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Exploring the anticancer activities of Sulfur and magnesium oxide through integration of deep learning and fuzzy rough set analyses based on the features of Vidarabine alkaloid

Abstract Drug discovery and development is a challenging and time-consuming process. Laboratory experiments conducted on Vidarabine showed IC50 6.97 µg∕mL, 25.78 µg∕mL, and ˃ 100 µg∕mL against non-small Lung cancer (A-549), Human Melanoma (A-375), and Human epidermoid Skin carcinoma (skin/epidermis) (A-431) respectively. To address these challenges, this paper presents an Artificial Intelligence (AI) model that combines the capabilities of Deep Learning (DL) to identify potential new drug candidates, Fuzzy Rough Set (FRS) theory to determine the most important chemical compound features, Explainable Artificial Intelligence (XAI) to explain the features’ importance in the last layer, and medicinal chemistry to rediscover anticancer drugs based on natural products like Vidarabine. The proposed model aims to identify potential new drug candidates. By analyzing the results from laboratory experiments on Vidarabine, the model identifies Sulfur and magnesium oxide (MgO) as new potential anticancer agents. The proposed model selected Sulfur and MgO based on Interpreting their promising features, and further laboratory experiments were conducted to validate the model’s predictions. The results demonstrated that, while Vidarabine was inactive against the A-431 cell line (IC50 ˃ 100 µg∕mL), Sulfur and MgO exhibited significant anticancer activity (IC50 4.55 and 17.29 µg/ml respectively). Sulfur displayed strong activity against A-549 and A-375 cell lines (IC50 3.06 and 1.86 µg/ml respectively) better than Vidarabine (IC50 6.97 and 25.78 µg/ml respectively). However, MgO showed weaker activity against these two cell lines. This paper emphasizes the importance of uncovering hidden chemical features that may not be discernible without the assistance of AI. This highlights the ability of AI to discover novel compounds with therapeutic potential, which can significantly impact the field of drug discovery. The promising anticancer activity exhibited by Sulfur and MgO warrants further preclinical studies.

NKAPL facilitates transcription pause-release and bridges elongation to initiation during meiosis exit

A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting

Abstract This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency’s Downscaler Model (DS) to predict Particulate Matter ( $$PM_{2.5}$$ ) levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time. Additionally, we contrast our methodology with recently created models that have been published in the literature, such as the Grey Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Henry Gas Solubility Optimization (HGSO), Barnacles Mating Optimizer (BMO), Whale Optimization Algorithm (WOA), and Manta Ray Foraging Optimization (MRFO). In particular, the proposed HHO-SVR model outperforms other approaches, establishing it as the optimal model based on its superior results.

Daily briefing: The brickbats hurled at scientists researching tobacco’s harms

Author Correction: Prospective de novo drug design with deep interactome learning

Foliar image-based characterization of airborne particulate matter in an urban area and its implications for remediation

Single-cell transcriptome atlas of lamprey exploring Natterin- induced white adipose tissue browning

Dual RNA sequencing of a co-culture model of Pseudomonas aeruginosa and human 2D upper airway organoids

How to be a brilliant ally to your neurodivergent lab mate

Rotation symmetry mismatch and interlayer hybridization in MoS2-black phosphorus van der Waals heterostructures

Surface plasmon resonance detection of anti-cancer drug flutamide by graphitic carbon nitride/chitosan nanocomposite

How to trick the immune system into attacking tumours

Anti-correlation of LacI association and dissociation rates observed in living cells

Abstract The rate at which transcription factors (TFs) bind their cognate sites has long been assumed to be limited by diffusion, and thus independent of binding site sequence. Here, we systematically test this assumption using cell-to-cell variability in gene expression as a window into the in vivo association and dissociation kinetics of the model transcription factor LacI. Using a stochastic model of the relationship between gene expression variability and binding kinetics, we performed single-cell gene expression measurements to infer association and dissociation rates for a set of 35 different LacI binding sites. We found that both association and dissociation rates differed significantly between binding sites, and moreover observed a clear anticorrelation between these rates across varying binding site strengths. These results contradict the long-standing hypothesis that TF binding site strength is primarily dictated by the dissociation rate, but may confer the evolutionary advantage that TFs do not get stuck in near-operator sequences while searching.

Comparison of 1D and 3D volume measurement techniques in NF2-associated vestibular schwannoma monitoring

Abstract To compare 1D (linear) tumor volume calculations and classification systems with 3D-segmented volumetric analysis (SVA), focusing specifically on their effectiveness in the evaluation and management of NF2-associated vestibular schwannomas (VS). VS were clinically followed every 6 months with cranial, thin-sliced (< 3 mm) MRI. We retrospectively reviewed and used T1-weighted post-contrast enhanced (gadolinium) images for both SVA and linear measurements. 3D-SVA was performed manually or combined with semiautomated segmentation by using axial planes. The maximum linear dimensions (MLD) were determined in three dimensions (anteroposterior, transverse, and craniocaudal planes) using axial and coronal planes. The MLD was cubed (MLD3), and orthogonal analysis (OA) was derived to establish comparability with the SVA. The Hannover and Koos classification was used to depict the size ratio in each MRI and tumor. A linear regression model was performed to compare 1D/classification systems to SVA, and the percentage deviation change of MLD3 and OA to SVA was established using a one-way multivariate variance analysis. 2586 SVA and 10344 linear measurements were performed in a cohort of 149 NF2 patients and 292 associated VS. All measurement techniques (MLD3, OA, KOOS, and Hannover) significantly (and strongly, r2 > 0.5) correlated with SVA (p < 0.001). The OA showed an even stronger positive correlation than the MLD3 to SVA. Smaller classified tumors (T1/T2, K1/K2) exhibited a low-moderate positive correlation (r2 = 0.23–0.44) compared to medium-sized (T3, K2/3) and large tumors (T4, K4; r2 = 0.54–0.76). Pre- and postoperative MLD3 and OA statistically significantly predict SVA (p < 0.001), but the postoperative correlation was weaker, particularly for MLD3 to SVA values. All analyses showed a large scatter range. In the percentage deviation analysis of MLD3 and OA from SVA, small tumors (K1/K2, T1/T2) were overestimated. Compared to the SVA, the MLD3 and especially the OA are a time-saving alternative for monitoring the tumor volume of NF2-associated VS. However, the scatter range in small/surgically reduced tumors is enormous. For this reason, they are not recommended for monitoring off-label therapy with Bevacizumab or for treatment decisions depending on a precise assessment of tumor volume and growth. Developing deep learning-based volume determinations in the future is essential to reduce SVA’s time intensity.

Revealed: Why the fatal Huntington’s gene takes so long to cause harm

Patterns of Klebsiella pneumoniae bacteremic dissemination from the lung

Optimal dose and type of exercise improve walking velocity in adults with Parkinson’s disease: a systematic review and Bayesian network meta-analysis

Interferon-α promotes HLA-B-restricted presentation of conventional and alternative antigens in human pancreatic β-cells

Abstract Interferon (IFN)-α is the earliest cytokine signature observed in individuals at risk for type 1 diabetes (T1D), but the effect of IFN-α on the antigen repertoire of HLA Class I (HLA-I) in pancreatic β-cells is unknown. Here we characterize the HLA-I antigen presentation in resting and IFN-α-exposed β-cells and find that IFN-α increases HLA-I expression and expands peptide repertoire to those derived from alternative mRNA splicing, protein cis-splicing and post-translational modifications. While the resting β-cell immunopeptidome is dominated by HLA-A-restricted peptides, IFN-α largely favors HLA-B and only marginally upregulates HLA-A, translating into increased HLA-B-restricted peptide presentation and activation of HLA-B-restricted CD8+ T cells. Lastly, islets of patients with T1D show preferential HLA-B hyper-expression when compared with non-diabetic donors, and islet-infiltrating CD8+ T cells reactive to HLA-B-restricted granule peptides are found in T1D donors. Thus, the inflammatory milieu of insulitis may skew the autoimmune response toward alternative epitopes presented by HLA-B, hence recruiting T cells with a distinct repertoire that may be relevant to T1D pathogenesis.

YOLOSeg with applications to wafer die particle defect segmentation

Comparative transcriptomics in serial organs uncovers early and pan-organ developmental changes associated with organ-specific morphological adaptation

Abstract Mice have evolved a new dental plan with two additional cusps on the upper molar, while hamsters were retaining the ancestral plan. By comparing the dynamics of molar development with transcriptome time series, we found at least three early changes in mouse upper molar development. Together, they redirect spatio-temporal dynamics to ultimately form two additional cusps. The mouse lower molar has undergone much more limited phenotypic evolution. Nevertheless, its developmental trajectory evolved as much as that of the upper molar and co-evolved with it. Among the coevolving changes, some are clearly involved in the new upper molar phenotype. We found a similar level of coevolution in bat limbs. In conclusion, our study reveals how serial organ morphology has adapted through organ-specific developmental changes, as expected, but also through shared changes that have organ-specific effects on the final phenotype. This highlights the important role of developmental system drift in one organ to accommodate adaptation in another.