Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Activity Tracker-Based Counselling and Live-Web Exercise on Breast Cancer Survivors during Italy COVID-19 Lockdown

Background: To prevent and fight the increase of daily sedentary time and to promote and stimulate the positive effects of physical activity and exercise on health, both traditional interventions and new strategies are important for breast cancer survivors (BCS). The research goal was to compare the effects of weekly personal feedback, based on objectively measured physical activity, on the trends of both daily sedentary time and on the physical activity of BCS (E− group) with those of an intervention also including online supervised physical exercise sessions (E+ group), during the Italy COVID-19 lockdown. Methods: The Italian COVID-19 emergency allowed the possibility to also observe the effects of social and personal limitations. A total of 51 BCS were studied over an 18-week period and had an objective registration of day-to-day sedentary time, physical activity, and sleep. Both subsamples received weekly or fortnight personal feedback. Data were analysed considering four key periods, according to the COVID-19 emergency steps. Results: Statistical analysis showed an additive effect for sedentary time and a multiplicative effect both for light-to vigorous and light-intensity physical activities. The E− group had a high overall sedentary time and a different trend of light-to vigorous and light-intensity physical activities, with a reduction from the 1st to the 2nd periods (national and personal restrictions), showing a significant rise just at the end of the national restrictions. Conclusions: The use of an activity tracker and its accompanying app, with the reception of weekly tailored advice and supervised online physical exercise sessions, can elicit proper physical activity recomposition in BCS in the COVID-19 era.
Pharmacokinetics and Bioavailability of Carprofen in Rainbow Trout (Oncorhynchus mykiss) Broodstock

The aim of this study was to determine the pharmacokinetics of carprofen following intravenous (IV), intramuscular (IM) and oral routes to rainbow trout (Oncorhynchus mykiss) broodstock at temperatures of 10 ± 1.5 °C. In this study, thirty-six healthy rainbow trout broodstock (body weight, 1.45 ± 0.30 kg) were used. The plasma concentrations of carprofen were determined using high-performance liquid chromatography and pharmacokinetic parameters were calculated using non-compartmental analysis. Carprofen was measured up to 192 h for IV route and 240 h for IM, and oral routes in plasma. The elimination half-life (t1/2λz) was 30.66, 46.11, and 41.08 h for IV, IM and oral routes, respectively. Carprofen for the IV route showed the total clearance of 0.02 L/h/kg and volume of distribution at steady state of 0.60 L/kg. For IM and oral routes, the peak plasma concentration (Cmax) was 3.96 and 2.52 μg/mL with the time to reach Cmax of 2 and 4 h, respectively. The bioavailability was 121.89% for IM route and 78.66% for oral route. The favorable pharmacokinetic properties such as the good bioavailability and long t1/2λz for IM and oral route of carprofen suggest the possibility of its effective use for the treatment of various conditions in broodstock.
Smart Health System to Detect Dementia Disorders Using Virtual Reality

Smart health technology includes physical sensors, intelligent sensors, and output advice to help monitor patients’ health and adjust their behavior. Virtual reality (VR) plays an increasingly larger role to improve health outcomes, being used in a variety of medical specialties including robotic surgery, diagnosis of some difficult diseases, and virtual reality pain distraction for severe burn patients. Smart VR health technology acts as a decision support system in the diseases diagnostic test of patients as they perform real world tasks in virtual reality (e.g., navigation). In this study, a non-invasive, cognitive computerized test based on 3D virtual environments for detecting the main symptoms of dementia (memory loss, visuospatial defects, and spatial navigation) is proposed. In a recent study, the system was tested on 115 real patients of which thirty had a dementia, sixty-five were cognitively healthy, and twenty had a mild cognitive impairment (MCI). The performance of the VR system was compared with Mini-Cog test, where the latter is used to measure cognitive impaired patients in the traditional diagnosis system at the clinic. It was observed that visuospatial and memory recall scores in both clinical diagnosis and VR system of dementia patients were less than those of MCI patients, and the scores of MCI patients were less than those of the control group. Furthermore, there is a perfect agreement between the standard methods in functional evaluation and navigational ability in our system where P-value in weighted Kappa statistic= 100% and between Mini-Cog-clinical diagnosis vs. VR scores where P-value in weighted Kappa statistic= 93%.
Current Trends on Green Wireless Sensor Networks

The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known that the radio is the biggest consumer of the available energy; (ii) energy-harvesting techniques, converting energy from not full-time available environmental sources and usually storing it; and (iii) energy transfer techniques, sharing energy resources from one node (either specialized or not) to another one. In this article, we survey the main contributions in these three areas and identify the main trending topics in recent research. A discussion and some future directions are also included.
Proof-of-Concept for the Analgesic Effect and Thermoregulatory Safety of Orally Administered Multi-Target Compound SZV 1287 in Mice: A Novel Drug Candidate for Neuropathic Pain

SZV 1287 (3-(4,5-diphenyl-1,3-oxazol-2-yl)propanal oxime) is a novel multi-target candidate under preclinical development for neuropathic pain. It inhibits amine oxidase copper containing 3, transient receptor potential ankyrin 1 and vanilloid 1 (TRPV1) receptors. Mainly under acidic conditions, it is transformed to the cyclooxygenase inhibitor oxaprozin, which is ineffective for neuropathy. Therefore, an enterosolvent capsule is suggested for oral formulation, which we investigated for nociception, basic kinetics, and thermoregulatory safety in mice. The antihyperalgesic effect of SZV 1287 (10, 20, 50, and 200 mg/kg, p.o.) was determined in partial sciatic nerve ligation-induced traumatic neuropathy by aesthesiometry, brain and plasma concentrations by HPLC, and deep body temperature by thermometry. Its effect on proton-induced TRPV1 activation involved in thermoregulation was assessed by microfluorimetry in cultured trigeminal neurons. The three higher SZV 1287 doses significantly, but not dose-dependently, reduced neuropathic hyperalgesia by 50% of its maximal effect. It was quickly absorbed; plasma concentration was stable for 2 h, and it entered into the brain. Although SZV 1287 significantly decreased the proton-induced TRPV1-mediated calcium-influx potentially leading to hyperthermia, it did not alter deep body temperature. Oral SZV 1287 inhibited neuropathic hyperalgesia and, despite TRPV1 antagonistic action and brain penetration, it did not influence thermoregulation, which makes it a promising analgesic candidate.
Fluorescence-Guided Surgery for High-Grade Gliomas: State of the Art and New Perspectives

High-grade gliomas are aggressive tumors that require multimodal management and gross total resection is considered to be the first crucial step of treatment. Because of their infiltrative nature, intraoperative differentiation of neoplastic tissue from normal parenchyma can be challenging. For these reasons, in the recent years, neurosurgeons have increasingly performed this surgery under the guidance of tissue fluorescence. Sodium fluoresceine and 5-aminolevulinic acid represent the 2 main compounds that allow real-time identification of residual malignant tissue and have been associated with improved gross total resection and radiological outcomes. Though presenting different profiles of sensitivity and specificity and further investigations concerning cost-effectiveness are need, Sodium fluoresceine, 5-aminolevulinic acid and new phluorophores, such as Indocyanine green, represent some of the most important tools in the neurosurgeon’s hands to achieve gross total resection.
Synthesis of Titanium Silicalite-1 with Small Crystal Size by Using Mother Liquid of Titanium Silicalite-1 As Seed

Titanium silicalite-1 (TS-1) with small crystal size was synthesized in a TPABr-ethylamine system using the mother liquid of nanosized TS-1 as seed. The as-synthesized small-crystal TS-1, the size of which was about 600 nm × 400 nm × 250 nm was characterized with X-ray diffraction (XRD), Fourier transform-infrared (FT-IR), ultraviolet–visible (UV–vis), UV-Raman, N2 sorption, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Its catalytic performance was evaluated in the epoxidation of propylene and the hydroxylation of phenol. The conversion of hydrogen peroxide and selectivity of propylene oxide (PO) in the propylene epoxidation reached 92 and 98 mol %, respectively. In the hydroxylation of phenol, small-crystal TS-1 also resulted in a high conversion of phenol.
Endometrial Inflammation at the Time of Insemination and Its Effect on Subsequent Fertility of Dairy Cows

Simple Summary A detailed understanding of cellular and molecular mechanisms in the bovine uterus is crucial to explain and avoid subfertility in dairy cows. Therefore, we examined the effect of inflammation in the bovine uterus in cows with no clinical signs of disease at the time of artificial insemination (AI) on subsequent pregnancy outcome. In a total of 71 healthy dairy cows, uterine cytology samples were collected by cytobrush technique within 10 min after insemination. Endometrial inflammation was investigated at the cellular and mRNA expression levels. All factors with a significant effect on fertility in our study were related to uterine polymorphonuclear neutrophil (PMN) migration, i.e., the first line of uterine defense. Cows with a proportion of ≥1% PMN had a 1.8-fold increased chance of pregnancy within 150 days postpartum compared to cows with fewer PMNs. From our results, we conclude that a certain level of inflammation at the molecular and cellular levels before the stimulus of AI might be favorable for cows’ fertility. Abstract Our objective was to investigate the level of endometrial immune response at artificial insemination (AI) and to relate it to subsequent fertility. From 71 healthy cows, endometrial cytobrush samples were taken at the first AI for cytological and mRNA analyses. Total RNA isolated from the cytobrushes was used for reverse transcription qPCR for selected transcripts. Animals were grouped into pregnant (PREG; n = 32) and non-pregnant (non-PREG; n = 39) cows following their first AI. The mRNA abundance of the neutrophil-related factor CEACAM1 and the chemokine CXCL5 was 1.2- (p = 0.03) and 2.0-fold (p = 0.04) greater in PREG than in non-PREG cows, respectively. Animals were further subdivided according to the number of inseminations until pregnancy (PREG1, n = 32; PREG2-3, n = 19) and in repeat breeder cows (RBC, n = 13). CEACAM1 and CXCL8 mRNA expression was 1.7- (p = 0.01) and 2.3-fold (p = 0.03) greater in PREG1 than in RBC, respectively. Cox regression showed that cows with PMN ≥ 1% had a 1.8-fold increased chance of pregnancy within 150 days postpartum compared with cows with fewer PMNs. We conclude that a certain level of inflammation before the stimulus of AI might be beneficial for subsequent fertility.
COVID-19 and mental well-being of nurses in a tertiary facility in Kenya

Background The 2019 coronavirus disease (COVID-19) epidemic is a global health emergency which has been shown to pose a great challenge to mental health, well-being and resilience of healthcare workers, especially nurses. Little is known on the impact of COVID-19 among nurses in sub-Saharan Africa. Methods A cross sectional study was carried out between August and November 2020 among nurses recruited from the Aga Khan University Hospital, Nairobi. The survey questionnaire consisted of six components- demographic and work title characteristics, information regarding care of COVID-19 patients, symptoms of depression, anxiety, insomnia, distress and burnout, measured using standardized questionnaires. Multivariable logistic regression analysis was performed to identify factors associated with mental health disorders. Results Of 255 nurses, 171 (67.1%) consented to complete the survey. The median age of the participants was 33.47 years, 70.2% were females and 60.8% were married. More than half, 64.9% were frontline workers directly engaged in COVID-19 care. Only 1.8% reported a prior history or diagnosis of any mental health disorder. Depression, anxiety, insomnia, distress, and burnout were reported in 45.9%, 48.2%, 37.0%, 28.8% and 47.9% of all nurses. Frontline nurses reported experiencing more moderate to severe symptoms of depression, distress and burnout. Furthermore, females reported more burnout as compared to males. Multivariate logistic regression analysis showed that after adjustment, working in the frontlines was an independent risk variable for depression and burnout. Conclusion This is one of the few studies looking at mental health outcomes among nurses during the COVID-19 pandemic in Kenya. Similar to other studies from around the world, nurses directly involved with COVID-19 patients reported higher rates of mental health symptoms. Burnout threatens to exacerbate the pre-existing severe nursing workforce shortage in low-resource settings. Cost-effective and feasible mitigating strategies, geared to low-middle income countries, are urgently needed to help cope with mental health symptoms during such a pandemic.
Larvicidal Activities against Aedes aegypti of Supernatant and Pellet Fractions from Cultured Bacillus spp. Isolated from Amazonian Microenvironments

The Aedes aegypti mosquito is the primary vector of Dengue, Chikungunya and Zika causing major problems for public health, which requires new strategies for its control, like the use of entomopathogenic microorganisms. In this study, bacteria from various Amazonian environments were isolated and tested for their pathogenicity to A. aegypti larvae. Following thermal shock to select sporulated Bacillus spp. , 77 bacterial strains were isolated. Molecular identification per 16S RNA sequences revealed that the assembled strains contained several species of the genus Bacillus and one species each of Brevibacillus, Klebsiella, Serratia, Achromobacter and Brevundimonas. Among the isolated Bacillus sp. strains, 19 showed larvicidal activity against A. aegypti. Two strains of Brevibacillus halotolerans also displayed larvicidal activity. For the first time, larvicidal activity against A. aegypti was identified for a strain of Brevibacillus halotolerans. Supernatant and pellet fractions of bacterial cultures were tested separately for larvicidal activities. Eight strains contained isolated fractions resulting in at least 50% mortality when tested at a concentration of 5 mg/mL. Further studies are needed to characterize the active larvicidal metabolites produced by these microorganisms and define their mechanisms of action.
Are Machine Learning Methods the Future for Smoking Cessation Apps?

Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention.
Surface Characteristics, Fluoride Release and Bond Strength Evaluation of Four Orthodontic Adhesives

Orthodontic adhesives have similar properties in terms of fluoride release, roughness, shear bond strength or cement debris for specific clinical conditions. Three commercial consecrated orthodontic adhesives (Opal Seal®, Blugloo®, Light Bond®) were compared with an experimental orthodontic material (C1). Brackets were bonded to enamel using a self-etch technique followed by adhesive application and then de-bonded 60 days later. Share bond strength evaluation, scanning electron microscopy, atomic force microscopy and fluoride release analysis were performed. The highest amount of daily and cumulative fluoride release was obtained for the experimental material, while the lowest value was observed for Opal Seal®. The materials evaluated in the current study presented adequate shear bond strength, with the experimental material having a mean value higher than Opal Seal and Blugloo. The atomic force microscopy measurements indicated that the smoothest initial sample is Opal Seal® followed by Light Bond®. Scanning electron microscopy evaluation indicated different aspects of cement debris on the enamel and/or bracket surface, according to the type of adhesive. The experimental material C1 presented adequate properties in terms of shear bond strength, fluoride release, roughness and enamel characteristics after de-bonding, compared to the commercial materials. Under these circumstances, it can be considered for clinical testing.
DDR1 Affects Metabolic Reprogramming in Breast Cancer Cells by Cross-Talking to the Insulin/IGF System

The insulin receptor isoform A (IR-A), a dual receptor for insulin and IGF2, plays a role in breast cancer (BC) progression and metabolic reprogramming. Notably, discoidin domain receptor 1 (DDR1), a collagen receptor often dysregulated in cancer, is involved in a functional crosstalk and feed forward loop with both the IR-A and the insulin like growth factor receptor 1 (IGF1R). Here, we aimed at investigating whether DDR1 might affect BC cell metabolism by modulating the IGF1R and/or the IR. To this aim, we generated MCF7 BC cells engineered to stably overexpress either IGF2 (MCF7/IGF2) or the IR-A (MCF7/IR-A). In both cell models, we observed that DDR1 silencing induced a significant decrease of total ATP production, particularly affecting the rate of mitochondrial ATP production. We also observed the downregulation of key molecules implicated in both glycolysis and oxidative phosphorylation. These metabolic changes were not modulated by DDR1 binding to collagen and occurred in part in the absence of IR/IGF1R phosphorylation. DDR1 silencing was ineffective in MCF7 knocked out for DDR1. Taken together, these results indicate that DDR1, acting in part independently of IR/IGF1R stimulation, might work as a novel regulator of BC metabolism and should be considered as putative target for therapy in BC.
Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series

Hierarchical time series is a set of data sequences organized by aggregation constraints to represent many real-world applications in research and the industry. Forecasting of hierarchical time series is a challenging and time-consuming problem owing to ensuring the forecasting consistency among the hierarchy levels based on their dimensional features. The excellent empirical performance of our Deep Long Short-Term Memory (DLSTM) approach on various forecasting tasks motivated us to extend it to solve the forecasting problem through hierarchical architectures. Toward this target, we develop the DLSTM model in auto-encoder (AE) fashion and take full advantage of the hierarchical architecture for better time series forecasting. DLSTM-AE works as an alternative approach to traditional and machine learning approaches that have been used to manipulate hierarchical forecasting. However, training a DLSTM in hierarchical architectures requires updating the weight vectors for each LSTM cell, which is time-consuming and requires a large amount of data through several dimensions. Transfer learning can mitigate this problem by training first the time series at the bottom level of the hierarchy using the proposed DLSTM-AE approach. Then, we transfer the learned features to perform synchronous training for the time series of the upper levels of the hierarchy. To demonstrate the efficiency of the proposed approach, we compare its performance with existing approaches using two case studies related to the energy and tourism domains. An evaluation of all approaches was based on two criteria, namely, the forecasting accuracy and the ability to produce coherent forecasts through through the hierarchy. In both case studies, the proposed approach attained the highest accuracy results among all counterparts and produced more coherent forecasts.
From Genetics to Histomolecular Characterization: An Insight into Colorectal Carcinogenesis in Lynch Syndrome

Lynch syndrome is a hereditary cancer-predisposing syndrome caused by germline defects in DNA mismatch repair (MMR) genes such as MLH1, MSH2, MSH6, and PMS2. Carriers of pathogenic mutations in these genes have an increased lifetime risk of developing colorectal cancer (CRC) and other malignancies. Despite intensive surveillance, Lynch patients typically develop CRC after 10 years of follow-up, regardless of the screening interval. Recently, three different molecular models of colorectal carcinogenesis were identified in Lynch patients based on when MMR deficiency is acquired. In the first pathway, adenoma formation occurs in an MMR-proficient background, and carcinogenesis is characterized by APC and/or KRAS mutation and IGF2, NEUROG1, CDK2A, and/or CRABP1 hypermethylation. In the second pathway, deficiency in the MMR pathway is an early event arising in macroscopically normal gut surface before adenoma formation. In the third pathway, which is associated with mutations in CTNNB1 and/or TP53, the adenoma step is skipped, with fast and invasive tumor growth occurring in an MMR-deficient context. Here, we describe the association between molecular and histological features in these three routes of colorectal carcinogenesis in Lynch patients. The findings summarized in this review may guide the use of individualized surveillance guidelines based on a patient’s carcinogenesis subtype.