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Background: Hypoxia-inducible factor-1α (HIF-1α) is a crucial transcription factor activated under hypoxic conditions, known to regulate genes associated with tumor survival, progression, and response to therapy. This study aimed to evaluate the prognostic significance of HIF-1α expression in patients with anal squamous cell carcinoma (ASCC) undergoing chemoradiation therapy. Methods: We conducted a retrospective analysis of 28 ASCC patients treated with intensity-modulated radiotherapy (IMRT) at our center from 2009 to 2022. HIF-1α expression was assessed via immunohistochemistry on formalin-fixed paraffin-embedded tissue specimens. Quantitative analysis of HIF-1α expression was performed, and its relationship with clinical outcomes, including disease-free survival (DFS), locoregional relapse-free survival (LRRFS), and overall survival (OS), was examined using Cox regression models. Furthermore, ASCC tissue specimens from 17 patients were analyzed for potential PIK3CA mutations using Sanger sequencing. Results: High HIF-1α expression was significantly associated with poorer DFS (p = 0.005), LRRFS (p = 0.012), and OS (p = 0.009). HIF1α expression was marginally significantly higher in males compared to females (p = 0.056) while there was no significant difference found based on tumor stage or p16 status. However, a positive correlation was identified between BMI and HIF-1α levels (Pearson correlation r = 0.5, p = 0.0084), suggesting a link between metabolic status and tumor hypoxia. Only one patient exhibited a PIK3CA mutation, preventing a reliable assessment of its correlation with HIF-1α expression. Conclusion: Our findings underscore the importance of HIF-1α as a potential biomarker for predicting survival outcomes in ASCC patients treated with chemoradiation. The association between higher BMI and increased HIF-1α expression may provide insights into the interplay between metabolic health and tumor biology in ASCC. Further studies with larger cohorts are needed to validate these findings and explore targeted therapies focusing on HIF-1α modulation.

Analysing complex diseases such as chronic inflammatory joint diseases (CIJDs), where many factors influence the disease evolution over time, is a challenging task. CIJDs are rheumatic diseases that cause the immune system to attack healthy organs, mainly the joints. Different environmental, genetic and demographic factors affect disease development and progression. The Swiss Clinical Quality Management in Rheumatic Diseases (SCQM) Foundation maintains a national database of CIJDs documenting the disease management over time for 19'267 patients. We propose the Disease Activity Score Network (DAS-Net), an explainable multi-task learning model trained on patients' data with different arthritis subtypes, transforming longitudinal patient journeys into comparable representations and predicting multiple disease activity scores. First, we built a modular model composed of feed-forward neural networks, long short-term memory networks and attention layers to process the heterogeneous patient histories and predict future disease activity. Second, we investigated the utility of the model's computed patient representations (latent embeddings) to identify patients with similar disease progression. Third, we enhanced the explainability of our model by analysing the impact of different patient characteristics on disease progression and contrasted our model outcomes with medical expert knowledge. To this end, we explored multiple feature attribution methods including SHAP, attention attribution and feature weighting using case-based similarity. Our model outperforms temporal and non-temporal neural network, tree-based, and naive static baselines in predicting future disease activity scores. To identify similar patients, a k-nearest neighbours regression algorithm applied to the model's computed latent representations outperforms baseline strategies that use raw input features representation.

Abstract Graphene presents practical applications in energy storage devices, especially supercapacitors. However, mainstream synthesis of graphene includes toxic chemical usage, which threatens the environment. With the recent attention shift to synthesizing nanomaterials from agro-waste due to their easy availability, cost-effectiveness, and, most importantly, their environmental friendliness, we present, in this work for the first time, a novel and green synthesis of few-layered graphene sheets using pomegranate peels as a precursor at a low temperature of 80 °C. The surface morphology and microstructural properties are determined by Transmission Electron Microscopy (TEM), Energy Dispersive X-Ray spectroscopy (EDX), X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), UV–visible spectroscopy (UV–vis), and the electrical properties determined by Hall Effect Measurement. The application as a supercapacitor is also examined using Cyclic Voltammetry (CV), Charge–Discharge Cycling (GCD), and Electrochemical Impedance Spectroscopy (EIS). The resulting supercapacitor delivers an areal capacitance of $$3.39 mF{cm}^{-2}$$ 3.39 m F cm - 2 at a current density of 15.6 μA $${cm}^{-2}$$ cm - 2 , making our synthesized graphene a good choice for electrochemical storage devices.

Abstract Introduction Stereotactic body radiotherapy (SBRT) is an ablative method for lung malignancies. Here, the definition of the gross target volume (GTV) is subject to interobserver variation. In this study, we aimed to evaluate the interobserver variability during SBRT and its dosimetric impact, as well as to introduce a semi‐automated delineation tool for both planning computer tomography (P‐CT) and cone beam CT (CBCT) to help to standardise GTV delineation and adaptive volume‐change registration. Methods The interobserver variation of GTV manual contours from five physicians was analysed in 15 patients after lung SBRT on free breathing (FB) P‐CT (n = 15) and CBCT (n = 90) before and after each fraction. The dosimetric impact from interobserver variations of GTV based on the original treatment plan was analysed. Next, the accuracy of an in‐house easy‐to‐use semi‐automated‐segmentation algorithm for pulmonary lesions was compared with gold standard contours in FB P‐CT and CBCT, as well as 4D P‐CT of additional 10 patients. Results The interobserver variability in manual contours resulted in violations of dose coverage of the planning target volume (PTV), which, in turn, resulted in compromised tumour control probability in contours from four physicians. The validation of the semi‐automated delineation algorithm using thorax phantom led to a highly reliable accuracy in defining GTVs. Comparing the unsupervised auto‐contours with the gold standard delineation revealed high equal high concordance for FB P‐CT, 4D P‐CT and CBCT, with a DSC of 0.83, 0.76 and 0.8, respectively. The supervised use of the semi‐automated delineation tool improved its accuracy, with DSCs of 0.86, 0.86 and 0.8 for FB P‐CT, 4D P‐CT and CBCT, respectively. The use of the algorithm was associated with a significantly shorter working time. The semi‐automated delineation tool can accurately register volume changes in CBCTs. Conclusion The segmentation algorithm provides a reliable, standardised and time‐saving alternative for manual delineation in lung SBRT in P‐CT and CBCT.

Abstract Background Anal cancer is a rare cancer with rising incidence. Despite the relatively good outcomes conferred by state-of-the-art chemoradiotherapy, further improving disease control and reducing toxicity has proven challenging. Developing and validating prognostic models using routinely collected data may provide new insights for treatment development and selection. However, due to the rarity of the cancer, it can be difficult to obtain sufficient data, especially from single centres, to develop and validate robust models. Moreover, multi-centre model development is hampered by ethical barriers and data protection regulations that often limit accessibility to patient data. Distributed (or federated) learning allows models to be developed using data from multiple centres without any individual-level patient data leaving the originating centre, therefore preserving patient data privacy. This work builds on the proof-of-concept three-centre atomCAT1 study and describes the protocol for the multi-centre atomCAT2 study, which aims to develop and validate robust prognostic models for three clinically important outcomes in anal cancer following chemoradiotherapy. Methods This is a retrospective multi-centre cohort study, investigating overall survival, locoregional control and freedom from distant metastasis after primary chemoradiotherapy for anal squamous cell carcinoma. Patient data will be extracted and organised at each participating radiotherapy centre (n = 18). Candidate prognostic factors have been identified through literature review and expert opinion. Summary statistics will be calculated and exchanged between centres prior to modelling. The primary analysis will involve developing and validating Cox proportional hazards models across centres for each outcome through distributed learning. Outcomes at specific timepoints of interest and factor effect estimates will be reported, allowing for outcome prediction for future patients. Discussion The atomCAT2 study will analyse one of the largest available cross-institutional cohorts of patients with anal cancer treated with chemoradiotherapy. The analysis aims to provide information on current international clinical practice outcomes and may aid the personalisation and design of future anal cancer clinical trials through contributing to a better understanding of patient risk stratification.

The spinning and weaving industry is one of the oldest industries known to man, and it began as a handicraft to which man was guided by his instinct, making for himself what he used to cover his body and protect it from the cold of winter, then it developed after a period of time using mechanical processes in this industry (5)The production of pile textiles represents a wide sector of the textile production sectors because of its importance in covering many functional and aesthetic dimensions of the consumer, It is unique from the common and commonly used types of textiles with its structural theories, design rules, and technical methods for its production, represented by the stages of production and equipment for the production of pile. (1)Webbing textiles are distinguished among all other textiles by their effectiveness towards highlighting and confirming the technical, aesthetic and functional trends of the third dimension of the fabric represented in thickness, which arises mainly from achieving the pile appearance of that type of fabrics, whether cut pile or uncut, each of which plays a major role in determining dimensions and performance rules Functional and aesthetic of pile weaving product (3).The research aims to take advantage of the applied methods of hand tufted carpets as a source for the design and production of textile pendants with Egyptian character and identity, and to achieve the objectives of the study, a group of (10) designs were made, through the foundations of design and technology of hand tufted carpets, using some Islamic of geometric, vegetarian, and Islamic Mafroka, to emphasize and enrich the aesthetic values of the fabric hanging design.A number of designs were selected and implemented using hand tufted carpet techniques (Tuft), and to determine the quality level of designs, their relevance implementation technology, and the achievement of the Egyptian identity, a tool (evaluation form) was designed and applied by referees specialized in the field, after the application of the questionnaire, its data were statistically processed and the best (5) designs were reached, in the end, the study reached a set of conclusions and recommendations, if applied, it can achieve an advanced level in the quality of hand tufting carpet industry in general, and the design and production of pile textile in particular.

In animal production systems around the world, brucellosis is a serious zoonotic disease that creates public health hazards and losses in economic terms. The aim of the study is to genotype and molecularly characterize Brucella melitensis (B. melitensis) and Brucella abortus (B. abortus) collected from different animal species and humans. A total of 50 isolates of Brucella species (16B. melitensis and 34B. abortus) were isolated from 1081 animal and human samples using a culture technique, followed by biochemical identification using the Vitek 2 compact system and proteomic identification using mass spectrometry technology. Molecular genotyping was performed on all isolates using multiplex real-time PCR. Six isolates from each genotype of Brucella species were selected and genetically evaluated by IS711 insertion sequences. Microchips-based real-time PCR for Brucella species identification was performed on twelve genetically characterized isolates as a first attempt. Forty-four (88%) isolates of Brucella species were detected using multiplex real-time PCR. Based on IS711 nucleotide sequencing, twelve isolates were phylogenetically clustered into their specific clusters. The results of the comparative analysis of conventional real time and microchips-based real time indicated that the later is faster and qualitatively more sensitive than conventional real time; however, further studies are needed to ensure that it is capable of serving as a gold standard alternative for Brucella species monitoring.

Trapezoidal cross-sectional spiral microfluidic channels showed high resolution and throughput in cell separation in bio-applications. The main challenges are the complexity and high cost of the fabrication process of trapezoidal cross-sectional channels on the micro-scale. In this work, we present the application of grayscale in microfluidic channel design to overcome the complexity of the fabrication process. We also use direct engraving with a CO2 laser beam on polymethyl methacrylate (PMMA) material to drastically reduce the microfluidic chip’s cost (to

This research, entitled (Combining the Two Prizes in a House with One Novel Syntactic Morphological Study) is an attempt to find out a linguistic phenomenon mentioned in our poetic heritage, and I wanted it to study its images, reasons, and motives for it with the arrangement of what was mentioned and its classification grammatically, morphologically and semantically, and the phenomenon of collecting the poet came between The winners in a house with a single novel are scattered in the stomachs of books and references, without collecting their scattered or surveying their pictures. I intended to write on this topic a study that clarifies his feelings, clarifies his concept, collects what is dispersed from him and reveals his rulings. It became clear to me that the motive for this phenomenon is to show the poet's ability to speak the language and to familiarize him with its grammar, his knowledge of the languages and dialects of Arabs, and to inform him of the ex-poems and their simulations, and his use of it if he had to adjust his weights in the face of some of his Arabic hues. I gathered twenty-five verses from the eyes of authentic and authentic Arabic poetry, and I ask God for success

Abstract Background Drug–drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pairs, it is nearly impossible to experimentally test all combinations and discover previously unobserved side effects. Therefore, machine learning based methods are being used to address this issue. Methods We propose a Siamese self-attention multi-modal neural network for DDI prediction that integrates multiple drug similarity measures that have been derived from a comparison of drug characteristics including drug targets, pathways and gene expression profiles. Results Our proposed DDI prediction model provides multiple advantages: (1) It is trained end-to-end, overcoming limitations of models composed of multiple separate steps, (2) it offers model explainability via an Attention mechanism for identifying salient input features and (3) it achieves similar or better prediction performance (AUPR scores ranging from 0.77 to 0.92) compared to state-of-the-art DDI models when tested on various benchmark datasets. Novel DDI predictions are further validated using independent data resources. Conclusions We find that a Siamese multi-modal neural network is able to accurately predict DDIs and that an Attention mechanism, typically used in the Natural Language Processing domain, can be beneficially applied to aid in DDI model explainability.

Base editors enable precise genetic alterations but vary in efficiency at different loci. Here the authors analyse ABEs and CBEs at over 28,000 integrated sequences to train BE-DICT, a machine learning model capable of predicting base editing outcomes.

PurposeTo describe the survival and toxicity outcome from a single-centre experience in patients with squamous cell cancer of the anal canal (SCC-AC), related to the impact of technological advances in diagnostics and radiation techniques.Material and MethodsA retrospective cohort study was performed after the approval of the institutional ethical committee (EK 478-21). We identified 142 patients in our registry, who received radical treatment for SCC-AC between 2000 and 2020. Fifty-five patients had FDG PET/CT for initial staging and target volume delineation, 87.33% received concomitant chemoradiotherapy (CRT), 64 patients were treated with 3-dimensional conformal radiotherapy (3DRT) between 2000-2009, and 78 patients with intensity-modulated radiotherapy (IMRT) between 2009-2020. Endpoints for the analysis included locoregional relapse-free survival (LRFS), disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS). Acute and late toxicities were also reported.ResultsAt a median follow-up of 31.2 months, the median overall survival was 135 months, 5-year LRFS was 73.1%, 5-year DFS was 65.3%, and 5-year CSS was 75.3%. The use of IMRT was associated with shorter treatment duration. In the univariate analysis, IMRT was associated with significantly improved DFS and CSS for the whole cohort and significantly improved DFS, OS, and CSS for patients who received CRT. In the multivariate analysis, IMRT was associated with the improvement of all survival paraments. The use of FDG PET/CT did not translate into an improvement in the survival outcomes in both univariate and multivariate analyses. Grade-3 and more dermatological toxicities occurred less frequently, but hematological toxicities were more frequent in the IMRT-group. Late side effects and colostomies were less frequently reported in the IMRT group.ConclusionThe use of IMRT in the management of SCC-AC was associated with improvement of the oncological outcomes with improved toxicity profiles in this long-term single-centre experience.

Abstract Background Patients increasingly turn to search engines and online content before, or in place of, talking with a health professional. Low quality health information, which is common on the internet, presents risks to the patient in the form of misinformation and a possibly poorer relationship with their physician. To address this, the DISCERN criteria (developed at University of Oxford) are used to evaluate the quality of online health information. However, patients are unlikely to take the time to apply these criteria to the health websites they visit. Methods We built an automated implementation of the DISCERN instrument (Brief version) using machine learning models. We compared the performance of a traditional model (Random Forest) with that of a hierarchical encoder attention-based neural network (HEA) model using two language embeddings, BERT and BioBERT. Results The HEA BERT and BioBERT models achieved average F1-macro scores across all criteria of 0.75 and 0.74, respectively, outperforming the Random Forest model (average F1-macro = 0.69). Overall, the neural network based models achieved 81% and 86% average accuracy at 100% and 80% coverage, respectively, compared to 94% manual rating accuracy. The attention mechanism implemented in the HEA architectures not only provided ’model explainability’ by identifying reasonable supporting sentences for the documents fulfilling the Brief DISCERN criteria, but also boosted F1 performance by 0.05 compared to the same architecture without an attention mechanism. Conclusions Our research suggests that it is feasible to automate online health information quality assessment, which is an important step towards empowering patients to become informed partners in the healthcare process.

In this study a quintuple band rectangular dielectric resonator antenna (RDRA) with a small frequency ratio and small size of 30 mm × 30 mm × 9.813 mm is introduced. The proposed RDRA covers the frequency bands of WLAN (2.4/5.2 GHz), WiMAX (3.5 GHz) and 5G (4.1/4.8 GHz) exploiting TE10y, TE211x, TE1δ1y, TE111y, and TE2δ1y modes, respectively. A rectangular slot aperture is optimized to serve as a resonator with resonance mode TE10y as well as to couple the electric field from microstrip line to the DRA to excite TE211x mode. Furthermore, a new feeding approach employing three vertical metallic strip pairs (VMSPs) is proposed to excite further TE1δ1y, TE111y, and TE2δ1y modes in the RDRA. The VMSPs are configured on both sides of the dielectric resonator (DR) along the y - direction to act as vertical electric current sources. The lengths, width, and positions of the VMSPs are carefully determined in order to attain the desired modes. The four RDRA modes have broadside patterns while the slot resonator mode has a pattern like a dipole. Good agreement between both measured and simulated results of the reflection coefficient, radiation pattern, and the gain is achieved.

Objective: To evaluate the efficacy of using neural response telemetry (NRT) thresholds in predicting behavioural thresholds during programming of cochlear implant in prelingual children. Method: Prospective study of 28 cochlear implants implanted with Nucleus 24 cochlear implant. We recorded NRT-thresholds on electrode numbers 1, 6, 11, 16 and 22 of the electrode array in each patient, the neural response thresholds were correlated with the behavioural map after six months of programming the device. Results: The mean neural response telemetry level was significantly higher than the mean threshold level (T-level) but lower than the comfortable level (C-level) in all the electrodes tested. NRT levels could statistically significantly predict T behavioural levels and comfortable behavioural levels, p

In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery.

The continued digitalization of medicine has led to an increased availability of longitudinal patient data that allows the investigation of novel and known diseases in unprecedented detail. However, to accurately describe any underlying pathophysiology and allow inter-patient comparisons, individual patient trajectories have to be synchronized based on temporal markers. In this pilot study, we use longitudinal data from critically ill ICU COVID-19 patients to compare the commonly used alignment markers “onset of symptoms,” “hospital admission,” and “ICU admission” with a novel objective method based on the peak value of the inflammatory marker C-reactive protein (CRP). By applying our CRP-based method to align the progression of neutrophils and lymphocytes, we were able to define a pathophysiological window that improved mortality risk stratification in our COVID-19 patient cohort. Our data highlights that proper synchronization of longitudinal patient data is crucial for accurate interpatient comparisons and the definition of relevant subgroups. The use of objective temporal disease markers will facilitate both translational research efforts and multicenter trials.

A dual-band mm-wave compact antenna is proposed in this paper. A quarter-wave half-slot resonator is used instead of a half-wave slot to achieve miniaturization of the antenna size. The antenna consists of two stacked half-slot resonators to radiate at two frequencies, f1 = 24 GHz and f2 = 28.5 GHz, with the same microstrip line feed. By using this stacked structure, a dual-band is obtained and the two resonant frequencies become independently matched with a minor effect on each other. Measurements show the proposed antenna has an impedance bandwidths of 6.3% and 15% at f1 and f2, respectively; furthermore, the antenna has good radiation characteristics. The antenna gains in addition to the radiation efficiencies at f1 and f2, are 3.5dBi, 92%, 4dBi, and 95% respectively. The equivalent circuit model for the proposed antenna is introduced to show the electrical behavior of the antenna. Finally, the antenna design is inserted in multiple-input multiple-output (MIMO) system. The proposed antenna is analyzed and optimized using ANSYS HFSS EM simulator and its equivalent circuit is performed by Agilent Advanced Design System (ADS). The simulated as well as measured results show good agreement. The designated antenna resembles good candidate for 5G wireless communication systems.

Choosing an optimal data fusion technique is essential when performing machine learning with multimodal data. In this study, we examined deep learning-based multimodal fusion techniques for the combined classification of radiological images and associated text reports. In our analysis, we (1) compared the classification performance of three prototypical multimodal fusion techniques: Early, Late, and Model fusion, (2) assessed the performance of multimodal compared to unimodal learning; and finally (3) investigated the amount of labeled data needed by multimodal vs. unimodal models to yield comparable classification performance. Our experiments demonstrate the potential of multimodal fusion methods to yield competitive results using less training data (labeled data) than their unimodal counterparts. This was more pronounced using the Early and less so using the Model and Late fusion approaches. With increasing amount of training data, unimodal models achieved comparable results to multimodal models. Overall, our results suggest the potential of multimodal learning to decrease the need for labeled training data resulting in a lower annotation burden for domain experts.

Non-steroidal anti-inflammatory drugs (NSAIDs) are generally utilized for numerous inflammatory ailments. The long-term utilization of NSAIDs prompts adverse reactions such as gastrointestinal ulceration, renal dysfunction and hepatotoxicity; however, selective COX-2 inhibitors prevent these adverse events. Various scientific approaches have been employed to identify safer COX-2 inhibitors, as in any case, a large portion of particular COX-2 inhibitors have been retracted from the market because of severe cardiovascular events. This study aimed to develop and synthesize a novel series of indomethacin analogues with potential anti-inflammatory properties and fewer side effects, wherein carboxylic acid moiety was substituted using DCC/DMAP coupling. This study incorporates the docking of various indomethacin analogues to detect the binding interactions with COX-2 protein (PDB ID: 3NT1). MD simulation was performed to measure the stability and flexibility of ligand–protein interactions at the atomic level, for which the top-scoring ligand–protein complex was selected. These compounds were evaluated in vitro for COX enzymes inhibition. Likewise, selected compounds were screened in vivo for anti-inflammatory potential using the carrageenan-induced rat paw oedema method and their ulcerogenic potential. The acute toxicity of compounds was also predicted using in silico tools. Most of the compounds exhibited the potent inhibition of both COX enzymes; however, 3e and 3c showed the most potent COX-2 inhibition having IC50 0.34 µM and 1.39 µM, respectively. These compounds also demonstrated potent anti-inflammatory potential without ulcerogenic liability. The biological evaluation revealed that the compound substituted with 4-nitrophenyl was most active.

This paper presents a high-efficiency CMOS rectifier based on an improved dynamic threshold reduction technique (DTR). The proposed DTR consists of a clamper circuit that biases the gates of pMOS diode switches through a capacitor and diode-connected pMOS transistor. The clamper is used to insert a negative dc level to the input RF signal; therefore, more negative RF signal can be obtained to bias the gates of the main rectifying pMOS devices during its conduction phase. This mechanism reduces the threshold voltage of the main pMOS transistors and increases their sensitivity to the RF input signal. The proposed rectifier is implemented in a 0.18-μm CMOS technology and tested. The measurement shows a peak power conversion efficiency of 86% and an output voltage of 0.52 V at an input power of -16.5 dBm and an input frequency of 402 MHz. The core area of chip excluding measurement pads is 0.024 mm2.

Abstract Background Green tea extract (GTE) has various health promoting effects on animals and humans. However, the effects of perinatal exposure to GTE on the behavioral aspects of offspring have not been elucidated thus far. GTE was provided for pregnant female mice at concentrations of either 20 or 50 g/L, beginning the day of conception until the third week after delivery, postnatal day 22 (PD 22). Mice pups were subjected to behavioral testing to assess sensory motor reflexes, locomotion, anxiety, and learning on various postnatal days. Results Perinatal exposure to GTE resulted in a significant reduction in body weight, as well as earlier body hair appearance and opening of the eyes. Sensory motor reflexes exhibited faster responses and significant stimulatory effects in pups exposed to GTE. During the adolescent period, male and female offspring exhibited increased locomotor activity (on PD 22), reduced anxiety and fear (on PD 25), and enhanced memory and learning abilities (on PD 30), all in both GTE treated groups. All blood counts (RBCs, WBCs, Hb, and platelets), and glucose, cholesterol, triglyceride, and low density lipoprotein concentrations were significantly lower in the GTE-treated pups; however, there was no effect on high density lipoprotein levels. Conclusion Our data provide evidence that the high dose of GTE (50 g/L) had higher anxiolytic properties and positive effects on locomotor activities and sensory motor reflexes, as well as learning and memory of the offspring than the low dose of GTE (20 g/L).

The involvement of secretin (SCT) and its receptor (SCTR) in angiotensin II (ANGII)-mediated osmoregulation by forming SCTR/ angiotensin II type 1 receptor (AT1R) heteromer is well established. In this study, we demonstrated that SCTR/AT1R complex can mediate ANGII-induced aldosterone secretion/release through potentiating calcium mobilization. Through IHC and cAMP studies, we showed the presence of functional SCTR and AT1R in the primary zona glomerulosa (ZG) cells of C57BL/6N (C57), and functional AT1R and non-functional SCTR in SCTR knockout (SCTR-/-) mice. Calcium mobilization studies revealed the important role of SCTR on ANGII-mediated calcium mobilization in adrenal gland. The fluo4-AM loaded primary adrenal ZG cells from the C57 mice displayed a dose-dependent increase in intracellular calcium influx ([Ca2+]i) when exposed to ANGII but not from the SCTR-/- ZG cells. Synthetic SCTR transmembrane (TM) peptides STM-II/-IV were able to alter [Ca2+]i in C57 mice, but not the mice with mutated STM-II/-IV (STM-IIm/IVm) peptides. Through enzyme immunoassay (EIA), we measured the aldosterone release from primary ZG cells of both C57 and SCTR-/- mice by exposing them to ANGII (10nM). SCTR-/- ZG cells showed impaired ANGII-induced aldosterone secretion compared to the C57 mice. TM peptide, STM-II hindered the aldosterone secretion in ZG cells of C57 mice. These findings support the involvement of SCTR/AT1R heterodimer complex in aldosterone secretion/release through [Ca2+]i.

Background and purpose: Postoperative imaging of cochlear implant needs to provide detailed information on the position of individual electrodes; the aim of this study was to evaluate visualization of individual electrodes and measurement of the electrode–modiolus distance (EMD) using dual energy CT (DECT) – with low radiation dose using virtual monochromatic spectral (VMS) imaging comparing the images quality and radiation dose with those by using multidetector CT (MDCT). Materials and methods: 25 pediatric patients who underwent cochlear implantation were imaged using DECT (15 patients) and MDCT (10 patients), and the image quality and radiation dose of DECT were compared to those of MDCT. Measurement of EMD was done for 5 electrodes and the results were correlated with neural response telemetry (NRT) and behavioural mapping levels. Results: A statistically significant difference between the radiation dose of DECT and MDCT was confirmed (p = 0.002) without a statistically significant difference in images quality (weighted K = −0.129 and PABAK = 0.533). Statistically significant correlations were found between EMD and NRT threshold, T (threshold) and C (maximum comfortable) levels with p =

Inflammation is critical for infection control and acts as an arsenal defense mechanism against invading microbes through activation of the host immune system. It works via its inflammasome components to sense the dangerous invading microorganism and send messages to the immune system to destroy them. To date, the function of bovine macrophage inflammasome and its relationship with actin has not been identified. This study aimed to investigate the activation of bovine inflammasome by phase one flagellin from Salmonella typhimurium and its interaction with actin. Bovine monocyte-derived macrophages were prepared and challenged with S. typhimurium SL1344 phase one flagellin. The results demonstrated the relationship between the flagellin-based activation of inflammasome and actin rearrangement. The flagellin-based activation of inflammasome promoted the activation and co-localization of F-actin and the inflammasome complex. Actin was remodeled to different degrees according to the stage of inflammasome activation. The actin redistribution varied from polymerization to filopodia, while at the stage of pyroptotic cell death, actin was broken down and interacted with activated inflammasome complexes. In conclusion, flagellin-dependent inflammasome activation and actin localization to the inflammasome at the stage of pyroptotic cell death may be of importance for appropriate immune responses, pending further studies to explore the exact cross-linking between the inflammasome complex and actin.

Abstract Background Type 1 diabetes mellitus (T1D) is a chronic autoimmune disease caused by the selective destruction of pancreatic β cells, followed by hyperglycemia, oxidative stress and the subsequent extensive impairment of immune cell functions, a phenomenon responsible for the development of chronic diabetic complications. Propolis, a natural bee product that is extensively used in foods and beverages, significantly benefits human health. Specifically, propolis exerts antioxidant, anti-inflammatory and analgesic effects that may improve diabetic complications. To further elucidate the potential benefits of propolis, the present study investigated the effect of dietary supplementation with propolis on the plasma cytokine profiles, free radical levels, lipid profile and lymphocyte proliferation and chemotaxis in a streptozotocin (STZ)-induced type I diabetic mouse model. Methods Thirty male mice were equally distributed into 3 experimental groups: group 1, non-diabetic control mice; group 2, diabetic mice; and group 3, diabetic mice supplemented daily with an ethanol-soluble derivative of propolis (100 mg/kg body weight) for 1 month. Results First, the induction of diabetes in mice was associated with hyperglycemia and significant decreases in the insulin level and the lymphocyte count. In this context, diabetic mice exhibited severe diabetic complications, as demonstrated by a significant decrease in the levels of IL-2, IL-4 and IL-7, prolonged elevation of the levels of pro-inflammatory cytokines (IL-1β, IL-6 and TNF-α) and reactive oxygen species (ROS) and altered lipid profiles compared with control non-diabetic mice. Moreover, antigen stimulation of B and T lymphocytes markedly reduced the proliferative capacity and chemotaxis of these cells towards CCL21 and CXCL12 in diabetic mice compared with control mice. Interestingly, compared with diabetes induction alone, treatment of diabetic mice with propolis significantly restored the plasma cytokine and ROS levels and the lipid profile to nearly normal levels. Most importantly, compared with untreated diabetic mice, diabetic mice treated with propolis exhibited significantly enhanced lymphocyte proliferation and chemotaxis towards CCL21 and CXCL12. Conclusion Our findings reveal the potential immuno-modulatory effects of propolis, which acts as a natural antioxidant to enhance the function of immune cells during diabetes.