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Abstract Deep learning has proven capable of automating key aspects of histopathologic analysis. However, its context-specific nature and continued reliance on large expert-annotated training datasets hinders the development of a critical mass of applications to garner widespread adoption in clinical/research workflows. Here, we present an online collaborative platform that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/ ). Specifically, PHARAOH uses a weakly supervised, human-in-the-loop learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched annotation by human experts. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed efficiently and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Moreover, by using our PHARAOH pipeline, we showcase how correlation of cohort-level cytoarchitectural features with accompanying biological and outcome data can help systematically devise interpretable morphometric models of disease. Both the custom model design and feature extraction pipelines are amenable to crowdsourcing, positioning PHARAOH to become a fully scalable, systems-level solution for the expansion, generalization and cataloging of computational pathology applications.

Fatemeh Khatami,1 Bagher Larijani,2,3 Shekoufeh Nikfar,3,4 Mandana Hasanzad,3,5 Kiarad Fendereski,6 Seyed Mohammad Tavangar1,7 1Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; 2Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; 3Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; 4Department of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran; 5Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; 6Pediateric Urology and Regenerative Medicine Research Center, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran; 7Department of Pathology, Dr. Shariati Hospital, Tehran University of Medical Sciences, Tehran, IranCorrespondence: Seyed Mohammad TavangarDepartment of Pathology, Dr. Shariati Hospital, Tehran University of Medical Sciences, Jalale Ale Ahmad Ave, Tehran, IranTel +98 218 490 2187Fax +98 218 863 3078Email Tavangar@ams.ac.irAbstract: Thyroid cancer is one of the most common endocrine malignancies, with increasing incidence all over the world. In spite of good prognosis for differentiated thyroid carcinoma, for an unknown reason, about 5–10% of the patients, the cancer will show aggressive behavior, develop metastasis, and be refractory to treatment strategies like radioactive iodine. Regarding the genetic information, each thyroid cancer patient can be considered as an individual unique one, with unique genetic information. Contrary to standard chemotherapy drugs, target therapy components aim at one or more definite molecular pathway on cancer cells, so their selection is underlying patient’s genetic information. Nowadays, several mutations and rearrangements including BRAF, VEGF receptors, RET, and RET/PTC, KDR, KIT, PDGFRA, CD274, and JAK2 are taken into account for the therapeutic components like larotrectinib (TRK inhibitor), vemurafenib, sunitinib, sorafenib, selumetinib, and axitinib. With the new concept of personalized treatment of thyroid cancer diagnoses, planning treatment, finding out how well treatment will work, and estimating a prognosis has changed for the better over the last decade.Keywords: personalized medicine, target therapy, molecular testing

Background and Aims: Feverfew (Tanacetum parthenium) is a valuable medicinal plant from Asteraceae family with various pharmacological and therapeutic properties. In this article we reviewed the various aspects of feverfew including botanical characteristics, cultivation and production and medicinal and clinical uses.Material and Methods: Google Scholar, PubMed, EBSCO, Directory of open access journals (DOAJ), EMBASE, and Web of Science were searched using the keywords feverfew and parthenolide. Results: The essential oil of feverfew is frequently used in pharmaceutical and cosmetic industries. Parthenolide is the most important active component of feverfew that is used for the treatment of a wide variety of diseases such as fever, migraine headache, and rheumatoid arthritis.Conclusion: Feverfew has several positive clinical uses and has the potential to be considered as a new drug for the treatment of some diseases.

Objective(s): To determine health needs of a population living in the Tehranchr('39')s 17th municipal district and identify appropriate strategies to deal with them. Methods: This research included 2 phases: the first phase was a qualitative study with 2 stages, focus group discussion, in-depth interview with key informants of the 17th district and expert panel methods with specialists. The second phase was a cross sectional study carried out with a closed questionnaire based on the results of phase 1. Results: The results of the first phase indicted that the presence of railway tracks in the 17th district was the most important problem affecting peoplechr('39')s health status. Comparing the opinions of community leaders and health professionals with those of local residents, and study of cause-effect chain, it was obvious that with the transfer of Tehran-Tabriz railway, the major problems of this area will decrease. On the basis of this we arranged a meeting and invited officials from the municipality and railway organization to convey problems. Conclusion: The findings indicated that it is possible to disseminate community-based investigations to improve publicchr('39')s health.

Abstract Background Lisdexamfetamine (LDX) and Methylphenidate (MPH) are stimulant agents that have been shown to provide significant benefits in the management of attention-deficit/hyperactivity disorder (ADHD) in patients. Aim This study aimed to assess the cost-effectiveness and the budget impact of LDX compared to MPH as the first-line treatment for ADHD. Methods A one-year cost-effectiveness analysis (CEA) was conducted to compare the effects of LDX and MPH in reducing disease symptoms and patient costs and improving quality of life (QoL) from a social perspective. Clinical data were obtained using the EQ-5D questionnaire. In contrast, economic data were sourced from the official website of the Iranian Food and Drug Association (FDA), the national book of tariffs, and specific questionnaires designed to evaluate patients' direct and indirect costs. 197 patients were included in the study, including individuals who sought psychiatric evaluation at a hospital in Mashhad and those who obtained ADHD medications from governmental pharmacies. The cost-effectiveness of the study medicine was assessed using the decision tree method, and the results were presented as the Incremental Cost-Effectiveness Ratio (ICER). Deterministic Sensitivity Analysis (DSA) and Probabilistic Sensitivity Analysis (PSA) were performed to assess the robustness of the findings. Additionally, a Budget Impact Analysis (BIA) was conducted over five years, considering three different scenarios, to evaluate the financial implications of incorporating LDX into the national pharmaceutical system. Results The ICER for LDX therapy compared to MPH was estimated at USD 264.28 (with an incremental cost of USD 54.9, incremental effectiveness of 0.208, and Quality-Adjusted Life Years (QALYs) gained of 0.765). The PSA indicated a 0.994% probability of LDX being cost-effective, considering a threshold of USD 2450 per QALY. Furthermore, the DSA revealed that the acquisition cost of LDX influenced the model's sensitivity. The BIA demonstrated that incorporating LDX into Iran's healthcare system would result in a financial burden of approximately $368,566 in the first year, representing an additional cost of $11,154 compared to the non-availability of this medicine and the use of previous medications. It is projected that by 2027, the financial burden of treating ADHD with LDX will reach approximately USD 443,879 over five years, amounting to an increase of $71,154 compared to the absence of this medicine. Conclusion From a social perspective, the inclusion of LDX in the treatment regimen for ADHD is associated with higher costs and an increased financial burden. However, based on our analysis, LDX appears to be a cost-effective choice for managing ADHD in Iran when compared to MPH.

The increasing use of artificial intelligence (AI) in education has raised questions about the implications of ChatGPT for teaching and learning. A systematic literature review was conducted to answer these questions, analyzing 112 scholarly articles to identify the potential benefits and challenges related to ChatGPT use in educational settings. The selection process was thorough to ensure a comprehensive analysis of the current academic discourse on AI tools in education. Our research sheds light on the significant impact of ChatGPT on improving student engagement and accessibility and the critical issues that need to be considered, including concerns about the quality and bias of generated responses, the risk of plagiarism, and the authenticity of educational content. The study aims to summarize the utilizations of ChatGPT in teaching and learning by addressing the identified benefits and challenges through targeted strategies. The authors outlined some recommendations that will ensure that the integration of ChatGPT into educational frameworks enhances learning outcomes while safeguarding academic standards.

Computational fluid dynamics (CFD) simulations are broadly used in many engineering and physics fields. CFD requires the solution of the Navier–Stokes (N-S) equations under complex flow and boundary conditions. However, applications of CFD simulations are computationally limited by the availability, speed, and parallelism of high-performance computing. To address this, machine learning techniques have been employed to create data-driven approximations for CFD to accelerate computational efficiency. Unfortunately, these methods predominantly depend on large labeled CFD datasets, which are costly to procure at the scale required for robust model development. In response, we introduce a weakly supervised approach that, through a multichannel input capturing boundary and geometric conditions, solves steady-state N-S equations. Our method achieves state-of-the-art results without relying on labeled simulation data, instead using a custom data-driven and physics-informed loss function and small-scale solutions to prime the model for solving the N-S equations. By training stacked models, we enhance resolution and predictability, yielding high-quality numerical solutions to N-S equations without hefty computational demands. Remarkably, our model, being highly adaptable, produces solutions on a 512 × 512 domain in a swift 7 ms, outpacing traditional CFD solvers by a factor of 1,000. This paves the way for real-time predictions on consumer hardware and Internet of Things devices, thereby boosting the scope, speed, and cost-efficiency of solving boundary-value fluid problems.

Abstract Background Pharmaceuticals have made an important contribution to global reductions in morbidity and mortality. To help save lives and improve health, it is important to be sure about equity to access to drugs, drug efficacy, quality and safety, and rational use of drugs, which are standardized National Drug Policy (NDP) objectives. NDP's indicators are useful to evaluate the pharmaceutical system performance in a country. Iran has adapted a National Drug List (NDL). Since management of drug supply in Iran takes place only for drugs that have been selected in NDL and this list is selected by the member of Iran Drug Selecting Committee (IDSC), thus evaluation of IDSC's decision making during last 5 years is an appropriate way to evaluate the implementation of drug supply system in the country. Methods To identify strengths and weaknesses of pharmaceutical policy formation and implementation in Iran, four standard questionnaires of the World Health Organization (WHO) were used. To assess the agreement between decisions of IDSC and standardized NDP indicators in the last 5 years (1998–2002), a weighted questionnaire by nominal group technique based on the questions that should be answered during discussion about one drug in IDSC was designed and used. Results There is a totally generics based NDP with 95% local production, that provides affordable access to drugs. The system, structures, and mechanisms were in place; however, they did not function properly in some topics. Assessment of 59 dossiers of approved drugs for adding to NDL during last 5 years showed that IDSC's members pay more attention to efficacy, safety, and rationality in use rather than accessibility and affordability. Conclusion Revision of drug system in term of implementation of the processes to achieve NDP's objectives is necessary to save public health. Clarification of NDP's objectives and their impact for IDSC's members will result in improvement of the equity in access to pharmaceuticals.

The Taliban is of the political studies issues that have been of interest in the Islamic world during the last three decades. The dynamism of this stream is tied to domestic and global crises. This group took power in 1996 after the widespread crises in Afghanistan; and in 2001, they were abolished by the attack of the United States of America. The problem surveyed in this article is how the Taliban's thought is related to the internal and external crises related to Afghan society. Using the four stages defined in Thomas Spriggans' research method, the main question of the article is, what do the Taliban think about the crises in Afghan society and the causes of these crises? and what is their ideal society and their prescriptions to overcome the crises of Afghan society? The findings of the research show that in the Taliban's view, the main problems and crises in the Afghan society are "political", "economic" and "ignorance" crises. Failure to follow the rules of Islam and the illegitimacy of the political system have also been considered the origin of these crises. The desired ideal of the Taliban to overcome the problems is an ideal society that is governed according to the tradition of the Prophet (PBUH) and the righteous caliphs and strives to establish an Islamic government, peace and security, and fights against cultivation, trade, and drug use. Prescriptive solutions in the Taliban's thought to solve crises are "unification of sovereignty", "elimination of jihadi parties", "implementing the duty of commanding the good and forbidding the bad" and "fighting corruption".