After tooth extraction, there can be consequences involving injury to the tissue surrounding the extracted tooth, which may lead to severe problems such as inflammation and infection. The wound healing process comprises inflammation, proliferation, and remodeling phases. Photobiomodulation is a therapy form that utilizes the interaction of a light source with tissue. This interaction can activate an increase in Adenosine Triphosphate (ATP), which subsequently triggers a chain reaction leading to the creation of new blood vessels and an increase in the number of fibroblasts. This study used a red laser light source with a power of 3.32 ± 0.01 mW, delivering a dose of 3.5 J to patients for extraction indications. The parameters observed included Interleukin 1_ (IL-1_), Prostaglandin E2 (PGE2), Human Beta defensin 2 (HBD2), and Gingival Index (GI). The results of testing saliva samples using the enzyme-linked immunosorbent test (ELISA) for the parameters IL-1_, PGE2, and HBD2 show a significant influence between the control and therapy groups. Meanwhile, GI revealed a significant influence of therapy on the wound-healing process. Using the Mann-Whitney U test, on day 1, the p-value was found to be 0.32, indicating no significant deference between the control and therapy groups. However, on the third day after the therapy was administered, the p-value was obtained as 0.01, signifying a significant deference between the control and therapy groups. On day 5, a p-value of 0.034 was obtained, signifying a significant deference between the control and therapy groups. Based on the research results, it can be observed that there is a decrease in the values of IL-1_, PGE2, HBD2, and GI. This indicates that local immune cells, including resident macrophages, are activated by pro-inflammatory mediators released in response to injury, and they play an essential role in accelerating wound healing.

Background: Human estrogen receptor alpha (ERα), which is known to play a role in mediating cell proliferation, metastasis, and resistance to apoptosis, is one of the targets of breast cancer therapies. Alpha mangostin (AM) is an active xanthone compound from Garcinia mangostana L. which has activity as an ERα inhibitor. Objectives: This research aims to predict the pharmacokinetic and toxicity, and to study the molecular interactions of AM derivatives with the ERα using computer-aided simulation approaches through molecular docking, molecular dynamic, and pharmacophore screening to develop novel anti-breast cancer agents. Methods: Marvinsketch and Chimera programs were used to design and optimize the structure of AM and its derivatives. For screening the pharmacokinetic and toxicity profiles, the PreADMET web was used. The AutoDockTools 1.5.6 and LigandScout 4.4.3 Advanced software were used to conduct the molecular docking simulation and pharmacophore screening, respectively, while the molecular dynamic simulation was performed using AMBER 16. The results were visualized by Biovia Discovery Studio. Results: Molecular docking using Autodock showed that FAT10 derivate has lower binding free energy (ΔG) (-12.04 kcal/mol) than AM (-8.45 kcal/mol) when docking to ERα and both performed the same hydrogen bond with Thr347. These support the results of the MMPBSA calculation on dynamic simulation which shows FAT10 (-58.4767 kcal/mol) has lower ΔG than AM (-42.7041 kcal/mol) and 4-OHT (- 49.0821 kcal/mol). The pharmacophore screening results also showed that FAT10 fitted the pharmacophore with a fit score of 47.08. Conclusion: From the results, it can be suggested that FAT10 has promising activity as ERα antagonist. Further in vitro and in vivo experiments should be carried out to support these in silico studies.

This study examines the influence of spirituality dimensions and tax equity dimensions on religious giving. Using a 2x2 between-subjects experiment, we manipulated tax savings, capped and non-capped and type of religious giving: zakat versus other religious giving. Our results indicate that all spirituality dimensions influence the amount of contributions to zakat and other religious giving. This influence is also different in capped and non-capped tax savings conditions. We find some support for the effect of vertical equity on the amount of these contributions. The main contribution of this study is twofold. First, we offer insights into the effect of spirituality and tax equity dimensions on religious giving, when tax savings are capped and uncapped. Second, we offer insights into the contrast between zakat payments and other religious giving. Our study can help policymakers develop better tax policies surrounding religious matters.

Abstract: CAR T-cell therapy is a promising approach for cancer treatment, utilizing a patient's own T-cells (autologous cell) or T-cells from a healthy donor (allogeneic cell) to target and destroy cancer cells. Over the last decade, significant advancements have been made in this field, including the development of novel CAR constructs, improved understanding of biology and mechanisms of action, and expanded clinical applications for treating a wider range of cancers. In this review, we provide an overview of the steps involved in the production of CAR T-cells and their mechanism of action. We also introduce different CAR T-cell therapies available, including their implementation, dosage, administration, treatment cost, efficacy, and resistance. Common side effects of CAR T-cell therapy are also discussed. The CAR T-cell products highlighted in this review are FDA-approved products, which include Kymriah® (tisagenlecleucel), Tecartus® (brexucabtagene autoleucel), Abecma® (Idecabtagene vicleucel), Breyanzi® (lisocabtagene maraleucel), and Yescarta® (axicabtagene ciloleucel). In conclusion, CAR T-cell therapy has made tremendous progress over the past decade and has the potential to revolutionize cancer treatment. This review paper provides insights into the progress, challenges, and future directions of CAR T-cell therapy, offering valuable information for researchers, clinicians, and patients.

Detecting forged handwriting is important in a wide variety of machine learning applications, and it is challenging when the input images are degraded with noise and blur. This article presents a new model based on conformable moments (CMs) and deep ensemble neural networks (DENNs) for forged handwriting detection in noisy and blurry environments. Since CMs involve fractional calculus with the ability to model nonlinearities and geometrical moments as well as preserving spatial relationships between pixels, fine details in images are preserved. This motivates us to introduce a DENN classifier, which integrates stenographic kernels and spatial features to classify input images as normal (original, clean images), altered (handwriting changed through copy-paste and insertion operations), noisy (added noise to original image), blurred (added blur to original image), altered-noise (noise is added to the altered image), and altered-blurred (blur is added to the altered image). To evaluate our model, we use a newly introduced dataset, which comprises handwritten words altered at the character level, as well as several standard datasets, namely ACPR 2019, ICPR 2018-FDC, and the IMEI dataset. The first two of these datasets include handwriting samples that are altered at the character and word levels, and the third dataset comprises forged International Mobile Equipment Identity (IMEI) numbers. Experimental results demonstrate that the proposed method outperforms the existing methods in terms of classification rate.

As the size of satellites scales down, low-power and compact propulsion systems such as the pulsed plasma thruster (PPT) are needed for stabilizing these miniature satellites in orbit. Most PPT systems are operated at 2 J or more of discharge energy. In this work, the performance of a PPT with a side-fed, tongue-flared electrode configuration operated within a lower discharge energy range of 0.5‒2.5 J has been investigated. Ablation and charring of the polytetrafluoroethylene propellant surface were analyzed through field-effect scanning electron microscopy imaging and energy-dispersive X-ray spectroscopy. When the discharge energy fell below 2 J, inconsistencies occurred in the specific impulse and the thrust efficiency due to the measurement of the low mass bit. At energy 2 J, the performance parameters are compared with other PPT systems of similar configuration and discussed in depth.

Studies of climate variation commonly rely on chemical and isotopic changes recorded in sequentially produced growth layers, such as in corals, shells, and tree rings, as well as in accretionary deposits-ice and sediment cores, and speleothems. Oxygen isotopic compositions (δ18O) of tooth enamel are a direct method of reconstructing environmental variation experienced by an individual animal. Here, we utilize long-forming orangutan dentitions (Pongo spp.) to probe recent and ancient rainfall trends on a weekly basis over ~3-11 years per individual. We first demonstrate the lack of any consistent isotopic enrichment effect during exclusive nursing, supporting the use of primate first molar teeth as environmental proxies. Comparisons of δ18O values (n=2016) in twelve molars from six modern Bornean and Sumatran orangutans reveal a high degree of overlap, with more consistent annual and bimodal rainfall patterns in the Sumatran individuals. Comparisons with fossil orangutan δ18O values (n=955 measurements from six molars) reveal similarities between modern and late Pleistocene fossil Sumatran individuals, but differences between modern and late Pleistocene/early Holocene Bornean orangutans. These suggest drier and more open environments with reduced monsoon intensity during this earlier period in northern Borneo, consistent with other Niah Caves studies and long-term speleothem δ18O records in the broader region. This approach can be extended to test hypotheses about the paleoenvironments that early humans encountered in southeast Asia.

Chatbots have the potential to increase people's access to quality health care. However, the implementation of chatbot technology in the health care system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings. This paper presents a protocol of a bibliometric analysis aimed at offering the public insights into the current state and emerging trends in research related to the use of chatbot technology for promoting health. In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplore, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in health care. Our search strategy includes keywords such as "chatbot," "virtual agent," "virtual assistant," "conversational agent," "conversational AI," "interactive agent," "health," and "healthcare." Five researchers who are AI engineers and clinicians will independently review the titles and abstracts of selected papers to determine their eligibility for a full-text review. The corresponding author (ZN) will serve as a mediator to address any discrepancies and disputes among the 5 reviewers. Our analysis will encompass various publication patterns of chatbot research, including the number of annual publications, their geographic or institutional distribution, and the number of annual grants supporting chatbot research, and further summarize the methodologies used in the development of health-related chatbots, along with their features and applications in health care settings. Software tool VOSViewer (version 1.6.19; Leiden University) will be used to construct and visualize bibliometric networks. The preparation for the bibliometric analysis began on December 3, 2021, when the research team started the process of familiarizing themselves with the software tools that may be used in this analysis, VOSViewer and CiteSpace, during which they consulted 3 librarians at the Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2340 papers. The official search phase started on July 27, 2023. Our goal is to complete the screening of papers and the analysis by February 15, 2024. Artificial intelligence chatbots, such as ChatGPT (OpenAI Inc), have sparked numerous discussions within the health care industry regarding their impact on human health. Chatbot technology holds substantial promise for advancing health care systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with health care consumers, delivering personalized care, and providing accurate health-related information and knowledge remain considerable challenges. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software used in their development, and their preferred functionalities among users. PRR1-10.2196/54349.