Second antiseizure medication monotherapy in patients with adult-onset epilepsy: A register-based analysis.

OBJECTIVE: Revision of therapy is fundamental in epilepsy care, since only half of patients achieve seizure freedom and tolerate the first antiseizure medication (ASM). We studied the selection and retention of second antiseizure medication monotherapy in adults who discontinued treatment with one of the three most frequently prescribed first ASMs, and the impact of age or brain comorbidities. METHODS: Using Swedish national registers, we conducted a population-based, retrospective cohort study from 2007 to 2019 on patients age ≥ 30 at the epilepsy diagnosis that had switched to a second monotherapy after the three most common initial monotherapies (n = 7369). Retention rates (RR) were estimated via Kaplan-Meier. Discontinuation of the second monotherapy was defined as 12-month prescription gap or initiation of a third ASM. Analyses were stratified by sex, age, and presence of stroke or dementia. RESULTS: The three most commonly prescribed second ASMs were carbamazepine, levetiracetam, and lamotrigine. The 1-year retention rate was 63-76% in all patients. For groups with stroke or dementia, the maximal 1-year RRs were 77% and 87%, respectively. After five years, retention rates ranged from 12% to 39%. There were no major differences between ASMs, apart from in patients discontinuing carbamazepine, where lamotrigine had a superior retention compared to levetiracetam as second monotherapy. SIGNIFICANCE: The three most often prescribed second ASMs seem to be suitable treatment options according to present guidelines. The second ASMs' retention rates were initially high in all studied patient groups but dropped to approximately the expected proportion of second monotherapy responders over the next five years. This suggests that therapy revision could be expedited.

Using Micro- and Macro-Level Network Metrics Unveils Top Communicative Gene Modules in Psoriasis.

(1) Background: Psoriasis is a multifactorial chronic inflammatory disorder of the skin, with significant morbidity, characterized by hyperproliferation of the epidermis. Even though psoriasis' etiology is not fully understood, it is believed to be multifactorial, with numerous key components. (2) Methods: In order to cast light on the complex molecular interactions in psoriasis vulgaris at both protein-protein interactions and transcriptomics levels, we studied a set of microarray gene expression analyses consisting of 170 paired lesional and non-lesional samples. Afterwards, a network analysis was conducted on the protein-protein interaction network of differentially expressed genes based on micro- and macro-level network metrics at a systemic level standpoint. (3) Results: We found 17 top communicative genes, all of which were experimentally proven to be pivotal in psoriasis, which were identified in two modules, namely the cell cycle and immune system. Intra- and inter-gene interaction subnetworks from the top communicative genes might provide further insight into the corresponding characteristic interactions. (4) Conclusions: Potential gene combinations for therapeutic/diagnostics purposes were identified. Moreover, our proposed workflow could be of interest to a broader range of future biological network analysis studies.

Cancer systems biology and modeling: microscopic scale and multiscale approaches.

Cancer has become known as a complex and systematic disease on macroscopic, mesoscopic and microscopic scales. Systems biology employs state-of-the-art computational theories and high-throughput experimental data to model and simulate complex biological procedures such as cancer, which involves genetic and epigenetic, in addition to intracellular and extracellular complex interaction networks. In this paper, different systems biology modeling techniques such as systems of differential equations, stochastic methods, Boolean networks, Petri nets, cellular automata methods and agent-based systems are concisely discussed. We have compared the mentioned formalisms and tried to address the span of applicability they can bear on emerging cancer modeling and simulation approaches. Different scales of cancer modeling, namely, microscopic, mesoscopic and macroscopic scales are explained followed by an illustration of angiogenesis in microscopic scale of the cancer modeling. Then, the modeling of cancer cell proliferation and survival are examined on a microscopic scale and the modeling of multiscale tumor growth is explained along with its advantages.

Reconstruction of an integrated genome-scale co-expression network reveals key modules involved in lung adenocarcinoma.

Our goal of this study was to reconstruct a "genome-scale co-expression network" and find important modules in lung adenocarcinoma so that we could identify the genes involved in lung adenocarcinoma. We integrated gene mutation, GWAS, CGH, array-CGH and SNP array data in order to identify important genes and loci in genome-scale. Afterwards, on the basis of the identified genes a co-expression network was reconstructed from the co-expression data. The reconstructed network was named "genome-scale co-expression network". As the next step, 23 key modules were disclosed through clustering. In this study a number of genes have been identified for the first time to be implicated in lung adenocarcinoma by analyzing the modules. The genes EGFR, PIK3CA, TAF15, XIAP, VAPB, Appl1, Rab5a, ARF4, CLPTM1L, SP4, ZNF124, LPP, FOXP1, SOX18, MSX2, NFE2L2, SMARCC1, TRA2B, CBX3, PRPF6, ATP6V1C1, MYBBP1A, MACF1, GRM2, TBXA2R, PRKAR2A, PTK2, PGF and MYO10 are among the genes that belong to modules 1 and 22. All these genes, being implicated in at least one of the phenomena, namely cell survival, proliferation and metastasis, have an over-expression pattern similar to that of EGFR. In few modules, the genes such as CCNA2 (Cyclin A2), CCNB2 (Cyclin B2), CDK1, CDK5, CDC27, CDCA5, CDCA8, ASPM, BUB1, KIF15, KIF2C, NEK2, NUSAP1, PRC1, SMC4, SYCE2, TFDP1, CDC42 and ARHGEF9 are present that play a crucial role in cell cycle progression. In addition to the mentioned genes, there are some other genes (i.e. DLGAP5, BIRC5, PSMD2, Src, TTK, SENP2, PSMD2, DOK2, FUS and etc.) in the modules.