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A well-known chemotherapy medication is a cisplatin, also referred to as cis-diamminedichloroplatinum or cisplatinum(II). Cancers such as bone metastases, lymphomas, germ cell tumors, and carcinomas can all be treated with it. Its mode has been takenpertaining to its capability to cross-link with purine biological branches, obstructing DNA repair processes, generating DNA damage, and, as a result, cancerous cells undergo apoptosis. Nevertheless, due to drug resistance and a number of unfavorable side effects, including severe kidney problems, allergic reactions, lowered immunity to infections, gastrointestinal problems and others, have also been used. For overcoming drug resistance and reducing adverse effects, cisplatin- based combination therapies with other pharmaceuticals have also garnered considerable investigation. This in-depth analysis looks at the isotopes of the properties cisplatin and associated platinum-based drugs, as well as how they can be is employed to treat a range of health maligancies. Particular focus is placed on its unfavorable side effects and molecular mechanisms of action. The current paper provides a pharmacological assessment of the drug, outlining its clinical applications, toxic effects, and mechanisms of resistance. The ability of cisplatin to form DNA adducts by crosslinking with urine bases on DNA has been connected to its mode of action. As a result, cancer cells experience apoptosis, which stops DNA damage from being repaired. The drug does, however, display certainly improved DNA damage repair, decreased drug accusation inside cells, and cisplatin deactivation in the cytosol are all signs of resistance. The drug also has some negative adverse consequences, including vomiting, kidney damage, cardiotoxicity, liver toxicity, and neurodegeneration.

The research of the MOFs for biological activities have garnered a lot of interest in current history. Because of their exceptionally wide surfaces, and permeability, Metal-organic framework are thought to be an exciting type of nanocarriers for the dlivery of pharmaceuticals. The unique characteristics of MOFs and their advantages as Nanomaterials for drug administration in therapeutic systemshave been reviewed in the first half of this paper. The most current techniques included hydrophilic group, pore encapsulated, covatent linkin, and using basic components of useful molecules. Strong bonds are used to join inorganic and organic units to create according to the utilization of metal-organic frameworks versality of the elements, structure, dimension, and usefulness, more that twenty thousdand different MOFs have been reported and investigated in the last ten years. An overview, different types of ligands, and numerous techniques for MOF synthesis are given at the beginning of the chapter. In many applications where MOFs are potential options, the special property of MOFs has been a significant problem. The latest scientific applications of MOFs for aptamer- specific intracellular drug, protein, and nucleic acid delivery were discussed. Finally, issues and opportunities were thoroughly covered to set the stage for the evolution of MOFs into an effective drug delivery system in the future.

•Eight studies, with 372 participants were included in meta-analysis.•Chia consumption significantly reduced DBP (-7.49 mmHg) and SBP (-5.61 mmHg) in adults.•Chia consumption significantly reduced WC (-1.46 cm) in adults in adults.•Chia consumption did not significantly change the BMI and weight in adults. Growing evidence has suggested that the consumption of chia seed can decrease blood pressure and obesity in adults. However, even studies have reported uncertain findings. The current meta-analysis aimed to assess the findings of randomized controlled trials (RCTs) on the efficacy of chia seed supplementation on blood pressure (systolic blood pressure [SBP], diastolic blood pressure [DBP]) and body composition (waist circumference [WC], weight, body mass index [BMI]) in adults. A systematic search of the literature was carried out in the PubMed, Web of Knowledge, Scopus, Cochrane Central Library, and EMBASE from inception up to October 2024. Data were extracted and analyzed using a random-effects model, and reported as weighted mean differences (WMD) with 95% confidence intervals (CI). A total of eight RCTs involving 372 participants were included in the meta-analysis. The results showed that chia consumption significantly reduced DBP (WMD: -7.49 mmHg; 95% CI: -9.64, -5.34; P < 0.001) and SBP (WMD: -5.61 mmHg; 95% CI: -8.77, -2.44; P = 0.001). Moreover, consuming chia seeds was linked to a notable decrease in WC (WMD: -1.46 cm; 95% CI: -2.68, -0.25; P = 0.01), but it had no significant effect on, BMI (WMD: -0.31 kg/m2; 95% CI: - 0.96, 0.34; P = 0.34) and weight (WMD: 0.09 kg; 95% CI: -0.76, 0.93; P = 0.84). Chia consumption can significantly reduce SBP, DBP, and WC in adults, but no significant impact was showed on BMI and weight. To verify these results, more studies involving a greater number of participants are required.

Pomegranate peel powder is used as a functional ingredient in the development of nutritional bars. Pomegranate ( Punica granatum ) is well known fruit belongs to punicaceae family having multiple health benefits, not only limited to its edible parts but also in its non-edible parts mostly the peel. Fruit wastes are rich source of nutrients, and can be used for the development of functional food products. Pomegranate peel is considered to be beneficial due to its functional and therapeutic properties as it is a source of many biological active components like polyphenols, tannins and flavonoids. Nutrient rich and ready-made foods are the demand of everyone due to their easy availability and cost effectiveness. Among the confectionary products, bars are liked by individuals of different age groups. Hence, nutritional properties of bars can be enhanced by using pomegranate peel powder. The current study was designed to develop bars enriched with pomegranate peel powder as a basic ingredient. Pomegranate peel powder is prepared and analyzed for proximate, mineral, total phenolic content, total flavonoid content and anti-oxidant potential (DPPH). By using pomegranate peel powder, oats and jaggery, bars were prepared. In this research, five treatments T 0 (0% pomegranate peel powder and 100% oats). T 1 (5% pomegranate peel powder and 95% oats), T 2 (10% pomegranate peel powder and 90% oats), T 3 (15% pomegranate peel powder and 85% oats) and T 4 (20% pomegranate peel powder and 80% oats) were used. The developed product is analyzed for proximate, mineral, total flavonoid contents, total phenolic content and anti-oxidant potential (DPPH). Proximate analysis of bars revealed that moisture, protein, fat, fiber, ash and nitrogen free extract ranges from T 0 to T4 (13.38±1.21 to 11.32±1.15, 9.56±0.92 to 8.32±1.14, 9.05±1.21 to 7.93±1.08, 5.23±0.82 to 16.89±0.64, 2.05±0.87 to 2.92±1.25 and 62.51±0.85 to 52.62±0.93 respectively. Phytochemical analysis of bars enriched with pomegranate peel powder revealed that total phenolic content, total flavonoid content and antioxidant potential of bars ranges from T 0 to T 4 (142.74±0.65 to 211.79±0.63 mg GAE/100g, 129.16±0.64 to 192±0.53 mg QE/100g and 41.35±0.82 to 64.57±0.69%) respectively. Mineral analysis of bars enriched with pomegranate peel powder revealed that calcium, Phosphorus, Potassium, Iron, Magnesium content ranged from T 0 to T 4 (25.42±0.63 to 31.06±0.58, 51.00±1.01 to 45.05±1.09, 59.46±1.13 to 79.15±0.28, 1.32±1.20 to 1.95±0.83 and 54.17±0.88±0.58 to 57.36±0.68 mg/100g respectively). Sensory evaluation is done for color, aroma, taste, texture overall acceptability. T 3 got maximum score. Then, the data obtained were evaluated by CRD design. On the basis of results revealed that treatment T 3 with 15% pomegranate peel powder was overall highly acceptable.

This study focuses on predicting the solubility of paracetamol and density of solvent using temperature (T) and pressure (P) as inputs. The process for production of the drug is supercritical technique in which the focus was on theoretical investigations of drug solubility and solvent density as well. Machine learning models with a two-input, two-output structure were developed and validated using experimental data on paracetamol solubility as well as density. Ensemble models with decision trees as base models, including Extra Trees (ETR), Random Forest (RFR), Gradient Boosting (GBR), and Quantile Gradient Boosting (QGB) were adjusted to predict the two outputs. The results are useful to evaluate the feasibility of process in improving the efficacy of the drug, i.e., its enhanced bioavailability. The hyper-parameters of ensemble models as well as parameters of decision tree tuned using WOA algorithm separately for both outputs. The Quantile Gradient Boosting model showed the best performance for mole fraction (drug solubility), while the R 2 score of 0.985 was determined. For density of solvent, the Extra Trees model performed the best with an R 2 equal to 0.997.

We investigated solubility variations of a medication in supercritical carbon dioxide with an insight into preparation of nanomedicines with improved aqueous solubility. As the case study, the solubility of famotidine (FAM) medicine in sc-CO 2 (supercritical carbon dioxide) was computed as a function of temperature and pressure, with a particular focus on modeling and predicting solubility and sc-CO 2 density. Three regression models with machine learning behavior including Quadratic Polynomial Regression (QPR), Weighted Least Squares (WLS), and Orthogonal Matching Pursuit (OMP) were employed to analyze the data, and Sequential Model-Based Optimization (SMBO) was utilized for hyper-parameter tuning. Among these models, the best-performing model for predicting FAM solubility was the QPR model, with an impressive coefficient of determination (R 2 ) of 0.95858 for all sets including training and validation. Additionally, QPR exhibited low MAPE of 1.64278E + 00, RMSE of 9.6833E-02, and a maximum error of 1.49480E-01, while exhibiting a higher maximum error of 18.99 kg/m³ for density predictions, indicating areas for potential improvement. These results highlight the accuracy and precision of the QPR model in predicting FAM solubility in sc-CO 2 . For the prediction of sc-CO 2 density, QPR again proved to be the most effective model with a remarkable R 2 score of 0.99733. This model achieved a low MAPE of 1.06004E-02, RMSE of 8.4072E + 00, and a maximum error of 1.89894E + 01. The QPR model demonstrates its exceptional capability in accurately predicting sc-CO 2 density in terms of temperature and pressure.

One of the problems with new medications is their poor water solubility that is possible to be addressed by using supercritical method. This study aims to predict the solubility of raloxifene and the density of supercritical CO 2 using temperature and pressure as inputs to analyze the supercritical processing for production of drug nanoparticles. Three regression models, Extra Trees (ET), Random Forest (RF), and Gradient Boosting (GB) were proposed and optimized using Gradient-based optimization to predict density and solubility of drug. In predicting the density of supercritical CO₂, GB attained an R² value of 0.986, reflecting an excellent agreement between its estimates and the true measurements. The model exhibited an RMSE of 23.20, indicating high accuracy, with a maximum error of 33.06. Regarding the solubility of raloxifene, the ET model yielded the highest R-squared score of 0.949, indicating a good fit to the data. The model exhibited an RMSE of 0.41, with a maximum error of 0.90. Comparatively, the RF and GB models obtained slightly lower precision, for the solubility of raloxifene. The RF model exhibited an RMSE of 0.55, while the GB model had an RMSE of 0.72. The optimized models were found to be reliable in predicting solubility and density within the supercritical processing field.

This study explores the practical applications of artificial intelligence (AI) in medical imaging, focusing on machine learning classifiers and deep learning models. The aim is to improve detection processes and diagnose diseases effectively. The study emphasizes the importance of teamwork in harnessing AI’s full potential for image analysis. Collaboration between doctors and AI experts is crucial for developing AI tools that bridge the gap between concepts and practical applications. The study demonstrates the effectiveness of machine learning classifiers, such as forest algorithms and deep learning models, in image analysis. These techniques enhance accuracy and expedite image analysis, aiding in the development of accurate medications. The study evidenced that technologically assisted medical image analysis significantly improves efficiency and accuracy across various imaging modalities, including X-ray, ultrasound, CT scans, MRI, etc. The outcomes were supported by the reduced diagnosis time. The exploration also helps us to understand the ethical considerations related to the privacy and security of data, bias, and fairness in algorithms, as well as the role of medical consultation in ensuring responsible AI use in healthcare.

Background People living with HIV (PLWH) are more vulnerable to infectious and non-infectious comorbidities due to chronic inflammation and immune dysfunction. Air pollution is a major global health risk, contributing to millions of deaths annually, primarily from cardiovascular and respiratory diseases. However, the link between air pollution and mortality risk in PLWH is underexplored. This systematic review assesses the association between exposure to pollutants such as particulate matter (PM), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), ozone (O 3 ), and carbon monoxide (CO) and mortality risk in PLWH. Methods A systematic search of PubMed, Web of Science, and Embase was conducted for studies published up to August 2024. Eligibility criteria included cohort, case-control, and cross-sectional studies assessing air pollution exposure and mortality in PLWH. Nested-Knowledge software was used for screening and data extraction. The Newcastle-Ottawa Scale was applied for quality assessment. A narrative approach and tabular summarization were used for data synthesis and presentation. Results Nine studies, mostly from China, demonstrated a significant association between long-term exposure to PM 1 , PM 2.5 , and PM 10 and increased risks of AIDS-related and all-cause mortality in PLWH. Hazard ratios for mortality increased by 2.38–5.13% per unit increase in PM concentrations, with older adults (> 60), females, and those with lower CD4 counts (< 500 cells/µL) being more vulnerable. Short-term exposure to ozone and sulfur dioxide also increased mortality risks, particularly during the warm season and in older populations. Specific pollutants like ammonium (NH4⁺) and sulfate (SO4²⁻) had the strongest links to elevated mortality. Conclusion Air pollution, especially fine particulate matter and ozone, is associated with a higher risk of mortality in PLWH. Targeted interventions to reduce pollution exposure in vulnerable subgroups are crucial. Further research is needed to confirm these findings in diverse regions and develop effective mitigation strategies.

Background Celiac disease (CD) is an autoimmune disorder characterized by gluten intolerance, primarily affecting the gastrointestinal system but potentially influencing cardiovascular health. Emerging evidence suggests an association between CD and myocardial infarction (MI), though studies have produced inconsistent results. This study aimed to systematically review and conduct a meta-analysis of existing literature to quantify the risk of MI in individuals diagnosed with CD. Methods A comprehensive literature search was performed across PubMed, Embase, and Web of Science up to August 2024. Studies were included if they investigated the association between CD and MI in adult populations and provided relevant effect estimates. Data from eligible studies were extracted, and a random-effects meta-analysis was conducted to calculate pooled hazard ratios (HRs) and odds ratios (ORs), along with an assessment of heterogeneity. Statistical analysis has been performed by R software (V 4.4). Results A total of 8 studies were included in the systematic review. Pooled HR analysis showed no significant association between CD and MI (HR = 1.143, 95% CI: 0.619–2.109), and pooled OR analysis also revealed non-significant results (OR = 0.879, 95% CI: 0.481–1.606). High heterogeneity was observed (I 2  = 86% for HR, 99% for OR). Conclusion This meta-analysis found no significant association between CD and MI. However, substantial heterogeneity across studies indicates variability in results, highlighting the need for further research with larger, more homogeneous cohorts to better understand cardiovascular risks in CD patients. Future studies should explore subgroups and the impact of gluten-free diet adherence.