Transcriptomic Analysis of Age-Associated Periventricular Lesions Reveals Dysregulation of the Immune Response.

White matter lesions (WML) are a common feature of the ageing brain associated with cognitive impairment. The gene expression profiles of periventricular lesions (PVL, n = 7) and radiologically-normal-appearing (control) periventricular white matter cases (n = 11) obtained from the Cognitive Function and Ageing Study (CFAS) neuropathology cohort were interrogated using microarray analysis and NanoString to identify novel mechanisms potentially underlying their formation. Histological characterisation of control white matter cases identified a subgroup (n = 4) which contained high levels of MHC-II immunoreactive microglia, and were classified as "pre-lesional." Microarray analysis identified 2256 significantly differentially-expressed genes (p ≤ 0.05, FC ≥ 1.2) in PVL compared to non-lesional control white matter (1378 upregulated and 878 downregulated); 2649 significantly differentially-expressed genes in "pre-lesional" cases compared to PVL (1390 upregulated and 1259 downregulated); and 2398 significantly differentially-expressed genes in "pre-lesional" versus non-lesional control cases (1527 upregulated and 871 downregulated). Whilst histological evaluation of a single marker (MHC-II) implicates immune-activated microglia in lesion pathology, transcriptomic analysis indicates significant downregulation of a number of activated microglial markers and suggests established PVL are part of a continuous spectrum of white matter injury. The gene expression profile of "pre-lesional" periventricular white matter suggests upregulation of several signalling pathways may be a neuroprotective response to prevent the pathogenesis of PVL

Study the beneficial role of laser irradiation combination with indirect swim-up sperm preparation technique against oxidative DNA damage in infertile men

Background: In order to offer successful assisted reproductive procedures, a variety of in vitro sperm preparation techniques were created to separate normal and motile spermatozoa from other constituents of the sample. Much research on the laser as a sperm motility stimulant has been undertaken, and the results have indicated that the Laser has a good effect on sperm activation in vitro and improves progressive forward movement.  Objective: This study is aimed to identify the differences in sperm activation by ISU with or without laser methods and compare them. And detection of oxidative damage to the DNA before and after laser by assessment of the 8-Hydroxydeoxyguanosine (8-OHDG) as a biomarker. Patients and Methods: The current study was conducted on 30 semen samples, divided into two groups (Asthenozoospermia and Normozoospermia individuals), during the period of attendance at the infertility clinic at the High Institute for Infertility Diagnosis and Assisted Reproductive Technologies, Al-Nahrain University. From November 2021 until March 2022. Each sperm sample was separated into three portions

Robust Automatic Modulation Classification Using Convolutional Deep Neural Network Based on Scalogram Information

This study proposed a two-stage method, which combines a convolutional neural network (CNN) with the continuous wavelet transform (CWT) for multiclass modulation classification. The modulation signals’ time-frequency information was first extracted using CWT as a data source. The convolutional neural network was fed input from 2D pictures. The second step included feeding the proposed algorithm the 2D time-frequency information it had obtained in order to classify the different kinds of modulations. Six different types of modulations, including amplitude-shift keying (ASK), phase-shift keying (PSK), frequency-shift keying (FSK), quadrature amplitude-shift keying (QASK), quadrature phase-shift keying (QPSK), and quadrature frequency-shift keying (QFSK), are automatically recognized using a new digital modulation classification model between 0 and 25 dB SNRs. Modulation types are used in satellite communication, underwater communication, and military communication. In comparison with earlier research, the recommended convolutional neural network learning model performs better in the presence of varying noise levels

Induction of apoptosis and autophagy via regulation of AKT and JNK mitogen-activated protein kinase pathways in breast cancer cell lines exposed to gold nanoparticles loaded with TNF-α and combined with doxorubicin

Gold nanoparticles (GNPs) tagged with peptides are pioneers in bioengineered cancer therapy. The aim of the current work was to elucidate the potential anticancer interactions between doxorubicin and GNPs loaded with tumor necrosis factor-alpha (TNF-α). To investigate whether GNPs loaded with TNF and doxorubicin could stimulate autophagy and apoptosis in breast cancer cells. Two human breast cancer cell lines, MCF-7 and AMJ-13, as well as different apoptotic and autophagy markers, were used. In both cell types, treatment with TNF-loaded GNPs in conjunction with doxorubicin increased the production of apoptotic proteins including Bad, caspase-3, caspase-7, and p53 with upregulation of the LC3-II and Beclin1 proteins. In addition, the findings showed that the mitogen-activated protein kinase signaling pathway was dramatically affected by the GNPs loaded with TNF-α and combined with doxorubicin. This had the effect of decreasing p-AKT while simultaneously increasing p-JNK1/2. The findings demonstrated that GNPs loaded with TNF-α and combined with doxorubicin can induce both autophagy and apoptosis in breast cancer cells. These results suggest that TNF- and doxorubicin-loaded GNPs provide a therapeutic option as a nanomedicine to inhibit the proliferation of breast cancer

Robust Automatic Modulation Classification Using Convolutional Deep Neural Network Based on Scalogram Information

This study proposed a two-stage method, which combines a convolutional neural network (CNN) with the continuous wavelet transform (CWT) for multiclass modulation classification. The modulation signals’ time-frequency information was first extracted using CWT as a data source. The convolutional neural network was fed input from 2D pictures. The second step included feeding the proposed algorithm the 2D time-frequency information it had obtained in order to classify the different kinds of modulations. Six different types of modulations, including amplitude-shift keying (ASK), phase-shift keying (PSK), frequency-shift keying (FSK), quadrature amplitude-shift keying (QASK), quadrature phase-shift keying (QPSK), and quadrature frequency-shift keying (QFSK), are automatically recognized using a new digital modulation classification model between 0 and 25 dB SNRs. Modulation types are used in satellite communication, underwater communication, and military communication. In comparison with earlier research, the recommended convolutional neural network learning model performs better in the presence of varying noise levels

Graphene oxide-induced, reactive oxygen species-mediated mitochondrial dysfunctions and apoptosis: high-dose toxicity in normal cells - supplementary figures

Aim: The cytotoxic effects of graphene oxide nanoparticles (GONPs) using MTT assays, observance of apoptotic markers, and oxidative stress were outlined. Materials & methods: Rat embryonic fibroblasts (REFs) and human epithelial breast cells (HBLs) were used at 250, 500 and 750 μg/ml concentrations. Results: Significant cytotoxic and apoptotic effects were observed. Analyses of CYP2E1 and malondialdehyde concentrations in REF and HBL-100 cell lines after exposing to GONPs confirmed the nanomaterials toxicity. However, the glutathione levels in REF and HBL-100 cell lines showed a substantial reduction compared with the control. The cytochrome CYP2E1, glutathione, malondialdehyde and caspase-3 alterations provided a plausible interlinked relationship. Conclusion: The study confirmed the GONPs cytotoxic effects on REF and HBL-100 cell lines. The outcome suggested caution in wide-spread applications of GONPs, which could have implications for occupational health also.</p

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population