Prophylactic Bacteriophage Administration More Effective than Post-infection Administration in Reducing Salmonella enterica serovar Enteritidis Shedding in Quail

Infections caused by Salmonella bacteria, often through poultry products, are a serious public health issue. Because of drawbacks associated with antibiotic prophylaxis, alternative treatments are sought. Bacterial viruses (bacteriophages) may provide an effective alternative, but concerns remain with respect to bacteriophage stability and effectiveness. To this end, we assessed the stability of a novel bacteriophage isolated from poultry excreta, siphovirus PSE, and its effectiveness in reducing Salmonella enterica serovar Enteritidis colonization in vitro and in vivo. Moreover, we sought to determine how the timing (prophylactic or therapeutic) and route (oral gavage or vent lip) of PSE administration impacted its effectiveness. Here we report that significant quantities of viable PSE bacteriophages were recovered following exposure to high and low pH, high temperatures, and bile salts, testifying to its ability to survive extreme conditions. In addition, we found that ileal lactic acid bacteria and Streptococcus spp. counts increased, but colibacilli and total aerobe counts decreased, in quail receiving phage PSE through both oral gavage and vent lip routes. In other experiments, we assessed the efficiency of PSE administration, in both prophylactic and therapeutic contexts, via either oral gavage or vent lip administration, on S. Enteritidis colonization of quail cecal tonsils. Our results demonstrate that administration of PSE as a preventive agent could reduce the S. Enteritidis colonization more effectively than post-challenge administration. Furthermore, oral administration of PSE phage is a more effective prophylactic tool for reduction of S. Enteritidis shedding in poultry than is vent lip administration

Effect of Probiotic, Thyme, Garlic and Caraway Herbal Extracts on the Quality and Quantity of Eggs, Blood Parameters, Intestinal Bacterial Population and Histomorphology in Laying Hens

In the current Iranian poultry industry, antibiotics are the most frequently used additive in feeds to increase the productivity. Regarding the negative effects on human health due to consuming chicken whose feeds contain antibiotics, finding an appropriate alternative is of a great importance. This study aims to find an appropriate and harmless feed additive to increase the quality and quantity of poultry eggs. Total of 60 laying hens which had been in production for 85 weeks were allocated in a completely randomized design considering five treatments with four replicates and three birds in each. Group one received a layer basal diet with no supplementation which served as control. The second, third and fourth groups received basal diet with 1 mL of herbal extracts (garlic, thyme and caraway) / L drinking water, respectively. The fifth group fed the basal diet plus 1 g of probiotic / kg diet. The number and weight of produced eggs were measured in a daily manner, feed consumption in weekly manner and the egg quality, yolk cholesterol, intestinal bacterial population and effect of treatments on the morphology of the small intestine were measured at the end of experiment after 8 weeks. All treatments showed no overall effect on quality and quantity of produced eggs in comparison with controls; however, the thyme and garlic extracts reduced the cholesterol of serum and yolk relative to the control. The herbal extracts caused a significant decrease in the intestinal bacterial population and probiotic increased villus height in ileum (

Watershed road network analysis with an emphasis on fire fighting management

The aim of this study is fire hazard zoning the Chehel-Chay watershed and analysis of road network in order to fire-fighting management. Using effective factors on fire occurrence, the fire hazard map of the study area produced by support vector machine algorithm and then was divided into four hazard classes. The road length and type were investigated in the each fire hazard classes. The results showed that most of occurred fires are located in the close distances of roads and forest areas. The results showed that road types and land cover are important in fire occurrences and suppression. In high dangerous zone, the roads pass through forestlands, but in low dangerous zone, the roads are passing from farmlands. The roads do not cover the half of area and do not pass at two third of high hazard class zones. Therefore, appreciate road network planning is necessary according to fire-fighting management. 

Effect of Chamomile, Wild Mint and Oregano Herbal Extracts on Quality and Quantity of Eggs, Hatchability, and Some Other Parameters in Laying Japanese Quails

According to various reports of beneficial effects of medicinal plants on the performance of broiler chickens and less extensively studies in laying poultries, this study was conducted to find an appropriate and harmless feed additive to enhance the quality and quantity of poultry eggs. The effect of three herbal extracts on quantity and quality of eggs, blood parameters, hatchability, intestinal bacterial population, and intestinal morphology in laying Japanese quail were investigated. The study was applied with 64, ten-week old laying Japanese quails for 8 weeks. The experiment was a completely randomized design with 4 treatments, 4 replications and 4 birds per replicate (the ratio of male to female 1:3). Experimental treatments involved: Control, with no additive in drinking water; chamomile extract; wild mint extract; and oregano extract. Herbal extracts were added 1 mL/L drinking water. The three treatments showed no significant effect on productivity, egg mass, FCR, egg weight, feed intake and qualitative indices of eggs; however, the herbal extracts specially the chamomile extract reduced the cholesterol of eggs (

Identification of parameters in photovoltaic models through a runge kutta optimizer

Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively

Upregulation of miR-210 promotes differentiation of mesenchymal stem cells (MSCs) into osteoblasts

Numerous studies indicated that microRNAs are critical in the regulation of cellular differentiation, by controlling the expression of underlying genes. The aim of this study was to investigate the effect of miR-210 upregulation on differentiation of human umbilical cord blood (HUCB)-derived mesenchymal stem cells (MSCs) into osteoblasts. MSCs were isolated from HUCB and confirmed by their adipogenic/osteogenic differentiation and flow cytometric analysis of surface markers. Pre-miR-210 was amplified from human DNA, digested and ligated with plenti-III-mir-green fluorescent protein (GFP) vector, and cloned in STBL4 bacteria. After confirmation with polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), the plenti-III-GFP segment bearing pre-miR-210 was transfected into MSCs by electroporation. Two control vectors, pmaxGFP and Scramble, were transfected separately into MSCs. The expression of miR-210 and genes related to osteoblast differentiation, i.e., runt-related transcription factor 2 (Runx2), alkaline phosphatase (ALP) and osteocalcin gene, in the three groups of transfected MSCs was analyzed 0, 7, 14, and 21 days of transfection by quantitative reverse transcription PCR (qRT-PCR). Overexpression of miR-210 was observed in MSCs transfected with miR-210-bearing plasmid, and this was significantly different compared to Scramble group (p < 0.05). Significantly increased expression of Runx2 (at day 7 and 14), ALP and osteocalcin genes (at all time points for both genes) was observed in MSCs with miR-210-bearing plasmid compared to controls. Overall, the overexpression of miR-210 in MSCs led to MSC differentiation into osteoblasts, most probably by upregulating the Runx2, ALP, and osteocalcin genes at different stages of cell differentiation. Our study confirms the potential of miRNAs in developing novel therapeutic strategies that could target regulatory mechanisms of cellular differentiation in various disease states

Artificial intelligence‐based digital scores of stromal tumour‐infiltrating lymphocytes and tumour‐associated stroma predict disease‐specific survival in triple‐negative breast cancer

Triple-negative breast cancer (TNBC) is known to have a relatively poor outcome with variable prognoses, raising the need for more informative risk stratification. We investigated a set of digital, artificial intelligence (AI)-based spatial tumour microenvironment (sTME) features and explored their prognostic value in TNBC. After performing tissue classification on digitised haematoxylin and eosin (H&E) slides of TNBC cases, we employed a deep learning-based algorithm to segment tissue regions into tumour, stroma, and lymphocytes in order to compute quantitative features concerning the spatial relationship of tumour with lymphocytes and stroma. The prognostic value of the digital features was explored using survival analysis with Cox proportional hazard models in a cross-validation setting on two independent international multi-centric TNBC cohorts: The Australian Breast Cancer Tissue Bank (AUBC) cohort (n = 318) and The Cancer Genome Atlas Breast Cancer (TCGA) cohort (n = 111). The proposed digital stromal tumour-infiltrating lymphocytes (Digi-sTILs) score and the digital tumour-associated stroma (Digi-TAS) score were found to carry strong prognostic value for disease-specific survival, with the Digi-sTILs and Digi-TAS scores giving C-index values of 0.65 (p = 0.0189) and 0.60 (p = 0.0437), respectively, on the TCGA cohort as a validation set. Combining the Digi-sTILs feature with the patient's positivity status for axillary lymph nodes yielded a C-index of 0.76 on unseen validation cohorts. We surmise that the proposed digital features could potentially be used for better risk stratification and management of TNBC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Deep learning based digital cell profiles for risk stratification of urine cytology images

Urine cytology is a test for the detection of high-grade bladder cancer. In clinical practice, the pathologist would manually scan the sample under the microscope to locate atypical and malignant cells. They would assess the morphology of these cells to make a diagnosis. Accurate identification of atypical and malignant cells in urine cytology is a challenging task and is an essential part of identifying different diagnosis with low-risk and high-risk malignancy. Computer-assisted identification of malignancy in urine cytology can be complementary to the clinicians for treatment management and in providing advice for carrying out further tests. In this study, we presented a method for identifying atypical and malignant cells followed by their profiling to predict the risk of diagnosis automatically. For cell detection and classification, we employed two different deep learning-based approaches. Based on the best performing network predictions at the cell level, we identified low-risk and high-risk cases using the count of atypical cells and the total count of atypical and malignant cells. The area under the receiver operating characteristic (ROC) curve shows that a total count of atypical and malignant cells is comparably better at diagnosis as compared to the count of malignant cells only. We obtained area under the ROC curve with the count of malignant cells and the total count of atypical and malignant cells as 0.81 and 0.83, respectively. Our experiments also demonstrate that the digital risk could be a better predictor of the final histopathology-based diagnosis. We also analyzed the variability in annotations at both cell and whole slide image level and also explored the possible inherent rationales behind this variability