Browse Articles

Interpretable and integrative deep learning for discovering brain-behaviour associations

Author Correction: A sedimentary ancient DNA perspective on human and carnivore persistence through the Late Pleistocene in El Mirón Cave, Spain

Stability indicating RP-HPLC technique for simultaneous estimation of nirmatrelvir and ritonavir in their new copackaged dosage form for COVID-19 treatment

Abstract RP-HPLC technique was developed and optimized for simultaneous identification and estimation of nirmatrelvir (NIR) and ritonavir (RIT) in their new copackaged tablet. Stability of nirmatrelvir (NIR) was studied after exposure to different five stress conditions; alkali, acid, heat, photo and oxidation degradation. The chromatographic separation was achieved using VDSpher PUR 100 ODS (4.6-mm x 15-mm), 3.5 μm column and mixture of 0.03 M potassium di-hydrogen phosphate buffer pH 4 and acetonitrile (45:55, v/v) as mobile phase. The column temperature was set at 40 °C, flow rate at 1mL/min and UV detection at 215 nm. The NIR and RIT retention times were 3.94 ± 0.08 min and 9.08 ± 0.1 min, respectively. Linear relationship was established in range of (1.5–105 µg/mL) for NIR and (1–70 µg/mL) for RIT with good reproducibility. The found mean percentage recoveries of nirmatrelvir (NIR) and ritonavir (RIT) were 100.03% and 99.85%, respectively. The developed method shows very good sensitivity as the LOQ and LOD were found to be 3.001 & 0.990 µg/mL, respectively for NIR and 2.765 & 0.912 µg/mL, respectively for RIT. The developed approach was validated concerning to ICH guidelines and applied successfully for the simultaneous estimation of NIR and RIT in their new copackaged dosage from. The results of assay using the proposed approach were compared statistically to the results found by applying the published one with good agreement.

Enhanced sampling of protein conformational changes via true reaction coordinates from energy relaxation

Physical and emotional abuse with internet addiction and anxiety as a mediator and physical activity as a moderator

Improving polyketide biosynthesis by rescuing the translation of truncated mRNAs into functional polyketide synthase subunits

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.