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ObjectivePotentially inappropriate prescribing is a global health issue with catastrophic consequences in the elderly population. Healthcare providers play a critical role in medication optimisation in elderly patients. The present study aims to explore the perceptions of healthcare professionals (prescribers) regarding the complexities of inappropriate prescribing practices in the elderly population.DesignA qualitative study using semistructured interviews was conducted. All the data were transcribed verbatim and analysed via Braun and Clarke’s thematic analysis approach.SettingPrescribers working in a tertiary care hospital in Karachi, Pakistan.ParticipantsPrescribers having more than 5 years of experience in elderly prescribing. Participants were selected using purposive sampling, and recruitment continued until the point of data saturation, meaning no new major themes emerged.Results13 prescribers, five females and eight males with an average experience of 15.3 years, were interviewed. The interviews lasted for an average of 15 min. The analysis revealed three primary themes: (1) inappropriate prescribing, characterised by knowledge and awareness of inappropriate prescribing and its assessment tools; (2) complexities in elderly prescribing, highlighting patient factors such as comorbidities, polypharmacy, psychological issues and socioeconomic challenges, as well as prescriber factors; and (3) interventions to improve prescribing, emphasising the role of pharmacists in enhancing medication safety, the importance of effective patient–prescriber relationships through counselling and the need for regulatory measures to monitor prescribing behaviours. Inadequate knowledge of standardised assessment tools such as the Screening Tool to Alert to Right Treatment/Screening Tool of Older Persons’ Prescriptions criteria, time constraints faced by prescribers and fragmented healthcare systems were some of the barriers identified by the respondents in medication optimisation for elderly individuals.ConclusionThe findings highlight the need for enhanced education on standardised assessment tools and the implementation of targeted interventions. A key recommendation is the integration of clinical pharmacists into care teams to optimise prescribing practices.

Mathematical models serve as essential tools to investigate brain aging, the onset of Alzheimer's disease (AD) and its progression. By studying the representation of the complex dynamics of brain aging processes, such as amyloid beta (Aβ) deposition, tau tangles, neuro-inflammation, and neuronal death. Sensitivity analyses provide a powerful framework for identifying the underlying mechanisms that drive disease progression. In this study, we present the first local sensitivity analysis of a recent and comprehensive multiscale ODE-based model of Alzheimer's Disease (AD) that originates from our group. As such, it is one of the most complex model that captures the multifactorial nature of AD, incorporating neuronal, pathological, and inflammatory processes at the nano, micro and macro scales. This detailed framework enables realistic simulation of disease progression and identification of key biological parameters that influence system behavior. Our analysis identifies the key drivers of disease progression across patient profiles, providing insight into targeted therapeutic strategies. We investigated a recent ODE-based model composed of 19 variables and 75 parameters, developed by our group, to study Alzheimer's disease dynamics. We performed single- and paired-parameter sensitivity analyses, focusing on three key outcomes: neural density, amyloid beta plaques, and tau proteins. Our findings suggest that the parameters related to glucose and insulin regulation could play an important role in neurodegeneration and cognitive decline. Second, the parameters that have the most important impact on cognitive decline are not completely the same depending on sex and APOE status. These results underscore the importance of incorporating a multifactorial approach tailored to demographic characteristics when considering strategies for AD treatment. This approach is essential to identify the factors that contribute significantly to neural loss and AD progression.

In an evolutionary era of medical education, “Artificial intelligence” (AI) is applied to replicate human intellect, encompassing abilities, logical reasoning and effective problem-solving skills. Previous research has explored the attitude of medical and dental students, toward the assimilation of AI in medicine; however, a significant gap exists in appraising the understanding and concerns of pharmacy students. Therefore, the current study was designed to explore undergraduate pharmacy students’ perceptions of integrating AI into education and practice. Methods : A cross-sectional study was conducted among final-year pharmacy students from different public and private sector universities in Karachi. The sample size on 60% anticipated response rate and 99% CI was calculated to be 390. Data was collected after acquiring ethical approval using convenient sampling. Frequency and percentage of the socio-demographic features were analyzed and then goodness of fit and Pearson’s chi-squared test of correlation was applied. Results were considered significant when p < 0.05. Results : The overall response rate of the study was 67%. More than 80% of the respondents were female. The students 35% (n = 202) strongly agreed and 59% (n = 334) agreed that AI plays an important role in healthcare, (χ2 = 505.6, p < 0.001). Around 79% (n = 453, χ2 = 384.3, p < 0.001) of students agreed on the replacement of patient care specialties with AI in the future, whereas 495 students (87%, χ2 = 682.3, p < 0.001) stated that they possess a strong comprehension of the fundamental principles governing the operation of AI. More than 80% of the students were comfortable in using AI terminologies (n = 475, χ2 = 598, p < 0.001) and 93% (n = 529, χ2 = 290, p < 0.001) were sure that AI inclusion in pharmacy education will develop a positive influence into the pharmacy curriculum (95%, n = 549, χ2 = 566.9, p < 0.001). A high and positive correlation was observed between the perception and willingness of students to adopt the AI changes in teaching undergraduate students (ρ = 0.491, p < 0.001). Furthermore, the outcomes showed students at private-sector universities stood out in computer literacy compared to public-sector universities (χ2 = 6.546, p < 0.05). Conclusion: The current outcomes revealed the higher willingness of pharmacy students towards AI-infused learning. They understood the prerequisite of having both formal and informal learning experiences on the clinical application, technological constraints, and ethical considerations of the AI tools to be successful in this endeavor. The policymakers must take action to ensure that future pharmacists have a strong foundation of AI literacy and take initiatives to foster the interests and abilities of imminent pharmacists who will spearhead innovation in the field.

The prevalence of potentially inappropriate medications (PIMs) in older adults populations is a significant concern, often leading to adverse drug events and increased health-care utilization. In the present study, we aim to evaluate the prevalence of PIMs among hospitalized older adults patients in Pakistan using STOPP (Screening Tool of Older Persons' Prescriptions) criteria version 3. A prospective observational study was conducted at a tertiary-care hospital in Karachi over 1 year from March 2023 to March 2024. Patients aged 60 years and above, prescribed at least one medication, were included. Data on demographics, comorbidities, and medications were collected and analyzed using the STOPP criteria to identify PIMs. Statistical analysis was performed using IBM SPSS Statistics version 21. To find the variables linked to PIM use, multivariable logistic regression analysis was used. The 95% CI and adjusted odds ratio (aOR) were used to measure the statistical association's strength. A p-value of less than 0.05 was deemed statistically significant. Among 450 participants, the median age was 67 years, with a predominance of male patients (55.3%). The prevalence of PIM use was 56.6%, and a total of 388 instances of PIM use were identified according to STOPP criteria version 3. Acetylsalicylic acid (18%) and pheniramine (11%) were the most frequent inappropriately prescribed medications. The multivariable logistic regression analysis revealed that polypharmacy and the presence of one or more comorbidities primarily influence the PIM use. The findings highlight a critical need for improved prescribing practices in the older adults population in Pakistan. Utilizing screening tools like the STOPP criteria can significantly enhance medication safety and optimize pharmacotherapy in this vulnerable group.

We detected Crimean-Congo hemorrhagic fever virus infections in 4 provinces of Pakistan during 2017-2018. Overall, seroprevalence was 2.7% in humans and 36.2% in domestic livestock. Antibody prevalence in humans was highest in rural areas, where increased contact with animals is likely.

The impact of traffic emitted nitrogen dioxide (NO 2 ) and fine particulate matter (PM 2.5 ) on various plant species ( Polyalthia longifolia , Swietenia mahagoni , Artocarpus heterophyllus ) during different seasons (summer, rainy, and winter) was studied in greater Dhaka city (traffic, residential, and control site), Bangladesh. Air pollution tolerance index (APTI) of these plant species was determined from the measured concentrations of total chlorophyll content (TCC), ascorbic acid (AAC), relative water content (RWC), and pH of the leaf extract. TCC and AAC concentrations in leaves species were determined with UV–visible spectrophotometer. NO 2 concentration was determined with Aeroquel (New Zealand), and PM 2.5 was determined with Aerocet (USA). The measured NO 2 and PM 2.5 concentrations at each sampling location were used to establish a relationship with the determined APTI values. The average value of TCC, AAC, and RWC was found much lower in traffic site compared than that of the control site. High pollution stress on plants was also found in traffic sites showing 30% lower APTI values. The plants become sensitive if the APTI value is smaller than or equal to 12 (APTI ≤ 12). However, the average APTI values in different seasons showed a significant variations and followed the sequence—rainy season (8.10) > summer (7.34) > winter (6.69). The plants had the highest sensitivity towards pollutants during winter time. The average APTI values also varied depending on the locations and plant species with a total average of 7.38 ± 1.17, which indicates the sensitivity of plants towards pollution. On average, the measured concentration of NO 2 was 223 ± 0.15 μg m −3 and PM 2.5 mass was 125.7 ± 0.05 μg m −3 . The APTI values showed a strong negative correlation with traffic emitted pollutants NO 2 ( R 2  = 0.86) and PM 2.5 ( R 2  = 0.70) indicating threats towards the plants survival.

2D pose estimation is a general problem in computer vision, where the main objective is to detect a person’s body key-points and estimate a 2D skeletonized pose of a person.Skeleton estimation is outbound as an essential part of body parts detection in many fields, such as healthcare, rehabilitation, sports and fitness, animation, gaming, augmented reality, robotics. These systems are based on neural network applications and able to give reliable, objective and cost-effective benefits. Various methods are available based on this topic and used to update existing systems. In this regard, in this work, we have proposed a method for skeleton-based angle detection where we have used MobileNet model. This model is developed based on the convolution neural network (CNN). At first, 18 key-points of the human body parts were generated through the model. After that, by using the extracted key-points the skeleton of the human body parts is generated by estimating key-points according to the body part pairs. Furthermore, based on the generated skeletons, different skeleton joint angles at different key-points are estimated. To evaluate the performance of the proposed model at different environmental conditions, a customized dataset was utilized. This approach shows 95.37% accuracy for key-points detection, for joint angle estimation the accuracy is 96.11%, and shows 96.667% accuracy for body part length measurement.

Background Onset and progression of Alzheimer's Disease (AD) are driven by complex interactions between various biological processes. Integrative mathematical models are useful tools to investigate age and pathology‐related trajectories. In turn, sensitivity analysis of mathematical models can provide insights into important dynamics. Method We performed a sensitivity analysis of our mathematical model of AD [1], which is specified by a system of 19 ordinary differential equations and 75 parameters. In our model, the population is stratified by sex and APOE status. Our analysis included simple one‐parameter & two‐parameter perturbation approaches with 10% variation in parameter values to reflect biological variability. We also generated virtual population samples on which we computed statistics, such as correlations between parameter values and outcomes. Our chosen outcomes were amyloid‐beta (Aβ) concentration, neuronal count (N) and tau concentration at 80 years of age after a 50‐year progression. Result Single parameter perturbations allowed us to assess how the value each of the 75 parameters affected model outcomes. As an example, in Figure 1 (a), we show how the value of a parameter related to pro inflammatory microglia affected the decrease in (N) over time. Figure 1 (b) illustrates parameters having the most impact on the neural count at 80 years of age, pointing to the importance of d_Ta, a parameter describing the rate of neuronal death caused by tumor necrosis factor alpha. To identify interactions between parameters, we investigated whether the effect of changing two parameters at a time differed from the sum of the individual effects. In Figure 1 (c), we grouped parameters according to their pathway of action and computed the maximal interaction strength between parameters of each group. Strong interactions are observed between neuronal dynamics and cytokines and pathological proteins. Conclusion This work may help identify therapeutic targets in the treatment of AD. It emphasizes the importance of tailoring strategies to patient characteristics and suggests that combinatorial approaches may be beneficial. References: [1] Chamberland, ´E., Moravveji, S., Doyon, N., Duchesne, S.(2024). A computational model of Alzheimer's disease at the nano, micro, and macroscales, Frontiers in Neuroinformatics, 18, 1348113.

Onset and progression of Alzheimer's Disease (AD) are driven by complex interactions between various biological processes. Integrative mathematical models are useful tools to investigate age and pathology-related trajectories. In turn, sensitivity analysis of mathematical models can provide insights into important dynamics. We performed a sensitivity analysis of our mathematical model of AD [1], which is specified by a system of 19 ordinary differential equations and 75 parameters. In our model, the population is stratified by sex and APOE status. Our analysis included simple one-parameter & two-parameter perturbation approaches with 10% variation in parameter values to reflect biological variability. We also generated virtual population samples on which we computed statistics, such as correlations between parameter values and outcomes. Our chosen outcomes were amyloid-beta (Aβ) concentration, neuronal count (N) and tau concentration at 80 years of age after a 50-year progression. Single parameter perturbations allowed us to assess how the value each of the 75 parameters affected model outcomes. As an example, in Figure 1 (a), we show how the value of a parameter related to pro inflammatory microglia affected the decrease in (N) over time. Figure 1 (b) illustrates parameters having the most impact on the neural count at 80 years of age, pointing to the importance of d_Ta, a parameter describing the rate of neuronal death caused by tumor necrosis factor alpha. To identify interactions between parameters, we investigated whether the effect of changing two parameters at a time differed from the sum of the individual effects. In Figure 1 (c), we grouped parameters according to their pathway of action and computed the maximal interaction strength between parameters of each group. Strong interactions are observed between neuronal dynamics and cytokines and pathological proteins. This work may help identify therapeutic targets in the treatment of AD. It emphasizes the importance of tailoring strategies to patient characteristics and suggests that combinatorial approaches may be beneficial. References: [1] Chamberland, ´E., Moravveji, S., Doyon, N., Duchesne, S.(2024). A computational model of Alzheimer's disease at the nano, micro, and macroscales, Frontiers in Neuroinformatics, 18, 1348113.

Abstract OP 29: Health Status 4, B302 (FCSH), September 4, 2025, 16:00 - 17:00 Return is often perceived as the end of the migration cycle, but for the low-skilled returnees, it signifies the challenges that will profoundly affect their health and wellbeing. Hence, return has become that pivotal stage where health disparities and abandonment are evident. This research aims to investigate post-return health disparities among involuntary returnees by comprehensive qualitative interviews performed in 2024 in rural areas of Bangladesh. This study explores how structural barriers such as lack of preparedness, restricted healthcare access, gendered social stigma can impede returnee’s health and overall wellbeing. Findings reveal significant health disparities rooted in the nature of return. The study reveals that a lack of preparedness in return due to abrupt deportation, lack of savings, fraudulent activities and global crisis like COVID-19 - intensifies mental and physical distress. Findings indicate that women from involuntary return face psychological distress, little awareness for healthcare services and institutional neglect. Many participants reported experiencing symptoms of anxiety, physical deterioration, and isolation without access to medical or emotional support. Despite growing migration from Bangladesh, reintegration policies remain fragmented, and healthcare systems lack the outreach and cultural sensitivity needed to support returnees in rural areas. The absence of a coordinated governmental framework leaves this population vulnerable to long-term health inequities. This research contributes to broader conversations on health equity by highlighting the lived realities of return migration in the Global South. It argues for inclusive, post-return support policies to ensure health equity, especially for those navigating forced or unplanned return.