Nutrients are chemical substances obtained from food. They have different roles in body.  Some are used as energy source, some as structural materials, and regulating agents. Nutrients may reduce the risks of some diseases. There are some recommendations about dietary intake of these nutrients for optimal health. This study aimed to estimate average calcium and magnesium content in day meal in a sample of students from Faculty of Medicine of Sarajevo University. A cross-sectional study was conducted during academic 2015/16 year at Faculty of Medicine of Sarajevo University. The survey covered 44 students. The research instrument was a self-administered questionnaire, by which 3-Day Diet Record was provided. The average intakes of calcium and magnesium were estimated using Nutritional analysis computer program (Nutrics Professional Nutrition Analysis Software). On daily level average intake of calcium was 718.39±311.14 mg in total sample and average intake of magnesium was 292.57±310.10 mg in total sample. Average Ca/Mg ratio was 2.45. In our sample cheese was top source of calcium with Ca/Mg ratio of 32.5, and bread was top source of magnesium with Ca/Mg ratio of 3.1. These results emphasize the importance of monitoring the food nutrition facts in order to achieve adequate nutrients intake.

We present a case of cytokine release syndrome (CRS) that occurred five days after vaccination with BTN162b2 (tozinameran), an mRNA COVID-19 vaccine, in a patient with colorectal cancer on long-standing anti-PD-1 monotherapy. The CRS was evidenced by raised inflammatory markers, thrombocytopenia, elevated cytokine levels (IFN-y/IL-2R/IL-18/IL-16/IL-10), and steroid responsiveness.

Introduction Achieving adequate resection margins during oral cancer surgery is important to improve patient prognosis. Surgeons have the delicate task of achieving an adequate resection and safeguarding satisfactory remaining function and acceptable physical appearance, while relying on visual inspection, palpation, and preoperative imaging. Intraoperative assessment of resection margins (IOARM) is a multidisciplinary effort, which can guide towards adequate resections. Different forms of IOARM are currently used, but it is unknown how accurate these methods are in predicting margin status. Therefore, this review aims to investigate: 1) the IOARM methods currently used during oral cancer surgery, 2) their performance, and 3) their clinical relevance. Methods A literature search was performed in the following databases: Embase, Medline, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar (from inception to January 23, 2020). IOARM performance was assessed in terms of accuracy, sensitivity, and specificity in predicting margin status, and the reduction of inadequate margins. Clinical relevance (i.e., overall survival, local recurrence, regional recurrence, local recurrence-free survival, disease-specific survival, adjuvant therapy) was recorded if available. Results Eighteen studies were included in the review, of which 10 for soft tissue and 8 for bone. For soft tissue, defect-driven IOARM-studies showed the average accuracy, sensitivity, and specificity of 90.9%, 47.6%, and 84.4%, and specimen-driven IOARM-studies showed, 91.5%, 68.4%, and 96.7%, respectively. For bone, specimen-driven IOARM-studies performed better than defect-driven, with an average accuracy, sensitivity, and specificity of 96.6%, 81.8%, and 98%, respectively. For both, soft tissue and bone, IOARM positively impacts patient outcome. Conclusion IOARM improves margin-status, especially the specimen-driven IOARM has higher performance compared to defect-driven IOARM. However, this conclusion is limited by the low number of studies reporting performance results for defect-driven IOARM. The current methods suffer from inherent disadvantages, namely their subjective character and the fact that only a small part of the resection surface can be assessed in a short time span, causing sampling errors. Therefore, a solution should be sought in the field of objective techniques that can rapidly assess the whole resection surface.

For vulvar squamous cell carcinoma (VSCC), the mainstay of treatment is surgical removal with tumour-free margins. Surgeons still operate without objective tools that provide margin-status. This study assesses Raman spectroscopy potentiality for distinguishing ex-vivo VSCC from healthy tissue in 11 patients. Grid-based Raman maps were obtained from processed spectra. Water content and C-H band ratio (2,910-2,966 cm-1 / 2810-2890 cm-1) were calculated per spectrum and used as linear discriminant parameters. Healthy tissue was differentiated from VSCC with 0.90 discriminative power, 0.79 sensitivity and 0.86 specificity.This is an important step towards the development of objective tools for VSCC surgical guidance.

In automated planning, the need for explanations arises when there is a mismatch between a proposed plan and the user’s expectation. We frame Explainable AI Planning as an iterative plan exploration process, in which the user asks a succession of contrastive questions that lead to the generation and solution of hypothetical planning problems that are restrictions of the original problem. The object of the exploration is for the user to understand the constraints that govern the original plan and, ultimately, to arrive at a satisfactory plan. We present the results of a user study that demonstrates that when users ask questions about plans, those questions are usually contrastive, i.e. “why A rather than B?”. We use the data from this study to construct a taxonomy of user questions that often arise during plan exploration. Our approach to iterative plan exploration is a process of successive model restriction. Each contrastive user question imposes a set of constraints on the planning problem, leading to the construction of a new hypothetical planning problem as a restriction of the original. Solving this restricted problem results in a plan that can be compared with the original plan, admitting a contrastive explanation. We formally define model-based compilations in PDDL2.1 for each type of constraint derived from a contrastive user question in the taxonomy, and empirically evaluate the compilations in terms of computational complexity. The compilations were implemented as part of an explanation framework supporting iterative model restriction. We demonstrate its benefits in a second user study.

This paper presents a comprehensive anatomic overview of normal pulmonary vascularization in an American mink (Neovison vison), with emphasis on venous and arterial peculiarities as regards its course and potential disruptions. The study is designed as macro-morphological and vascular network analysis of lungs of fifteen minks of different age and gender. Dissection is conducted along with injection corrosion method in order to clearly visualize the vascular topology, bronchial tree, arterial and venous trees, and their interconnections. The specimens exhibit consistence in the arterial branching pattern with negligible range of alterations. It was noticed that upon leaving the right ventricle of heart, pulmonary trunk divides ventrally from the site of tracheal bifurcation. The divisions were identified as left and right pulmonary arteries, which then ramified in the left and right lung, respectively. Left a. pulmonalis further divides into two major branches ramus lobi cranialis and ramus lobi caudalis, while the right a. pulmonalis gives five major branches for lobes in the right lung. Total of five pulmonary veins leave pulmonary lobes and enter left atrium of the heart. Mink has a distinct niche in biomedical research, proving as a suitable model to enhance the understandings of the various diseases. Gained insights are valuable as reference values for examination of the vasculature in other Mustelidae species, recognition of potential deviations and vascular remodeling due to respiratory diseases.

Acquisition of downlink channel state information (CSI) is an important procedure performed at the base station (BS) for high quality wireless communication in frequency division duplexing (FDD) communication system. Generally, the downlink CSI is fed back to the BS through the user equipment (UE). Compared with traditional methods, neural network (NN) can effectively compress the downlink CSI, thus greatly reducing the feedback overhead. However, the generalization of the NN is poor, hence it is necessary to train a NN from scratch whenever there is a change in the wireless channel environment. Nevertheless, training a NN this way requires huge data and time cost in 5G massive MIMO systems. In this paper, deep transfer learning (DTL) is proposed to solve the problem of high training cost of the downlink CSI feedback NN. In a new wireless environment, our proposed technique utilises relatively small number of samples to fine-tune a pre-trained model, in order to obtain a new model with low training cost. The performance of this model is shown to be comparable with that of the NN trained with large samples. Experiment results demonstrate the effectiveness and superiority of the proposed method.

In massive multiple input multiple output (MIMO) systems, the base station (BS) requires channel state information (CSI) to better utilize the available spatial diversity and multiplexing gains. However, in frequency division duplex (FDD) systems, user equipment (UE) needs to keep on feeding downlink CSI back to the BS, thereby consuming precious bandwidth resources. In this paper, we propose a deep learning (DL) based downlink CSI limited feedback scheme, called FullyConv, which is composed of all convolutional layers to compress and decompress the downlink CSI. FullyConv will improve reconstruction accuracy and robustness as well as reduce the time and space complexity, thus enhancing the system feasibility. Experimental results demonstrate that the FullyConv has a gain of nearly 5 dB compared to baseline. The performance of the FullyConv degrades slightly in the noisy uplink channel, which shows the robustness of FullyConv. Meanwhile, the complexity of the model composed of time complexity and space complexity is significantly reduced.

This paper proposes a decentralized automatic modulation classification (DecentAMC) method using light network and model aggregation. Specifically, the lightweight network is designed by separable convolution neural network (S-CNN), in which the separable convolution layer is utilized to replace the standard convolution layer and most of the fully connected layers are cut off, the model aggregation is realized by a central device (CD) for edge device (ED) model weights aggregation and multiple EDs for ED model training. Simulation results show that the model complexity of S-CNN is decreased by about 94% while the average CCP is degraded by less than 1% when compared with CNN and that the proposed AMC method improves the training efficiency when compared with the centralized AMC (CentAMC) using S-CNN.

Malignant salivary gland tumors form 11% of all head and neck tumors. Salivary duct carcinoma (SDC) of the parotid gland is an uncommon and highly aggressive tumor accounting for 2% of all salivary gland malignancies. The fourth edition of the WHO Classification of Head and Neck Tumors reported global annual incidence of 0.4–2.6/100,000 people. Standard treatment for SDC is wide surgical resection along with lymph node dissection followed by adjuvant radiation therapy. The role of adjuvant chemotherapy is not known. Here, we present a case of an SDC of the parotid gland in a 55-year-old female. She presented with a painless swelling behind her right ear. Surgery was performed, and total right parotidectomy was done along with selective neck dissection (levels II, III, VA). The pathology report concluded that it was a G3 (poorly differentiated) SDC (micropapillary variant) with lymphovascular and perineural invasion. The clinical stage of the disease was T4aN2bM0. Adjuvant therapy consisted of concurrent radiation (TD 66 Gy) and chemotherapy with weekly paclitaxel (45 mg/m2), carboplatin (AUC 1.5), and trastuzumab (2 mg/kg; THC) for 6 weeks followed by 4 cycles of THC every 3 weeks. Adjuvant trastuzumab was continued for a total of 1 year. She is still without the evidence of the disease 7 years later. The consensus regarding the use of adjuvant chemotherapy does not exist. Based on all the data that have been presented before, the conclusion is that we need new treatment modalities to improve the outome of this aggressive disease.

Erythema ab igne (EAI) is a localized, hyperpigmented and reticulated dermatosis at sites of chronic heat exposure. Within longstanding skin lesions of EAI, hyperkeratotic lesions may emerge and can potentially transform into pre-malignant or malignant skin lesions. A 55-year-old woman presented for the evaluation of multiple hyperkeratotic lesions along with a reticular patterned hyperpigmentation on her right knee, an area that had repeated and prolonged exposure to a heat source over a period of several months. Based on her clinical history and the physical examination of her lesions, she was diagnosed as having a hyperkeratotic form of EAI. A skin biopsy was performed to rule out malignant alteration, but the histopathological findings were supportive of keratosis lichenoides chronica.

Corvids appear to be capable of adjusting their behaviour according to another’s perspective, knowledge and desire. For example, Eurasian jays have been found to employ a variety of cache protection strategies to minimise cache loss by responding to cues about the visual perspective or current desire of an observing conspecific. However, it is not known whether these jays (or any other corvid) can integrate multiple cues about different mental states and perform the optimal response accordingly. Across five experiments, we found little evidence that our Eurasian jays responded to either the visual perspective or current desire of another agent. In Experiments 1 and 2 we investigated whether Eurasian jays can limit the risk of cache loss by responding simultaneously to cues about the desire and perspective of a potential conspecific pilferer. Building on established paradigms, we used opaque and clear barriers to manipulate the observer’s visual access to cache locations, and specific satiety to manipulate the observer’s desire towards different types of food. Across both experiments the jays’ caching pattern provided no evidence that they could integrate information about the observer’s desire and perspective. Moreover, the results were also inconsistent with the previously reported effects that jays protect their caches by responding to either the visual access or specific satiety of the observer independently. To gain further insight into these unexpected results, we conducted three more experiments. In Experiments 3 and 4, we attempted to replicate the previous finding that Eurasian jays prefer to cache behind an opaque barrier over a clear barrier when observed by a conspecific than when caching in private. In Experiment 5, we further investigated the previous finding that jays preferentially cache a type of food that had been eaten to satiety by a conspecific over a food that had not been eaten by the conspecific. Experiments 3, 4 and 5 found no significant effects in the direction of the previously reported effects, questioning their robustness. We conclude by discussing the implications of our study for the field of corvid cognition and highlight several key issues that affect the reliability of comparative cognition research.

Magnesium based materials are considered promising biodegradable metals for orthopedic bone implant applications as they exhibit similar density and elastic modulus to that of bone, biodegradability, and excellent osteogenic properties. The use of Mg based biomaterials eliminates the limitations of currently used implant materials such as stress shielding and the need for the second surgery. Recently, the development of Mg-based implants has attracted significant attention. Additive manufacturing is one of the effective techniques to develop Mg based implants. Additive manufacturing which could be named 3D printing is a transformative and rapid method of producing industrial parts with in the acceptable dimensional range. Therefore, recent investigations have tried to apply this method for the development of Mg-based implants. This state-of-the-art review focuses on the additive manufacturing of Mg biodegradable materials and their in-vitro corrosion and degradation, and mechanical properties. The future directions to develop Mg biodegradable materials are reported through summarization of current achievements.