Immunomodulatory effects of Kaempferol on microglial and Macrophage cells during the progression of diabetic retinopathy.

BACKGROUND: Diabetic retinopathy (DR) stands as a prevalent secondary complication of diabetes, notably Type 1 Diabetes Mellitus (T1D), characterized by immune system involvement potentially impacting the retinal immune response mediated by microglia. Early stages of DR witness blood-retinal barrier permeabilization, facilitating peripheral immune cell interaction with the retinal immune system. Kaempferol (Kae), known for its potent anti-inflammatory activity, presents a promising avenue in DR treatment by targeting the immune mechanisms underlying its onset and progression. Our investigation delves into the molecular intricacies of innate immune cell interaction during DR progression and the attenuation of inflammatory processes pivotal to its pathology. METHODS: Employing in vitro studies, we exposed HAPI microglial and J774.A1 macrophage cells to pro-inflammatory stimuli in the presence or absence of Kae. Ex vivo and in vivo experiments utilized BB rats, a T1D animal model. Retinal explants from BB rats were cultured with Kae, while intraperitoneal Kae injections were administered to BB rats for 15 days. Quantitative PCR, Western blotting, immunofluorescence, and Spectral Domain - Optical Coherence Tomography (SD-OCT) facilitated survival assessment, cellular signaling analysis, and inflammatory marker determination. RESULTS: Results demonstrate Kae significantly mitigates inflammatory processes across in vitro, ex vivo, and in vivo DR models, primarily targeting immune cell responses. Kae administration notably inhibits proinflammatory responses during DR progression while promoting an anti-inflammatory milieu, chiefly through microglia-mediated synthesis of Arginase-1 and Hemeoxygenase-1(HO-1). In vivo, Kae administration effectively preserves retinal integrity amid DR progression. CONCLUSIONS: Our findings elucidate the interplay between retinal and systemic immune cells in DR progression, underscoring a differential treatment response predominantly orchestrated by microglia's anti-inflammatory action. Kae treatment induces a phenotypic and functional shift in immune cells, delaying DR progression, thereby spotlighting microglial cells as a promising therapeutic target in DR management.

Fabrication and characterization of nanodelivery platform based on chitosan to improve the anticancer outcome of sorafenib in hepatocellular carcinoma.

Chitosan nanoparticles (CS NPs) showed promising results in drug, vaccine and gene delivery for the treatment of various diseases. The considerable attention towards CS was owning to its outstanding biological properties, however, the main challenge in the application of CS NPs was faced during their size-controlled synthesis. Herein, ionic gelation reaction between CS and sodium tripolyphosphate (TPP), a widely used and safe CS cross-linker for biomedical application, was exploited. The development of nanodelivery platform, namely Sorafenib-loaded chitosan nanoparticles (SF-CS NPs), was constructed in order to improve SF drug delivery to human Hepatocellular Carcinoma (HepG2) cell lines. The NPs were artificially fabricated using an ionic gelation technique. A number of CS NPs that had been loaded with an SF were prepared using different concentrations of sodium tripolyphosphate (TPP). These concentrations were 2.5, 5, 10, and 20 mg/mL, and they are abbreviated as SF-CS NPs 2.5, SF-CS NPs 5.0, SF-CS NPs 10, and SF-CS NPs 20 respectively. DLS, FTIR, XRD, HRTEM, TGA, and FESEM with EDX and TEM were used for the physiochemical characterisation of SF-CS NPs. Both DLS and HRTEM techniques demonstrated that smaller particles were produced when the TPP content was raised. In a PBS solution with a pH of 4.5, the SF exhibited efficient release from the nanoparticles, demonstrating that the delivery mechanism is effective for tumour cells. The cytotoxicity investigation showed that their anticancer effect against HepG2 cell lines was significantly superior than that of free SF. In addition, the nanodrug demonstrated an absence of any detectable toxicity to normal adult human dermal fibroblast (HDFa) cell lines. This is a step towards developing a more effective anticancer medication delivery system with sustained-release characteristics, which will ultimately improve the way cancer is managed.

Fault Diagnostics and Tolerance Analysis of a Microgrid System Using Hamilton-Jacobi-Isaacs Equation and Game Theoretic Estimations in Sliding Mode Observers.

This paper focuses on robustness and sensitivity analysis for sensor fault diagnosis of a voltage source converter based microgrid model. It uses robust control parameters such as minimum sensitivity parameter (H-), maximum robustness parameter (H∞), and compromised both (H-/H∞), being incorporated in the sliding mode observer theory using the game theoretic saddle point estimation achieved through convex optimization of constrained LMIs. The approach used works in a way that the mentioned robust control parameters are embedded in Hamilton-Jacobi-Isaacs-Equation (HJIE) and are also used to determine the inequality version of HJIE, which is, in terms of the Lyapunov function, faults/disturbances and augmented state/output estimation error as its variables. The stability analysis is also presented by negative definiteness of the same inequality version of HJIE, and additionally, it also gives linear matrix inequalities (LMIs), which are optimized using iterative convex optimization algorithms to give optimal sliding mode observer gains enhanced with robustness to maximal preset values of disturbances and sensitivity to minimal preset values of faults. The enhanced sliding mode observer is used to estimate states, faults, and disturbances using sliding mode observer theory. The optimality of sliding mode observer gains for sensitivity of the observer to minimal faults and robustness to maximal disturbance is a game theoretic saddle point estimation achieved through convex optimization of LMIs. The paper includes results for state estimation errors, faults' estimation/reconstruction, fault estimation errors, and fault-tolerant-control performance for current and potential transformer faults. The considered faults and disturbances in current and potential transformers are sinusoidal nature composite of magnitude/phase/harmonics at the same time.

Consequences of SARS-CoV-2 Infection in Pregnant Women and Their Infants: A Systematic Review.

Coronavirus disease 2019 (COVID-19) is a worldwide health problem, particularly for pregnant women. This review assesses the effects of COVID-19 on pregnant women and their infants. A systematic search was performed of studies published on PubMed, Web of Science, Google Scholar, and Embase from January 2020 to January 2021, without restriction by language. This review included 27 studies (22 from China, one from the United States, one from Honduras, one from Italy, one from Iran, and one from Spain), which cumulatively evaluated 386 pregnant women with clinically confirmed COVID-19 and their 334 newborns. Of the 386 pregnant women, 356 had already delivered their infants, four had medical abortions at the time of research, 28 were still pregnant, and two died from COVID-19 before they were able to give birth. Cesarean sections were performed on 71% of pregnant women with COVID-19 to give birth. Fever and cough were common symptoms among women. Premature rupture of membranes, distress, and preterm birth were pregnancy complications. Low birth weight and a short gestational age were common outcomes for newborns. The common laboratory findings among pregnant women were lymphopenia, leukocytosis, and elevated levels of C-reactive protein. Chest computed tomography revealed abnormal viral lung changes in 73.3% of women. Eleven infants tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. There was no evidence of vertical transmission. Most infants were observed to have lymphopenia and thrombocytopenia. The clinical features of pregnant women were found to be similar to those of generally infected patients. There is evidence of adverse pregnancy and neonatal outcomes caused by COVID-19.

Engineered Nanomaterials: The Challenges and Opportunities for Nanomedicines.

The emergence of nanotechnology as a key enabling technology over the past years has opened avenues for new and innovative applications in nanomedicine. From the business aspect, the nanomedicine market was estimated to worth USD 293.1 billion by 2022 with a perception of market growth to USD 350.8 billion in 2025. Despite these opportunities, the underlying challenges for the future of engineered nanomaterials (ENMs) in nanomedicine research became a significant obstacle in bringing ENMs into clinical stages. These challenges include the capability to design bias-free methods in evaluating ENMs' toxicity due to the lack of suitable detection and inconsistent characterization techniques. Therefore, in this literature review, the state-of-the-art of engineered nanomaterials in nanomedicine, their toxicology issues, the working framework in developing a toxicology benchmark and technical characterization techniques in determining the toxicity of ENMs from the reported literature are explored.

QuPWM: Feature Extraction Method for Epileptic Spike Classification.

Epilepsy is a neurological disorder ranked as the second most serious neurological disease known to humanity, after stroke. Inter-ictal spiking is an abnormal neuronal discharge after an epileptic seizure. This abnormal activity can originate from one or more cranial lobes, often travels from one lobe to another, and interferes with normal activity from the affected lobe. The common practice for Inter-ictal spike detection of brain signals is via visual scanning of the recordings, which is a subjective and a very time-consuming task. Motivated by that, this article focuses on using machine learning for epileptic spikes classification in magnetoencephalography (MEG) signals. First, we used the Position Weight Matrix (PWM) method combined with a uniform quantizer to generate useful features from time domain and frequency domain through a Fast Fourier Transform (FFT) of the framed raw MEG signals. Second, the extracted features are fed to standard classifiers for inter-ictel spikes classification. The proposed technique shows great potential in spike classification and reducing the feature vector size. Specifically, the proposed technique achieved average sensitivity up to 87% and specificity up to 97% using 5-folds cross-validation applied to a balanced dataset. These samples are extracted from nine epileptic subjects using a sliding frame of size 95 samples-points with a step-size of 8 sample-points.

Multiplex PCR is a Rapid, Simple and Cheap Method for Direct Diagnosis of M. tuberculosis from Sputum Samples.

INTRODUCTION: Rapid diagnosis of M. tuberculosis directly from sputum samples is a challenging process. This study aimed to design and evaluate a multiplex-PCR method for direct diagnosis of M. tuberculosis from sputum specimens. MATERIALS AND METHODS: 46 suspected tuberculosis patients and 25 apparently healthy individuals were enrolled in the study. Sputa were collected from the study population and processed by cold ZN stain. DNA was extracted from each sample and processed by Multiplex PCR and Genotype Mycobacteria CM. RESULTS: Out of the 46 Tuberculosis suspected patients, 22 (47.8%) revealed positive Acid fast ba- cilli (AFB), while 19 (41.3%) showed positive by both multiplex PCR and Genotype Mycobacte- ria CM. The overall sensitivity of multiplex PCR and smear microscopy were 100% while the specificity were 100, and 86.3%, respectively. CONCLUSION: Multiplex PCR method using two different sets of primers in combination with other diagnostic tools such as X-Rays and smear Microscopy are cheap, rapid and reliable methods for the diagnosis of M. tuberculosis from clinical samples and are able to identify most of the smear positive cases with valuable accuracy.

Emergence of Heteroresistance Mycobacterium Tuberculosis in Saudi Arabia.

PURPOSE: Heteroresistant Mycobacterium tuberculosis (MTB) is defined as a group of drug-susceptible and resistant bacteria in a single clinical specimen from tuberculosis (TB) patients. Heteroresistance of MTB is considered a preliminary stage to full resistance. The present study aimed to determine the heteroresistance in Mycobacterium tuberculosis in Tabuk province, in the north of the Kingdom of Saudi Arabia. METHOD: GenoType MTBDRplus assay was used to determine mutations associated with isoniazid and rifampicin resistance. RESULTS: A total number of 46 confirmed M. tuberculosis positive sputum samples were scanned for heteroresistance. The present study revealed 3 (6.5%) heteroresistant mutations to either rpoB gene alone, 2 (4.4%) to rpoB and 1 (2.2%) to inhA genes. CONCLUSION: The detection of heteroresistant mutations could guide the initiation of an appropriate regimen of treatment.

Hybrid model for efficient prediction of poly(A) signals in human genomic DNA.

Polyadenylation signals (PAS) are found in most protein-coding and some non-coding genes in eukaryotes. Their accurate recognition improves understanding gene regulation mechanisms and recognition of the 3'-end of transcribed gene regions where premature or alternate transcription ends may lead to various diseases. Although different methods and tools for in-silico prediction of genomic signals have been proposed, the correct identification of PAS in genomic DNA remains challenging due to a vast number of non-relevant hexamers identical to PAS hexamers. In this study, we developed a novel method for PAS recognition. The method is implemented in a hybrid PAS recognition model (HybPAS), which is based on deep neural networks (DNNs) and logistic regression models (LRMs). One of such models is developed for each of the 12 most frequent human PAS hexamers. DNN models appeared the best for eight PAS types (including the two most frequent PAS hexamers), while LRM appeared best for the remaining four PAS types. The new models use different combinations of signal processing-based, statistical, and sequence-based features as input. The results obtained on human genomic data show that HybPAS outperforms the well-tuned state-of-the-art Omni-PolyA models, reducing the classification error for different PAS hexamers by up to 57.35% for 10 out of 12 PAS types, with Omni-PolyA models being better for two PAS types. For the most frequent PAS types, 'AATAAA' and 'ATTAAA', HybPAS reduced the error rate by 35.14% and 34.48%, respectively. On average, HybPAS reduces the error by 30.29%. HybPAS is implemented partly in Python and in MATLAB available at https://github.com/EMANG-KAUST/PolyA_Prediction_LRM_DNN.