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Barium Bismuth Ferrite nanoparticles having the general formula Ba1-xBixFeO3 were successfully made by the sol-gel system, using nitrates as the fundamental material. Tartaric acid and polyvinyl alcohol were used as chelating agents. The synthesized nanoparticle powder was sintered at 650 ̊C and 750 ̊C for 3 h. Filtering with dilute nitric acid and distilled water is used to remove the impurities. The structural, optical, and dielectric properties were determined at room temperature. Using X-ray diffraction (XRD) analysis, structure, lattice parameter, microstrain, and dislocation density were calculated. Using the Scherrer equation, the peak's crystalline size was ascertained. In FTIR, the vibrational modes of the octahedral and tetrahedral metal complexes in the sample have been studied using the wavenumber in this range. The synthesized nanocomposite contains morphological features, such as interconnecting agglomerates with spherical, irregular, or non-uniform elongated shapes, as demonstrated by SEM images. The activation energy for relaxation and activation energy for conduction was measured from the Cole-Cole plot and the AC conductivity versus frequency plot respectively. The determined activation energies from the two investigations were relatively close to one another. The Barium Bismuth Ferrite nanoparticles samples at room temperature indicate that the samples have implicit candidates for information storage devices for spintronic devices. In the high frequency range, all samples depending upon the temperature demonstrate excellent dielectric behaviour and small dielectric loss, and with stable dielectric constant, these samples make their prospective for the practical application in spintronic devices the main goal of this work.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised histologically by the death of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the presence of Lewy bodies in various parts of the brain. The SNpc is a compact structure in the midbrain that plays a vital role in motor coordination and movement control by producing a chemical substance called dopamine, which is integral for controlling the initiation, velocity, and fluidity of voluntary movement sequences. The causes of most cases of PD (known as ‘sporadic’ or ‘idiopathic’ PD) are still unknown but involve complex interactions between genetic and environmental factors. PD is the second most common neurodegenerative disorder after Alzheimer’s disease, affecting 1% of the population over the age of 60 and reaching approximately 5% at 85. The prevalence is rising due to ageing populations. According to the Parkinson Disease Foundation, about 10 million people worldwide have PD, one million of them in the USA, 1.2 million in Europe, and two million projected in China by 2030. One out of 500 individuals in the UK are affected, and it is expected that this number will rise threefold in the next 50 years. There is currently no proven disease-modifying therapy. The diagnosis of PD requires the presence of bradykinesia (slowness of movements) in addition to muscle rigidity or tremor or postural instability. Approximately 20% of patients do not develop a tremor. The manifestations of PD are not limited to motor impairments. Prompt diagnosis of PD is important in order to provide patients with appropriate treatment and information on prognosis. However, an accurate early diagnosis can be challenging because the movement symptoms can overlap with other conditions. Doctors make the diagnosis of PD based on clinical evaluation, interpreting information gained predominantly through history-taking and examination of the patient. Sometimes brain imaging may be requested to help support the clinical diagnosis, but there are currently no tests that are wholly sensitive or specific for Parkinson’s. The rate of misdiagnosis of PD is approximately 10–25%, and the average time required to achieve 90% accuracy is 2.9 years. Autopsy is still the gold standard for the confirmation of the disease. Therefore, this project designed an advanced convolution neural network model to predict Parkinson disease from both image and voice data. In general, existing ML algorithms such as SVM, and Random Forest will not filter data multiple times so its prediction accuracy is less hence CNN is used in this project, which filter data multiple times using neuron values so its prediction accuracy can be better. This project uses WAVE and SINE images of normal and Parkinson disease patients for imaging data and UCI Parkinson recorded voice is used for voice samples. [ABSTRACT FROM AUTHOR]

Abstract Background Immune checkpoint inhibitor (ICI) therapies represent a major advance in treating a variety of advanced-stage malignancies. Nevertheless, only a subset of patients benefit, even when selected based on approved biomarkers such as PD-L1 and tumor mutational burden. New biomarkers are needed to maximize the therapeutic ratio of these therapies. Methods In this retrospective cohort, we assessed a 27-gene RT-qPCR immuno-oncology (IO) gene expression assay of the tumor immune microenvironment and determined its association with the efficacy of ICI therapy in 67 advanced-stage NSCLC patients. The 27-gene IO test score (IO score), programmed cell death ligand 1 immunohistochemistry tumor proportion score (PD-L1 TPS), and tumor mutational burden (TMB) were analyzed as continuous variables for response and as binary variables for one-year progression free survival. The threshold for the IO score was prospectively set based upon a previously described training cohort. Prognostic implications of the IO score were evaluated in a separate cohort of 104 advanced-stage NSCLC patients from The Cancer Genome Atlas (TCGA) who received non-ICI therapy. Results The IO score was significantly different between responders or non-responders (p = 0.007) and associated with progression-free survival (p = 0.001). Bivariate analysis established that the IO score was independent of PD-L1 TPS and TMB in identifying patients benefiting from ICI therapy. In a separate cohort of late-stage NSCLC patients from TCGA, the IO score was not prognostic of outcome from non-ICI-treated patients. Conclusions This study is the first application of this 27-gene IO RT-qPCR assay in a clinical cohort with outcome data. IO scores were significantly associated with response to ICI therapy and prolonged progression-free survival. Together, these data suggest the IO score should be further studied to define its role in informing clinical decision-making for ICI treatment in NSCLC.

Abstract Background Papillary breast lesions may be benign, atypical, and malignant lesions. Pathological and clinical differentiation of breast papillomas can be a challenge. Unlike malignant lesions, benign breast papillomas are not classically associated with lymph node and distant metastasis. We report a unique case of a recurrent, benign breast papilloma presenting as an aggressive malignant tumor. Case presentation Our patient was a 56-year-old postmenopausal African American woman who was followed in the breast clinic with a long history of multiple breast papillomas. She underwent multiple resections over the course of 7–9 years. After being lost to follow-up for 2 years, she once again presented with a slowly enlarging left breast mass. Subsequent imaging revealed a predominantly cystic mass in the left breast, as well as a suspicious hypermetabolic internal mammary node and a hypermetabolic nodule in the pretracheal space. Biopsy of the internal mammary node demonstrated papillary neoplasm with benign morphology and immunostains positive for estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2/Neu. Due to the clinical picture concerning for malignancy, the patient was then started on endocrine therapy with palbociclib and letrozole before surgery. She then underwent simple mastectomy and sentinel lymph node dissection with negative nodes and pathology once again revealing benign papillary neoplasm. She underwent adjuvant chest wall radiation for 6 weeks and received letrozole following completion of her radiation therapy. She was without evidence of disease 30 months after surgery. Conclusions We present an unusual case of multiple recurrent peripheral papillomas with entirely benign histologic features exhibiting malignant behavior over a protracted period of many years, with an invasion of pectoralis musculature and possibly internal mammary and mediastinal nodes. Her treatment course included multiple surgeries (ultimately mastectomy), radiation therapy, and endocrine therapy.

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

BackgroundThere are poorer outcomes following ST elevation myocardial infarction in blacks compared to white patients despite comparable door‐to‐reperfusion time. We hypothesized that delays to hospital presentation may be contributory. Methods and ResultsWe conducted a retrospective analysis of the 1144 patients admitted for STEMI in our institution from 2008 to 2013. The door‐to‐balloon time (D2BT) and symptom‐onset‐to‐door time (SODT) were compared by race. Bivariate analysis was done comparing the median D2BT and SODT. Stratified analyses were done to evaluate the effect of race on D2BT and SODT, accounting for insurance status, age, sex and comorbidities. The mean age was 59±13 years; 56% of this population was black and 41% was white. Males accounted for 66% of this population. The median D2BT was 60 minutes (interquartile range [IQR] 42–82), and median SODT was 120 minutes (IQR 60–720). There was no significant difference in D2BT by race (P=0.86). Black patients presented to the emergency room (ER) later than whites (SODT=180 [IQR 60–1400] vs 120 [IQR 60–560] minutes, P