Shaheed Benazir Bhutto University

Medicinal plants are major sources of bioactive polyphenols. The plants of Rheum genus contain valuable phenolic compounds which exhibit various important pharmacological activities. The current study was designed to investigate the polyphenols in Rheum wittrockii roots through bioassays and extraction/ isolate them via chromatophilic methods. The roots biomass of R. wittrockii was extracted with methanol and fractionated into four sub-fractions, i.e. n-hexane, chloroform, ethyl acetate and n-butanol (FG1-FG4). Total Phenolic Content (TPC) assay was used to affirm the number of total phenolic contents equivalent to gallic acid. Antioxidant activities were measured via 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay. α-amylase enzyme inhibition activities were performed using standard methods. Antibacterial activities were performed against Methicillin-Resistant Staphylococcus Aureus (MRSA) in dose dependent manner for all the fractions. The FG2 fraction was subjected to series of chromatographic separation using silica gel column to isolate the main phenolic compounds. Chloroform fraction (FG2) showed TPC of 12.5 mg/g GAE, strong antioxidant activities i.e. 85±0.22% at dose of 100 mg/mL (IC50= 6.25 mg/mL) against DPPH and also showed strong α-amylase inhibition activities with IC50 values of 432 µg/mL, compared to IC50 of standard acarbose (125 µg/mL). It was active antibacterial against MRSA at lower doses. The FG2 fraction was further subjected to various chromatographic separations to obtain three known phenolic compounds, protocatecutic acid (1), isovanilliic acid (2) and epipinoresinol (3) in appreciable quantities for the first time from this plant. The structures of all the three compounds were elucidated through FT-IR, NMR and Mass spectral studies. The present investigation concluded that the roots of Rheum wittrockii is a good source of phenolics. Its chloroform fraction showed potent antioxidant, anti-diabetic and antibacterial effects probably due to its three compounds isolated for the first time. The compounds are valuable pharmaceuticals with promising biological actions.

Abstract This article proposes a novel multitask learning framework that integrates contrastive learning and natural language generation (NLG) to enhance medical image classification and report generation. The goal is to improve disease classification accuracy and interpretability in medical diagnostics. The model architecture consists of a Vision Transformer (ViT) as a visual encoder, a transformer-based text encoder, and a multimodal decoder. The visual encoder processes medical images, while the text encoder handles disease-related text prompts. These components are trained jointly using image-text contrastive loss and language generation loss. Evaluations on the MIMICCXR and Chexpert datasets show that the model with NLG (Plain + NLG) outperforms the baseline contrastive learning model (Plain) in disease classification. For example, in the MIMICCXR dataset, the accuracy for Atelectasis increased from 17.44%(Plain) to 41.5% (Plain + NLG), and for Cardiomegaly, it improved from 19.25% to 47.4%. In Chexpert, the accuracy for Atelectasis increased from 12.5% to 58.5%, and for Pleural Effusion, from 61.10% to 64.0%. The model also demonstrated improvements in F1 scores, particularly for complex diseases like Cardiomegaly and Consolidation. The proposed multitask framework effectively combines contrastive learning with NLG, leading to improved disease classification and medical report generation. This approach has potential clinical applications by enhancing AI’s interpretability and accuracy in medical decision-making.

People affected by vision impairment experience significant challenges in mobility and daily life activities. In this paper, a smart assistive navigation system is proposed to address mobility challenges and to enhance the independence of visually impaired individuals. Three modules are integrated into the proposed system. The vision module detects obstacles and interactive objects such as doors, chairs, people, fire extinguishers, etc. The depth cam-based distance module provides the distance of detected objects and obstacles. The voice module provides auditory feedback to visually impaired individuals about the detected objects and obstacles that fall under the pre-defined threshold distance. Finally, the proposed system is optimized in terms of performance and user experience. Jetson Nano is used to reduce the cost of the overall system; however, it has compatibility issues with many of the latest object detection models. The YOLOv5n model is used considering compatibility for object detection, but it has low Mean Average Precision (mAP) and frame rate. To improve the performance of the vision module, various hyperparameters of YOLOv5n are fine-tuned along with transfer learning to enhance the mAP@50 from the original 0.457 to 0.845 and mAP@50-95 from 0.28 to 0.593. Tensor-RT optimization is employed to increase the frame rate to deploy the model in a real scenario. The real-time experimentation shows that the proposed system successfully alerts users to key objects, hazards, and obstacles which enables independent and confident navigation.

Type 2 diabetes mellitus (T2DM), caused by hyperglycemia, insulin resistance, inflammation, oxidative stress, and endothelial dysfunction, leads to cardiovascular morbidity and mortality. Sodium-glucose cotransporter-2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP-1RAs) are classes of therapeutic agents that have shown cardiovascular effects on top of glycemic regulation. In this narrative review, existing evidence on pharmacological mechanisms and cardiovascular outcome data of the two drug classes are synthesized. The main effects of SGLT2 inhibitors are cardioprotective by hemodynamic and natriuretic mechanisms, enhanced myocardial energetics, and renal protection, and strong decreases in heart failure hospitalization and chronic kidney disease progression. Conversely, GLP-1 receptor agonists have anti-atherosclerotic and anti-inflammatory properties, which reduce major adverse cardiovascular events and stroke to a significant degree. Comparative analysis points to complementary processes and phenotype-specific advantages. Incorporation of these agents into modern management of T2DM is a paradigm shift in outcomes-based cardiometabolic care. There are also research gaps and future therapeutic implications.

Distributed randomized in-network localised load-balancing (DRILL) is one of the most commonly used protocols and relies on the “power of two choices” algorithm. However, this is a sub-optimal load balancing solution due to under-sampling and high number of candidate ports. Along with per-packet granularity, this sub-optimality increases the occurrence of out-of-order packets. Therefore, in this paper, we proposes a novel power of two choices-based load balancing protocol to overcome these shortcomings. Designated as state aware multiple-choice routing (SAMCR) for load balancing in data centers, it builds a Kalman filtering algorithm; to approximate queue length states using the developed processes and measurement models. Furthermore, it uses the estimated queue length states in an adaptive threshold-based covariance matrix to track the most optimal decision in the system. Mathematical simulations as well as an NS-3 discrete event network simulator were used to evaluate the superior performance of SAMCR over other state-of the art approaches in terms of system load, flow completion time and throughput for most types of flows.