Miracle-dz/UniFAR-FTUs huggingface dataset:
Polyimide-Based Nanocomposites with Binary CeO2/Nanocarbon Fillers: Conjointly Enhanced Thermal and Mechanical Properties

To design novel polymer materials with optimal properties relevant to industrial usage, it would seem logical to modify polymers with reportedly good functionality, such as polyimides (PIs). We have created a set of PI-based nanocomposites containing binary blends of CeO2 with carbon nanoparticles (nanocones/discs or nanofibres), to improve a number of functional characteristics of the PIs. The prime novelty of this study is in a search for a synergistic effect amidst the nanofiller moieties regarding the thermal and the mechanical properties of PIs. In this paper, we report on the structure, thermal, and mechanical characteristics of the PI-based nanocomposites with binary fillers. We have found that, with a certain composition, the functional performance of a material can be substantially improved. For example, a PI containing SO2-groups in its macrochains not only had its thermal stability enhanced (by ~20 °C, 10% weight loss up to 533 °C) but also had its stiffness increased by more than 10% (Young’s modulus as high as 2.9–3.0 GPa) in comparison with the matrix PI. In the case of a PI with no sulfonic groups, binary fillers increased stiffness of the polymer above its glass transition temperature, thereby widening its working temperature range. The mechanisms of these phenomena are discussed. Thus, this study could contribute to the design of new composite materials with controllable and improved functionality.
Interleukin‐6‐mediated resistance to immunotherapy is linked to impaired myeloid cell function

High serum levels of interleukin‐6 (IL‐6) correlate with poor prognosis and chemotherapy resistance in several cancers. The underlying mechanisms and its effects on immunotherapy are largely unknown. To address this, we developed a human papillomavirus type 16 (HPV16)‐associated tumor model expressing IL‐6 to investigate the impact of tumor‐expressed IL‐6 during cisplatin chemotherapy and HPV16 synthetic long peptide vaccination as immunotherapy. The effects of tumor‐produced IL‐6 on tumor growth, survival and the tumor microenvironment were analyzed. Our data demonstrated that tumor‐produced IL‐6 conferred resistance to cisplatin and therapeutic vaccination. This was not caused by a changed in vitro or in vivo growth rate of tumor cells, or a changed sensitivity of tumor cells to chemotherapy or T‐cell‐mediated killing. Furthermore, no overt differences in the frequencies of tumor‐infiltrating subsets of T cells or CD11b+ myeloid cells were observed. IL‐6, however, affected the systemic and local function of myeloid cells, reflected by a strong reduction of major histocompatibility complex (MHC) class II expression on all major myeloid cell subtypes. Resistance to both therapies was associated with a changed intratumoral influx of MHC class II+ myeloid cells toward myeloid cells with no or lower MHC class II expression. Importantly, while these IL‐6‐mediated effects provided resistance to the immunotherapy and chemotherapy as single therapies, their combination still successfully mediated tumor control. In conclusion, IL‐6‐mediated therapy resistance is caused by an extrinsic mechanism involving an impaired function of intratumoral myeloid cells. The fact that resistance can be overcome by combination therapies provides direction to more effective therapies for cancer.
Biosensor Real-Time Affective Analytics in Virtual and Mixed Reality Medical Education Serious Games: Cohort Study

Background The role of emotion is crucial to the learning process, as it is linked to motivation, interest, and attention. Affective states are expressed in the brain and in overall biological activity. Biosignals, like heart rate (HR), electrodermal activity (EDA), and electroencephalography (EEG) are physiological expressions affected by emotional state. Analyzing these biosignal recordings can point to a person’s emotional state. Contemporary medical education has progressed extensively towards diverse learning resources using virtual reality (VR) and mixed reality (MR) applications. Objective This paper aims to study the efficacy of wearable biosensors for affect detection in a learning process involving a serious game in the Microsoft HoloLens VR/MR platform. Methods A wearable array of sensors recording HR, EDA, and EEG signals was deployed during 2 educational activities conducted by 11 participants of diverse educational level (undergraduate, postgraduate, and specialist neurosurgeon doctors). The first scenario was a conventional virtual patient case used for establishing the personal biosignal baselines for the participant. The second was a case in a VR/MR environment regarding neuroanatomy. The affective measures that we recorded were EEG (theta/beta ratio and alpha rhythm), HR, and EDA. Results Results were recorded and aggregated across all 3 groups. Average EEG ratios of the virtual patient (VP) versus the MR serious game cases were recorded at 3.49 (SD 0.82) versus 3.23 (SD 0.94) for students, 2.59 (SD 0.96) versus 2.90 (SD 1.78) for neurosurgeons, and 2.33 (SD 0.26) versus 2.56 (SD 0.62) for postgraduate medical students. Average alpha rhythm of the VP versus the MR serious game cases were recorded at 7.77 (SD 1.62) μV versus 8.42 (SD 2.56) μV for students, 7.03 (SD 2.19) μV versus 7.15 (SD 1.86) μV for neurosurgeons, and 11.84 (SD 6.15) μV versus 9.55 (SD 3.12) μV for postgraduate medical students. Average HR of the VP versus the MR serious game cases were recorded at 87 (SD 13) versus 86 (SD 12) bpm for students, 81 (SD 7) versus 83 (SD 7) bpm for neurosurgeons, and 81 (SD 7) versus 77 (SD 6) bpm for postgraduate medical students. Average EDA of the VP versus the MR serious game cases were recorded at 1.198 (SD 1.467) μS versus 4.097 (SD 2.79) μS for students, 1.890 (SD 2.269) μS versus 5.407 (SD 5.391) μS for neurosurgeons, and 0.739 (SD 0.509) μS versus 2.498 (SD 1.72) μS for postgraduate medical students. The variations of these metrics have been correlated with existing theoretical interpretations regarding educationally relevant affective analytics, such as engagement and educational focus. Conclusions These results demonstrate that this novel sensor configuration can lead to credible affective state detection and can be used in platforms like intelligent tutoring systems for providing real-time, evidence-based, affective learning analytics using VR/MR-deployed medical education resources.
In Vitro or In Vivo Models, the Next Frontier for Unraveling Interactions between Malassezia spp. and Hosts. How Much Do We Know?

Malassezia is a lipid-dependent genus of yeasts known for being an important part of the skin mycobiota. These yeasts have been associated with the development of skin disorders and cataloged as a causal agent of systemic infections under specific conditions, making them opportunistic pathogens. Little is known about the host–microbe interactions of Malassezia spp. , and unraveling this implies the implementation of infection models. In this mini review, we present different models that have been implemented in fungal infections studies with greater attention to Malassezia spp. infections. These models range from in vitro (cell cultures and ex vivo tissue), to in vivo (murine models, rabbits, guinea pigs, insects, nematodes, and amoebas). We additionally highlight the alternative models that reduce the use of mammals as model organisms, which have been gaining importance in the study of fungal host–microbe interactions. This is due to the fact that these systems have been shown to have reliable results, which correlate with those obtained from mammalian models. Examples of alternative models are Caenorhabditis elegans, Drosophila melanogaster, Tenebrio molitor, and Galleria mellonella. These are invertebrates that have been implemented in the study of Malassezia spp. infections in order to identify differences in virulence between Malassezia species.
Effects of Propylene Glycol on Negative Energy Balance of Postpartum Dairy Cows

Simple Summary After calving, the milk production of dairy cows increases rapidly, but the nutrient intake cannot meet the demand for milk production, forming a negative energy balance. Dairy cows in a negative energy balance have an increased risk of developing clinical or subclinical ketosis. The ketosis in dairy cows has a negative impact on milk production, dry matter intake, health, immunity, and reproductive performance. Propylene glycol can be used as an important gluconeogenesis in ruminants and can effectively inhibit the formation of ketones. Supplementary propylene glycol to dairy cows during perinatal is an effective method to alleviate the negative energy balance. This review summarizes the reasons and consequences of negative energy balance as well as the mechanism and effects of propylene glycol in inhibiting a negative energy balance in dairy cows. In addition, the feeding levels and methods of using propylene glycol to alleviate negative energy balance are also discussed. Abstract With the improvement in the intense genetic selection of dairy cows, advanced management strategies, and improved feed quality and disease control, milk production level has been greatly improved. However, the negative energy balance (NEB) is increasingly serious at the postpartum stage because the intake of nutrients cannot meet the demand of quickly improved milk production. The NEB leads to a large amount of body fat mobilization and consequently the elevated production of ketones, which causes metabolic diseases such as ketosis and fatty liver. The high milk production of dairy cows in early lactation aggravates NEB. The metabolic diseases lead to metabolic disorders, a decrease in reproductive performance, and lactation performance decline, seriously affecting the health and production of cows. Propylene glycol (PG) can alleviate NEB through gluconeogenesis and inhibit the synthesis of ketone bodies. In addition, PG improves milk yield, reproduction, and immune performance by improving plasma glucose and liver function in ketosis cows, and reduces milk fat percentage. However, a large dose of PG (above 500 g/d) has toxic and side effects in cows. The feeding method used was an oral drench. The combination of PG with some other additives can improve the effects in preventing ketosis. Overall, the present review summarizes the recent research progress in the impacts of NEB in dairy cows and the properties of PG in alleviating NEB and reducing the risk of ketosis.
Recent Advances Regarding the Therapeutic Potential of Adapalene

Adapalene (ADP) is a representative of the third retinoids generation and successfully used in first-line acne treatment. ADP binds to retinoic acid nuclear receptors. The comedolytic, anti-inflammatory, antiproliferative, and immunomodulatory are the known ADP effects. Its safety profile is an advantage over other retinoids. ADP recently was found to be effective in the treatment of several dermatological diseases and photoaging besides the utility in the treatment of acne vulgaris. New biological effects of adapalene with therapeutic potential are highlighted in this review paper. Thus, adapalene could be a valuable therapeutic drug into the treatment of several types of cancer. Additionally, some neurodegenerative diseases could be treated with a suitable formulation for intravenous administration. The antibacterial activity against methicillin-resistant Staphylococcus aureus of an analogue of ADP has been proven. In different therapeutic schemes, ADP is more effective in combination with other active substances. New topical combinations with adapalene include ketoconazole (antifungal), mometasone furoate (anti-inflammatory corticosteroid), nadifloxacin (fluoroquinolone), and alfa and beta hydroxy acids. Combination with oral drugs is a new trend that enhances the properties of topical formulations with adapalene. Several studies have investigated the effects of ADP in co-administration with azithromycin, doxycycline, faropenem, isotretinoin, and valganciclovir. Innovative formulations of ADP also aim to achieve a better bioavailability, increased efficacy, and reduced side effects. In this review, we have highlighted the current studies on adapalene regarding biological effects useful in various treatment types. Adapalene has not been exploited yet to its full biological potential.
Pharmacoinformatics and hypothetical studies on allicin, curcumin, and gingerol as potential candidates against COVID-19-associated proteases

Abstract Medicinal plants have been known to provide the essential raw material for the majority of antiviral drugs. This study demonstrated the putative inhibitory potential of curcumin, allicin, and gingerol towards cathepsin K, COVID-19 main protease, and SARS-CoV 3 C-like protease. The pharmacokinetic properties were predicted through the SwissADME server while the corresponding binding affinity of the selected phytocompounds towards the proteins was computed using PyRx-Python Prescription 0.8 and the binding free energy were computed based on conventional molecular dynamics using LARMD server. The ADMET properties revealed all the drugs possess drug-like properties. Curcumin has the highest binding affinities with all the selected proteases while allicin has the lowest binding affinities towards the proteases. Moreover, it was observed that curcumin exhibited the highest binding free energy of −17.90 ± 0.23, −18.21 ± 0.25, and −9.67 ± 0.08 kcal/mol for Cathepsin K, COVID-19 main protease, and SARS-CoV 3 C-like protease, respectively. Based on the activities of the phytocompounds against coronavirus target proteases involved in the viral entry as evident from the results, the study, therefore, suggests that these phytocompounds could be valuable for the development of drugs useful for the prevention of coronavirus entry and replication. Communicated by Ramaswamy H. Sarma
Effects of hay provision and presentation on cognitive development in dairy calves

In the dairy industry, feeding management has considerable influence on calf behavioral development, yet there is limited understanding of how aspects of diet or accommodating more varied feeding behavior may affect cognitive development in young calves. The objective of this study was to evaluate effects of provision and presentation of hay on the cognitive ability of pre-weaned dairy calves. Individually-housed Holstein heifer calves were assigned at birth to 1 of 3 treatments: pelleted starter only (n = 10), hay (chopped to 5 cm) and starter provided in separate buckets (n = 12), or hay and starter offered as a mixture (n = 11). During week 5 of age, calves were tested daily in a learning task consisting of a T-maze with a milk reward (0.2 L milk) placed in one arm. Calves were subjected to an initial learning and reversal learning stage, where the reward location was changed to the opposite arm of the maze. Calves received 5 sessions/d until they met learning criterion (moving directly to correct side in 3 consecutive sessions) for initial and reversal learning. Dietary treatment did not affect pass rate or the number of sessions required to pass the initial learning stage. During the reversal learning stage, calves provided only starter had a lower pass rate (0.038, during first 8 testing session) early during testing than calves provided hay separately (0.20; P = 0.020) and tended to have a lower pass rate than calves provided hay as a mixture (0.14; P = 0.057). Calves provided only starter also tended to require more sessions to meet the learning criterion (15.8) than both calves provided hay separately (10.8; P = 0.089) and as a mixture (11.8; P = 0.10). Calves provided hay also kicked less and spent more time sniffing or licking the testing area. The results of this experiment indicate that provision of hay may affect behavioral flexibility in dairy calves.
Effects of Eyestalk Ablation on the Molting,Growth and Gonad Development of Procambarus clarkia

In the pool test in 1996,the unilateral eyestalk and bilateral eyestalk of Procambarus clarkia were ablated by burn extrusion method.It was found that after the eyestalk was ablated,the molting of Procambarus clarkia quickened,the molting period shortened and the growth speed of Procambarus clarkia increased.The molting frequency,the growth speed and gonad development of Procambarus clarkia in 3 treatment groups were bilateral groupunilateral groupCK group and there was specially significant difference between the groups(P0.01). After the unilateral eyestalk and bilateral eyestalk were cut Procambarus clarkia picked up the mayuration of his ovary and a specially significant diference was found among groups.
Clinical characteristics of coronavirus disease 2019 patients with diarrhea in Daegu

Background/Aims Coronavirus disease 2019 (COVID-19) can reportedly cause gastrointestinal symptoms. Therefore, we investigated the clinical characteristics of COVID-19 patients with diarrhea. Methods We included 118 COVID-19 patients admitted to a single hospital from February 20 to March 31, 2020. Medical records with clinical characteristics, laboratory data, treatment course, and clinical outcomes were compared based on the presence or absence of diarrhea. Prognostic factors for disease severity and mortality in COVID-19 were also assessed. Results Among patients, 54 (45.8%) had diarrhea, whereas seven (5.9%) had only diarrhea. The median age of patients with diarrhea was 59 years (44 to 64), and 22 (40.7%) were male. Systemic steroid use, intensive care unit admission, septic shock, and acute respiratory distress syndrome were less frequent in the diarrhea group than in the non-diarrhea group. No significant differences were observed in total hospital stay and mortality between groups. On multivariate analysis, age (odds ratio [OR], 1.06; 95% confidence interval [CI], 1.01 to 1.12; p = 0.044), diabetes (OR, 3.00; 95% CI, 1.25 to 20.47; p = 0.042), and dyspnea (OR, 41.19; 95% CI, 6.60 to 823.16; p < 0.001) were independent risk factors for septic shock. On Cox regression analysis, diabetes (hazard ratio [HR], 4.82; 95% CI, 0.89 to 26.03; p = 0.043) and chronic obstructive pulmonary disease (HR, 16.58; 95% CI, 3.10 to 88.70; p = 0.044) were risk factors for mortality. Conclusions Diarrhea was present in 45.8% of patients and was a common symptom of COVID-19. Although patients with diarrhea showed less severe clinical features, diarrhea was not associated with disease severity or mortality.
A genetic analysis of a Spanish population with early onset Parkinson’s disease

Introduction Both recessive and dominant genetic forms of Parkinson’s disease have been described. The aim of this study was to assess the contribution of several genes to the pathophysiology of early onset Parkinson’s disease in a cohort from central Spain. Methods/patients We analyzed a cohort of 117 unrelated patients with early onset Parkinson’s disease using a pipeline, based on a combination of a next-generation sequencing panel of 17 genes previously related with Parkinson’s disease and other Parkinsonisms and CNV screening. Results Twenty-six patients (22.22%) carried likely pathogenic variants in PARK2, LRRK2, PINK1, or GBA. The gene most frequently mutated was PARK2, and p.Asn52Metfs*29 was the most common variation in this gene. Pathogenic variants were not observed in genes SNCA, FBXO7, PARK7, HTRA2, DNAJC6, PLA2G6, and UCHL1. Co-occurrence of pathogenic variants involving two genes was observed in ATP13A2 and PARK2 genes, as well as LRRK2 and GIGYF2 genes. Conclusions Our results contribute to the understanding of the genetic architecture associated with early onset Parkinson’s disease, showing both PARK2 and LRRK2 play an important role in Spanish Parkinson’s disease patients. Rare variants in ATP13A2 and GIGYF2 may contribute to PD risk. However, a large proportion of genetic components remains unknown. This study might contribute to genetic diagnosis and counseling for families with early onset Parkinson’s disease.
Is there a role for fetal interventions in gastroschisis management? – An updated comprehensive review

We conducted a comprehensive evidence‐based review on the epidemiology and current standard of care of gastroschisis management as well as the pathophysiology, rationale and feasibility of fetal therapy as a viable alternative. Gastroschisis is a periumbilical abdominal wall defect characterized by abdominal viscera herniation in utero. It affects 4 in 10 000 live births, but the prevalence has steadily increased in recent years. Gastroschisis is typically diagnosed on routine second‐trimester ultrasound. The overall prognosis is favorable, but complex gastroschisis, which accounts for about 10% to 15% of cases, is associated with a higher mortality, significant disease burden and higher healthcare costs due to long‐ and short‐term complications. The current standard of care has yet to be established but generally involves continued fetal surveillance and multidisciplinary perinatal care. Postnatal surgical repair is achieved with primary closure, staged silo closure or sutureless repair. Experimental animal studies have demonstrated the feasibility of in utero closure, antiinflammatory therapy and prenatal regenerative therapy. However, reports of early preterm delivery and amnioinfusion trials have failed to show any benefit in humans. Further experimental studies and human trials are necessary to demonstrate the potential benefit of fetal therapy in gastroschisis.
Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

Pneumonia is a contagious disease that causes ulcers of the lungs, and is one of the main reasons for death among children and the elderly in the world. Several deep learning models for detecting pneumonia from chest X-ray images have been proposed. One of the extreme challenges has been to find an appropriate and efficient model that meets all performance metrics. Proposing efficient and powerful deep learning models for detecting and classifying pneumonia is the main purpose of this work. In this paper, four different models are developed by changing the used deep learning method; two pre-trained models, ResNet152V2 and MobileNetV2, a Convolutional Neural Network (CNN), and a Long Short-Term Memory (LSTM). The proposed models are implemented and evaluated using Python and compared with recent similar research. The results demonstrate that our proposed deep learning framework improves accuracy, precision, F1-score, recall, and Area Under the Curve (AUC) by 99.22%, 99.43%, 99.44%, 99.44%, and 99.77%, respectively. As clearly illustrated from the results, the ResNet152V2 model outperforms other recently proposed works. Moreover, the other proposed models—MobileNetV2, CNN, and LSTM-CNN—achieved results with more than 91% in accuracy, recall, F1-score, precision, and AUC, and exceed the recently introduced models in the literature.