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Brazil’s extensive coastline, tropical and subtropical climate, and well-preserved environment represent a conducive setting for dirofilariosis, a zoonotic mosquito-borne disease. Although this condition has long been recognised in the country, it has been relatively under-studied, and it is currently considered to be an emerging disease. Diagnosis, treatment, and control remain challenging due to the extensive gaps in knowledge. In order to help address this issue, this review aims to (i) summarise the available literature on the distribution of Dirofilaria spp. in Brazilian dogs over the last decade, (ii) review case reports of dirofilariosis in cats, wild animals, and humans over the last twenty years, and (iii) highlight the benefits of taking a One Health approach to managing this disease. While there have been several prevalence studies in dogs, disease distribution is poorly characterised in cats, and little is known about the occurrence of the parasite in wildlife. Human cases are sporadically reported, and no large-scale studies have been undertaken to date. Evidence indicates that Dirofilaria immitis is the main species circulating in Brazil, although Dirofilaria repens has also been detected. Molecular studies have also suggested the circulation of a highly virulent form of D. immitis, which may be genetically distinct from those of the Old World. A programme of epidemiological, ecological, genomic, and pathogenicity-based studies is required to quantify the impact of dirofilariosis in Brazil on both veterinary and public health and to inform others on its control.

Approximately 20% of patients with ulcerative colitis (UC) develop acute severe UC (ASUC), for which intravenous systemic steroid therapy and possibly infliximab-based rescue therapy are generally imposed. However, there are no significant guideline recommendations on ASUC regarding vedolizumab as an alternative in this setting. A case report was presented where a patient with steroid-dependent UC developed ASUC induced by second-line chemotherapy. Treatment with intravenous methylprednisolone was imposed, but there was no reduction in bowel movements in the days following admission. Rescue therapy with infliximab was contraindicated because of the oncologic history. Surgical consultation, contraindicated colectomy, and administration of vedolizumab 300 mg were initiated. After infusion with vedolizumab, there was a significant reduction in bowel movements starting the day after infusion until normalisation of bowel movements within three days and the concomitant normalisation of inflammatory indices. The patient is currently in clinical remission, on therapy with vedolizumab 108 mg subcutaneously every two weeks, and is in oncologic follow-up for pulmonary neoplasm. This case highlights the novel potential of vedolizumab as a possible rescue therapy in ASUC, especially in special populations, where it may offer a better safety profile. Although cyclosporine and infliximab still represent the mainstays of salvage therapy for steroid-refractory ASUC, new therapeutic agents may also be effective, such as vedolizumab, ustekinumab, and anti-Janus kinase agents.

Peritoneopericardial diaphragmatic hernia (PPDH) is an opening between the pericardial sac and the pleuroperitoneal membrane. Pneumopericardium is an infrequent complication of PPDH. This condition is a serious circumstance in which free gas accumulates in the pericardial sac. The present report describes the occurrence of pneumopericardium and pericardial effusion after elective ovariectomy in a dog affected by PPDH. The presence of an umbilical and diaphragmatic hernia was highlighted during ovariectomy, and a pneumopericardium was seen during an X-ray exam. At the time of admission to the hospital, the dog was asymptomatic. The diagnosis was performed by X-ray and ultrasonographic exams. Computed tomography examination confirmed the diagnosis and directed for a surgical approach of the congenital defect. Surgery resulted in resolution of PPDH and of pneumopericardium.

Ozone has the potential to improve the shelf life of fruits and vegetables by its antimicrobial properties, but the influence on the aroma properties has not been studied comprehensively at the molecular level. Therefore, the impact of ozone on odour-active compounds, total titratable acidity, ascorbic acid, weight loss, and fungal growth during storage is investigated in oranges and bananas. The fruits were stored at room temperature for 11 days either with or without ozone treatment. Interestingly, no significant impact of the ozone treatment on the contents of the selected odour-active compounds in bananas and oranges was observable. Furthermore, no impact on titratable acidity, weight loss, and ascorbic acid content were observable as a consequence of the ozone treatment at the respective ozone concentrations. In addition, the inhibitive effects of the growth and sporulation of moulds were demonstrated in this study. Finally, the data from this research show that ozonation has the potential of improving the shelf life of fruits without compromising their quality.

Organizations engaged in business, regardless of the industry in which they operate, must be able to extract knowledge from the data available to them. Often the volume of customer and supplier data is so large, the use of advanced data mining algorithms is required. In particular, machine learning algorithms make it possible to build predictive models in order to forecast customer demand and, consequently, optimize the management of supplies and warehouse logistics. We base our analysis on the use of the XGBoost as a predictive model, since this is now considered to provide the more efficient implementation of gradient boosting, shown with a numerical comparison. Preliminary tests lead to the conclusion that the XGBoost regression model is more accurate in predicting future sales in terms of various error metrics, such as MSE (Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and WAPE (Weighted Absolute Percentage Error). In particular, the improvement measured in tests using WAPE metric is in the range 15–20%.

The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by retrieving device and app usage patterns. However, a still uncovered research topic is to extend the concepts of behavioral and context-aware biometric to take into account all the sensing data provided by the Internet of Things (IoT) and the smart city, in the shape of user habits. In this paper, we propose a meta-model-driven approach to mine user habits, by means of a combination of IoT data incoming from several sources such as smart mobility, smart metering, smart home, wearables and so on. Then, we use those habits to seamlessly authenticate users in real time all along the smart city when the same behavior occurs in different context and with different sensing technologies. Our model, which we called WoX+, allows the automatic extraction of user habits using a novel Artificial Intelligence (AI) technique focused on high-level concepts. The aim is to continuously authenticate the users using their habits as behavioral biometric, independently from the involved sensing hardware. To prove the effectiveness of WoX+ we organized a quantitative and qualitative evaluation in which 10 participants told us a spending habit they have involving the use of IoT. We chose the financial domain because it is ubiquitous, it is inherently multi-device, it is rich in time patterns, and most of all it requires a secure authentication. With the aim of extracting the requirement of such a system, we also asked the cohort how they expect WoX+ will use such habits to securely automatize payments and identify them in the smart city. We discovered that WoX+ satisfies most of the expected requirements, particularly in terms of unobtrusiveness of the solution, in contrast with the limitations observed in the existing studies. Finally, we used the responses given by the cohorts to generate synthetic data and train our novel AI block. Results show that the error in reconstructing the habits is acceptable: Mean Squared Error Percentage (MSEP) 0.04%.

Background: Diabetic retinopathy (DR) is a neurovascular disease, characterized by a deficiency of brain-derived neurotrophic factor (BDNF), a regulator of autophagy. Beta-hydroxybutyrate (BHB), previously reported as a protective agent in DR, has been associated with BDNF promotion. Here, we investigated whether systemic BHB affects the retinal levels of BDNF and local autophagy in diabetic mice with retinopathy; Methods: C57BL/6J mice were administered with intraperitoneal (i.p.) streptozotocin (STZ) (75 mg/kg) injection to develop diabetes. After 2 weeks, they received i.p. injections of BHB (25–50–100 mg/kg) twice a week for 10 weeks. Retinal samples were collected in order to perform immunofluorescence, Western blotting, and ELISA analysis; Results: BHB 50 mg/kg and 100 mg/kg significantly improved retinal BDNF levels (p < 0.01) in diabetic mice. This improvement was negatively associated with autophagosome–lysosome formations (marked by LC3B and ATG14) and to higher levels of connexin 43 (p < 0.01), a marker of cell integrity. Moreover, BHB administration significantly reduced M1 microglial activation and autophagy (p < 0.01); Conclusions: The systemic administration of BHB in mice with DR improves the retinal levels of BDNF, with the consequent reduction of the abnormal microglial autophagy. This leads to retinal cell safety through connexin 43 restoration.

The organized large-scale retail sector has been gradually establishing itself around the world, and has increased activities exponentially in the pandemic period. This modern sales system uses Data Mining technologies processing precious information to increase profit. In this direction, the extreme gradient boosting (XGBoost) algorithm was applied in an industrial project as a supervised learning algorithm to predict product sales including promotion condition and a multiparametric analysis. The implemented XGBoost model was trained and tested by the use of the Augmented Data (AD) technique in the event that the available data are not sufficient to achieve the desired accuracy, as for many practical cases of artificial intelligence data processing, where a large dataset is not available. The prediction was applied to a grid of segmented customers by allowing personalized services according to their purchasing behavior. The AD technique conferred a good accuracy if compared with results adopting the initial dataset with few records. An improvement of the prediction error, such as the Root Mean Square Error (RMSE) and Mean Square Error (MSE), which decreases by about an order of magnitude, was achieved. The AD technique formulated for large-scale retail sector also represents a good way to calibrate the training model.

Background and purpose: pericoronary fat over-inflammation might lead to the development and destabilization of coronary plaque in patients with pre-diabetes (PDM). Notably, pericoronary fat could over-express the sodium-glucose cotransporter 2 (SGLT2) and leptin, along with decreased sirtuin 6 (SIRT6) expression in PDM vs. normoglycemic (NG) patients undergoing coronary artery bypass grafting (CABG) for acute myocardial infarction (AMI). However, in the current study, we evaluated inflammatory markers, SGLT2, SIRT6, and leptin levels in pericoronary fat and, subsequently, 12-month prognosis comparing PDM to NG subjected to CABG for AMI. In addition, we evaluated in PDM patients the effects of metformin therapy on SIRT6 expression, leptin, and SGLT2 levels, and assessed its beneficial effect on nitrotyrosine and inflammatory cytokine levels. Methods: we studied AMI patients referred for CABG, divided into PDM and NG-patients. PDM patients were divided into never-metformin users and metformin users. Finally, we evaluated major adverse cardiac events (MACE) at a 12-month follow-up. Results: the MACE was 9.1% in all PDM and 3% in NG patients (p < 0.05). Metformin users presented a significantly lower MACE rate in PDM than never-metformin users (p < 0.05). PDM showed higher inflammatory cytokines, 3-nitrotyrosine levels, SGLT2, and leptin content, and decreased SIRT6 protein levels in pericoronary fat compared to NG-patients (p < 0.05). PDM never-metformin-users showed higher SGLT2 and leptin levels in pericoronary fat than current-metformin-users (p < 0.05). Conclusions: metformin therapy might ameliorate cardiovascular outcomes by reducing inflammatory parameters, SGLT2, and leptin levels, and finally improving SIRT6 levels in AMI-PDM patients treated with CABG.

The characterization of a quarry site which is suitable for railway ballast aggregate production represents a big challenge for the mining industry. The knowledge of structural discontinuities within local geological materials is fundamental to guide mining operations, optimize investments, and guarantee quarry security. This research work presents an innovative methodology for the subsurface investigation of a quarry excavation area down to a depth of about 50 m in Falconara Albanese, Calabria, Italy. The proposed methodological approach incorporates photogrammetry, drone technology, and GPR data acquisition and processing. Photogrammetry represents the first step for obtaining a 3D topographical model reconstruction of the whole quarry, helping to detail the acquisition approach and properly plan the subsequent drone survey. In particular, two 120 MHz antennas have been mounted on the drone and two profiles have been acquired above and across the quarry. Results show the presence of fractured material and demonstrate the applicability of the method for identification of areas that are more suitable for railway ballast production. The presented method is therefore capable of detecting subsurficial fractures at a quarry site by means of a relatively fast and cost-effective procedure. Results are achieved within the framework of an industry project.

Toll-like receptors (TLRs) are key receptors through which infectious and non-infectious challenges act with consequent activation of the inflammatory cascade that plays a critical function in various acute and chronic diseases, behaving as amplification and chronicization factors of the inflammatory response. Previous studies have shown that synthetic analogues of lipid A based on glucosamine with few chains of unsaturated and saturated fatty acids, bind MD-2 and inhibit TLR4 receptors. These synthetic compounds showed antagonistic activity against TLR4 activation in vitro by LPS, but little or no activity in vivo. This study aimed to show the potential use of N-palmitoyl-D-glucosamine (PGA), a bacterial molecule with structural similarity to the lipid A component of LPS, which could be useful for preventing LPS-induced tissue damage or even peripheral neuropathies. Molecular docking and molecular dynamics simulations showed that PGA stably binds MD-2 with a MD-2/(PGA)3 stoichiometry. Treatment with PGA resulted in the following effects: (i) it prevented the NF-kB activation in LPS stimulated RAW264.7 cells; (ii) it decreased LPS-induced keratitis and corneal pro-inflammatory cytokines, whilst increasing anti-inflammatory cytokines; (iii) it normalized LPS-induced miR-20a-5p and miR-106a-5p upregulation and increased miR-27a-3p levels in the inflamed corneas; (iv) it decreased allodynia in peripheral neuropathy induced by oxaliplatin or formalin, but not following spared nerve injury of the sciatic nerve (SNI); (v) it prevented the formalin- or oxaliplatin-induced myelino-axonal degeneration of sciatic nerve. SIGNIFICANCE STATEMENT We report that PGA acts as a TLR4 antagonist and this may be the basis of its potent anti-inflammatory activity. Being unique because of its potency and stability, as compared to other similar congeners, PGA can represent a tool for the optimization of new TLR4 modulating drugs directed against the cytokine storm and the chronization of inflammation.

In the last two decades, reports of canine heartworm (HW) infection have increased even in non-endemic areas, with a large variability in prevalence data due to the diagnostic strategy employed. This study evaluated the relative performance of two microtiter plate ELISA methods for the detection of HW antigen in determining the occurrence of Dirofilaria immitis in a dog population previously tested by the modified Knott’s test and SNAP 4Dx Plus test. The prevalence of this infection in the sheltered dog population (n = 363) from a high-risk area for HW infection was 44.4% according to the modified Knott’s test and 58.1% according to a point-of-care antigen ELISA. All serum samples were then evaluated by a microtiter plate ELISA test performed with and without immune complex dissociation (ICD). The prevalence increased from 56.5% to 79.6% following ICD, indicating a high proportion of samples with immune complexing. Comparing these results to that of the modified Knott’s test, the samples negative for microfilariae (mfs) and those positive only for D. repens mfs demonstrated the greatest increase in the proportion of positive results for D. immitis by ELISA following ICD. While the ICD method is not recommended for routine screening, it may be a valuable secondary strategy for identifying HW infections in dogs.

The potential of near infrared spectroscopy (NIRS) in the wavelength range of 1000–2500 nm for predicting quality parameters such as total soluble solids (TSS), acidity (TA), firmness, and individual sugars (glucose, fructose, sucrose, and xylose) for two cultivars of apples (“Braeburn” and “Cripps Pink”) was studied during the pre- and post-storage periods. Simultaneously, a qualitative investigation on the capability of NIRS to discriminate varieties, harvest dates, storage periods and fruit inhomogeneity was carried out. In order to generate a sample set with high variability within the most relevant apple quality traits, three different harvest time points in combination with five different storage periods were chosen, and the evolution of important quality parameters was followed both with NIRS and wet chemical methods. By applying a principal component analysis (PCA) a differentiation between the two cultivars, freshly harvested vs. long-term stored apples and, notably, between the sun-exposed vs. shaded side of apples could be found. For the determination of quality parameters effective prediction models for titratable acid (TA) and individual sugars such as fructose, glucose and sucrose by using partial least square (PLS) regression have been developed. Our results complement earlier reports, highlighting the versatility of NIRS as a fast, non-invasive method for quantitative and qualitative studies on apples.