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Artificial intelligence based personalized student feedback system -Sisu Athwala’ to enhance exam performance of medical undergraduates

Background In medical education, mentoring and feedback play crucial roles. Providing feedback on exam performance is a vital component as it allows students to improve. Feedback has to be tailor made and specific to the individual student. This needs lot of time and human resources, which are always not in abundance. Use of artificial intelligence (AI) is a promising proposition yet it comes with the integral problem of generating inaccurate responses by the Large language models (LLM). To alleviate and minimize this, we have developed our unique model ‘Sisu Athwala’ using retrieval augment generation (RAG) with custom LLM’s. Objective To design and implement an AI-based tool using RAG to provide customized feedback to medical students to enhance their exam performance, minimizing the risk of generating inaccurate responses by the LLM’s. To evaluate the AI tool by expert student mentors and by the end users. Methods The study was conducted at the Faculty of Medicine, University of Peradeniya, Sri Lanka. An AI based feedback tool was developed powered by Generative Pre-trained Transformers-4 (GPT-4) LLM using a RAG pipeline. Expert instruction sets were used to develop the data base through embedding model to minimize potential inaccuracies and biases. To generate user queries, students were provided with a self-evaluation form which was processed using Representative Vector Summarization (RVS). Hence most critical concerns of each student are distilled and captured accurately, minimizing noise or irrelevant details. The role of the AI tool was defined as a counsellor during Pre-processional alignment allowing professional manner throughout the interaction. User queries were processed using Open AI Application Programming Interface (API), utilizing GPT-4-turbo LLM. Students were invited to engage in conversations with the newly developed feedback tool. The AI tool was evaluated by the expert student mentors, as per its ability to give personalized feedback, use varied language expressions, and to introduce novel perspectives to students. End user perception on the use of AI tool was assessed using a questionnaire. Results Post implementation end user survey of the Sisu Athwala AI tool was largely positive. 92% mentioned the advices given by the tool on stress management were helpful. 60% believed that the study techniques suggested were useful. While further 60% thought they are comfortable using the tool. 52% find the advices on exam performances were helpful. In their open comments some suggested to have the tool as a mobile APP. 15 expert student mentors took part in evaluating the tool. 100% agreed that it effectively addressed key points of student strengths and identifies areas for improvements going by the Pendleton model. 90% agreed that Sisu- Athwala gives clear actionable plans. Conclusion Sisu Athwala AI tool provided comprehensive tailor made feedback and guidance to medical students which was well received by the end users. Expert student mentors evaluation of the material generated by the AI tool were quite positive. Though this is not a replacement for human mentors it supports mentoring to be delivered circumventing the human resource constraints.

Acoustic multiple transmission peaks in Thue-Morse structures based on lateral resonators

Lysine p-nitroanilide impairs cellular energetics and potentiates statin-induced cytotoxicity in RD rhabdomyosarcoma cells

Statins are clinically effective drugs for treating dyslipidemia and have been proposed as promising antineoplastic and adjuvant agents in cancer therapy for years due to their impact on dysregulated cell growth processes, including cell signaling, energetics, and membrane synthesis. Despite being potent inhibitors of mevalonate synthesis and its downstream products, their limited clinical success highlights the need to further explore their mechanistic effects. Leveraging the observed sensitivity of muscle cells to atorvastatin in clinical settings and utilizing untargeted metabolomic analysis of atorvastatin-treated RD rhabdomyosarcoma cells, we identified reduced levels of aminoadipic acid, an intermediate in lysine catabolism. We investigated whether metabolic sensitization of RD cells to lysine-related metabolites (lysine, aminoadipic acid, pipecolic acid, glutamic acid, α-ketoglutarate, and lysine-p-nitroanilide) prior to atorvastatin treatment enhances its cytotoxic effects. Metabolic sensitization or reprogramming involves cellular processes wherein cells adapt their metabolism to environmental changes, reflecting alterations in enzymatic activity, transport, and stress response thresholds. These adaptations enable cells to cope with specific environmental pressures but may impair their ability to respond to other stressors or stimuli. To evaluate the impact of metabolic supplementation, we analyzed cellular stress response markers via western blot. The results revealed that lysine-p-nitroanilide increased BiP, the master regulator of the unfolded protein response, and augmented the phosphorylation at threonine 172 of AMPK, an indicator of altered cellular energetics. Further analysis demonstrated that combining lysine-p-nitroanilide with atorvastatin disrupted mitochondrial homeostasis and reduced glycolysis, both desirable outcomes in antineoplastic treatments. Lysine-p-nitroanilide acts as an in vitro inhibitor of α-aminoadipic semialdehyde synthase, enzyme essential for lysine metabolism via the saccharopine pathway. However, we demonstrated that it is catabolically cleaved to p-nitroanilide, with this molecule driving the cytotoxic activity observed in our experiments. Although lysine metabolism was not fully suppressed by lysine-p-nitroanilide, these findings provide valuable insights for developing novel therapies for rhabdomyosarcoma.

Laser cooling traps more antimatter atoms than ever before

In vitro anticancer studies of new derivatives based on the furanocoumarin scaffold

A feature recognition and detection algorithm for pine wilt disease trees based on FLMP-YOLOv8

Pine wilt disease, a highly contagious forest disease caused by the pine wood nematode and primarily transmitted via its insect vector, the pine sawyer beetle (Monochamus spp.), poses a significant threat to forest ecosystems. Accurate detection of infected trees is vital for effective prevention and control. This study pioneers the detection of pine wilt disease-infected trees in the China’s Qinba Mountain region, where the complex terrain and uneven forest distribution thinder feature extraction of diseased trees. To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. This study provides a more reliable and cost-effective method for detecting trees infected with pine wilt disease.

Glycolysis-related MiRNA signature predicts prognosis, recurrence risk, and therapeutic responses in hepatocellular carcinoma

Influence of inter-fractional respiratory motion changes on dose delivery accuracy in dynamic conformal arc lung stereotactic body radiotherapy: A phantom study

Purpose To evaluate the influence of inter-fractional respiratory motion variation on dose delivery accuracy in dynamic conformal arc lung stereotactic body radiotherapy (SBRT) using glass dosimeter and QUASAR TM respiratory motion phantom. Materials and Methods Four-dimensional computed tomography (4D-CT) was acquired using a sinusoidal respiratory waveform (amplitude: 10 mm, breaths per minute [BPM]: 20). Three glass dosimeters were positioned at the superior edge, geometric center, and inferior edge of the tumor target. Internal target volume (ITV)-SBRT and gated-SBRT plans were created and delivered under nine respiratory conditions (BPM: 10–30, amplitude: 5–30 mm). Treatment was considered acceptable if the delivered dose to the glass dosimeters remained within the D 5% –D 95% of the gross tumor volume. Results BPM had minimal effect on ITV-SBRT, with the doses delivered to the target remaining within the acceptable range for all BPMs. However, amplitude significantly affected SBRT accuracy. For ITV-SBRT, increase in amplitude caused underdose at both the superior and inferior edges. In gated-SBRT, higher amplitude led to significant underdosing at superior edge of the target than that observed in ITV-SBRT, while inferior edge remained within the acceptable dose range. Underdose worsened with increasing amplitude, and 10 mm increase from the reference caused it to fall below the acceptable range (D 95% ). Conclusion Respiratory motion significantly affects dose delivery accuracy in lung SBRT, with amplitude playing a critical role. An amplitude increase of ≥ 10 mm from CT acquisition during SBRT delivery resulted in a significant target underdosing below the clinically acceptable threshold.

Cyclical grief in Israeli women after IVF and medical termination

Central obesity rather than BMI is associated with chronic pain: A cross-sectional analysis of NHANES

Background Chronic pain is a prevalent and debilitating condition that poses a major public health burden. Most existing research on obesity and pain has focused on general obesity, typically assessed using body mass index (BMI). However, BMI fails to capture fat distribution and may not adequately reflect metabolic risks associated with pain. Central obesity, characterized by abdominal fat accumulation, has been increasingly recognized as a more relevant predictor of chronic disease, but its relationship with chronic pain remains underexplored in population-based studies. Methods Data from 2,511 adults in the National Health and Nutrition Examination Survey (NHANES) were analyzed. Weighted logistic regression was used to assess the association between anthropometric indexes, including A Body Shape Index (ABSI), Waist Circumference (WC), Body Roundness Index (BRI), and BMI, and chronic pain. Subgroup and sensitivity analyses were conducted to test robustness. Restricted cubic spline (RCS) was applied to examine nonlinear relationships. Receiver operating characteristic (ROC) analysis compared the predictive performance of the anthropometric indicators. Results Higher ABSI was significantly associated with increased odds of chronic pain, even after adjusting for a wide range of covariates including BMI (adjusted OR for highest vs. lowest quartile: 1.74; 95% CI: 1.16–2.59; P  = 0.010). In contrast, BRI, WC, and BMI were not significantly associated with chronic pain. RCS analysis indicated a linear relationship between ABSI and chronic pain. ROC analysis showed that central obesity indicators (ABSI, BRI, and WC) had better discriminative ability than BMI. Findings were consistent across subgroup and sensitivity analyses. Conclusion Central obesity, as measured by ABSI, is significantly associated with chronic pain, independent of BMI and other risk factors. These findings highlight the importance of incorporating central obesity indicators into public health and clinical strategies for chronic pain prevention and management.

Quantitative holographic analysis in stallion spermatozoa following cryopreservation

Abstract This study employs Holographic tomography (HT) to examine structural and biophysical changes occurring during the cryopreservation of stallion sperm. HT is an advanced imaging technique that integrates digital holography with tomography to achieve three-dimensional, quantitative reconstructions of objects without the need for treatment or reporter dyes. By using refractive index (RI) intervals to represent specific structural regions of sperm cells, variations in optical density, surface area, volume, and dry mass across different cryopreservation extenders and donors have been quantified. Three main sperm components, (i) nuclear region, (ii) post-acrosomal region and midpiece and (iii) whole cell were identified and discriminated based on different RI. Our results revealed significant differences in volume of post-acrosomal region and midpiece among stallions as well as between fresh and frozen/thawed sperm, whereas no significant differences were observed between freezing extenders, aligning with our findings on sperm kinetics. A significant stallion-extender interaction underscores the need to personalize the sperm freezing process.

Polypharmacy and frailty among aging World Trade Center responders

Background During and after the 9/11 rescue and recovery efforts, World Trade Center (WTC) responders were exposed to environmental hazards that may accelerate aging and increase frailty. This study examines the relationship between polypharmacy and frailty among WTC responders to inform strategies that mitigate medication-related risks in high-risk, aging populations. Methods We included WTC responders aged 50 and older who attended at least one clinical monitoring visit at WTC Health Program between 2017–2019. Frailty was assessed using the WTC-specific Clinical Frailty Index, and associations with polypharmacy (concurrent use of 5 or more medications) and fall-risk increasing drugs (FRIDs) use were evaluated through multivariable logistic regression models adjusting for demographic, employment, health, and WTC exposure data. Results Among 6,966 WTC responders, 55% met the criteria for polypharmacy and 7.6% used FRIDs. Frailty was independently associated with both polypharmacy (OR 1.15, p < 0.001) and FRID use (OR 1.11, p < 0.001). Older age (OR 1.08, p < 0.001), obesity (OR 1.92, p < 0.001 for BMI ≥ 30), protective service occupations (OR 1.30, p = 0.002), and chronic conditions such as gastroesophageal reflux disease (OR 1.71, p < 0.001), obstructive airway disease (OR 2.24, p < 0.001), and upper respiratory disease (OR 1.85, p < 0.001) were associated with higher odds of polypharmacy. In contrast, male sex (OR 0.81, p = 0.018) and construction occupations (OR 0.73, p = 0.001) were associated with lower odds of polypharmacy. Female sex (OR 1.64, p < 0.001), smoking (current: OR 1.55, p = 0.013; former: OR 1.30, p = 0.014), and mental health conditions such as anxiety (OR 1.66, p = 0.004), depression (OR 2.85, p < 0.001), and post-traumatic stress disorder (OR 1.72, p < 0.001) were associated with higher odds of FRID use. Conclusions We found a high prevalence of polypharmacy and FRID use among aging WTC responders, with frailty significantly associated with both. Our findings underscore the need to optimize medication management for aging WTC responders, which may impact their healthy aging.

Development and evaluation of a low-cost 3D-printed simulator for ossicular prosthesis placement

Morphological diversity in the honeyeater hyolingual apparatus and its relationship with nectarivory

Honeyeaters (Aves, Meliphagidae) are a speciose clade of nectarivorous birds, and there is immense diversity in the degree to which different species within the family rely on nectar. Honeyeater tongues are commonly described as similar to a paintbrush, with this morphology being interpreted as an adaptation for increasing nectar extraction efficiency. However, there has been limited work documenting the degree of interspecific diversity in tongue morphology across the family or the extent to which such diversity correlates with dependance on nectar. This information is also lacking for the hyoid bones, the structures responsible for moving the tongue in and out of the mouth. We aimed to fill this knowledge gap by examining honeyeater tongues and hyoids from across the family. We found that there are six distinct tongue types across the Meliphagidae, and that certain genera such as Acanthorhynchus and Phylidonyris have a unique tongue morphologies. Using phylogenetic generalized least square regressions, we found that tongue length (not size corrected) and the proportion of tongue that is bristled were both positively correlated to degree of nectarivory, while tongue length (relative to bill length), tongue depth (relative to bill depth) and tongue width (relative to bill width) were not correlated to nectarivory. Finally, we found no correlation between hyoid length (relative to bill length) and nectarivory, suggesting that the capacity for further tongue protrusion is unrelated to nectar dependence in honeyeaters. Similar studies should be conducted across other groups of avian nectarivores to expand our understanding of dietary ecomorphology beyond bill shape, which has been the focus of the majority of research on food handling adaptations in birds thus far.

Early damage detection in bridges using a variational autoencoder–based hybrid unsupervised learning framework

Correction: Automatic detection of expressed emotion from five-minute speech samples: Challenges and opportunities

Prosumer Web City: a novel energy market framework for enabling dynamic bidding and scalable integration of distributed energy participants

Use of a new micropattern tape method to detect chirality shifts in differentiating C2C12 cells

Chirality is an intrinsic property of cells manifested as left-right (LR) asymmetry in terms of cellular morphology and organization, which influences cell behavior, migration, and tissue development. Traditional in vitro methods used to study cell chirality often require complex fabrication methods, limiting their accessibility and reproducibility. Here, we present a novel micropattern tape method that facilitates fabrication of high-quality rectangular micropatterns useful for efficient, high-throughput analysis of cell chirality. Using this method, we characterized chirality of C2C12 myoblasts and MC3T3-E1 osteoblasts, which respectively exhibit clockwise (CW) and counterclockwise (CCW) chirality relative to the long axis of the rectangle. We used the method to analyze how cellular differentiation impacts chirality and observed striking reversal of C2C12 cell chirality upon bone morphogenic protein-2 (BMP2)-induced osteoblastic differentiation. These results demonstrate that our micropattern tape method can effectively detect dynamic change of cell chirality during differentiation.

Biological activity, UHPLC-MS phytochemical profiling, and computational studies of the leaf extract of Acridocarpus socotranus

Characterizing rurality using the All of Us Research Program data

Rural communities experience disproportionately higher rates of chronic diseases, less access to healthcare services, and poorer health outcomes compared to their urban counterparts in the United States. However, inconsistencies in how rurality is defined across biomedical research, including limitations in geographic detail within large-scale datasets, present significant challenges for reliably studying rural health outcomes. This study aimed to develop and apply an operational rurality scale using 3-digit ZIP codes to characterize rural participation in the All of Us Research Program and to examine associations between rurality, delayed care, and healthcare affordability. Publicly available information from the Federal Office of Rural Health Policy and the Environmental Systems Research Institute was integrated to generate a continuous rurality scale at the 3-digit ZIP code level. A Kolmogorov-Smirnov test identified statistically significant differences in the geographic distribution of those who had delayed access to care (P < 0.001) and those with difficulties affording care (P < 0.001). The proposed continuous rurality scale is reproducible and extensible in several ways within the All of Us Workbench, as it provides a framework for categorizing participants by geolocation and facilitates standardized analyses of rurality-related research questions.