bluuebunny/crossref_metadata_2025 huggingface dataset:
The Efficacy and Safety of Roxadustat for Anemia in Hemodialysis Patients with Chronic Kidney Disease: A Meta-Analysis of Randomized Controlled Trials
Authors: Geng Yunling , Zhang Shuaixing , Cao Zijing , Tang Jingyi , Cui Hailan , Dong Zhaocheng , Liu Yuning , Liu Weijing
Published: November 2024

Background: Patients undergoing hemodialysis (HD) for chronic kidney disease (CKD) often encounter anemia. Roxadustat has not only undergone phase II-III clinical trials in patients suffering from CKD and undergoing HD; a number of post-marketing clinical studies have been conducted using the drug. This article was to assess the effectiveness and safety of roxadustat in managing anemia among patients with CKD undergoing HD. Methods: A thorough search was performed across eight databases, including PubMed, Web of Science, Cochrane Library, Embase, Wan Fang, China National Knowledge Infrastructure (CNKI), Chongqing VIP (CQ VIP), and SinoMed to identify randomized clinical trials (RCTs) examining the effectiveness and safety of roxadustat in managing anemia among individuals suffering from CKD and undergoing HD. This search included studies from the inception of these databases to April 2023. Results: Two phase II, one phase III, and 16 post-marketing studies with 1688 participants were included. Serum iron (SI), transferrin, and total iron-binding capacity (TIBC) levels changed from baseline (∆SI, ∆transferrin, and ∆TIBC) and were significantly more increased for roxadustat than for erythropoiesis-stimulating agents (ESAs): MD 2.55, (95% CI 1.51 to 3.60), p < 0.00001; MD 0.55, (95% CI 0.41 to 0.69), p < 0.00001; and MD 6.54, (95% CI 4.50 to 8.59), p < 0.00001, respectively. Roxadustat was not inferior to ESAs with regard to increasing Hb (∆Hb) levels [MD 1.17 (95% CI 0.71 to 1.63), p < 0.00001] (g/dL). No statistically significant distinctions of the ∆ferritin, ∆hepcidin, and transferrin saturation (TSAT) from baseline (∆TSAT) level were identified between roxadustat and ESAs. C-reactive protein (CRP) levels changed from baseline (∆CRP) and were significantly more reduced for roxadustat than for ESAs. As for safety, the analysis indicated no notable difference in the occurrence of adverse events (AEs) and serious adverse events (SAEs) between roxadustat and ESAs. Conclusions: This meta-analysis demonstrated that roxadustat outperformed ESAs in enhancing SI, transferrin, and TIBC levels while also decreasing CRP levels. Roxadustat was not inferior to ESAs in terms of improving Hb levels and safety. These findings suggest that roxadustat was well tolerated and a potent alternative to ESAs in managing anemia among patients suffering from CKD and undergoing HD.
Enhancing Environmental and Human Health Management Through the Integration of Advanced Revitalization Technologies Utilizing Artificial Intelligence
Authors: Volf Mirela , Vučemilović Ante , Dobrović Željko
Published: November 2024

Pollution can be broadly defined as the presence of contaminants or energy sources detrimental to ecosystems and human health. The human organism serves as a valuable indicator of ecosystem contamination. However, understanding physiological disorders and correlating specific contaminants with disease development is a complex and arduous task, necessitating extensive scientific research spanning years or even decades. To facilitate a more rapid and precise understanding of the physiological impairments induced by various contaminants, a comprehensive approach is indispensable. This review proposes a model for such an approach, which involves the systematic collection and analysis of data from ecosystem contamination monitoring, integrated with biomedical data on compromised physiological conditions in humans across different temporal and spatial scales. Given the complexity and sheer volume of data, alongside the imperative for strategic decision-making, this model leverages the capabilities of artificial intelligence (AI) tools. Although this paper exemplifies the model by investigating the effects of contaminants on the human organism, the model is adaptable to all ecosystem components, thereby supporting the conservation of plant and animal species.
Cytomegalovirus Biology Viewed Through a Cell Death Suppression Lens
Authors: Mocarski Edward S.
Published: November 2024

Cytomegaloviruses, species-specific members of the betaherpesviruses, encode an impressive array of immune evasion strategies committed to the manipulation of the host immune system enabling these viruses to remain for life in a stand-off with host innate and adaptive immune mechanisms. Even though they are species-restricted, cytomegaloviruses are distributed across a wide range of different mammalian species in which they cause systemic infection involving many different cell types. Regulated, or programmed cell death has a recognized potential to eliminate infected cells prior to completion of viral replication and release of progeny. Cell death also naturally terminates replication during the final stages of replication. Over the past two decades, the host defense potential of known programmed cell death pathways (apoptosis, necroptosis, and pyroptosis), as well as a novel mitochondrial serine protease pathway have been defined through studies of cytomegalovirus-encoded cell death suppressors. Such virus-encoded inhibitors prevent virus-induced, cytokine-induced, and stress-induced death of infected cells while also moderating inflammation. By evading cell death and consequent inflammation as well as innate and adaptive immune clearance, cytomegaloviruses represent successful pathogens that become a critical disease threat when the host immune system is compromised. This review will discuss cell death programs acquired for mammalian host defense against cytomegaloviruses and enumerate the range of modulatory strategies this type of virus employs to balance host defense in favor of lifelong persistence.
Establishment of a Real-Time Reverse Transcription Recombinase-Aided Isothermal Amplification (qRT-RAA) Assay for the Rapid Detection of Bovine Respiratory Syncytial Virus
Authors: Hou Guanxin , Zhu Siping , Li Hong , Li Chihuan , Liu Xiaochen , Ren Chao , Zhu Xintong , Shi Qiumei , Zhang Zhiqiang
Published: November 2024

Background: Bovine respiratory syncytial virus (BRSV) is a significant cause of bovine respiratory disease, resulting in significant losses to the cattle industry. For rapid detection of BRSV, a real-time recombinase-aided isothermal amplification assay (qRT-RAA) based on the F gene of BRSV was developed in this study. Results: The developed qRT-RAA assay showed good exponential amplification of the target fragment in 20 min at a constant temperature of 39 °C. And this assay displayed a high specificity for BRSV, without cross-reactions with Infectious Bovine Rhinotracheitis Virus (IBRV), Bovine Parainfluenza Virus Type 3 (BPIV3), Bovine Viral Diarrhea Virus (BVDV), and Bovine Coronavirus (BCoV). With the standard RNA of BRSV serving as a template, the limit of detection for qRT-RAA was 102 copies/μL. We examined ninety-seven clinical samples from cattle with respiratory disease using this method and determined a positive rate of 7.2% (7/97), consistent with results using the classical PCR method reported previously. Conclusions: A qRT-RAA assay for BRSV detection was established in this study. The method is specific and sensitive and can be completed within 20 min at 39 °C. These works demonstrate that the generated qRT-RAA assay is an effective diagnostic tool for rapidly detecting BRSV in resource-limited settings, which may be applied for the clinical detection of BRSV.
The Role of Dual Mutations G347E and E349D of the Pigeon Paramyxovirus Type 1 Hemagglutinin–Neuraminidase Protein In Vitro and In Vivo
Authors: Chen Yu , Gong Junhao , Zhan Tiansong , Wang Mingzhan , Hu Shunlin , Liu Xiufan
Published: November 2024

Pigeon Newcastle disease (ND) is the most common viral infectious disease in the pigeon industry, caused by pigeon paramyxovirus type 1 (PPMV-1), a variant of chicken-origin Newcastle disease virus (NDV). Previous studies have identified significant amino acid differences between PPMV-1 and chicken-origin NDV at positions 347 and 349 in the hemagglutinin–neuraminidase (HN) protein, with PPMV-1 predominantly exhibiting glycine (G) at position 347 and glutamic acid (E) at position 349, while most chicken-origin NDVs show E at position 347 and aspartic acid (D) at position 349. However, the impact of these amino acid substitutions remains unclear. In this study, we generated a recombinant virus, NT-10-G347E/E349D, by introducing the G347E and E349D dual mutations into a PPMV-1 strain NT-10 using reverse genetics. The biological characteristics of NT-10 and NT-10-G347E/E349D were compared both in vitro and in vivo. In vitro, the G347E and E349D dual mutations reduce NT-10′s replication and neuraminidase activity in pigeon embryo fibroblast (PEF) cells while enhancing both in chicken embryo fibroblast (CEF) cells. Additionally, these mutations decrease NT-10′s binding affinity to the α-2,6 sialic acid receptor while significantly increasing its affinity for the α-2,3 receptor. In vivo, NT-10-G347E/E349D exhibited reduced pathogenicity in pigeons but increased pathogenicity in chickens compared to the parental NT-10 strain. The mutations also reduced the pigeon-to-pigeon transmission of NT-10 but enhanced its transmission from pigeons to chickens. Notably, significant antigenic differences were observed between NT-10 and NT-10-G347E/E349D, as an inactivated vaccine based on NT-10 provided full protection against NT-10 challenge in immunized pigeons but only 67% mortality protection against NT-10-G347E/E349D. Overall, these findings underscore the critical role of amino acids at positions 347 and 349 in PPMV-1 infection, pathogenicity, and transmission, providing a theoretical foundation for the scientific prevention and control of PPMV-1.
Study on the Spatiotemporal Evolution Pattern of Frazil Ice Based on CFD-DEM Coupled Method
Authors: Liu Fang , Li Hongyi , Zhao Xin , Chen Yunfei
Published: November 2024

Frazil ice is the foundation for all other ice phenomena, and its spatiotemporal evolution is critical for regulating ice conditions in rivers and channels, as well as for preventing and controlling ice damage. This paper investigates the dynamic transport pattern of frazil ice during the early stages of winter freezing in water conveyance channels based on a CFD-DEM coupled numerical model, and derives predictive formulae for the spatiotemporal evolution of frazil ice and floating ice. First, static repose angle simulations and slope sliding simulations were used to calibrate the contact parameters between frazil ice particles and between frazil ice and the channel bed, ensuring the accurate calculation of contact forces in the model. On this basis, the processes of frazil ice transport, aggregation, and upward movement in water transfer channels were simulated, and the influence of contact parameters on simulation results was analyzed, showing a significant effect when the ice concentration was high. Numerical results indicate that the amount of suspended frazil ice is positively correlated with the frazil ice generation rate and water depth, with minimal influence from the flow velocity; the amount of floating ice increases linearly along the channel, with growth positively correlated with the frazil ice generation rate and water depth, and negatively correlated with the flow velocity. Predictive formulae correlating frazil ice and floating ice amounts with the flow velocity, water depth, and other factors were proposed based on numerical results. There is good agreement between the predictive and numerical results: the maximum APE between the predicted and simulated values of suspended frazil ice is 13.24%, and the MAPE is 6.32%; the maximum APE between the predicted and simulated values of floating ice increment is 7.80%, and the MAPE is 2.89%. The proposed prediction formulae can provide a theoretical basis for accurately predicting ice conditions during the early stages of winter freezing in rivers and channels.
Quartile Regression and Ensemble Models for Extreme Events of Multi-Time Step-Ahead Monthly Reservoir Inflow Forecasting
Authors: Weekaew Jakkarin , Ditthakit Pakorn , Kittiphattanabawon Nichnan , Pham Quoc Bao
Published: November 2024

Amidst changing climatic conditions, accurately predicting reservoir inflows in an extreme event is challenging and inevitable for reservoir management. This study proposed an innovative strategy under such circumstances through rigorous experimentation and investigations using 18 years of monthly data collected from the Huai Nam Sai reservoir in the southern region of Thailand. The study employed a two-step approach: (1) isolating extreme and normal events using quantile regression (QR) at the 75th, 80th, and 90th quantiles and (2) comparing the forecasting performance of individual machine learning models and their combinations, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Multiple Linear Regression (MLR). Forecasting accuracy was assessed at four lead times—3, 6, 9, and 12 months—using ten-fold cross-validation, resulting in 16 model configurations for each forecast period. The results show that combining quantile regression (QR) to distinguish between extreme and normal events with hybrid models significantly improves the accuracy of monthly reservoir inflow forecasting, except for the 9-month lead time, where the XG model continues to deliver the best performance. The top-performing models, based on normalized scores for 3-, 6-, 9-, and 12-month-ahead forecasts, are XG-MLR-75, RF-XG-80, XG-75, and XG-RF-75, respectively. Another crucial finding of this research is the uneven decline in prediction accuracy as lead time increases. Notably, the model performed best at t + 9, followed by t + 3, t + 12, and t + 6, respectively. This pattern is influenced by model characteristics, error propagation, temporal variability, data dynamics, and seasonal effects. Improving the accuracy and efficiency of hybrid model forecasting can greatly enhance hydrological operational planning and management.
Climate Change-Driven Hydrological Shifts in the Kon-Ha Thanh River Basin
Authors: Vu Cong Huy , Nguyen Binh Quang , Tran Thanh-Nhan-Duc , Vo Duong Ngoc , Arshad Arfan
Published: November 2024

Climate change is projected to bring substantial changes to hydroclimatic extremes, which will affect natural river regimes and have wide-ranging impacts on human health and ecosystems, particularly in Central Highland Vietnam. This study focuses on understanding and quantifying the projected impacts of climate change on streamflow in the Kon-Ha Thanh River basin, using the Soil and Water Assessment Tool (SWAT) between 2016 and 2099. The study examined projected changes in streamflow across three time periods (2016–2035, 2046–2065, and 2080–2029) under two scenarios, Representative Conversion Pathways (RCPs) 4.5 and 8.5. The model was developed and validated on a daily scale with the model performance, yielding good performance scores, including Coefficient of Determination (R2), Nash-Sutcliffe Efficiency (NSE), and Root Mean Squared Error (RMSE) values of 0.79, 0.77, and 50.96 m3/s, respectively. Our findings are (1) streamflow during the wet season is projected to increase by up to 150%, particularly in December, under RCP 8.5; (2) dry season flows are expected to decrease by over 10%, beginning in May, heightening the risk of water shortages during critical agricultural periods; and (3) shifts in the timing of flood and dry seasons are found toward 2099 that will require adaptive measures for water resource management. These findings provide a scientific foundation for incorporating climate change impacts into regional water management strategies and enhancing the resilience of local communities to future hydroclimatic challenges.
Hydrochemical and Isotopic Characterization of Groundwater in the Nakivale Sub-Catchment of the Transboundary Lake Victoria Basin, Uganda
Authors: Hyeroba Emmanuel Nabala , Kalin Robert M. , Mukwaya Christine
Published: November 2024

This study characterized groundwater resources for the Nakivale sub-catchment of the transboundary Victoria Basin in Uganda using classical hydrochemical and stable isotopic approaches. Groundwater in the study area is essential for domestic, agricultural, and industrial uses. As a sub-domain of the larger Victoria Basin, it also plays a crucial role in shaping the hydrological characteristics of this vital transboundary basin, both in terms of quality and quantity fronts. This makes its sustainable management and development vital. The predominant groundwater type is Ca-SO4, with other types including Ca-HCO3, Na-Cl, Na-HCO3, and Ca-Mg-SO4-Cl. Hydrochemical facies analysis highlights the importance of rock–water interactions in controlling groundwater chemistry, mainly through incongruent chemical weathering of Ca-rich plagioclase feldspars and the oxidation of sulfide minerals, such as pyrite, which are prevalent in the study area. Groundwater recharge is primarily influenced by the area’s topography, with recharge zones characterized by lineament networks, located in elevated areas. Stable isotope analyses indicate that groundwater mainly originates from local precipitation, while tritium data suggest the presence of both recent and older groundwater (likely over 20 years old). The study’s comprehensive approach and findings contribute significantly to the understanding of groundwater systems in the region, thus providing valuable insights for policymakers and stakeholders involved in water resource management and development strategies.
The association between cancer risk assessment tool use and GP consultation duration: an observational study
Authors: Fletcher Emily , Campbell John , Pitchforth Emma , Mounce Luke Timothy Allan , Hamilton William , Abel Gary A.
Published: November 2024

Background: England is short of General Practitioners (GPs). GP consultation rates, consultation duration, and workload are increasing. Electronic clinical decision support (eCDS) tools assist decision-making for screening, diagnosis, and risk-management. Cancer detection is one area where tools are designed to support GPs. Electronic risk assessment tools (eRATs) estimate risk of current cancer based on symptoms. We aimed to explore any association between eRATs impact and GP workload and workflow during consultations. Methods: Thirteen practices participating in a cluster randomised controlled trial of eRATs (ERICA) were recruited to an observational sub-study. We compared the average duration of consulting sessions and consultations where eRATs were or were not activated, using mixed effects regression models. Results: there was no evidence that sessions where an eRAT was activated were, on average, longer than sessions where no eRATs had been activated. However, individual consultations involving an eRAT were longer on average by 3.96 minutes (95% CI: 3.45 to 4.47; p<0.001), when compared with consultations with no eRATs , after adjusting for a range of session and consultation characteristics. Conclusions: There was no evidence to suggest that eRATs should not be used to support GPs in early cancer diagnosis from a workload perspective. eRATs were not associated with increased workload across a session. Definitive findings regarding the clinical effectiveness of eRATs, not the related workload/workflow implications, will ultimately determine whether the use of eRATs should be rolled out more widely.
Trabajo remoto, estrés percibido y salud mental durante la pandemia por COVID-19
Authors: Veliz Huanca Fátima Stefanie , Mucha López Diana Carolina , Guzmán Meza Maritza Emperatriz
Published: November 2024

La pandemia de COVID-19 ha transformado radicalmente el panorama laboral a nivel mundial, impulsando una rápida adopción del trabajo remoto. Esta investigación tuvo como objetivo determinar la relación entre el trabajo remoto, el estrés percibido y la salud mental en empleados del Colegio Tecnólogo Médico del Perú durante la pandemia. Se empleó un enfoque cuantitativo con método hipotético-deductivo y un diseño correlacional múltiple de corte transversal. Los resultados revelan una fuerte relación positiva entre el trabajo remoto y el estrés percibido (0,698), mientras que un entorno remoto con menor estrés se asocia a una mejor salud mental ocupacional (0,829). La alta calificación con la dimensión espiritual (0.937) indica un impacto positivo en la salud mental, aunque el estrés tiene una influencia moderada en esta área (0.642). Las dimensiones comportamentales (0.755) y cognitivas (0.729) también se ven afectadas. Se concluye que el trabajo remoto requiere estrategias efectivas para gestionar el estrés y promover la salud mental.
PENGARUH EARNING PER SHARE (EPS), RETURN ON ASSET (ROA), CURRENT RATIO (CR), PRICE BOOK VALUE (PBV) TERHADAP HARGA SAHAM (STUDI KASUS PADA PERUSAHAAN YANG TERDAFTAR PADA LQ45 DI BURSA EFEK INDONESIA (BEI))
Authors: Rindu Rindu Joshua Parulian Nainggolan , H. Manao Stephen Cornelius , Sinuraya Bella Ristha Sinuraya , Nasution M Irsan
Published: November 2024

This research aims to test and explain the partial and dominant influence of Earning Per Share (EPS), Return On Assets (ROA), Current Raio (CR), Price Book Value (PBV) on the share prices of companies listed on LQ45 on the Indonesia Stock Exchange (IDX). The analytical method used in this research is a quantitative method by testing classical assumptions and statistical analysis, namely multiple linear regression analysis. The sampling method used was purposive sampling. The variables in this research consist of EPS, ROA, CR and PBV as independent variables, and share price as the dependent variable with a total sample of 60 from 30 companies. The results of the analysis show that EPS and ROA have a significant effect on share prices. while CR and PBV do not have a significant effect on stock returns. For further research, it is hoped that other research variables can be added, for example Net Profit Margin (NPM), Debt to Equity Ratio (DER) or Price Earning Ratio (PER).
Pengaruh Current Ratio dan Non Performing Loan Terhadap Return on Asset Pada Perusahaan Perbankan yang Terdaftar Di Bursa Efek Indonesia Periode 2019-2022
Authors: Purba Harapan Kindo Purba
Published: November 2024

ABSTRAK
Pengkajian ini bertarget guna mengamati dampak “Current Ratio(CR) dan Non Performing Loan (NPL) terhadap Return On Asset (ROA) pada perusahaan perbankan yang Terdaftar di Bursa Efek Indonesia (BEI) selama 4 tahun dari 2019 sampai dengan 2022” dengan jumlah sampel 11 perusahaan, memakai tehnik Purposive sampling menjadi cara penetapan sampelnya. Pengkajian ini memakai jenis data sekunder. Metode data anaisis yang dipakai ialah regresi liner berganda
Melalui perolehan uji-t CR bernilai sign sejumlah 0,481 > 0,05 yang dimaknai bila CR tidak berdampak pada ROA perusahaan yang terverifikasi di BEI sejak 2019-2022.NPL bernilai sign sejumlah 0,073 > 0,05 dimaknai dengan parsial tidak berdampak pada ROA perusahaan yang terverifikasi di BEI sejak 2019-2022. Dari pengujian F nilai CR serta NPL bernilai sign sejumlah 0,164 > 0,05 yang artinya CR dan NPL secara simultan tidak berdampak pada ROA perusahaan yang terverifikasi di BEI sejak 2019- 2022.
PENGARUH LIKUIDITAS DAN PROFITABILITAS TERHADAP HARGA SAHAM PERUSAHAAN PERBANKAN YANG TERDAFTAR DI BEI PERIODE 2020-2022
Authors: Fitria Fariza , Sinaga Wulan Dari , Faris Salman , Ndruru Hati Nurani
Published: November 2024

Penelitian ini bertujuan untuk mengetahui Pengaruh Likuiditas Dan Profitabilitas Terhadap Harga Saham Perusahaan Perbankan Yang Terdaftar Di Bursa Efek Indonesia Periode 2020-2022. Penelitian ini merupakan jenis penelitian Kuantitatif. Populasi dalam penelitian ini adalah Perusahaan Perbankan yang Terdaftar di Bursa Efek Indonesia yang berjumlah 32 perusahaan.Teknik pengumpulan data menggunakan dokumentasi laporan keuangan yang di publishkan di website resmi Bursa Efek Indonesia yaitu www.idx.co.id . Teknik analisis yang digunakan adalah regresi linear berganda. Hasil penelitian ini menunjukkan bahwa Likuiditas tidak berpengaruh signifikan terhadap harga saham, Profitabilitas memiliki pengaruh signifikan terhadap harga saham dan secara simultan likuiditas dan profitabilitas Bersama sama memepengaruhi harga saham perusahaan perbankan yang terdaftar di bei periode 2020-2022
PENGARUH RASIO LIKUIDITAS DAN SOLVABILITAS TERHADAP KINERJA KEUANGAN PERUSAHAAN
Authors: Rahmi Namira ufrida , Simanjorang Bella Rosa , Pasaribu Ridoi Gilbert , Fan Yulis
Published: November 2024

Financial performance is really needed by companies to determine the good and bad of the company's financial condition in a certain period. One way of measuring company performance is using financial ratio analysis. This research aims to examine the effect of liquidity ratios (CR) and Solvency (DER) on financial performance (ROA) in pharmaceutical companies for the 2021-2023 period, both partially and simultaneously. The samples were taken using purposive sampling technique. A sample of 14 companies was obtained from a total population of 15 companies. The sample was selected using a purposive sampling technique. The analysis technique used was descriptive statistics, multiple linear regression. , classical assumption test, hypothesis test (T test and F test) and coefficient of determination. Data processing using the SPSS 26 program. Partial test results show that solvency has a negative and significant effect on financial performance. Liquidity has a positive and significant effect on financial performance. Activities has no effect on financial performance. Meanwhile, simultaneous test results show that liquidity, solvency and activity together have an effect on financial performanc
Automatic classification of Giardia infection from stool microscopic images using deep neural networks
Authors: Yarahmadi Pezhman , Ahmadpour Ehsan , Moradi Parham , Samadzadehaghdam Nasser
Published: November 2024

Introduction: Giardiasis is a common intestinal infection caused by the Giardia lamblia parasite, and its rapid and accurate diagnosis is crucial for effective treatment. The automatic classification of Giardia infection from stool microscopic images plays a vital role in this diagnosis process. In this study, we applied deep learning-based models to automatically classify stool microscopic images into three categories, namely, normal, cyst, and trophozoite. Methods: Unlike previous studies focused on images acquired from drinking water samples, we specifically targeted stool samples. In this regard, we collected a dataset of 1610 microscopic digital images captured by a smartphone with a resolution of 2340 × 1080 pixels from stool samples under the Nikon YS100 microscope. First, we applied CLAHE (Contrast Limited Adaptive Histogram Equalization) histogram equalization a method to enhance the image quality and contrast. We employed three deep learning models, namely Xception, ResNet-50, and EfficientNet-B0, to evaluate their classification performance. Each model was trained on the dataset of microscopic images and fine-tuned using transfer learning techniques. Results: Among the three deep learning models, EfficientNet-B0 demonstrated superior performance in classifying Giardia lamblia parasites from stool microscopic images. The model achieved precision, accuracy, recall, specificity, and F1-score values of 0.9599, 0.9629, 0.9619, 0.9821, and 0.9607, respectively. Conclusion: The EfficientNet-B0 showed promising results in accurately identifying normal, cyst, and trophozoite forms of Giardia lamblia parasites. This automated classification approach can provide valuable assistance to laboratory science experts and parasitologists in the rapid and accurate diagnosis of giardiasis, ultimately improving patient care and treatment outcomes.
Targeting hypoxia-inducible factor 1 alpha augments synergistic effects of chemo/immunotherapy via modulating tumor microenvironment in a breast cancer mouse model
Authors: Rashid Mohsen , Ramezani Mina , Molavi Ommoleila , Ghesmati Zeinab , Baradaran Behzad , Sabzichi Mehdi , Ramezani Fatemeh
Published: November 2024

Introduction: The immunosuppressive context of the tumor microenvironment (TME) is a significant hurdle in breast cancer (BC) treatment. Combinational therapies targeting cancer core signaling pathways involved in the induction of TME immunosuppressive milieu have emerged as a potent strategy to overcome immunosuppression in TME and enhance patient therapeutic outcomes. This study presents compelling evidence that targeting hypoxia-inducible-factor-1 alpha (Hif-1α) alongside chemotherapy and immune-inducing factors leads to substantial anticancer effects through modulation of TME. Methods: Chitosan (Cs)/Hif-1alpha siRNA nano-complex was synthesized by siRNA adsorption methods. Nanoparticles were fully characterized using dynamic light scattering and scanning electron microscope. Cs/Hif-1α siRNA cytotoxicity was measured by MTT assay. The anticancer effects of the combinational therapy were assessed in BALB/c bearing 4T1 tumors. qPCR and western blotting were applied to assess the expression of some key genes and proteins involved in the induction of immunosuppression in TME. Results: Hif-1α siRNA was successfully loaded in chitosan nanoparticles. Hif-1α siRNA nanocomplexes significantly inhibited the expression of Hif-1α. Triple combination therapy (Paclitaxel (Ptx) + Imiquimod (Imq) + Cs/Hif-1α siRNA) inhibited tumor growth and downregulated cancer progression genes while upregulating cellular-immune-related cytokines. Mice without Cs/Hif-1α siRNA treatments revealed fewer cancer inhibitory effects and more TME immunosuppressive factors. These results suggest that the inhibition of Hif-1α effects synergize with Ptx and Imq to inhibit cancer progression more significantly than other combinational treatments. Conclusion: Combining Hif-1α siRNA with Ptx and Imq is promising as a multimodality treatment. It has the potential to attenuate TME inhibitory effects and significantly enhance the immune system's ability to combat tumor cell growth, offering an inspiration of hope in the fight against BC.
Recombinant receptor-binding motif of spike COVID-19 vaccine candidate induces SARS-CoV-2 neutralizing antibody response
Authors: Samiei-Abianeh Hossein , Nazarian Shahram , Kordbacheh Emad , Felegary Alireza
Published: November 2024

Introduction: The SARS-CoV-2 pandemic necessitates effective therapeutic solutions. The receptor-binding motif (RBM) is a subdomain of the spike protein's receptor-binding domain (RBD) and is critical for facilitating the binding of SARS-CoV-2 to the human ACE2 receptor. This study investigates the use of the receptor-binding motif (RBM) domain as an immunogen to produce potent neutralizing antibodies against SARS-CoV-2. Methods: The RBM gene was codon-optimized and cloned into the pET17b vector for expression in E. coli BL21 (DE3) cells, induced with 1 mM IPTG. The recombinant RBM protein was purified using Ni-NTA affinity chromatography. After validating the recombinant RBM by Western blotting with anti-His tag antibodies, BALB/c mice were immunized with 20 µg of the purified RBM protein. Anti-RBM IgG was subsequently purified using protein G resin, and its neutralizing capacity was assessed using the Pishtaz Teb Zaman Neutralization Assay Kit. Results: The recombinant RBM protein, with a molecular weight of 10 kDa, was expressed as inclusion bodies. the typical yield of purification was 27 mg/L of bacterial culture. The neutralization test demonstrated a concentration of 36 µg/mL of neutralizing antibodies in the immunized serum, preventing the spike protein from binding to ACE2. Conclusion: Our study demonstrated that anti-RBM antibodies exhibited neutralization effects on SARS-CoV-2. These findings provide evidence for the development of a vaccine candidate through the induction of antibodies against the RBM, necessitating further studies with adjuvants suitable for human use to evaluate its potential for human vaccination.
Application of Bioinformatics and Molecular Dynamics Simulation for Designing FGF7 Mutants Stable in Urea Solution
Authors: Dastmalchi Siavoush , Alizadeh Ali Akbar
Published: November 2024

Background: FGF7 is a potent cytoprotective and regenerative protein on the injured epithelial tissues, indicating its potential therapeutic effect in conditions such as mucositis, ulcerative colitis, and cutaneous wound formation induced by chemotherapy or radiotherapy. However, FGF7 low stability prevents its usability as a pharmaceutical product, necessitating the design and production of stable FGF7 mutants. Methods: In the current study, bioinformatics and MD simulations approaches were used for identifying potential stable mutants of FGF7. For this, the model structure of FGF7 was constructed and subjected to MAESTROweb service to identify stability conferring mutations. The identified potential mutants of FGF7 were MD simulated in an explicit denaturant condition and their stabilities were evaluated. The complexes of FGF7 mutants with FGFR2 receptor were also examined to investigate the receptor binding ability of proposed mutants. Results: Investigation of stability parameters showed that introducing A104S mutations in FGF7 may greatly improve its stability in urea solution. Calculation of binding energies indicated that A104S mutant interacts better then wild type FGF7 with the receptor. The structural stability and binding capability of A104S were comparable to that of A120C mutation, as an experimentally determined stability conferring mutation, which was used in whole process to indicate the validity of employed MD simulation for predicting the effect of mutation on FGF7 stability. Conclusion: The results of current study showed that A104S mutant of FGF7 has a potential to be evaluated further for the purpose of presenting a therapeutic agent effective in conditions such as mucositis, ulcerative colitis, and cutaneous wound formation induced by chemotherapy or radiotherapy.
Comparison of Different Approaches for Calculating LOD and LOQ in an HPLC-Based Analysis Method
Authors: Kaviani Raha
Published: November 2024

Background: Sensitivity in the determination of the drug concentration is critical in pharmaceutical analysis. This research investigates several approaches for determining two sensitivity parameters, the Limit of Detection (LOD) and the Limit of Quantification (LOQ), in the analysis of the drug concentration using High-Performance Liquid Chromatography (HPLC). Methods: The study evaluates the FDA’s Lower Limit of Quantification (LLOQ) parameter, following global standards and quantitatively comparing sensitivity parameters for an established HPLC-UV method for the analysis of carbamazepine and phenytoin. Results: The study found that the LOD and LOQ values obtained by different methods varied significantly. The signal-to-noise ratio (S/N) method provided the lowest LOD and LOQ values for both drugs, while the standard deviation of the response and slope (SDR) method resulted in the highest values. This highlights the variability in sensitivity depending on the method used. Conclusion: The results show significant differences among calculated sensitivity values, emphasizing the influence of methodological variations on sensitivity values. It recommends following FDA criteria in chromatographic-based pharmaceutical analysis to improve the accuracy of drug concentration determination.
Earnings Persistence Determinants in Indonesia’s Automotive Industries
Authors: Latifah Yuni , Hernawati Erna
Published: November 2024

The financial performance of companies, particularly as reflected in financial statements, is crucial for decision-making. Earnings persistence, which is defined as the ability of a company to maintain stable earnings from one period to the next, is a key indicator in assessing the financial health and stability of a company. This research aims to analyze the patterns, trends, and best strategies in the use of digital technology and digital transformation implemented by companies in the global market.  The study's population includes automotive sub-sector companies, and their components listed on the Indonesia Stock Exchange (IDX) from 2019 to 2023, with purposive sampling serving as the sampling method, and 18 companies were selected as the samples. The technique applied for data analysis is the multiple regression method. The discoveries from this research prove that company size, accrual reliability, and book tax differences do not significantly affect earnings consistency. Meanwhile, the debt ratio has a significant effect on earnings persistence.
Keywords: Firm Size; Accrual Reliability; Book Tax Differences; Debt Asset to Ratio; Earning Persistence.
THE METHOD OF CALCULATING THE RANGE OF VISIBILITY A TELEVISION CHANNEL IN THE HYDROSPHERE BASED ON THE JOHNSON CRITERION
Authors: Shimanskaya M.S.
Published: November 2024

in the context of rapidly developing technologies, improving the efficiency of telecommunications equipment for underwater vehicles is an urgent task to strengthen national security and support scientific research in our country. Vision systems are an integral part of modern underwater vehicles, serve for navigation and orientation in conditions of limited visibility underwater, are used for mapping the seabed, detecting and identifying objects, as well as monitoring marine flora and fauna. This article proposes a method for calculating the visibility range of a television channel on underwater vehicles, which can increase the effectiveness of their search performance.
DETECTION AND ELIMINATION OF INFORMATION SECURITY THREATS AND VULNERABILITIES IN THE OPERATION OF MOBILE SEARCH GROUPS
Authors: Artemova S.V. , Bakaev A.A. , Mityakov E.S. , Vegera J.G. , Shuvalova A.M.
Published: November 2024

During search and rescue operations, special attention should be paid to information security when working with mobile search and rescue teams. Therefore, there is a need to find methods for detecting and eliminating threats and vulnerabilities that arise during the operation of mobile search groups. To save the lives of victims, it is necessary not only to be in good physical shape, but also to always be extremely careful when it comes to processing confidential information, transmitting tactical and strategic plans for solving tasks. Therefore, an important task is to increase the effectiveness of protecting confidential information in mobile groups during search and rescue operations. In the process of studying threats and vulnerabilities to information security in the work of mobile search groups, it is proposed to use methods of logical and statistical analysis. It is necessary to create a list of methods and recommendations to increase the level of protection of processing, transmission and analysis of confidential information when working with mobile search teams in search and rescue organizations. These recommendations will increase the effectiveness of the protection of processed information, and will have a beneficial effect in volunteer search and rescue organizations in the future, since within the framework of information security, the risk of threats will decrease and it will be possible to correctly neutralize vulnerabilities.
Big Data Mining for Chronic Disease Prediction using Principal Component Analysis and eXtreme Gradient Boosting
Authors: Choubey Shruti Bhargava , Chitra T. , Hephzipah J. Jasmine
Published: November 2024

Big data mining has revised health care with advanced analytics running on huge data sets, thereby improving immensely in disease detection and management. In this paper, a new approach of chronic disease detection is proposed, which combines PCA-based feature selection with the XGB classifier. It focuses only on the most important features, making analysis more efficient and accurate. This technique projects complicated medical data, be it biomarkers or clinical parameters, into a lower dimension called principal components, capturing essential variability but discarding less important information. In this way, PCA will reduce the dimensionality of the dataset, making the analysis more efficient and accurate. This technique projects complicated medical data into a lower dimension called principal components, capturing essential variability but getting rid of less important information. After extracting the features using PCA, XGB is used for the classification that would lead to the identification and prediction of chronic diseases. Boosting algorithms in XGB are very powerful and could handle complex relationships between data points easily and accurately, greatly enhancing model performance. The approach returned remarkable results with precision of 98.8%, recall of 98%, and an F1-score of 98.7%, hence considerably good enough to decide between healthy vs. chronic conditions. Moreover, it achieves high efficiency with the proposed model, reducing processing time to just 8 seconds and thus making the model very practical to be applied in real-world scenarios.
Automated Detection and Management of Leaf Diseases in Plants using Image Processing and Machine Learning
Authors: Manohar Gunaselvi , Harika D. , Chand S. Ravi
Published: November 2024

This paper presents a system designed to detect and monitor diseases found on the leaves of plants through the aid of image processing and machine learning mechanisms. The system can diagnose the problem from the images it receives from leaves and recommend specific treatment. Common users are able to front-end an image of the leaf through a simple web interface that feeds the images into a central database. In response to an image query, the system uses the GLCM method to feed texture characteristics in and to employ K - means clustering in segmenting disease regions. After this, a Support Vector Machine (SVM) polynomial kernel-based classifier is used for the multi-class classification of the diseases of the leaves. It also has features that entails information on the diseases identified and treatment plans for them, improving disease control plans. Experimental assessments show that the developed system is truly capable of analyzing the peculiarity of diseased leaves and categorizing the diseases with high prediction accuracy, pointing to a promising application of the proposed system in agricultural industry and plant pathology science. Some possible enhancements for the future could be the expansion of more functionalities or creating a mobile app aimed at enhancing the program’s usage. The proposed system will not only help in moving the agricultural industry forward by providing a dependable, easy to use, error-free system for early diagnosis of diseases and taking initial steps in the mitigation process but also result in improved quality and quantity of produce.
Clinical Prediction using Machine Learning-based IoT for E-Healthcare Systems
Authors: Hemalatha P. K. , Choubey Shruti Bhargava
Published: November 2024

Machine learning is one of the fundamental approaches to nature the data produced by Internet of Things (IoT) and has drastic enhancements to enhance the decision-making fields such as education, security, business, and especially the healthcare sector. This integration of ML with IoT makes it possible for healthcare systems to generate patterns from big data which can improve the efficiency of predictive analytics and referrals. It also helps in the development of templates for medical records and real-time patient care, and the identification of patient diagnosis electronically. Though, performance of the ML algorithms can be affected by the dataset on the basis of which clinical decisions are made. Such variations clearly demonstrate the requirement to gain a deeper understanding of how several ML approaches work in terms of IoT data analysis for healthcare. In the proposed study we are testing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithms for tracking health parameters. We first employ the above-mentioned approach for training and validation of the ML models on the database available at UCI. In the testing phase, the IoT devices record actual data of heart rate, blood pressure, posture, and temperature to forecast a health anomaly. Subsequently, an examination of accuracy of the aforementioned prediction is performed on the cloud stored data with reference to those produced by K-nearest neighbour (KNN) algorithm. Such an approach makes it possible to give adequate consideration to the offered assessment of the role of ML in increasing the efficiency of IoT-based healthcare systems with regard to early detection and comprehensive intervention practices. In this way, this research help to consider variability in prognostic outcomes for more accurate and precise clinical decision-making processes, and thus contribute in enhancing patient care and health in IoT environment.
Профилактика и лечение тромбоэмболических осложнений в гинекологии
Authors: Р.И. Рагимова
Published: November 2024

Цель: улучшить результаты профилактики ранних тромбоэмболических осложнений (ТЭО) в гинекологической практике. Материал и методы. Представлен обзор литературы по ТЭО после гинекологических операций. Частота встречаемости ТЭО и тромбоэмболии легочной артерии и процент летальных исходов. Дана классификация больных и их разделение на 4 группы в зависимости от степени риска послеоперационных венозных тромботических осложнений в оперативной гинекологии. Результаты. Подробно изложены причинные и предрасполагающие факторы и патогенез тромбоэмболии легочной артерии. Особый акцент в статье делается на наследственные нарушения свертывающей системы крови – тромбофилию. Отражены классические признаки проявления ТЭО и методы диагностики ТЭО с использованием современных лабораторных (Д-димер и др.) и инструментальных методов, включая мультидетекторную компьютерную томографию. Предложены методы лечения (медикаментозный и инвазивный) и профилактики (медикаментозные, механические и комбинированные) ранних послеоперационных ТЭО в оперативной гинекологии. Заключение. Проведенный анализ позволит значительно улучшить вопросы ранней диагностики, выбора способа лечения и профилактики ранних послеоперационных ТЭО после гинекологических операций.

Objective: to improve the results of the prevention of early thromboembolic complications (TEO) in gynecological practice. Material and methods. A review of the literature on the feasibility study after gynecological surgery is presented. The frequency of occurrence of feasibility study and thromboembolism of the pulmonary artery and the percentage of deaths. The classification of patients and their division into 4 groups is given depending on the degree of risk of postoperative venous thrombotic complications in operative gynecology. Results. Causal and predisposing factors and pathogenesis of pulmonary embolism are described in detail. Particular emphasis in the article is placed on hereditary disorders of the blood coagulation system thrombophilia. The classic signs of the manifestation of the feasibility study and the diagnostic methods of the feasibility study using modern laboratory (Ddimer, etc.) and instrumental methods, including multidetector computed tomography are reflected. The methods of treatment (medication and invasive) and prophylaxis (medication, mechanical and combined) of early postoperative feasibility studies in operative gynecology are proposed. Conclusion The analysis will significantly improve the issues of early diagnosis, the choice of treatment and prevention of early postoperative feasibility studies after gynecological operations.