KLE University

Metal ions are vital for living and environmental system. Consequently, their detection and monitoring has been considered great significance by the chemists. Scientists are developing new approaches to recognize these cations. It was successfully demonstrated that fluorescent sensors developed on organic structural framework are effective in detecting all kinds of metal ions and offer a number of benefits, including inexpensiveness, simple synthesis and high sensitivity. In present work, a novel and versatile Schiff base chemosensor 6-(2-((E)-2hydroxybenzylidene)hydrazine-1-carbonyl)-N-(2-(2-((E)-hydroxybenzylidene)hydrazine-1-carbonyl)phenyl) picolinamide (HSH) was synthesized for quick recognition of Al3+ ions through fluorescence turn-on response at lambda max. 473 nm in DMF:H2O (1:1). The HSH exhibited high fluorescence property by inhibiting ESIPT process, -C=N- isomerization and enhanced ICT character upon coordinated with Al3+ ions. The stoichiometric ratio (1:2) of the HSH towards Al3+ ions was confirmed by ESI-MS and Job's plot analysis. The obtained detection limit and binding constant value for HSH were found to be 11.93 nM and 8.69 x 106 M- 1 respectively. The HSH was utilized for bio-imaging application in HeLa cells. In addition, the sensing properties of HSH were studied on economical test along with TLC kits for the recognition of Al3+ ions. The findings of the present investigation suggest that the HSH could be serve as potent chemosensor for Al3+ ions.

The expanding use of Large Language Models (LLMs) in scientific writing raises critical concerns regarding research accountability, integrity, and transparency. This study aimed to develop and content-validate the CAREFUL-AI framework and apply it to evaluate the scientific quality of AI-generated research manuscripts. A three-round Delphi process with 10 multidisciplinary experts was conducted to develop the CAREFUL-AI framework. Content validity was assessed using item-level and scale-level content validity indices. Using standardized, locked prompts, 150 complete scientific research manuscripts across five study designs were generated by six freely accessible LLM platforms ChatGPT (GPT-5.2), Claude (Claude 4.5), Gemini (Gemini 3 Flash), Grok (Grok 4.1), DeepSeek (DeepSeek-V3.2), and Meta AI (Llama 4 Maverick). Manuscripts were evaluated by the respective prompt administrators using the CAREFUL-AI framework. The framework demonstrated strong content validity (I-CVI: 0.83-1.00; S-CVI/Ave: 0.92). Overall, 38.0% of manuscripts were rated high quality, 52.7% moderate quality, and 9.3% low quality. Claude generated the highest proportion of high-quality manuscripts, while Meta AI produced all low-quality outputs. Reproducibility and uncertainty handling consistently received the lowest domain scores across models. The CAREFUL-AI framework provides researchers with a structured tool to critically appraise AI-generated manuscripts, helping safeguard methodological rigor, transparency, and evidence reliability in scientific writing. Substantial variability exists in the quality of AI-generated manuscripts. Although Claude demonstrated comparatively stronger performance, persistent deficiencies in key integrity domains indicate that AI-assisted manuscript generation requires robust human oversight. CAREFUL-AI provides a content-validated framework to support ethical governance and editorial oversight of AI-assisted research.

Background:Reportedly, around 6.3% of individuals over the age of 30 in Asia-Pacific countries suffer from stages II and III chronic obstructive pulmonary disease (COPD). Among various rehabilitation strategies, Inspiratory Muscle Training (IMT) and Autogenic Drainage (AD) are emerging as effective interventions aimed at improving respiratory function and symptom control. This study aimed to evaluate and compare the effects of IMT and AD in hospitalized COPD patients, focusing on outcomes related to lung function, quality of life, bronchial hygiene, and respiratory muscle performance. Methods:Out of 59 screened subjects, 40 met the inclusion criteria and were enrolled. They were randomly allocated into two groups: Group A received AD and Group B underwent IMT. Both groups participated in a structured intervention protocol for two weeks. A total of 35 participants completed the study. Results:Post-intervention, both groups showed significant improvement in forced vital capacity (FVC): Group A (-0.61 ± 0.52; P = .0089) and Group B (-0.46 ± 0.67; P = .0233). Chest expansion at Point B (measured at nipple level ) increased significantly in both groups (P = .0002) and at Point A (measured at axillary level) in Group A (P = .0283). While other outcomes, namely FEV1/FVC (P = .5), St George Respiratory Questionnaire (P = .6), Maximal Inspiratory Pressure (P = .6), and 6-minute walk test (P = .9), demonstrated positive trends, although not statistically significant. Conclusion:Both the AD and IMT groups demonstrated statistically significant increases in FVC and chest expansion following the intervention, especially at the axillary level in the AD group and at the nipple level in both groups. Although not statistically significant, other variables such as the FEV1/FVC ratio, quality of life scores, inspiratory pressure, and functional exercise capacity demonstrated clinically useful trends. Keywords:chronic obstructive pulmonary disease, respiratory therapy, rehabilitation, pulmonary function tests, inspiratory muscle training, autogenic drainage.

AI and the IoT are revolutionizing drug development and discovery. By utilizing real-time data and sophisticated algorithms, researchers can rapidly identify potential drug candidates and assess their efficacy. In drug research, AI plays a vital role in predicting the pharmacological properties of lead compounds from their chemical structures, thereby improving the efficiency of medication discovery and development. It is possible to develop predictive models that estimate the key features of solubility, bioavailability, and toxicity by utilizing machine learning methods. These models provide a blueprint for designing new molecules with improved pharmacological attributes, thereby boosting the efficacy and safety of potential therapies. The integration of the IoT in the pharmaceutical industry accelerates the production of new drugs for conditions requiring treatment, reducing both time and costs. Utilizing cell-in-a-chip and organ-in-a-chip devices that replicate human physiological conditions, the early evaluation of potential formulations is significantly expedited. By analysing patient data and electronic medical records more effectively, AI enhances clinical trial design and speeds up patient recruitment. AI also helps optimise trial design by identifying which patient subgroups are more likely to respond to therapies. Real-time monitoring is enabled by AI-driven wearables, which ensure accurate data collection and the necessary modifications to trial protocols to improve patient safety. The chapter also examines how AI and IoT are used in drug research, including target identification, drug design, and clinical trials. Lastly, it addresses potential issues and constraints associated with these technologies, as well as future directions for this field's research and development. In conclusion, this chapter has shown how AI and the Internet of Things could drastically improve the development and discovery processes, leading to better and more efficient treatments for a wide range of ailments.