Multidrug-Resistant Pathogens in Wound Infections: A Systematic Review.

This systematic review aimed to explore the antimicrobial activity of a silver-containing gelling fiber dressing against multidrug-resistant organisms (MDROs) in wound infections. It particularly focuses on burn wounds and evaluates its potential clinical significance in combating antimicrobial resistance. A comprehensive literature search was conducted across multiple databases over the past ten years. It is used to identify relevant studies addressing MDRO infections in wound care and exploring novel antimicrobial approaches. The included studies underwent rigorous methodological assessment. Additionally, the data were synthesized to evaluate the efficacy of silver-containing dressings in inhibiting MDRO growth and eradicating biofilm-associated bacteria. Moreover, this review revealed that silver-containing dressings have constant in vitro antimicrobial activity against 10 MDROs over seven days in simulated wound fluid. However, inhibitory and bactericidal effects were consistently observed against free-living and biofilm phenotypes. The findings suggest potential clinical significance in managing MDRO infections in wounds. This highlights its role in mitigating treatment failure and antimicrobial resistance. Despite the promising implications for wound management practices, this study acknowledges some limitations. In vitro models and the absence of direct clinical validation have also been included. However, the review explains the importance of new approaches. Nanotechnology has been used to address antimicrobial resistance in wound care. Thus, further research and innovation are needed to improve patient outcomes and combat antimicrobial resistance.

Radiolabeling and preclinical animal model evaluation of DTPA coupled 99mTc-labelled flutamide complex ([99mTc]DTPA-FLUT) as a potential radiotracer for cancer imaging.

BACKGROUND: Advances in molecular imaging strategies have had an effect on precise diagnosis and treatment. Research has been intensified to develop more effective and versatile radiopharmaceuticals to uplift diagnostic efficiency and, consequently, the treatment. PURPOSE: To label the flutamide (FLUT) coupled with diethylenetriamine pentaacetate (DTPA) with technetium-99 m (99mTc) and to evaluate its binding efficiency with rhabdomyosarcoma (RMS) cancer cells. MATERIAL AND METHODS: Radiolabeling of FLUT with 185 MBq freshly eluted 99mTcO4-1 was carried out via DTPA bifunctional chelating agent using stannous chloride reducing agent at pH 5. The labeled compound was assessed for its purity using chromatography analysis, stability in saline and blood serum, AND charge using paper electrophoresis. Normal biodistribution was studied using a mouse model, while binding affinity with RMS cancer cells was studied using an internalization assay. The in vivo accumulation of RMS cancer cells in a rabbit model was monitored using a SPECT gamma camera. RESULTS: Radiolabeling reaction displayed a pharmaceutical yield of 97% and a stability assay showed >95% intact radiopharmaceutical up to 6 h in saline and blood serum. In vitro internalization studies showed the potential of [99mTc]DTPA-FLUT to enter into cancer cells. This biodistribution study showed rapid blood clearance and minimum uptake by body organs, and scintigraphy displayed the [99mTc]DTPA-FLUT uptake by lesion, induced by RMS cancer cell lines in rabbit. CONCLUSION: Stable, newly developed [99mTc]DTPA-FLUT seeks its way to internalize into RMS cancer cells, indicating it could be a potential candidate for the diagnosis of RMS cancer.

Evaluation of diethyl 4-(5-bromo-1H-indol-3-yl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate: synthesis, anti-corrosion potential, and biomedical applications.

The pursuit of advanced multifunctional compounds has gained significant momentum in recent scientific endeavours. This study is dedicated to elucidating the synthesis, rigorous characterization, and multifaceted applications-encompassing anti-corrosion, antimicrobial, and antioxidant properties-of Diethyl 4-(5-bromo-1H-indol-3-yl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate. The 1,4-dihydropyridine derivative was meticulously synthesized through a strategic reaction of ethyl acetoacetate, ammonium acetate, and 5-bromoindole-3-carboxaldehydein the ethanol medium at 60  C. Subsequent spectral validations were conducted using sophisticated techniques, namely FTIR, NMR, and Mass spectrometry, resulting in data that perfectly resonated with the hypothesized chemical structure of the compound. Its anti-corrosive potential was assessed on mild steel subjected to an aggressive acidic environment, employing comprehensive methodologies like gravimetric analysis, Tafel polarization, and EIS. Concurrently, its antimicrobial prowess was ascertained against a spectrum of bacterial and fungal pathogens viz., Staphylococcus aureus, Bacillus subtilis, Escherichia coli, Pseudomonas, Candida albicansandAspergillusniger, leveraging the disc diffusion method and using Gentamicin as a reference standard.The empirical results illustrated a substantial decrement in corrosion rates with ascending concentrations of the organic compound, achieving an apex of anti-corrosive efficacy at 81.89% for a concentration of 2 × 103 M. Furthermore, the compound outperformed Gentamicin in antimicrobial screenings, manifesting superior efficacy against all tested pathogens. The antioxidant potential, quantified using the DPPH free radical scavenging assay against ascorbic acid as a benchmark, was found to have an IC50 value of 113.964 ± 0.076 µg/ml.This comprehensive investigation accentuates the paramount potential of the synthesized dihydropyridine derivative in diverse domains-from industrial applications as a corrosion inhibitor to therapeutic avenues given its pronounced antimicrobial and antioxidant capabilities. The compelling results obtained pave the way for expansive research and development initiatives cantered around this multifaceted compound.

Valve-in-Valve Transcatheter Aortic Valve Replacement Versus Redo-Surgical Aortic Valve Replacement in Patients With Aortic Stenosis: A Systematic Review and Meta-analysis.

Aortic stenosis is a common and significant valve condition requiring bioprosthetic heart valves with transcatheter aortic valve replacement (TAVR) being strongly recommended for high-risk patients or patients over 75 years. This meta-analysis aimed to pool existing data on postprocedural clinical as well as echocardiographic outcomes comparing valve-in-valve (ViV)-TAVR to redo-surgical aortic valve replacement to assess the short-term and medium-term outcomes for both treatment methods. A systematic literature search on Cochrane Central, Scopus, and Medline (PubMed interface) electronic databases from inception to August 2023. We used odds ratios (OR) for dichotomous outcomes and mean differences (MD) for continuous outcomes. Twenty-four studies (25,216 patients) were pooled with a mean follow-up of 16.4 months. The analysis revealed that ViV-TAVR group showed a significant reduction in 30-day mortality (OR 0.50, 95% confidence interval [CI] 0.43 to 0.58, p <0.00001), new-onset atrial fibrillation (OR 0.34, 95% CI 0.17 to 0.67, p = 0.002), major bleeding event (OR 0.28, 95% CI 0.17 to 0.45, p <0.00001) and lower rate of device success (OR 0.25, 95% CI 0.12 to 0.53, p = 0.0003). There were no significant differences between either group when assessing 1-year mortality, stroke, myocardial infarction, postoperative left ventricular ejection fraction, and effective orifice area. ViV-TAVR cohort showed a significantly increased incidence of paravalvular leaks, aortic regurgitation, and increased mean aortic valve gradient. ViV-TAVR is a viable short-term option for elderly patients with high co-morbidities and operative risks, reducing perioperative complications and improving 30-day mortality with no significant cardiovascular adverse events. However, both treatment methods present similar results on short-term to medium-term complications assessment.

Updates on traditional methods for combating malaria and emerging Wolbachia-based interventions.

The escalating challenge of malaria control necessitates innovative approaches that extend beyond traditional control strategies. This review explores the incorporation of traditional vector control techniques with emerging Wolbachia-based interventions. Wolbachia, a naturally occurring bacteria, offers a novel approach for combatting vector-borne diseases, including malaria, by reducing the mosquitoes' ability to transmit these diseases. The study explores the rationale for this integration, presenting various case studies and pilot projects that have exhibited significant success. Employing a multi-dimensional approach that includes community mobilization, environmental modifications, and new biological methods, the paper posits that integrated efforts could mark a turning point in the struggle against malaria. Our findings indicate that incorporating Wolbachia-based strategies into existing vector management programs not only is feasible but also heightens the efficacy of malaria control initiatives in different countries especially in Pakistan. The paper concludes that continued research and international collaboration are imperative for translating these promising methods from the laboratory to the field, thereby offering a more sustainable and effective malaria control strategy.

Dissecting the genetic and causal relationship between sleep-related traits and common brain disorders.

BACKGROUND: There is a profound connection between abnormal sleep patterns and brain disorders, suggesting a shared influential association. However, the shared genetic basis and potential causal relationships between sleep-related traits and brain disorders are yet to be fully elucidated. METHODS: Utilizing linkage disequilibrium score regression (LDSC) and bidirectional two-sample univariable Mendelian Randomization (UVMR) analyses with large-scale GWAS datasets, we investigated the genetic correlations and causal associations across six sleep traits and 24 prevalent brain disorders. Additionally, a multivariable Mendelian Randomization (MVMR) analysis evaluated the cumulative effects of various sleep traits on each brain disorder, complemented by genetic loci characterization to pinpoint pertinent genes and pathways. RESULTS: LDSC analysis identified significant genetic correlations in 66 out of 144 (45.8 %) pairs between sleep-related traits and brain disorders, with the most pronounced correlations observed in psychiatric disorders (66 %, 48/72). UVMR analysis identified 29 causal relationships (FDR<0.05) between sleep traits and brain disorders, with 19 associations newly discovered according to our knowledge. Notably, major depression, attention-deficit/hyperactivity disorder, bipolar disorder, cannabis use disorder, and anorexia nervosa showed bidirectional causal relations with sleep traits, especially insomnia's marked influence on major depression (IVW beta 0.468, FDR = 5.24E-09). MVMR analysis revealed a nuanced interplay among various sleep traits and their impact on brain disorders. Genetic loci characterization underscored potential genes, such as HOXB2, while further enrichment analyses illuminated the importance of synaptic processes in these relationships. CONCLUSIONS: This study provides compelling evidence for the causal relationships and shared genetic backgrounds between common sleep-related traits and brain disorders.

AI in Hand Surgery: Assessing Large Language Models in the Classification and Management of Hand Injuries.

Background: OpenAI's ChatGPT (San Francisco, CA, USA) and Google's Gemini (Mountain View, CA, USA) are two large language models that show promise in improving and expediting medical decision making in hand surgery. Evaluating the applications of these models within the field of hand surgery is warranted. This study aims to evaluate ChatGPT-4 and Gemini in classifying hand injuries and recommending treatment. Methods: Gemini and ChatGPT were given 68 fictionalized clinical vignettes of hand injuries twice. The models were asked to use a specific classification system and recommend surgical or nonsurgical treatment. Classifications were scored based on correctness. Results were analyzed using descriptive statistics, a paired two-tailed t-test, and sensitivity testing. Results: Gemini, correctly classifying 70.6% hand injuries, demonstrated superior classification ability over ChatGPT (mean score 1.46 vs. 0.87, p-value < 0.001). For management, ChatGPT demonstrated higher sensitivity in recommending surgical intervention compared to Gemini (98.0% vs. 88.8%), but lower specificity (68.4% vs. 94.7%). When compared to ChatGPT, Gemini demonstrated greater response replicability. Conclusions: Large language models like ChatGPT and Gemini show promise in assisting medical decision making, particularly in hand surgery, with Gemini generally outperforming ChatGPT. These findings emphasize the importance of considering the strengths and limitations of different models when integrating them into clinical practice.

Bile acid/fatty acid integrated nanoemulsomes for nonalcoholic fatty liver targeted lovastatin delivery; stability, in-vitro, ex-vivo, and in-vivo analyses.

BACKGROUND: Controlled and targeted drug delivery to treat nonalcoholic fatty liver disease (NAFLD) can benefit from additive attributes of natural formulation ingredients incorporated into the drug delivery vehicles. METHODS: Lovastatin (LVN) loaded, bile acid (BA) and fatty acid (FA) integrated nanoemulsomes (NES) were formulated by thin layer hydration technique for synergistic and targeted delivery of LVN to treat NAFLD. Organic phase NES was comprised of stearic acid (StA) with garlic (GL) and ginger (GR) oils, separately. Ursodeoxycholic acid (UDA) and linoleic acid (LiA) were individually incorporated as targeting moieties. RESULTS: Stability studies over 90 days showed average NES particle size, surface charge, polydispersity index (PDI), and entrapment efficiency (EE) values of 270 ± 27.4 nm, -23.8 ± 3.5 mV, 0.2 ± 0.04 and 81.36 ± 3.4%, respectively. Spherical NES were observed under a transmission electron microscope (TEM). In-vitro LVN release depicted non-fickian release mechanisms from GL and GR oils-based NES. Ex-vivo permeation of BA/FA integrated NES through isolated rat intestines showed greater flux than non-integrated ones. CONCLUSION: Liver histopathology of experimental rats together with in-vivo lipid profiles and liver function tests (LFTs) illustrated that these NES possess the clinical potential to be promising drug carriers for NAFLD.

Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care.

In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA's responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.

Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review.

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI's role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI's role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers' effectiveness and well-being.

Evaluating the Effectiveness and Safety of Evinacumab in Treating Hypercholesterolemia and Hypertriglyceridemia: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

BACKGROUND: Cardiovascular disease remains a significant global health concern, with high low-density lipoprotein cholesterol (LDL-C) levels contributing to an increased risk. Familial hypercholesterolemia (FH) further complicates its management, necessitating additional lipid-lowering therapies. Evinacumab, an angiopoietin-like protein 3 monoclonal antibody, has emerged as a potential treatment, particularly for patients with FH, by effectively reducing LDL-C and triglyceride levels. This meta-analysis aimed to evaluate the efficacy and safety of evinacumab across diverse patient populations. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, relevant randomized controlled trials (RCTs) were systematically retrieved from multiple databases until November 24, 2023. The inclusion criteria were studies comparing evinacumab (at doses of 5 and 15 mg) to placebo, with outcomes focusing on lipid levels and adverse events. Standardized protocols were employed for data extraction and quality assessment, and statistical analysis was conducted using RevMan software. RESULTS: Four RCTs, involving 270 patients, were included in the analysis. The analysis revealed significant reductions in lipid markers, particularly with the 15-mg dose of evinacumab, including triacylglycerols (standard mean difference [SMD] = -6.09, 95% confidence interval [CI] - 14.53 to 2.36, P = 0.16), total cholesterol (SMD = - 6.20, 95% CI - 11.53 to - 0.88, P = 0.02), high-density lipoprotein cholesterol (SMD = - 0.79, 95% CI - 1.27 to - 0.31, P = 0.001), LDL-C (SMD = - 4.58, 95% CI - 9.13 to - 0.03, P = 0.05), apolipoprotein (Apo) B (SMD = - 4.01, 95% CI - 7.53 to - 0.46, P = 0.03), and Apo C3 (SMD = - 7.67, 95% CI - 12.94 to - 2.41, P = 0.004). Adverse event analysis revealed no significant association, indicating good tolerability. CONCLUSION: High-dose evinacumab (15 mg) consistently demonstrated efficacy in reducing cholesterol and other lipid markers, with favorable tolerability. Further research is warranted to comprehensively assess its safety and clinical effectiveness, emphasizing the need for additional data to support its use in managing cardiovascular disease.

Geochemical insights of arsenic mobilization into the aquifers of Punjab, Pakistan.

It is well known that groundwater arsenic (As) contamination affects million(s) of people throughout the Indus flood plain, Pakistan. In this study, groundwater (n = 96) and drilled borehole samples (n = 87 sediments of 12 boreholes) were collected to investigate geochemical proxy-indicators for As release into groundwater across floodplains of the Indus Basin. The mean dissolved (µg/L) and sedimentary As concentrations (mg/kg) showed significant association in all studied areas viz.; lower reaches of Indus flood plain area (71 and 12.7), upper flood plain areas (33.7 and 7.2), and Thal desert areas (5.3 and 4.7) and are indicative of Basin-scale geogenic As contamination. As contamination in aquifer sediments is dependent on various geochemical factors including particle size (3-4-fold higher As levels in fine clay particles than in fine-coarse sand), sediment types (3-fold higher As in Holocene sediments of floodplain areas vs Pleistocene/Quaternary sediments in the Thal desert) with varying proportion of Al-Fe-Mn oxides/hydroxides. The total organic carbon (TOC) of cored aquifer sediments yielded low TOC content (mean = 0.13 %), which indicates that organic carbon is not a major driver (with a few exceptions) of As mobilization in the Indus Basin. Alkaline pH, high dissolved sulfate and other water quality parameters indicate pH-induced As leaching and the dominance of oxidizing conditions in the aquifers of upper flood plain areas of Punjab, Pakistan while at the lower reaches of the Indus flood plain and alluvial pockets along the rivers with elevated flood-driven dissolved organic carbon (exhibiting high dissolved Mn and Fe and a wide range of redox conditions). Furthermore, we also identified that paired dissolved AsMn values (instead of AsFe) may serve as a geochemical marker of a range of redox conditions throughout Indus flood plains.

Distortionless, free-breathing, and respiratory resolved 3D diffusion weighted imaging of the abdomen.

PURPOSE: Abdominal imaging is frequently performed with breath holds or respiratory triggering to reduce the effects of respiratory motion. Diffusion weighted sequences provide a useful clinical contrast but have prolonged scan times due to low signal-to-noise ratio (SNR), and cannot be completed in a single breath hold. Echo-planar imaging (EPI) is the most commonly used trajectory for diffusion weighted imaging but it is susceptible to off-resonance artifacts. A respiratory resolved, three-dimensional (3D) diffusion prepared sequence that obtains distortionless diffusion weighted images during free-breathing is presented. Techniques to address the myriad of challenges including: 3D shot-to-shot phase correction, respiratory binning, diffusion encoding during free-breathing, and robustness to off-resonance are described. METHODS: A twice-refocused, M1-nulled diffusion preparation was combined with an RF-spoiled gradient echo readout and respiratory resolved reconstruction to obtain free-breathing diffusion weighted images in the abdomen. Cartesian sampling permits a sampling density that enables 3D shot-to-shot phase navigation and reduction of transient fat artifacts. Theoretical properties of a region-based shot rejection are described. The region-based shot rejection method was evaluated with free-breathing (normal and exaggerated breathing), and respiratory triggering. The proposed sequence was compared in vivo with multishot DW-EPI. RESULTS: The proposed sequence exhibits no evident distortion in vivo when compared to multishot DW-EPI, robustness to B0 and B1 field inhomogeneities, and robustness to motion from different respiratory patterns. CONCLUSION: Acquisition of distortionless, diffusion weighted images is feasible during free-breathing with a b-value of 500 s/mm2, scan time of 6 min, and a clinically viable reconstruction time.

Computational Fluid Dynamics (CFD) Analysis of Bioprinting.

Regenerative medicine has evolved with the rise of tissue engineering due to advancements in healthcare and technology. In recent years, bioprinting has been an upcoming approach to traditional tissue engineering practices, through the fabrication of functional tissue by its layer-by-layer deposition process. This overcomes challenges such as irregular cell distribution and limited cell density, and it can potentially address organ shortages, increasing transplant options. Bioprinting fully functional organs is a long stretch but the advancement is rapidly growing due to its precision and compatibility with complex geometries. Computational Fluid Dynamics (CFD), a carestone of computer-aided engineering, has been instrumental in assisting bioprinting research and development by cutting costs and saving time. CFD optimizes bioprinting by testing parameters such as shear stress, diffusivity, and cell viability, reducing repetitive experiments and aiding in material selection and bioprinter nozzle design. This review discusses the current application of CFD in bioprinting and its potential to enhance the technology that can contribute to the evolution of regenerative medicine.

Heart failure survival prediction using novel transfer learning based probabilistic features.

Heart failure is a complex cardiovascular condition characterized by the heart's inability to pump blood effectively, leading to a cascade of physiological changes. Predicting survival in heart failure patients is crucial for optimizing patient care and resource allocation. This research aims to develop a robust survival prediction model for heart failure patients using advanced machine learning techniques. We analyzed data from 299 hospitalized heart failure patients, addressing the issue of imbalanced data with the Synthetic Minority Oversampling (SMOTE) method. Additionally, we proposed a novel transfer learning-based feature engineering approach that generates a new probabilistic feature set from patient data using ensemble trees. Nine fine-tuned machine learning models are built and compared to evaluate performance in patient survival prediction. Our novel transfer learning mechanism applied to the random forest model outperformed other models and state-of-the-art studies, achieving a remarkable accuracy of 0.975. All models underwent evaluation using 10-fold cross-validation and tuning through hyperparameter optimization. The findings of this study have the potential to advance the field of cardiovascular medicine by providing more accurate and personalized prognostic assessments for individuals with heart failure.

Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression.

Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body's cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.

Characteristics and health implications of fine particulate matter near urban road site in Islamabad, Pakistan.

The escalating issue of air pollution has become a significant concern in urban regions, including Islamabad, Pakistan, due to the rise in air pollutant emissions driven by economic and industrial expansion. To gain a deeper understanding of air pollution, a study was conducted during winter 2022-2023, assessing physical, chemical, and biological factors in Islamabad. The findings revealed that the average concentration of fine particulate matter (PM2.5) was notably greater than the World Health Organization (WHO) guidelines, reaching 133.39 µg/m³. Additionally, the average concentration of bacteria (308.64 CFU/m³) was notably greater than that of fungi (203.55 CFU/m³) throughout the study. Analytical analyses, including SEM-EDS and FTIR, showed that the PM2.5 in Islamabad is composed of various particles such as soot aggregates, coal fly ash, minerals, bio-particles, and some unidentified particles. EF analysis distinguished PM2.5 sources, enhancing understanding of pollutants origin, whereas Spearman's correlation analysis elucidated constituent interactions, further explaining air quality impact. The results from the Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-OES) indicated a gradual increase in the total elemental composition of PM2.5 from autumn to winter, maintaining high levels throughout the winter season. Furthermore, a significant variation was found in the mass concentration of PM2.5 when comparing samples collected in the morning and evening. The study also identified the presence of semi-volatile organic compounds (SVOCs) in PM2.5 samples, including polycyclic aromatic hydrocarbons (PAHs) and phenolic compounds, with notable variations in their concentrations. Utilizing health risk assessment models developed by the US EPA, we estimated the potential health risks associated with PM2.5 exposure, highlighting the urgency of addressing air quality issues. These findings provide valuable insights into the sources and composition of PM2.5 in Islamabad, contributing to a comprehensive understanding of air quality and its potential environmental and health implications.