Time Manner Research Articles

Timeliness and Completeness of Endometrial Cancer Pathology Reports Before and After the FIGO 2023 Endometrial Cancer Staging Guidelines Update

Introduction: Uterine cancer is the most common gynecologic malignancy among women in the United States with an estimated 66,200 new cases and 13,030 deaths in 2023.1 These cancers are divided into endometrial adenocarcinomas (EAC) and uterine sarcomas. In 2013, the Cancer Genome Atlas (TCGA) proposed four new molecular classifications based on genome and exome sequencing of EACs including POLE-mutant, mismatch repair deficiency (MMRd), p53 abnormal, and no specific molecular profile (NSMP).4 In June of 2023, the International Federation of Gynecology and Obstetrics (FIGO) published an updated staging system for uterine cancer to incorporate these molecular indices along with the traditional anatomical distribution of disease and histology used in older FIGO staging guidelines.5 The primary objective of this project was to analyze the timeliness and completeness of pathology reports and IHC staining of patients who underwent hysterectomies for uterine cancer at Detroit Medical Center from October 2022 – December 2023. Methods: Patients were identified from the list of patients presented at the Karmanos Cancer Insitute’s (KCI) Gynecologic Oncology Tumor Board. Given the June publication timing of the FIGO 2023 staging guidelines, this served as the intervention for the study. The pre-intervention group consists of surgical cases from October 2022 through June 2023, and the post-intervention group consists of cases from July 2023-December 2023. Differences in time from surgery to pathology report, surgery to IHC result, pathology report to IHC result as well as completeness of IHC results were compared pre- and post-intervention. Because neither group was normally distributed, a MannWhitney-U test was used to calculate P-value. IHC result completeness pre- and post-intervention was calculated using a Fisher exact test. Results: A total of 106 cases were included, of which 67 were preintervention (63%) and 39 were postintervention. The median number of days from surgery to final pathology report was statistically significant (p=0.003) between the groups. The median number of days from pathology report to IHC result was not a statistically significant difference (p=0.19). The median number of days from surgery to completed IHC also not statistically significant. In terms of IHC staining, there was a statistically significant difference in the number of specimens pre- and post-intervention for p53 (p=0.004) and HER2 (p=0.04). There was no change in the MMR and MSI testing between the two groups. Conclusions: Following the implementation of the FIGO 2023 staging guidelines in June of 2023, there was a significant decrease in the time from surgery to finalized pathology report and a significant change in the completeness of staining for p53 and HER2 on hysterectomy specimens. By finalizing the results of both pathology and IHC for endometrial cancer specimens in a timelier manner, patients can be informed of their diagnosis sooner. Additionally, with a complete pathologic and molecular diagnosis, our gynecologic oncologists can also more efficiently and effectively communicate within a multidisciplinary team of providers including radiation oncology and genetic counseling colleagues to formulate a treatment plan.

The Public Health Implications of the Global Financial Crisis

The Global Financial Crisis resulted in significant economic problems that subsequently led to a public health crisis worldwide. For example, the unemployed suffered from more severe physical and mental health problems than the employed due to the loss of employer-sponsored health insurances. The crisis further exacerbated racial health inequalities that subsequently worsened maternal and child health outcomes, which negatively affected health status of the future generations. This paper provides a comprehensive discussion of the influence that this crisis has had on physical and mental health, health inequalities, and the intergenerational health impact. To respond to the health implications, the government implemented the Affordable Care Act and the Continuation of Health Coverage (CORBA). In the future, policymakers need to increase social safety net programs, especially government health insurance, for vulnerable populations so that another global financial crisis will likely not have such a negative and profound impact on public health outcomes. The motivation of this paper is to emphasize that higher-quality and more comprehensive unemployment health insurance that covers a broader and more diverse set of populations should become available for the unemployed during economic downturns. This article concludes that policymakers need to design health policies in a way that prevents future financial crises from depriving low-income and unemployed people of their right to health care. Policies must become proactive in advocating for health equity and creating opportunities for people to access medical services in a timelier manner, especially during times of financial downturns.

"Better than my professor?" How to develop artificial intelligence tools for higher education.

Artificial Intelligence (AI) tools are currently designed and tested in many fields to improve humans' ability to make decisions. One of these fields is higher education. For example, AI-based chatbots ("conversational pedagogical agents") could engage in conversations with students in order to provide timely feedback and responses to questions while the learning process is taking place and to collect data to personalize the delivery of course materials. However, many existent tools are able to perform tasks that human professionals (educators, tutors, professors) could perform, just in a timelier manner. While discussing the possible implementation of AI-based tools in our university's educational programs, we reviewed the current literature and identified a number of capabilities that future AI solutions may feature, in order to improve higher education processes, with a focus on distance higher education. Specifically, we suggest that innovative tools could influence the methodologies by which students approach learning; facilitate connections and information attainment beyond course materials; support the communication with the professor; and, draw from motivation theories to foster learning engagement, in a personalized manner. Future research should explore high-level opportunities represented by AI for higher education, including their effects on learning outcomes and the quality of the learning experience as a whole.

Nilotinib Exerts a Therapeutic Approach via JAK/STAT Pathway and Cytokine Network in Chronic Myeloid Leukemia Cells

Aim: Chronic myeloid leukemia (CML) displays a constitutive tyrosine kinase (TK) activity which in turn leads to the activation of various signaling pathways and the outcome of leukemic phenotype. Activated STAT5A and STAT5B from JAK/STAT pathway induce cell growth, proliferation, differentiation, and survival of leukemic cells which are promoted by a cytokine network. Since the second-generation tyrosine kinase inhibitor nilotinib has the advantage of inhibiting this oncogenic TK activity; we aimed to investigate the underlying mechanism of its therapeutic approach and how it induced apoptosis via analyzing the forthcoming molecular targets of the pathway. 
 Methods: By Nilotinib treatments, cell viability and proliferation assays, apoptotic analysis, expressional regulations of STAT5A&5B mRNA transcripts, protein expression levels, and also cytokines’ expressional assessments were determined in CML model K562 cells, in vitro.
 Results: Nilotinib treatment in a time and dose-dependent manner assessed a therapeutic approach by decreasing leukemic cell proliferation and survival; inducing leukemic cell apoptosis, down-regulating STAT5A&5B mRNA, and protein expression levels, and regulating cytokine expressional network. 
 Conclusion: Nilotinib-mediated therapeutics could be dependent on targeting JAK/STAT pathway members STAT5A and STAT5B, besides; regulating the cytokine network might be another underlying mechanism for sensitization and response of K562 cells to nilotinib in leukemia pathogenesis.

Nanoplastics causes heart aging/myocardial cell senescence through the Ca2+/mtDNA/cGAS-STING signaling cascade

BackgroundNanoplastics (NPs) are now a new class of pollutants widely present in the soil, atmosphere, freshwater and marine environments. Nanoplastics can rapidly penetrate cell membranes and accumulate in human tissues and organs, thus posing a potential threat to human health. The heart is the main power source of the body. But up to now, the toxicological effects of long-term exposure to nanoplastics on the heart has not been revealed yet.ResultsWe evaluated the effects of long term exposure of nanoplastics on cardiac cell/tissue in vitro and in vivo model. Furthermore, we explored the molecular mechanism by which nanoplastics exposure causes myocardial cell senescence. Immunohistochemistry, indirect immunofluorescence and ELISA were performed to detect the effects of nanoplastics on heart aging. We found that nanoplastics were able to induce significant cardiac aging through a series of biochemical assays in vivo. In vitro, the effects of nanoplastics on cardiac cell were investigated, and found that nanoplastics were able to internalize into cardiomyocytes in time and dose-dependant manner. Further biochemical analysis showed that nanoplastics induces cardiomyocytes senescence by detecting a series of senescence marker molecules. Molecular mechanism research shows that nanoplastics may cause mitochondrial destabilization by inducing oxidative stress, which leads to the leakage of mtDNA from mitochondria into the cytoplasm, and then cytoplasm-localized mt-DNA activates the cGAS-STING signaling pathway and promotes inflammation response, ultimately inducing cardiomyocytes senescence.ConclusionsIn this work, we found that nanoplastics exposure induces premature aging of heart. Current research also reveals the molecular mechanism by which nanoplastics induces cardiomyocyte senescence. This study laid the foundation for further studying the potential harm of nanoplastics exposure on heart.Graphical abstract

(178) Climacturia: Emerging Type of Urinary Incontinence, Its Prevalence, Risk Factors, and Management: A Systematic Review and Prevalence Meta-analysis

Abstract Introduction Climacturia is an emerging type of urinary incontinence which occurs immediately following orgasm. Both men and women may suffer from climacturia in equivocal rates. This phenomenon is becoming more problematic as it impacts physiological and psychosexual aspect of well-being and quality of life. While awareness of climacturia becomes evident, studies are inadequate to support evidenced-based management in primary and urologic care settings. Objective This study aimed to conduct a systematic review and prevalence meta-analysis of climacturia and investigate its risk factors and evidenced-based management. Methods A systematic review of 24 articles were conducted following PRISMA 2020 guidelines. The PubMed, CINAHL, and Cochrane Library database were searched using the word “climacturia” and phrase “orgasm associated urinary incontinence” respectively. Search limiters applied included literature written in English from year of inception to 2022. Title and abstract reviews were conducted on 78 articles. Articles which did not discuss climacturia, duplicates, and previous systematic reviews were excluded after full article review. Results The review accounts a total of N = 7798 subjects which are composed of N = 7629 (97.83%) males and N = 169 (2.17%) females with age range of 18 to 80 years old. A total of N = 2, 039 (26%) participants are with climacturia, N = 87 or 4.26% are females and N = 1, 931 or 95.74% are males. The risk factors associated with climacturia include sexually active male, history of prostate cancer after prostatectomy within 12 months of surgery or radiation therapy, increased Body Mass Index (BMI), erectile dysfunction (ED), increased urethral width, presence of urinary incontinence, pain-associated orgasm and loss of penile length after pelvic surgery, use of aides to obtain an erection, and increased incontinence scores for men. For women, the risk factors are high scores of female sexual function index (FSFI) and detrusor overactivity. The management for females is limited to pharmacologic therapy with use of antimuscarinic agents. While, behavioral therapy, pharmacologic regimen, pelvic floor muscle training (PFMT), surgery (sling procedure) are the management options for affected men. Conclusions Climacturia affects males more than females, however, the evidence to support the efficacy of treatment options for both sexes are equally inadequate. Primary care and Advanced Practice Providers in primary and urologic care settings are at best position to query patients for the presence of and manage climacturia in timely manner. Further research is needed to increase evidence supporting effective management of climacturia for both men and women. Disclosure No.

Prevention of DMBA-induced Mammary Tumors from Pulmonary Metastases by Saussurea costus (Falc.) Lipsc Root Ethanolic Extract in Sprague Dawley Rats

Background: Breast cancer is an important problem in human females and therapies used for treatment often found to produce side effects. The present study was conducted to evaluate the effect of Saussurea costus root extract (SC) against metastatic breast cancer to the lungs. Methods: The anti-neoplastic activity of SC extract was assessed by calculating the % cytotoxicity on Leydig cell testicular tumour cells-540 (LC-540). The anti-metastatic effect of SC extract at 3 different doses (100, 250 and 500 mg/kg BW) for a period of 18 weeks against 12-dimethylbenz (a) anthracene (DMBA) induced mammary tumours in Sprague Dawley (SD) female rats was studied through radiography, gross lesion scoring, histopathology and immunohistochemistry (MMP-9). Result: SC extract exhibited the highest anti-neoplastic activity in a time and concentration-dependent manner. After 72 h of duration SC extract exhibited highest anti-neoplastic activity at 200 μg of concentration. On radiological and gross pathological examination, the inhibition of metastases was maximal in the rats treated with highest dose of the extract as compared with only DMBA treated group. The histopathological changes and immunohistochemical expression of metastases marker MMP-9 in lung tissue were more severe in the rats treated with DMBA only and were reduced in the groups treated with SC extract in a dose-dependent manner. SC extract has potential anti-neoplastic activity and prevents the rate of metastases from mammary tumours to lungs. Further research in exploring the possible therapeutic usage of SC extract against various neoplastic conditions is warranted.

Implementation of the EQIPD Quality System

The EQIPD Quality System (QS) was conceptualized and established by an international consortium consisting of academic and industrial partners to ensure that non-regulated biomedical research will be conducted according to Good Research Practice expectations. The QS supports researchers to reflect on and improve internal practices by providing a systematic framework and guidance for implementing the EQIPD QS in a time and cost effective manner. This report describes the content of the EQIPD QS with its key features and 18 Core Requirements (CR) in more detail. It gives a short background on each CR and hands on examples on how they were addressed by two different research labs in their respective laboratory environments. Thereby, this article provides examples and direction for other research labs who aim to implement the QS as well. The final paragraphs discuss the potential benefits of the QS in respect to different user groups and stakeholders within the scientific community and summarize the overall governance structure of the EQIPD framework.

The investigation of cytotoxic and apoptotic activity of Cl-amidine on the human U-87 MG glioma cell line.

Peptidyl (protein) arginine deiminases (PADs) provide the transformation of peptidyl arginine to peptidyl citrulline in the presence of calcium with posttranslational modification. The dysregulated PAD activity plays an important role on too many diseases including also the cancer. In this study, it has been aimed to determine the potential cytotoxic and apoptotic activity of chlorine-amidine (Cl-amidine) which is a PAD inhibitor and whose effectiveness has been shown in vitro and in vivo studies recently on human glioblastoma cell line Uppsala 87 malignant glioma (U-87 MG) forming an in vitro model for the glioblastoma multiforme (GBM) which is the most aggressive and has the highest mortality among the brain tumors. In the study, the antiproliferative and apoptotic effects of Cl-amidine on GBM cancer model were investigated. The antiproliferative effects of Cl-amidine on U-87 MG cells were determined by 4-[3-(4-iodophenyl)-2-(4-nitrophenyl)-2H-5-tetrazolio]-1,3-benzene disulfonate method at the 24th and 48th hours. The apoptotic effects were analyzed by Annexin V and Propidium iodide staining, caspase-3 activation, and mitochondrial membrane polarization (5,5', 6,6'-tetrachloro-1,1', 3,3' tetraethyl benzimidazolyl carbocyanine iodide) methods in the flow cytometry. It has been determined that Cl-amidine exhibits notable antiproliferative properties on U-87 MG cell line in a time and concentration-dependent manner, as determined through the 4-[3-(4-iodophenyl)-2-(4-nitrophenyl)-2H-5-tetrazolio]-1,3-benzene disulfonate assay. Assessment of apoptotic effects via Annexin V and Propidium iodide staining and 5,5', 6,6'-tetrachloro-1,1', 3,3' tetraethyl benzimidazolyl carbocyanine iodide methods has revealed significant efficacy, particularly following a 24-hour exposure period. It has been observed that Cl-amidine induces apoptosis in cells by enhancing mitochondrial depolarization, independently of caspase-3 activation. Furthermore, regarding its impact on healthy cells, it has been demonstrated that Cl-amidine shows lower cytotoxic effects when compared to carmustine, an important therapeutic agent for glioblastoma. The findings of this study have shown that Cl-amidine exhibits significant potential as an anticancer agent in the treatment of GBM. This conclusion is based on its noteworthy antiproliferative and apoptotic effects observed in U-87 MG cells, as well as its reduced cytotoxicity toward healthy cells in comparison to existing treatments. We propose that the antineoplastic properties of Cl-amidine should be further investigated through a broader spectrum of cancer cell types. Moreover, we believe that investigating the synergistic interactions of Cl-amidine with single or combination therapies holds promise for the discovery of novel anticancer agents.

A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency

Unconventional events exacerbate the imbalance between regional transportation demand and limited road network resources. Scientific and efficient path planning serves as the foundation for rapidly restoring equilibrium to the road network. In real large-scale road networks, especially during emergencies, it is usually difficult to obtain or predict accurate dynamic traffic network flows in real-time, which is used to support equilibrium path planning. Moreover, the traditional iterative methods cannot meet the real-time demand of emergency equilibrium path planning decision generation. To solve the above problems, this paper proposes a hybrid control architecture for path planning based on equilibrium traffic assignment theory. The architecture introduces the travelers’ real-time travel data and constructs a spatio-temporal neural network, which captures the evolution of traffic network loads. Adaptive multi-graph fusion technology is used to mix the background traffic flow data and the traveler’s real-time Origin–Destination (OD) data, to mine the dynamic correlation between the traffic state and the travelers’ travel. Based on the real-time prediction of dynamic network states, equilibrium mapping learning is carried out to pre-allocate potential travel demands and construct equilibrium traffic graphs based on system optimization traffic assignment. Finally, individual evacuation path strategies are generated online in a data-driven manner in real time to achieve improved resilience in the transportation system.

Advances in machine learning with chemical language models in molecular property and reaction outcome predictions.

Molecular properties and reactions form the foundation of chemical space. Over the years, innumerable molecules have been synthesized, a smaller fraction of them found immediate applications, while a larger proportion served as a testimony to creative and empirical nature of the domain of chemical science. With increasing emphasis on sustainable practices, it is desirable that a target set of molecules are synthesized preferably through a fewer empirical attempts instead of a larger library, to realize an active candidate. In this front, predictive endeavors using machine learning (ML) models built on available data acquire high timely significance. Prediction of molecular property and reaction outcome remain one of the burgeoning applications of ML in chemical science. Among several methods of encoding molecular samples for ML models, the ones that employ language like representations are gaining steady popularity. Such representations would additionally help adopt well-developed natural language processing (NLP) models for chemical applications. Given this advantageous background, herein we describe several successful chemical applications of NLP focusing on molecular property and reaction outcome predictions. From relatively simpler recurrent neural networks (RNNs) to complex models like transformers, different network architecture have been leveraged for tasks such as de novo drug design, catalyst generation, forward and retro-synthesis predictions. The chemical language model (CLM) provides promising avenues toward a broad range of applications in a time and cost-effective manner. While we showcase an optimistic outlook of CLMs, attention is also placed on the persisting challenges in reaction domain, which would optimistically be addressed by advanced algorithms tailored to chemical language and with increased availability of high-quality datasets.

Abstract TP214: In-Silico versus In-Vitro Evaluation of a New Stent-Retriever Design: A Novel Approach to Pre-Clinical Device Development

Introduction: In-vitro evaluation (analysis in a bench top flow model) is the gold standard for pre-clinical investigation of devices being developed for endovascular thrombectomy (EVT). In-silico evaluation (computer simulated analysis in a virtual stroke model) has the potential to test and optimize large number of stent-retriever design variations in a relatively time and cost-effective manner. We aim to validate the utility of in-silico evaluation when compared with in-vitro evaluation for the SuperNova Stent-retriever. Methods: In-silico analysis was performed using a virtual thrombectomy model, built using data from a fine-grained finite-element model, to estimate the probability of successful recanalization. Neurovascular anatomy of the virtual model was built based on digital subtraction angiography of an in-vitro model (Sim Agility, Mentice, Inc). Gravity Medical Technology’s SuperNova Stent-retriever was the device under investigation. Multiple thrombectomy scenarios were defined and validation analysis was performed. Results: We defined multiple thrombectomy scenarios including 1 cm red blood clot in M1 artery, 2 cm bifurcation M1-M2 red blood clot (Y-shaped clot), 1 cm white blood clot in M1 artery, and 1 cm white blood clot in superior M2 artery. The figure below demonstrates a pictorial representation of the first scenario- 1 cm red blood clot in M1 artery followed by mechanical thrombectomy simulation using the SuperNova stent-retriever. Per our in-vitro analysis (10 thrombectomy experiments), the rate of first pass effect and the rate of complete recanalization after a maximum of 3 passes using SuperNova stent-retriever was 50% and 90%, respectively. Preliminary data suggests 97% concordance. Conclusion: In-silico analysis has the potential to improve and expedite pre-clinical thrombectomy device development. Further studies are required to better understand the scope and potential of this technology.

Time- and Concentration-Dependent Stimulation of Oxidative Stress in Chondrocytes by Intracellular Soluble Urate.

Gout could result in irreversible bone erosion, and chondrocyte might be involved in the process. Increased soluble urate is the early stage of gout and is strongly oxidative. To explore the effect of intracellular urate on the oxidative status of chondrocytes. A chondrocyte model was used. Serial concentrations of exogenous urate were incubated with chondrocytes for increasing amounts of time. Reactive oxygen species (ROS), oxidant, and anti-oxidant molecules were measured with biochemical assays, rt-PCR, and western blot. A urate transport inhibitor and oxidative inhibitors were used to confirm the effect of exogenous urate. All concentrations of exogenous urate stimulated the production of ROS in a time- and concentration-dependent manner, as well as oxidant molecules, including hydrogen peroxide (H2O2), nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, nitric oxide (NO) inducible nitric oxide synthase (iNOS), and these effects, could be inhibited by oxidant inhibitors. However, anti-oxidant molecules, including acidic leucine-rich nuclear phosphoprotein-32A (ANP32A), ataxia-telangiectasia mutated (ATM), heme oxygenase-1 (HO-1), and the transcription factor nuclear factor erythroid 2 (NF-E2)-related (Nrf2), was decreased by high concentrations of exogenous urate after prolonged incubation, but not by low to medium concentrations of exogenous urate. By inhibiting soluble urate trafficking, benzbromarone significantly suppressed the effect of urate stimulus on the oxidant and anti-oxidant molecules. Intracellular soluble urate could regulate chondrocyte redox balance in a time and concentration-dependent manner, and would be a target for regulating and protecting chondrocyte function in the early gout stage.

Analysis of the combustion modes in a rocket-based combined cycle combustor using unsupervised machine learning methodology

Combustion mode analysis is essential to a rocket-based combined cycle (RBCC) combustor because it may experience multiple combustion modes during the operation. In this study, a method based on an autoencoder and a K-means algorithm was proposed for combustion mode analysis. Flame chemiluminescence images and schlieren images of three combustion modes observed in an RBCC combustor were used to evaluate this method. Two autoencoders that followed the same encoder–decoder architecture were developed separately to generate the latent space representations of flame chemiluminescence images and schlieren images. In the latent space, the centroids and boundaries of different combustion modes were determined using the K-means algorithm. Each autoencoder was trained using 750 images and tested using another 3000 images. The method achieved an accuracy up to 99% on both flame chemiluminescence images and schlieren images. The images generated by the decoder suggested that the autoencoder captured the important features (e.g., primary reaction zone and shock wave) of the reacting flow field. The autoencoder developed for flame chemiluminescence images also successfully detected the combustion mode transition during an ignition process, which suggested that it had the potential to monitor the combustion mode in a real time manner. However, the autoencoder failed on monitoring combustion mode transition when it came to the schlieren images because the optical access of the training data was not exactly the same. Therefore, it is essential to ensure that the optical accesses of different combustion modes are exactly the same when schlieren images are employed for combustion mode analysis.

A modified bandwidth allocation algorithm for XGPON network

Abstract In the current era of everything online, home-users are demanding high speed internet access at their figure tip. The giga-bit (XGPON) fiber based network has proven its capacity to provide higher speed internet at a light-speed. However in such network effectiveness is directly relies on the bandwidth allocation to the each of the user in timely manner. In this paper, modified bandwidth allocation algorithm is proposed to prove its capacity to support larger user with effective bandwidth allocation. The throughput result of this proposed DBA is carried out in network simulator-3 (NS-3), which is improved by 3 % over the existing DBA like Giant, Proportional DBA etc., at little increase in the delay performance of the TCONT-4 based traffic.

Predicting financial distress using current reports: A novel deep learning method based on user-response-guided attention

Effective financial distress prediction (FDP) can discover a company's potential financial risks and support relevant decisions in a timely manner. Previous studies on FDP have mostly focused on using financial indicators and periodic reports. Compared with periodic reports, current reports disclose major events in a timelier manner. But leveraging the information in current reports involves the critical challenges of capturing the complex semantics and measuring the importance of heterogeneous events. To this end, we propose a novel deep learning method, a user-response-guided deep attention network (URGDAN), to predict financial distress using current reports. In the proposed method, we construct a deep learning architecture to integrate financial indicators, current report texts, and user responses. URGDAN leverages the user responses to current reports to guide the semantic feature representation of the reports, it also identifies event information that has a significant correlation with company financial distress. Empirical evaluation shows that URGDAN significantly improves predictive performance and can accurately determine the importance of different current reports. Our work provides practical implications for creditors and investors.

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

The consequences of climate change include extreme weather events, such as heavy rainfall. As a result, many places around the world are experiencing an increase in flood risk. The aim of this research was to assess the usefulness of selected machine learning models, including artificial neural networks (ANNs) and eXtreme Gradient Boosting (XGBoost) v2.0.3., for predicting peak stormwater levels in a small stream. The innovation of the research results from the combination of the specificity of small watersheds with machine learning techniques and the use of SHapley Additive exPlanations (SHAP) analysis, which enabled the identification of key factors, such as rainfall depth and meteorological data, significantly affect the accuracy of forecasts. The analysis showed the superiority of ANN models (R2 = 0.803–0.980, RMSE = 1.547–4.596) over XGBoost v2.0.3. (R2 = 0.796–0.951, RMSE = 2.304–4.872) in terms of forecasting effectiveness for the analyzed small stream. In addition, conducting the SHAP analysis allowed for the identification of the most crucial factors influencing forecast accuracy. The key parameters affecting the predictions included rainfall depth, stormwater level, and meteorological data such as air temperature and dew point temperature for the last day. Although the study focused on a specific stream, the methodology can be adapted for other watersheds. The results could significantly contribute to improving real-time flood warning systems, enabling local authorities and emergency management agencies to plan responses to flood threats more accurately and in a timelier manner. Additionally, the use of these models can help protect infrastructure such as roads and bridges by better predicting potential threats and enabling the implementation of appropriate preventive measures. Finally, these results can be used to inform local communities about flood risk and recommended precautions, thereby increasing awareness and preparedness for flash floods.

Effects of rosmarinic acid and doxorubicine on an ovarian adenocarsinoma cell line (OVCAR3) via the EGFR pathway.

We aimed to reveal the effects of rosmarinic acid (RA), which has come to the forefront with its antitumor and antioxidant properties in many studies recently in the ovarian adenocarcinoma cell line, on the epidermal growth factor receptor (EFGR) signaling pathway in the presence of doxorubicin (DOX). Ovarian adenocarcinoma cell line (OVCAR3) and human skin keratinocyte cell line human skin keratinocyte cell line (HaCaT) were used as control. (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) test was applied to determine the effect of RA and DOX on the proliferation of OVCAR3 and HaCaT cells. Bcl2 expression and epidermal growth factor receptor (EGFR) and western blot analysis were performed to determine the expression levels of the markers. It was determined that RA (IC50 = 437.6 μM) and DOX (IC50 = 0.08 μM) have the ability to inhibit the proliferation of OVCAR3 cells and induce apoptosis in a 72-hour time and dose-dependent manner. Western blot showed that the expression level of Bcl-2 and EGFR in OVCAR3 cells was down-regulated by RA and DOX. Apoptosis in OVCAR3 cells can potentially be induced by RA via the EGFR pathway, and RA may be a potent agent for cancer therapy.

Effectiveness of Using the School Budget Work Plan Application in Reporting Accountability of School Operational Assistance Funds

Implementing the school budget work plan application is closely related to implementing School Operational Assistance Funds in education.. This research aims to determine the effectiveness of the School Budget Work Plan Application in reporting accountability for School Operational Assistance funds at the West Java Provincial Education Service. The research method uses a qualitative approach with descriptive analysis. Research informants using a purposive sampling technique. Data collection techniques use observation and interviews based on primary and secondary data, then credibility, transferability, dependability, and confirmability tests are carried out to validate the data. The research shows that the School Operational Assistance funds management through the School Budget Work Plan Application system can be effective based on the concept of effectiveness theory. This is demonstrated by an understanding of the School Operational Assistance funds program so that successful management of School Operational Assistance funds is achieved in an appropriate, timely manner, achieving goals and achieving absolute truth through technical guidelines at the West Java Provincial Education Office. Thus, the novelty of this research is to look at the effectiveness of transitioning the management and reporting of school operational assistance funds through the School Budget Work Plan Application system so that it can guarantee accountability in reporting funds.

Probing the Cellular Fate of the Protein Corona around Nanoparticles with Nanofocused X-ray Fluorescence Imaging.

X-ray fluorescence imaging (XRF-imaging) with subcellular resolution is used to study the intracellular integrity of a protein corona that was pre-formed around gold nanoparticles (AuNP). Artificial proteins engineered to obtain Gd coordination for detection by XRF-imaging were used to form the corona. Indications about the degradation of this protein corona at a cellular and subcellular level can be observed by following the Au and Gd quantities in a time and spatial-dependent manner. The extended acquisition times necessary for capturing individual XRF-imaging cell images result in relatively small sample populations, stressing the need for faster image acquisition strategies in future XRF-imaging-based studies to deal with the inherent variability between cells. Still, results obtained reveal degradation of the protein corona during cellular trafficking, followed by differential cellular processing for AuNP and Gd-labelled proteins. Overall, this demonstrates that the dynamic degradation of the protein corona can be tracked by XRF-imaging to a certain degree.