can be well explained by the binary-resistor model combined with energy perturbations induced by the pulse voltage. We also verified that the conduction mechanism in multilayered NiO/Pt nanowires is dominated by the hopping of holes. Our bottom-up approach and proposed mechanism explain the controllable multilevel memory effect and facilitate sound device design to encourage their universal adoption.
The 45,X/46,XY phenotype varies widely and a high index of suspicion is important to ensure early diagnosis. Cardiac and renal malformations should be screened ultrasonically at diagnosis and thyroid status should be monitored annually. Growth hormone effectively improves adult height in short girls. Prophylactic gonadectomy is indicated for those with intra-abdominal streaks or dysgenetic gonads to prevent the development of a malignancy.
Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible.
Lactobacillus plantarum TWK10 (LP10) is a probiotic known to improve endurance exercise performance. Here, we analyze the proteomics and metagenomic changes in a LP10 supplemented mouse model. Male ICR mice were divided into two groups ( n = 8) to receive by oral gavage either vehicle or of LP10 for 6 weeks. Proteins changes by LP10 treatment were subjected to the Ingenuity Pathway Analysis (IPA) to provide corroborative evidence for differential regulation of molecular and cellular functions affecting metabolic processes. Fecal samples were obtained from each mouse, and the microbial community profile analyzed by pyrosequencing of the 16S rRNA genes. Of the 880 identified proteins, 25 proteins were significantly downregulated and 44 proteins were significantly upregulated in the LP10 treated compared to vehicle group. LP10 supplementation shift in the gut microbiota to butyrate‐producing members and provided from lipid oxidation since peroxisomal fatty acid oxidation in liver.
QFG-IT assay was more sensitive for the diagnosis of TB disease than TST in an intermediate burden population with universal neonatal BCG vaccination. The increased recognition of BCG induced osteitis in recent years has alerted physicians that BCG induced lesions should be suspected when TST is positive but QFG-IT is negative. Despite higher costs for QFG-IT than TST, they have additional value for the diagnosis of active TB and should be performed when a diagnosis of TB remains in doubt.
Rationale: Paragangliomas (PGLs) are rare neuroendocrine tumors that are strongly influenced by genetics, and succinate dehydrogenase-deficient PGLs appear to constitute one of the most important categories. Interestingly, somatic PGLs only possess genomic alterations involving the SDHB and SDHD subunits, and no SDHA alterations have been described. Here, we are presenting the clinical and genetic analyses of 2 cases with the first somatic SDHA variant identified in PGLs. Patient concerns: Here, we reported 2 family members with the diagnosis of PGL. Patient 1 is a 55-year-old woman with a functionally perigastric PGL that co-occurred with a gastric gastrointestinal stromal tumor (GIST), and patient 2 is a 43-year-old woman with a nonfunctionally pericardial PGL, who was the younger sister of the first patient. Diagnoses: Imaging surveys of the 2 cases depicted the presence of a perigastric and a pericardial mass, respectively. A diagnosis of paragangliomas was established by immunohistochemistry (IHC). Interventions: Both patients underwent single-stage resection of the lesion after preoperative oral α-adrenoceptor therapy for 2 weeks. We later performed comprehensive genomic profiling on the tumor samples, including PGL and GIST from patient 1 and PGL from patient 2, and searched for novel actionable mutations, including in all succinate dehydrogenase subunits, as the IHC results were negative for SDHB . Outcomes: Both patients had an uneventful recovery after surgery and the sequencing showed a novel somatic variant in the SDHA gene on chromosome 5q11 (c.1945_1946delTT). Regular follow-up with biochemical testing and image studies showed no evidence of recurrence after a year for patient 1 and 6 years for patient 2. Lessons: PGLs often lead to considerable diagnostic difficulty due to their multiple anatomical locations and variable symptoms, as presented by our cases. The comprehensive use of images and plasma/urine catecholamine measurement can aid the diagnosis of PGLs. In addition, our findings also demonstrate the usefulness and importance of genetic analysis of SDHA mutations in patients exhibiting SDHB IHC-negative PGL. Additional studies utilizing comprehensive genomic profiling are needed to identify the group of PGLs harboring this SDHA genomic alteration.
BACKGROUND Melanoma is the most serious form of skin cancer, and the treatment can be challenging if the disease progresses to the metastatic stage. Depth of invasion is a good prognostic factor for predicting outcome. However, no good outcome prediction system that combines the staging system with other chronic systemic diseases is available to date. We investigated melanoma-related data from a population-based database and developed an outcome prediction tool for melanoma patients via machine learning. OBJECTIVE Build up a prediction tool for melanoma patients METHODS The clinical data of patients with melanoma were extracted from Taiwan’s National Health Insurance Research Database between 2008 and 2015 and were analysed in this study. Clinical data including demographic, pathologic, staging, and treatment data from melanoma patients over 18 years old were abstracted and collected. Prognostic factors were analyzed. Logistic regression (LR), random forest (RF) modelling, and multivariate adaptive regression splines (MARS) were applied to calculate predicted overall survival (OS). A 5-fold cross-validation method was applied. Two age groups (≥64 years old as the older age group and <64 years old as the general population group) with different prognostic factors were identified, and prognostic models for survival outcomes were built. RESULTS A total of 3481 patients were enrolled in our study. The 1-, 3-, and 5-year overall survival rates were 92.2%, 80.1%, and 70.3%, respectively. The Cox proportional hazard model showed that older age, male sex, higher grade, higher clinical stage, larger tumour size, positive surgical margins, no surgical intervention, and a higher Charlson comorbidity index (CCI) were associated with higher hazard ratios. LR, RF, and MARS techniques were used to validate the overall survival without tracking time, the accuracy of the MARS model for the <64-year-old patients and ≥64-year-old patients was 90.4% and 80.7%, respectively, with 3-, and 5-year the accuracy of prediction models are 94% and 89.6%. CONCLUSIONS Machine learning techniques offer excellent survival prediction in melanoma patients. Age-based survival prediction models may be applied for better clinical decision making. CLINICALTRIAL N/A
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