Notes

Innovative cancer of the lung introducing using cancerous pericardial effusion.
Melanins are a group of dark insoluble pigments found widespread in nature. In mammals, the brown-black eumelanins and the reddish-yellow pheomelanins are the main determinants of skin, hair, and eye pigmentation and play a significant role in photoprotection as well as in many biological functions ensuring homeostasis. Due to their broad-spectrum light absorption, radical scavenging, electric conductivity, and paramagnetic behavior, eumelanins are widely studied in the biomedical field. The continuing advancements in the development of biomimetic design strategies offer novel opportunities toward specifically engineered multifunctional biomaterials for regenerative medicine. Melanin and melanin-like coatings have been shown to increase cell attachment and proliferation on different substrates and to promote and ameliorate skin, bone, and nerve defect healing in several in vivo models. Herein, the state of the art and future perspectives of melanins as promising bioinspired platforms for natural regeneration processes are highlighted and discussed.Vaccination is a critical and reliable strategy for controlling the spread of influenza viruses in populations. Conventional seasonal split vaccines (SVs) for influenza evoke weaker immune responses than other types of vaccines, such as inactivated whole-virion vaccines, although SVs are highly safe compared to other types. Here, we assessed the potential of the lipid nanoparticle (LNP) we developed as an adjuvant for conventional influenza SV as an antigen in mice. The LNP did not induce the production of cytokines such as interleukin-6 (IL-6) and IL-12 p40 by dendritic cells or the expression of co-stimulatory molecules on these cells in vitro. In contrast, an SV adjuvanted with LNP improved SV-specific IgG1 and IgG2 responses and the Th1 response compared to the SV alone in mice. In addition, SV adjuvanted with an LNP gave superior protection against the influenza virus challenge over the SV alone and was as effective as SV adjuvanted with aluminum salts in mice. The LNP did not provoke inflammatory responses such as inflammatory cytokine production and inflammatory immune cell infiltration in mice, whereas aluminum salts induced inflammatory responses. These results suggest the potential of the LNP as an adjuvant without inflammatory responses for influenza SVs. Our strategy should be useful for developing influenza vaccines with enhanced efficacy and safety.Obstructive sleep apnea (OSA) and central serous chorioretinopathy (CSC) are in terms of nosography different pathologies, however they share a stress-related physio-pathogenetic component, not yet explored in depth. Therefore, the aim of the present study was to ascertain whether OSA and CSC share a common profile, specifically in cortisol production focusing on the cortisol awake response (CAR), the area under curve (AUCCAR) and the SLOPECAR compared with healthy matched controls. Furthermore, standardized self-administered questionnaires were used to identify mental health status related to depression, anxiety and subjective stress perception levels in the study populations. The results showed hypothalamus-pituitary-adrenal (HPA) axis activity anomalies, represented by a flattening CAR in the OSA group and a statistically significant increase in cortisol production in CSC patients at awakening. Saracatinib concentration This disarrangement of the HPA axis activity associated with elevated distress and mental health scores, and its presence in both patients with OSA and patients with CSC, might represent the shared path explaining the stress-related component in these diseases. Further research is needed to investigate the psycho-neuro-endocrinological aspects of OSA and CSC to determine whether psychoeducation on effective stress coping strategies might be of value in improving the quality of life of OSA and CSC patients.Although sarcopenia is characterized by a loss of muscle strength and skeletal muscle mass, few studies have evaluated the effect of muscle strength on hepatocellular carcinoma (HCC) patients. We evaluated the impact of sarcopenia-related factors (grip strength (GS) and the skeletal muscle index (SMI)) on the survival among lenvatinib-treated unresectable HCC (u-HCC) patients. This single-center cohort study was conducted at a university hospital. The study population included 63 lenvatinib-treated u-HCC patients managed between April 2018 and April 2020. A decreased GS and decreased SMI were found in 21 (33.3%) and 22 (34.9%) patients, respectively. The overall survival (OS) of the normal GS group was significantly higher than that of the decreased GS group, while that of the normal and decreased SMI groups did not differ markedly. There were no significant differences in the progression-free survival between the normal GS and decreased GS groups or the normal SMI and decreased SMI groups. A multivariate Cox proportional hazards model showed that modified albumin-bilirubin-grade (mALBI) 2b (hazard ratio (HR) 4.39) and a decreased GS (HR 3.55) were independently associated with an increased risk of poor prognosis. In addition to the hepatic functional reserve, a decreased GS was a poor prognostic factor in lenvatinib-treated u-HCC patients.Railway inspection has always been a critical task to guarantee the safety of the railway transportation. The development of deep learning technologies brings new breakthroughs in the accuracy and speed of image-based railway inspection application. In this work, a series of one-stage deep learning approaches, which are fast and accurate at the same time, are proposed to inspect the key components of railway track including rail, bolt, and clip. The inspection results show that the enhanced model, the second version of you only look once (YOLOv2), presents the best component detection performance with 93% mean average precision (mAP) at 35 image per second (IPS), whereas the feature pyramid network (FPN) based model provides a smaller mAP and much longer inference time. Besides, the detection performances of more deep learning approaches are evaluated under varying input sizes, where larger input size usually improves the detection accuracy but results in a longer inference time. Overall, the YOLO series models could achieve faster speed under the same detection accuracy.
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