Utjecaj dodataka začinskog bilja na kvalitetu mozzarele

In this study, 3 different spice mixes were added just after blanching to mozzarella cheese produced by high moisture production method. The dough was kneaded and filled into to fibrous cases. After filling process, cheeses were stored for 28 days at 4 °C and 85 % of relative humidity. The following characteristics were measured: color parameters, milk acidity, total dry matter, maturation index, total aerobic mesophilic bacteria, coliform bacteria, coagulase positive staphylococci, lactic acid bacteria, species of Lactococcus bacteria, proteolytic bacteria, lipolytic bacteria and mold /yeast count were examined on 0, 5, 15,21 and 28 days after storage. Although L* (lightness) and a* (redness) values decreased during storage period, while moreover b* (yellowness) values increased. In addition acidity, dry matter and maturation index values increased during storage. Total aerobic mesophilic bacteria, lactic acid bacteria, Lactococcus spp., lipolytic bacteria and mold/ yeast counts decreased, but proteolytic bacteria count increased.U ovom radu istražen je utjecaj 3 različite mješavine začinskog bilja u proizvodnji mozzarella sira. Sirno tijesto gnječeno je i punjeno u sirne marame, i uskladišteno 28 dana na 4 °C i relativnu vlagu 85 %. Istraženi su parametri boje, kiselosti mlijeka, ukupne suhe tvari i indeks zrenja. Također je istražen ukupan broj aerobno mezofilnih bakterija, te broj koliformnih bakterija, koagulaza pozitivnih stafilokoka, bakterija mliječne kiseline, proteolitičkih i lipolitičkih bakterija, te kvasaca i plijesni. Mikrobiološke analize su provedene 0., 5.,15., 21., i 28. dana skladištenja. Intenzitet žute boje povećao se tijekom skladištenja sira, dok se intenzitet bijele i crvene boje smanjivao. Parametri kiselosti, suhe tvari i indeksa zrenja povećani su tijekom zrenja. Utvrđeno je smanjenje ukupnog broj aerobno mezofilnih bakterija, bakterija mliječne kiseline, Lactococcus spp., lipolitičkih bakterija te broja kvasaca i plijesni tijekom skladištenja, dok je broj proteolitičkih bakterija porastao

Symmetric Dense Inception Network for Simultaneous Cell Detection and Classification in Multiplex Immunohistochemistry Images

Deep-learning based automatic analysis of the multiplex immunohistochemistry (mIHC) enables distinct cell populations to be localized on a large scale, providing insights into disease biology and therapeutic targets. However, standard deep-learning pipelines performed cell detection and classification as two-stage tasks, which is computationally inefficient and faces challenges to incorporate neighbouring tissue context for determining the cell identity. To overcome these limitations and to obtain a more accurate mapping of cell phenotypes, we presented a symmetric dense inception neural network for detecting and classifying cells in mIHC slides simultaneously. The model was applied with a novel stop-gradient strategy and a loss function accounted for class imbalance. When evaluated on an ovarian cancer dataset containing 6 cell types, the model achieved an F1 score of 0.835 in cell detection, and a weighted F1-score of 0.867 in cell classification, which outperformed separate models trained on individual tasks by 1.9% and 3.8% respectively. Taken together, the proposed method boosts the learning efficiency and prediction accuracy of cell detection and classification by simultaneously learning from both tasks

Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners.

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role 'between' brain and behavior. We discuss implications and possible avenues for future studies

Functional immune characterization of HIV-associated non-small-cell lung cancer.

Dear Editor, In the combined anti-retroviral therapy (cART) era, non-small cell lung cancer (NSCLC) is a highly incident cause of morbidity and mortality in people living with HIV (PLHIV)[1]. The immune-pathogenesis of NSCLC and HIV infection both rely on programmed-death 1 (PD-1) receptor-ligand interaction as a mechanism to induce T-cell exhaustion. To date, PLHIV have been excluded from clinical trials of immune-checkpoint inhibitors (ICPI), on the presumption that anti-tumour immunity might be compromised by HIV infection. To verify this, we evaluated the clinico-pathologic significance of PD-ligands expression in a consecutive series of 221 archival NSCLC samples, 24 of which were HIV-associated (Table S1)

Phenotypic Characteristics of the Tumour Microenvironment in Primary and Secondary Hepatocellular Carcinoma

(1) Background: The intra-tumoural heterogeneity (ITH) of hepatocellular carcinoma (HCC) and its microenvironment (TME) across primary and secondary disease is poorly characterised. (2) Methods: Intra-tumoural (IT) and peri-tumoural (PT) staining of matched primary and secondary samples was conducted to evaluate the distribution of CD4+/FOXP3+ and CD8+/PD1+ T-cells. Samples underwent PD-L1/2 immunostaining, tumour mutational burden (TMB) evaluation, and high-resolution T-cell receptor (TCR) sequencing to derive T-cell clonality and targeted transcriptomics. (3) Results: We analysed 24 samples from matched primary (n = 11) and secondary (n = 13; 5 synchronous, 6 metachronous) deposits, 11 being extrahepatic (84.6%). IT CD8+ density was lower than PT in both primary (p = 0.005) and secondary deposits (p = 0.01), consistent with immune exclusion. PD-L1+ tumours displayed higher IT and PT CD8+/PD1+ cell density compared to PD-L1- (p < 0.05), and primary IT infiltrate was enriched in CD4+/FOXP3+ cells, compared to PT regions (p = 0.004). TCR-sequencing demonstrated enrichment of the top T-cell clonotype in secondary versus primary HCC (p = 0.02), without differences in overall productive clonality (p = 0.35). TMB was similar across primary versus secondary HCC (p = 0.95). While directed gene set analysis demonstrated the uniformity of transcriptional signatures of individual immune cell types, secondary deposits demonstrated higher COLEC12 (p = 0.004), CCL26 (p = 0.02), CD1E (p = 0.02) and CD36 (p = 0.03) expression with downregulation of CXCL1 (p = 0.03), suggesting differential regulation of innate immunity. (4) Conclusion: Immune exclusion is a defining feature of the HCC TME. Despite evidence of homogeneity in somatic TMB, secondary HCC is characterised by the expansion of a distinct T-cell clonotype and differential regulation of innate immune pathways