An assessment of haematological and serum biochemical indices in Salmo trutta caspius

Haematological and biochemical serum analyses were carried out in 131 fish specimens in three age and size groups including smolt, juvenile and breeder. Of these groups, smolt fish were 40 specimens in the average total length range 195.45±12.426mm and had an average weight of 84±15.5g, juvenile fish were 25 individuals and their average total length and weight were 267±20.877mm and 217.20±22.013g, respectively. The 41 breeder fish had an average total length of 578.24±68.487mm and an average weight of 1994.2±775.379g. Another 25 breeder fish were also sampled in the spawning season for blood biochemical analyses had an average total length range of 617±59.9mm and weighed on average 2227.5±647.5g each. Red blood cell counts were 866600 per mm super(3) and 1259400 per mm ^(3) in smolt and breeder fish respectively. The average hematocrit was 48.39% in smolt and 44.29% in breeder fish. The average hemoglobin was 8.85g/dl in smolt and 10.91g/dl in breeder fish. White blood cell count was 8781.58 per mm ^(3) in smolt and 5217.65 per mm ^(3) in breeder fish. Other measurements were as follows: Lymphocyte 90.57% in smolt and 73.22% in breeders, Neutrophil 5.12% in smolt and 16.92% in breeders, Monocyte 1.27% in smolt and 4.24% in breeders, and clotting time was 282.34 seconds in smolt and 291.47 seconds in breeder fish. We also measured MCV, MCH and MCHC in smolt and breeder fish along with biochemical parameters. The glucose level was 2.97mmol/1 in juvenile and 1.99mmol/1 in breeder fish. The cholesterol level was 4.26mmol/l in juvenile and 7.06mmol/1 in breeders. The triglyceride amount was 2.35mmol/l in juvenile and 2.47mmol/l in breeder specimens and the calcium level was 2.44 in juvenile and 2.61 mmol/1 in breeder fish

Yield of protein recovery and degree of hydrolysis associated protein hydrolysates from Bighead Carp (Aristichthys nobilis) by using enzymes

The present study compared the properties of Bighead carp (Aristichthys nobilis) visceral, viscera use of commercial enzymes (Alcalase, Papain, and Protamex), recovery protein and degree of hydrolysis in the various temperatures and times of hydrolysis process. According to results, increase in time and temperature in hydrolysis process, led to increase in both recovery protein and degree of hydrolysis. Maximum of these parameters were observed at 55°C and 60min. minimum of parameters were observed at 35°C and 15min. In addition, among enzymes being investigated, Alcalase had high rate of recovery protein and degree of hydrolysis in equal temperature and times different from others enzymes (Papain and Protamex). Maximum mean of recovery protein for Alcalase was %51.38±2.39 in 55°C and 60 min of hydrolysis process. Also, maximum degree of hydrolysis for Alcalaqse was more than 20% at 55°C and 60 min of hydrolysis process. In conclusion, use of Alcalase enzyme induces product protein hydrolysates from Bighead Carp (Aristichthys nobilis) viscera that have suitable quality than others enzymes (Papain, and Protamex)

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients: COVID-19 prognostic modeling using CT radiomics and machine learning

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95: 0.81�0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95: 0.81�0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients. © 2022 The Author

The evolutionary ecology of fatty-acid variation : Implications for consumer adaptation and diversification

The nutritional diversity of resources can affect the adaptive evolution of consumer metabolism and consumer diversification. The omega-3 long-chain polyunsaturated fatty acids eicosapentaenoic acid (EPA; 20:5n-3) and docosahexaenoic acid (DHA; 22:6n-3) have a high potential to affect consumer fitness, through their widespread effects on reproduction, growth and survival. However, few studies consider the evolution of fatty acid metabolism within an ecological context. In this review, we first document the extensive diversity in both primary producer and consumer fatty acid distributions amongst major ecosystems, between habitats and amongst species within habitats. We highlight some of the key nutritional contrasts that can shape behavioural and/or metabolic adaptation in consumers, discussing how consumers can evolve in response to the spatial, seasonal and community-level variation of resource quality. We propose a hierarchical trait-based approach for studying the evolution of consumers' metabolic networks and review the evolutionary genetic mechanisms underpinning consumer adaptation to EPA and DHA distributions. In doing so, we consider how the metabolic traits of consumers are hierarchically structured, from cell membrane function to maternal investment, and have strongly environment-dependent expression. Finally, we conclude with an outlook on how studying the metabolic adaptation of consumers within the context of nutritional landscapes can open up new opportunities for understanding evolutionary diversification