PD-L1 and Tumor Infiltrating Lymphocytes as Prognostic Markers in Resected NSCLC

IntroductionImmune checkpoint inhibition has shifted treatment paradigms in non-small cell lung cancer (NSCLC). Conflicting results have been reported regarding the immune infiltrate and programmed death-ligand 1 (PD-L1) as a prognostic marker. We correlated the immune infiltrate and PD-L1 expression with clinicopathologic characteristics in a cohort of resected NSCLC.MethodsA tissue microarray was constructed using triplicate cores from consecutive resected NSCLC. Immunohistochemistry was performed for CD8, FOXP3 and PD-L1. Strong PD-L1 expression was predefined as greater than 50% tumor cell positivity. Matched nodal samples were assessed for concordance of PD-L1 expression.ResultsOf 522 patients, 346 were node-negative (N0), 72 N1 and 109 N2; 265 were adenocarcinomas (AC), 182 squamous cell cancers (SCC) and 75 other. Strong PD-L1 expression was found in 24% cases. In the overall cohort, PD-L1 expression was not associated with survival. In patients with N2 disease, strong PD-L1 expression was associated with significantly improved disease-free (DFS) and overall survival (OS) in multivariate analysis (HR 0.49, 95%CI 0.36–0.94, p = 0.031; HR 0.46, 95%CI 0.26–0.80, p = 0.006). In this resected cohort only 5% harboured EGFR mutations, whereas 19% harboured KRAS and 23% other. KRAS mutated tumors were more likely to highly express PD-L1 compared to EGFR (22% vs 3%). A stromal CD8 infiltrate was associated with significantly improved DFS in SCC (HR 0.70, 95%CI 0.50–0.97, p = 0.034), but not AC, whereas FOXP3 was not prognostic. Matched nodal specimens (N = 53) were highly concordant for PD-L1 expression (89%).ConclusionPD-L1 expression was not prognostic in the overall cohort. PD-L1 expression in primary tumor and matched nodal specimens were highly concordant. The observed survival benefit in N2 disease requires confirmation.     

HLA class I and class II associations in dengue viral infections in a Sri Lankan population

Background

HLA class I and class II alleles have been shown to be associated with the development of dengue hemorrhagic fever (DHF)/dengue shock syndrome (DSS) in different populations. However, the majority of studies have been based on limited numbers of patients. In this study we aimed to investigate the HLA-class I and class II alleles that are positively and negatively associated with the development of DSS in a cohort of patients with DHF and also the alleles associated with development of DHF during primary dengue infections in a Sri Lankan population.

Methodology/Principal Findings

The allele frequencies of HLA class I and class II alleles were compared in 110 patients with DHF and 119 individuals from the population who had never reported a symptomatic dengue infection at the time of recruitment. We found that HLA-A*31 (corrected P = 0.01) and DRB1*08 (corrected P = 0.009) were associated with susceptibility to DSS when infected with the dengue virus, during secondary dengue infection. The frequency of DRB1*08 allele was 28.7 times higher than in the normal population in patients with DSS. HLA-A*31 allele was increased 16.6 fold in DHF who developed shock when compared to those who did not develop shock. A*24 (corrected P = 0.03) and DRB1*12 (corrected P = 0.041) were strongly associated with the development of DHF during primary dengue infection.

Conclusions/Significance

These data suggest that certain HLA alleles confer susceptibility/protection to severe dengue infections. As T cell epitope recognition depend on the HLA type of an individual, it would be now important to investigate how epitope specific T cells associate with primary and secondary dengue infections and in severe dengue infections.     

Habitat‐related seed germination traits in alpine habitats

Understanding the key aspects of plant regeneration from seeds is crucial in assessing species assembly to their habitats. However, the regenerative traits of seed dormancy and germination are underrepresented in this context. In the alpine zone, the large species and microhabitat diversity provide an ideal context to assess habitat‐related regenerative strategies. To this end, seeds of 53 species growing in alpine siliceous and calcareous habitats (6230 and 6170 of EU Directive 92/43, respectively) were exposed to different temperature treatments under controlled laboratory conditions. Germination strategies in each habitat were identified by clustering with k‐means. Then, phylogenetic least squares correlations (PGLS) were fitted to assess germination and dormancy differences between species’ main habitat (calcareous and siliceous), microhabitat (grasslands, heaths, rocky, and species with no specific microhabitats), and chorology (arctic–alpine and continental). Calcareous and siliceous grasslands significantly differ in their germination behaviour with a slow, mostly overwinter germination and high germination under all conditions, respectively. Species with high overwinter germination occurs mostly in heaths and have an arctic–alpine distribution. Meanwhile, species with low or high germinability in general inhabit in grasslands or have no specific microhabitat (they belong to generalist), respectively. Alpine species use different germination strategies depending on habitat provenance, species’ main microhabitat, and chorotype. Such differences may reflect adaptations to local environmental conditions and highlight the functional role of germination and dormancy in community ecology.     

Models of classical conditioning

Classical conditioning is a basic form of associative learning in the animal kingdom. Many paradigmatic features of classical conditioning appear to be conserved throughout species and phyla and are independent of stimulus nature. This paper presents an analysis of trial-based and real-time models of classical conditioning which are mathematical abstractions of the underlying processing principles. Various models are reviewed and in a formal analysis, their capability of simulating and explaining classical conditioning is investigated. Since every existing model fails to simulate some particular conditioning phenomena and since some modelling approaches are not appropriate for detailed mathematical analysis, new model components will be introduced that overcome most of the weaknesses observed in the other models.     

Fungal pretreatment to enhance the yield of phytochemicals and evaluation of α-amylase and α-glucosidase inhibition using Cinnamomum zeylanicum (L.) quills pressurized water extracts

Bioactive compounds entrapped in plant materials can be effectively recovered using fungal enzymes. Cinnamomum zeylanicum Sri Wijaya (SW) and Sri Gemunu (SG) accessions and commercially available C. zeylanicum (CC) were subjected to fungal pretreatment and extracted with pressured water (PWE, 0·098 MPa). Thirteen fungal species were isolated and the substrate utilization ability of the species was tested using cellulose, pectin and lignin (indirectly). Total phenolic content (TPC, Folin–Ciocalteu method), proanthocyanidin content (PC, vanillin method) and α-amylase and α-glucosidase inhibitory potential of the extracts were evaluated. The anti-diabetic drug, Acarbose was used as the positive control. Trichoderma harzianum (MH298760) showed the highest cell lysis ability and hence was used for the microbial pretreatment process. Extracts of SW treated with T. harzianum species (Pre-SW) gave the highest percentage yield (4·08% ± 0·15%), significantly potent inhibition (P < 0·05) of α-amylase and α-glucosidase activities (IC₅₀ 57 ± 8 and 36 ± 8 μg ml⁻¹ respectively), TPC (2·24 ± 0·02 mg gallic acid equivalent g⁻¹), and PC (48·2 ± 0·4 mg of catechin equivalent g⁻¹) compared to Pre-SG, Pre-CC and nontreated samples. Trichoderma harzianum treatment can enhance the hypoglycaemic properties, PC and TPC of Cinnamon extracts and provide new insights into the recovery of phytochemicals.     

Anaerobic bacteria in clinical infections

The findings of 275 cultures from routine clinical specimens obtained from lesions in different sites of body, during a period of 11 months, are presented. The clinical specimens were obtained from surgical wounds, abdominal infections, orthopaedic operations, biliary tract infections and pleuropulmonary infections. The total number of positive cultures including both aerobes and anaerobes was 203 out of 275 (73.8%). Of the 38 cultures positive for anaerobes, 29 (76.3%) grew both aerobic and anaerobic bacteria, while in nine (23.7%) cultures only anaerobes were found. A total of 42 strains of anaerobic bacteria were isolated. The majority of them were found in clinical specimens obtained from abdominal infections (62%), while a low percentage (3.6%) was found in specimens from orthopaedic operations. Strains belonging to the genus Bacteroides were the most frequently isolated anaerobes, accounting for 35.7% of the total, followed by Clostridia 28.5%, Peptostreptococci 23.8% and Prevotella 12%.     

Sex ratio of offspring of patients with prostatic cancer

We have examined the fertility of 354 patients with prostatic cancer and 597 controls who had married at least once and formed part of a population-based case-control study in Alberta. Considering offspring of both sexes, the mean fertility of the patients was 3.17, not significantly different from the mean of 3.11 for the controls. However, the sex ratios were significantly different, 56.5% of the patients'' offspring being sons, compared with 50.1% of the controls'' offspring. The difference in sex ratio persisted after adjustment for ethnic group, level of education and age at first marriage, which were found to be risk factors for prostatic cancer.     

Kinetic models of odor transduction implemented as artificial neural networks

We present a formal model of olfactory transduction corresponding to the biochemical reaction cascade found in chemosensory neurons. It assumes that odorants bind to receptor proteins which, in turn, activate transducer mechanisms corresponding to second messenger-mediated processes. The model is reformulated as a mathematically equivalent artificial neural network (ANN). To enable comparison of the computational power of our model, previously suggested models of chemosensory transduction are also presented in ANN versions. In ANNs, certain biological parameters, such as rate constants and affinities, are transformed into weights that can be fitted by training with a given experimental data set. After training, these weights do not necessarily equal the real biological parameters, but represent a set of values that is sufficient to simulate an experimental set of data. We used ANNs to simulate data recorded from bee subplacodes and compare the capacity of our model with ANN versions of other models. Receptor neurons of the nonpheromonal, general odor-processing subsystem of the honeybee are broadly tuned, have overlapping response spectra, and show highly nonlinear concentration dependencies and mixture interactions, i.e., synergistic and inhibitory effects. Our full model alone has the necessary complexity to simulate these complex response characteristics. To account for the complex response characteristics of honeybee receptor neurons, we suggest that several different receptor protein types and at least two second messenger systems are necessary that may interact at various levels of the transduction cascade and may eventually have opposing effects on receptor neuron excitability.