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This article reports the first available human retrieval data following the use of a new fixation system for tumor prostheses. The compliant prestress (CPS) fixation system obviates the need for long intramedullary stems. The CPS was designed to provide a stable, high-pressure, motion-free bone-implant interface that would prevent aseptic loosening and allow osseointegration at the bone-implant interface. At 1 0 months, the fourth patient in the human trial required amputation. Backscatter electron microscopy revealed a buttress of new bone had formed along 70% of the bone-metal interface, with excellent bony ingrowth (average: 42%) into the transverse, porous-coated titanium interface.

After surgery at a HVC, the volume of adjuvant radiation therapy center was not significantly associated with overall survival. Significant predictors of survival included age, subsite, T stage, and extracapsular extension.

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Information generated by sensors that collect a patient's vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case the electrocardiogram (ECG). Processing uses a neural classifier, Kohonen competitive neural networks, detecting if the ECG shows any cardiac arrhythmia. In fact, it is possible to classify an ECG signal and thereby detect if it is exhibiting or not any alteration, according to normality.

Due to the need for management, control, and monitoring of information in an effient way. The hospital automation has been the object of a number of studies owing to constantly evolving technologies. However, many hospital processes are still manual in private and public hospitals. Thus, the aim of this study is to model and simulate of medical care provided to patients in the Intensive Care Unit (ICU), using stochastic Petri Nets and their possible use in a number of automation processes.

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In Romania, breast cancer (BC) is the most common malignancy in women. However, there is limited data on the prevalence of predisposing germline mutations in the population in the era of precision medicine, where molecular testing has become an indispensable tool in cancer diagnosis, prognosis, and therapeutics. Therefore, we conducted a retrospective study to determine the prevalence, mutational spectrum, and histopathological prediction factors for hereditary breast cancer (HBC) in Romania. A cohort of 411 women diagnosed with BC selected upon NCCN v.1.2020 guidelines underwent an 84-gene NGS-based panel testing for breast cancer risk assessment during 2018–2022 in the Department of Oncogenetics of the Oncological Institute of Cluj-Napoca, Romania. A total of 135 (33%) patients presented pathogenic mutations in 19 genes. The prevalence of genetic variants was determined, and demographic and clinicopathological characteristics were analyzed. We observed differences among BRCA and non-BRCA carriers regarding family history of cancer, age of onset, and histopathological subtypes. Triple-negative (TN) tumors were more often BRCA1 positive, unlike BRCA2 positive tumors, which were more often the Luminal B subtype. The most frequent non-BRCA mutations were found in CHEK2, ATM, and PALB2, and several recurrent variants were identified for each gene. Unlike other European countries, germline testing for HBC is still limited due to the high costs and is not covered by the National Health System (NSH), thus leading to significant discrepancies related to the screening and prophylaxis of cancer.