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Luteolin, a polyphenolic plant flavonoid, has been attributed with numerous beneficial properties like anti-cancer, antioxidant, and anti-inflammatory action. Luteolin has been reported earlier to be neuroprotective in models of spinal cord injury and traumatic brain injury and also induces neurite outgrowth in PC12 cells. However, the effect of luteolin on early differentiation, which might be important for its beneficial effects, is unknown. In this report, we show that luteolin negatively affects early differentiation of embryonic stem cells, hampering the formation of embryoid bodies. At later stages of differentiation, luteolin specifically inhibits neuronal differentiation, where the expression of early neuronal markers is suppressed, whereas luteolin treatment does not inhibit expression of meso- and endodermal markers. Further, in a developing zebrafish model, luteolin treatment leads to fewer numbers of mitotic cells in the brain. These specific effects of luteolin on neuronal differentiation could possibly be due to its ability to inhibit the lysine acetyltransferase, p300, since the structurally closely related p300 non-inhibitor flavonoid, apigenin, does not inhibit neuronal differentiation. These results show that luteolin perturbs neuronal differentiation of embryonic stem cells.

Chimeric Antigen Receptor-T (CAR-T) cell therapy is effective for relapsed/refractory B-acute lymphoblastic leukemia (r/r B-ALL) but is not universally available. We developed a novel humanized CD19-directed CAR-T (HCAR19) approved for Phase 1/1b/2 trials. Patients aged 3–25 years were enrolled with r/r B-ALL and ineligible for allogeneic stem cell transplant. Lymphodepletion utilized standard-dose fludarabine and cyclophosphamide. A 3 + 3 design testing 3 dose-ranges was used to determine Phase-2 Dose (P2D): Dose-A, 1 × 10 6 HCAR19 cells/kg, Dose-B, 3–5 × 10 6 /kg, and Dose-C, 10–15 × 10 6 /kg. Primary endpoint was overall response rate (ORR) at day-30 on bone-marrow flow-cytometry. From May-2021 to September-2023 12 patients [median age-14 (range: 5–24) years] were enrolled with median bone marrow blasts 19.5% at screening. Cytokine release syndrome occurred in 10 (83%) patients, predominantly Grades 1–2, and Grade-2 immune-cell associated neurotoxicity (ICANS) in 1. All patients had Grade-3 cytopenia. ORR was 91.7% (11/12), complete response (CR) in 8 (66.7%) and partial response in 3 (25%). Seven of 8 CRs were at Dose-levels B and C, all of which were sustained till 12 months follow-up. Patients who received dose levels below 3 × 10 6 /kg, or did not achieve CR, had early loss of response or rapid progression. HCAR19 demonstrated safety, manageable toxicity, and durable remissions. and P2D was determined as 5–10 × 10 6 HCAR19-cells/kg. Clinical trial registration The study is registered in the Clinical Trials Registry- India (CTRI/2021/05/033348 and CTRI/2023/03/050689).

The objective of this study was to explore a log of WhatsApp messages exchanged among members of the health care group Doctors For You (DFY) while they were providing medical relief in the aftermath of the Nepal earthquake in April 2015. Our motivation was to identify medical resource requirements during a disaster in order to help government agencies and other responding organizations to be better prepared in any upcoming disaster. A large set of WhatsApp (WhatsApp Inc, Mountain View, CA) messages exchanged among DFY members during the Nepal earthquake was collected and analyzed to identify the medical resource requirements during different phases of relief operations. The study revealed detailed phase-wise requirements for various types of medical resources, including medicines, medical equipment, and medical personnel. The data also reflected some of the problems faced by the medical relief workers in the earthquake-affected region. The insights from this study may help not only the Nepalese government, but also authorities in other earthquake-prone regions of the world to better prepare for similar disasters in the future. Moreover, real-time analysis of such online data during a disaster would aid decision-makers in dynamically formulating resource-mapping strategies. (Disaster Med Public Health Preparedness. 2017;11:652-655).

[This corrects the article DOI: 10.3389/fnmol.2019.00041.].

Urban commuters in India often face transportation challenges during their daily travels. Traditional feedback methods, such as surveys and hotlines, struggle to scale effectively due to the large population in Indian cities. In this context, social media platforms such as Twitter/X present a practical alternative. where commuters’ complaints are often voiced through short, informal posts. These commuter complaints represent various ongoing issues in India’s urban transportation sector. Hence they are important for urban planners, policymakers, and transportation authorities to gain real-time insights into public concerns. However, an efficient framework is needed to automatically identify transportation-related concerns from the vast pool of social media posts and then generate a concise summary highlighting the most pressing issues, so that the policymakers/authorities can understand the key challenges and respond effectively to them. This study proposes a framework that utilizes generative AI and Natural Language Processing (NLP) to automatically identify and summarize transportation-related complaints from social media posts. To improve the quality of summarization, a novel prompt is developed for systematically summarizing transportation-related concerns and grievances. Findings indicate that this prompt significantly enhances summarization performance with the GPT-4 Turbo LLM. Notably, GPT-4-Turbo using proposed prompt achieves a ROUGE score of 0.86, surpassing the widely used LexRank algorithm, which scores 0.45.

In the aftermath of a disaster event, it is of utmost important to ensure efficient allocation of emergency resources (e.g. food, water, shelter, medicines) to locations where the resources are needed (need-locations). There are several challenges in this goal, including the identification of resource-needs and resource-availabilities in real time, and deciding a policy for allocating the available resources from where they are available (availability-locations) to the need-locations. In recent years, social media, and especially microblogging sites such as Twitter, have emerged as important sources of real-time information on disasters. There have been some attempts to identify resource-needs and resource-availabilities from microblogging sites. However, there has not been much work on having a policy for optimized and real-time resource allocation based on the information obtained from microblogs. Specifically, the allocation of critical resources must be done in an optimal way by understanding the utility of emergency resources at various need-locations at a given point of time. This paper attempts to develop such a utility-driven model for optimized resource allocation in a post-disaster scenario, based on information extracted from microblogs in real time. Experiments show that the proposed model achieves much better allocation of resources than baseline models—the allocation by the proposed model is not only more efficient in terms of quickly bringing down resource-deficits at various need-locations, but also more fair in distributing the available resources among the various need-locations.

Due to population growth and rapid urbanization in Indian cities, transportation has evolved as a critical concern affecting a large number of commuters everyday. Hence it is important for the urban planners, policymakers, and transportation authorities of India to know about the different public grievances/concerns regarding transportation. This study aims to uncover valuable information about specific transport-related complaints/grievances in Indian cities from the vast pool of user-generated content on social media platforms such as Twitter. As an initial step, we have explored the broad sentiment of commuters in six Indian metropolitan cities about the existing transportation systems, and created a dataset that broadly classify tweets into negative and positive sentiments. Next, we have identified a set of fine-grained complaints/grievances in these tweets, and thus created the first dataset containing transport-related tweets labelled into various specific complaints/grievances in a multi-label setting. To our knowledge, there is no existing dataset that labels tweets according to specific concerns raised in the posts. We apply several classification models on the dataset, for classifying transportation-related tweets into the specific complaints/grievances. We further conducted a city-wise analysis to better comprehend the specific transport-related complaints prevalent in each Indian city.

PurposeThis is a theoretical paper in the field of structuralist macroeconomics. The paper focuses on commodity price fluctuation which has emerged as one of the major macroeconomic factors with significant bearing on the relationship between the agricultural and nonagricultural sectors.Design/methodology/approachThe paper develops a dual economy model consisting of an agricultural sector and an industrial sector. The commodity price is subject to the fluctuations due to the fact that stock of primary goods is an asset which is sensitive to speculations. The paper considers a standard methodology of dynamic adjustment process involving change in stock of agricultural goods and price of agricultural goods under perfect foresight. The saddle path properties of the equilibrium are also examined.FindingsThe paper shows that the balanced budget fiscal expansion, capital account liberalization and the agricultural expansion lead to expansion of the industrial sector as well as level of employment. The increase in world interest rate may lead to contraction of the industrial sector and depress employment.Originality/valueWe will consider the openness of the economy in explaining how different macroeconomic policies and capital account liberalization generate multiple cross effects on the inter-connectedness between agricultural and the non-agricultural sector. The paper will discuss the issue of employment generation in conjunction with commodity price fluctuation. We depart from the literature by using Taylor rule under which interest rate is fixed by the Central Bank such that money supply becomes endogenous.