Notes

Floor composition of AgPd single-atom alloy driver within an oxidative environment.
9 (95% confidence interval 1.6-2.4)]. During subsequent follow-up years, the SIR fell to 0.7 (95% confidence interval 0.5-0.8). Findings for Alzheimer's disease and VTE were similar.

People with dementia have an increased risk of a cancer diagnosis during the first year following VTE, perhaps related to increased surveillance, and a lower risk thereafter. Overall risk is similar to that of the general population.
People with dementia have an increased risk of a cancer diagnosis during the first year following VTE, perhaps related to increased surveillance, and a lower risk thereafter. Overall risk is similar to that of the general population.
Measurement of abnormal Red Blood Cell (RBC) deformability is a main indicator of Sickle Cell Anemia (SCA) and requires standardized quantification methods. Ektacytometry is commonly used to estimate the fraction of Sickled Cells (SCs) by measuring the deformability of RBCs from laser diffraction patterns under varying shear stress. In addition to estimations from model comparisons, use of maximum Elongation Index differences (ΔEImax) at different laser intensity levels was recently proposed for the estimation of SC fractions.

Implement a convolutional neural network to accurately estimate rigid-cell fraction and RBC concentration from laser diffraction patterns without using a theoretical model and eliminating the ektacytometer dependency for deformability measurements.

RBCs were collected from control patients. Rigid-cell fraction experiments were performed using varying concentrations of glutaraldehyde. Serial dilutions were used for varying the concentration of RBC. A convolutional neural network was constructed using Python and TensorFlow.

Measurements and model predictions show that a linear relationship between ΔEImax and rigid-cell fraction exists only for rigid-cell fractions less than 0.2. The proposed neural network architecture can be used successfully for both RBC concentration and rigid-cell fraction estimations without a need for a theoretical model.
Measurements and model predictions show that a linear relationship between ΔEImax and rigid-cell fraction exists only for rigid-cell fractions less than 0.2. The proposed neural network architecture can be used successfully for both RBC concentration and rigid-cell fraction estimations without a need for a theoretical model.During the first hit of SARS-COVID pandemic, an important reorganization of Healthcare Services has been done, and new protocols and pathways to protect frails patients as oncological patients were designed. The second hit of pandemic had stressed these new pathways and suggest to health-workers some improvements for safer management of patents.We reported our experience in organizing the clinical pathway of neoadjuvant therapy candidate patients based on the execution of sentinel lympho-node biopsy and the placement of implantable venous access port in the same access to operating room before neoadjuvant chemotherapy suggesting a possible organizational model. In the period October-December 2020 we have included in this new type of path twelve patients and we have not registered any cases of COVID among the patients included. We think this new path, adopted amid the second hit, will be useful for all Breast Units that are facing the challenge of guaranteeing the highest standards of care in a historical moment where the health emergency occupies the efforts of health workers and the economic resources of health systems.Intracystic papillary carcinoma (IPC) is a rare tumor with good prognosis that occurs in only 5% to 7.5% of male breast cancer. We report a case of a 46-year-old man who presented a brown nipple discharge a few months ago. He had a bilateral IPC and an invasive ductal carcinoma on the right breast. A double mastectomy was then performed with a bilateral sentinel lymph node biopsy, and he received chemotherapy, radiotherapy, and hormonotherapy. Two years after the diagnosis, the patient recovered and was free of recurrence. Considering the scarcity of this tumor type, we conducted a systematic literature review on the PubMed of all the cases of IPC in men. The clinical presentation, imaging, and treatment of the 43 case reports from the 41 articles selected were described. Furthermore, no clear guidelines for IPC management are available. Conservative surgery should also be preferred, and a sentinel lymph node biopsy should be performed systematically. Y-27632 order Moreover, radiotherapy should be proposed in the case of conservative surgery, and hormone therapy could be proposed in the case of invasive IPC or IPC associated with a ductal carcinoma in situ.
Male breast cancer is a considerably rare condition and only accounts for 1% of all breast cancer cases. Due to limited public awareness, the condition is likely to present late, leading to late diagnosis and treatment worsening morbidity and mortality. This article aims to identify the focus and most influential research on male breast cancer. Objective Identify the most influential papers in male breast cancer.

Search on Web of Science using the search terms 'Male', 'Breast Cancer' and "Male breast cancer" to identify all full manuscripts in English language and were ranked by the total number of citations. The top 100 articles were then further analysed according to subject, author, journal, year and country of publications.

The mean number of citations per paper was 96. Most cited paper was by Thorlacius, S etal.evaluating the relationship between BRCA2 and female breast cancer, prostate cancer, pancreatic cancer and ovarian cancer. Cancer is the journal with the most published papers and received min this field were published in high impact journals and had accumulated at least 100 citations to date, reflecting their quality and impact. By collating the most influential publications in this field, this analysis can serve to identify knowledge gaps in male breast cancer research as well as to help identify what makes a paper impactful and citable.
To develop an ensemble a deep transfer learning model of CT images for predicting pathologic complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC).

The data were obtained from the public dataset 'QIN-Breast' from The Cancer Imaging Archive (TCIA). CT Images were gathered before and after the first cycle of NAC. CT images of 121 breast cancer patients were used to train and test the model. Among these patients, 58 achieved a pCR and 63 showed a non-pCR based pathology examination of surgical results after NAC. The dataset was split into training and testing subsets with a ratio of 73. In addition, the number of training samples in the dataset was increased from 656 to 1,968 by performing an image augmentation method. Two deep transfer learning models namely, DenseNet201 and ResNet152V2, and the ensemble model with a concatenation of two models, were trained and tested using CT images.

The ensemble model obtained the highest accuracy of 100% on the testing dataset. Furthermore, we received the best performance of 100% in recall, Precision and f1-score value for the ensemble model.
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