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The rapid advancement of Deoxyribonucleic acid (DNA) sequencing technology has gained more attention, especially in interpreting high-dimensional, low-sample-size microarray data for disease identification. However, conventional gene selection techniques struggle to identify optimal biomarker subsets from gene data within a feasible time. To address this, we propose a novel hybrid method for robust cancer classification and biomarker discovery. To reduce the dimensionality of gene data while preserving biologically meaningful patterns, in the first stage of our approach, Kernel Principal Component Analysis (KPCA) is utilized. The refined gene subsets are then processed by the Multi-Population Gravitational Search Algorithm (GSA) known as MPKGSA with Opposition-Based Learning (OBL). The hybridization mechanism involves using OBL to generate a set of opposite solutions for each population, which is then integrated into the GSA update process. This process provides a more diverse exploration of the search space, preventing premature convergence on suboptimal gene subsets. The effectiveness of MPKGSA was evaluated on six microarray cancer datasets and a breast cancer single-nucleotide polymorphism (SNP) dataset from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). Numerical results demonstrate that MPKGSA excels at balancing convergence and diversity, achieving high prediction accuracy with minimal biomarker subsets. Furthermore, it outperformed existing meta-heuristic methods, selecting a small number of gene biomarkers strongly correlated with the biological response class, confirming its utility for precise cancer identification and classification.

Focused ultrasound and microbubbles can non-invasively and locally deliver therapeutics and imaging agents across the blood–brain barrier. Uniform treatment and minimal adverse bioeffects are critical to achieve reliable doses and enable safe routine use of this technique. Towards these aims, we have previously designed a rapid short-pulse ultrasound sequence and used it to deliver a 3 kDa model agent to mouse brains. We observed a homogeneous distribution in delivery and blood–brain barrier closing within 10 min. However, many therapeutics and imaging agents are larger than 3 kDa, such as antibody fragments and antisense oligonucleotides. Here, we evaluate the feasibility of using rapid short-pulses to deliver higher-molecular-weight model agents. 3, 10 and 70 kDa dextrans were successfully delivered to mouse brains, with decreasing doses and more heterogeneous distributions with increasing agent size. Minimal extravasation of endogenous albumin (66.5 kDa) was observed, while immunoglobulin (~ 150 kDa) and PEGylated liposomes (97.9 nm) were not detected. This study indicates that rapid short-pulses are versatile and, at an acoustic pressure of 0.35 MPa, can deliver therapeutics and imaging agents of sizes up to a hydrodynamic diameter between 8 nm (70 kDa dextran) and 11 nm (immunoglobulin). Increasing the acoustic pressure can extend the use of rapid short-pulses to deliver agents beyond this threshold, with little compromise on safety. This study demonstrates the potential for deliveries of higher-molecular-weight therapeutics and imaging agents using rapid short-pulses.

Tirzepatide represents a breakthrough in metabolic therapeutics as a dual agonist of glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) receptors1. This systematic review synthesizes evidence on its pharmacological mechanisms, clinical efficacy, safety profile, and broader implications for type 2 diabetes and obesity management. Drawing from major clinical trials and mechanistic studies, Tirzepatide demonstrates superior glycemic control (mean HbA1c reduction of 1.8-2.6%) and weight loss (up to 25% body weight reduction) compared to single GLP-1 agonists2,3, attributed to synergistic receptor co-activation. Safety data indicate transient gastrointestinal effects as primary concerns, with low hypoglycemia risk4. Future research should explore long-term outcomes and pharmacogenomic influences to optimize personalized therapy.

This paper reviews the work in the areas of machine learning’s role in bolstering renewable energy within smart grids. As the global shift towards eco-friendly energy sources such as wind and solar gains momentum, the challenge lies in managing these unpredictable energy sources efficiently. Innovative learning techniques are emerging as potential solutions to these challenges, optimising the use and benefits of renewable energies. Furthermore, the landscape of energy distribution is evolving, with a growing emphasis on automated decision-making software. Central to this evolution is machine learning, with its applications spanning a range of sectors. These include enhancing energy efficiency, seamlessly integrating green energy sources, making sense of vast data sets within smart grids, forecasting energy consumption patterns, and fortifying the security of power systems. Through a comprehensive review of these areas, this paper highlights the potential of machine learning in paving the way for a greener, more efficient energy future.

Background. Acrolein is a major component of environmental pollutants, cigarette smoke, and is also formed by heating cooking oil. We evaluated the interstrain variability of response to subchronic inhalation exposure to acrolein among inbred mouse strains for inflammation, oxidative stress, and tissue injury responses. Furthermore, we studied the response to acrolein vapor in the lung mucosa model using human primary bronchial epithelial cells (PBEC) cultured at an air-liquid interface (ALI) to evaluate the findings of mouse studies. Methods. Female 129S1/SvlmJ, A/J, BALB/cByJ, C3H/HeJ, C57BL/6J, DBA/2J, and FVB/NJ mice were exposed to 1 part per million (ppm) acrolein or filtered air for 11 weeks. Total cell counts and protein concentrations were measured in bronchoalveolar lavage (BAL) fluid to assess airway inflammation and membrane integrity. PBEC-ALI models were exposed to acrolein vapor (0.1 and 0.2 ppm) for 30 minutes. Gene expression of proinflammatory, oxidative stress, and tissue injury-repair markers was assessed (cut off: ≥2 folds; p<0.05) in the lung models. Results. Total BAL cell numbers and protein concentrations remained unchanged following acrolein exposure in all mouse strains. BALB/cByJ, C57BL/6J, and 129S1/SvlmJ strains were the most affected with an increased expression of proinflammatory, oxidative stress, and/or tissue injury markers. DBA/2J, C3H/HeJ, A/J, and FVB/NJ were affected to a lesser extent. Both matrix metalloproteinase 9 (Mmp9) and tissue inhibitor of metalloproteinase 1 (Timp1) were upregulated in the strains DBA/2J, C3H/HeJ, and FVB/NJ indicating altered protease/antiprotease balance. Upregulation of lung interleukin- (IL-) 17b transcript in the susceptible strains led us to investigate the IL-17 pathway genes in the PBEC-ALI model. Acrolein exposure resulted in an increased expression of IL-17A, C, and D; IL-1B; IL-22; and RAR-related orphan receptor A in the PBEC-ALI model. Conclusion. The interstrain differences in response to subchronic acrolein exposure in mouse suggest a genetic predisposition. Altered expression of IL-17 pathway genes following acrolein exposure in the PBEC-ALI models indicates that it has a central role in chemical irritant toxicity. The findings also indicate that genetically determined differences in IL-17 signaling pathway genes in the different mouse strains may explain their susceptibility to different chemical irritants.

Whole spent hen meat of Indian commercial layer bird (BV-300 breed) was enzymatically hydrolyzed using Flavourzyme® derived from Aspergillus oryzae. Different time, temperature, and pH combinations generated through response surface methodology (RSM) were tested to find the optimal hydrolysis condition at which maximum antioxidant potential and degree of hydrolysis can be achieved. Hydrolysis for 30 min at a temperature of 53.9°C and pH of 6.56 was found suitable for achieving high degree of hydrolysis and antioxidant activity. Antioxidant potential at optimized conditions was estimated at 93.26% by DPPH radical scavenging assay and 2.32 mM TEAC by FRAP assay. Amino acid profiling of the hydrolysate correlated very well with SDS-PAGE profiling. SDS-PAGE results confirmed that 30 min hydrolysis time was enough to produce low molecular weight peptides (2–5 kDa) with high antioxidant potential. Antioxidant rich Indian spent hen meat hydrolysate powder was economically produced using spray drying. Sensory analysis revealed that 10% hydrolysate powder had satisfactory overall acceptability and has potential to be used in health/functional foods at this concentration. This is the first study wherein optimum hydrolysis conditions for Indian spent hen meat have been reported.

A novel optimization technique is proposed based on ocean water currents. The combination of all-natural forces determines ocean current speed, further when resultant of all these forces nears to zero, stability is attained. Ocean water current optimization is the natural way to reach the stability point or the optimized point. The proposed evolutionary algorithm simulates the involved forces to generate Ocean Current Speed Index (OCSI), which further converges to the stability point. Compared to other nature-inspired algorithms, the proposed algorithm converges quickly due to an improved gradient descent algorithm. OWCO doesn’t get struck in local minima as the exploratory property of the algorithm uses highly mutating forces to generate new possible search spaces. Results with respect to CEC 2021 benchmark concludes that the proposed algorithm outperforms other contemporary algorithms in the context of application and empirical evaluations. This optimization algorithm is tested for D = 10 and D = 20 in CEC 2021 and found to be scalable with better exploration and convergence for single objective bound constraint problems.

Methane-based inflammable underground coal mine environment has led to catastrophic losses in the past. Migration of methane from the working seam and desorption region above and below the seam causes explosion hazard. In this study, the computational fluid dynamics (CFD)-based simulations of a longwall panel in a methane-rich inclined coal seam of the Moonidih mine in India established that the ventilation parameters greatly influence the methane flow in the longwall tailgate and porous medium of the goaf. The field survey and CFD analysis revealed that methane accumulation on the “rise side” wall of the tailgate is attributable to the geo-mining parameters. Further, the turbulent energy cascade was observed to impact the distinct dispersion pattern along the tailgate. The numerical code was used to investigate the changes in ventilation parameters made to dilute the methane concentration in the longwall tailgate. Methane concentration in the tailgate outlet decreased from 2.4 to 1.5% as the inlet air velocity increased from 2 to 4 m/s. The oxygen ingress into the goaf increased from 0.5 to 4.5 lps as the velocity was increased, causing the explosive zone in the goaf to expand from 5 to 100 m. Amongst all velocity variations, the lowest level of gas hazard was observed at an inlet air velocity of 2.5 m/s. This study, thus, demonstrated the ventilation-based numerical method to assess the coexistence of gas hazard in the goaf and longwall workings. Moreover, it provided impetus to the necessity of novel strategies to monitor and mitigate the methane hazard in U-type longwall mine ventilation.