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Abstract Clinical samples are routinely inactivated before molecular assays to prevent pathogen transmission. Antibody-based assays are sensitive to changes in analyte conformation, but the impact of inactivation on the analyte detectability has been overlooked. This study assessed the effects of commonly used inactivation-methods, Triton X-100 (0.5%) and heat (60 °C, 1 h), on cytokine/chemokine detection in plasma, lung aspirates, and nasopharyngeal samples. Heat significantly reduced analyte detectability in plasma (IL-12p40, IL-15, IL-16, VEGF, IL-7, TNF-β) by 33–99% (p ≤ 0.02), while Triton X-100 minimally affected analytes in plasma and nasopharyngeal samples (11–37%, p ≤ 0.04) and had no significant impact on lung aspirates. Structural analysis revealed that cytokines affected by heat had more hydrophobic residues and higher instability-indices. As the protein-detectability was affected differently in different sample types, the sample environment could also influence protein stability. This underscores the importance of selecting the most suitable inactivation methods for clinical samples to ensure accurate cytokine/chemokine analysis in both clinical and research settings.

Abstract As global environmental challenges intensify, manufacturing firms face increasing pressure to innovate sustainably. Green innovation, characterized by the development of environmentally friendly products, processes, and technologies, has become essential for firms striving to remain competitive. This study aims to investigate the influence of key factors—green logistics, green finance, and green technology—on green innovation within manufacturing firms, while exploring the mediating role of green technology in these relationships. A multi-method approach was employed, combining partial least squares structural equation modeling, fuzzy-set qualitative comparative analysis, and necessity condition analysis. 447 responses were collected from manufacturing companies in Dhaka city, Bangladesh, using structured questionnaires. The analysis revealed that green logistics and green finance have a significant positive impact on green innovation, while the influence of the green work environment was found to be positive but statistically insignificant. Additionally, green technology was identified as a significant mediator in the relationships between green finance, green logistics, and green innovation. This study offers a comprehensive green innovation model while green technology is a mediator. Furthermore, this study advances the resource-based view theory by integrating green technology as a pivotal resource that enhances a firm's competitive advantage in sustainable markets. By adopting a multi-method approach, this research provides a rigorous examination of the research questions, offering a comprehensive understanding of the dynamic interactions between green finance, green logistics, and green technology in driving innovation. Thus, this research has thought provoking implications to prioritize investments in green finance, logistics, and technology, manufacturing firms can enhance their competitiveness, improve operational efficiency, and meet evolving environmental regulations and consumer preferences.

Abstract Background Obstetric anesthesia guidelines are essential for standardizing obstetric anesthesia practices globally and ensuring high-quality patient care. However, practices may vary across different settings, and there are limited data from Arab countries. This study aims to gain insights into obstetric anesthesia practices in several major hospitals across Arab countries. Methods A questionnaire was emailed to 85 obstetric anesthesiologists/anesthesia chairpersons in major hospitals, including academic medical institutions and central hospitals, across 11/22 Arab countries. This survey gathered data on key structural and process-related obstetric anesthesia indicators. Results Out of 85 contacted, we had 56 responses (65.8%), with 41 being fully completed, providing insights into obstetric anesthesia indicators. Regarding structure: 31 (76%) hospitals had an operating room adjacent to the delivery room, all had intensive care units, and 22 (54%) had a lead obstetric anesthesiologist. For equipment, 19 (46%) had a video laryngoscope in the delivery suite, and 20 (49%) occasionally used ultrasound for epidurals. Regarding process: 33 (81%) held regular meetings, and 21 (51%) conducted research. Before epidural and spinal procedures, 26 (63%) and 28 (68%) required coagulation studies for patients without a history of hemorrhagic complications, while 38 (93%) and 36 (88%) mandated a platelet count, respectively. For labor analgesia, 34 (83%) primarily used epidurals, and 15 (37%) placed preemptive catheters in high-risk pregnancies. For cesarean delivery, 40 (98%) used spinals, with 16 (39%) using intrathecal morphine and 22 (54%) administering aspiration prophylaxis before general anesthesia. Regarding spinal-induced hypotension, 6 (15%) used prophylactic phenylephrine infusion. Conclusion This survey highlights variations in obstetric anesthesia practices among various major hospitals in several Arab countries, compared to international recommendations. It emphasizes the need for obstetric anesthesia registries in the Arab world for future research. Further studies are required to outline country-specific practices, improve resource allocation, and enhance obstetric population safety and satisfaction.

Abstract Background The clinicopathological parameters such as residual tumor, grade, the International Federation of Gynecology and Obstetrics (FIGO) score are often used to predict the survival of ovarian cancer patients, but the 5-year survival of high grade serous ovarian cancer (HGSOC) still remains around 30%. Hence, the relentless pursuit of enhanced prognostic tools for HGSOC, this study introduces an unprecedented gene expression-based molecular prognostic score (mPS). Derived from a novel 20-gene signature through Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression, the mPS stands out for its predictive prowess. Results Validation across diverse datasets, including training and test sets (n = 491 each) and a large HGSOC patient cohort from the Ovarian Tumor Tissue Analysis (OTTA) consortium (n = 7542), consistently shows an area-under-curve (AUC) around 0.7 for predicting 5-year overall survival. The mPS’s impact on prognosis resonates profoundly, yielding an adjusted hazard-ratio (HR) of 6.1 (95% CI: 3.65–10.3; p

Diabetes is a chronic disease characterized by high levels of sugar (glucose) in excess of normal limits, about 1.5 million deaths each year are directly attributed to diabetes. The purpose of this study is to obtain the optimal cluster analysis results on grouping medical record data on diabetes and obtain the silhouette coefficient value of the optimal cluster analysis results on grouping medical record data on diabetes at TK-II Putri Hijau Kesdam I / B Hospital, Medan-North Sumatra by using the divisive analysis algorithm and determining the variables that cause the patient to develop diabetes. The results showed that the optimal cluster using the divisive analysis algorithm was 2 clusters with a value of 0.468582 which stated that the cluster structure formed in this grouping was the right cluster. And for variables that cause patients to develop diabetes, namely age (X2) and blood sugar level (X8). it is because these two variables have the highest average value among the two clusters and all these variables.

Coastal land transformation has been identified as a topic of research in many countries around the world. Several studies have been conducted to determine the causes and impacts of land transformation. However, much less is understood about coupling change detection, factors, impacts, and adaptation strategies for coastal land transformation at a global scale. This review aims to present a systematic review of global coastal land transformation and its leading research areas. From 1,741 documents of Scopus and Web of Science, 60 studies have been selected using the PRISMA-2020 guideline. Results revealed that existing literature included four leading focus areas regarding coastal land transformation: change detection, driving factors, impacts, and adaptation measures. These focus areas were further analyzed, and it was found that more than 80% of studies used Landsat imagery to detect land transformation. Population growth and urbanization were among the major driving factors identified. This review further identified that about 37% of studies included impact analysis. These studies identified impacts on ecosystems, land surface temperature, migration, water quality, and occupational effects as significant impacts. However, only four studies included adaptation strategies. This review explored the scope of comprehensive research in coastal land transformation, addressing change detection, factor and impact analysis, and mitigation-adaptation strategies. The research also proposes a conceptual framework for comprehensive coastal land transformation analysis. The framework can provide potential decision-making guidance for future studies in coastal land transformation.

In this study, gamma activity concentrations, gross alpha and gross beta activity of natural radionuclides, and heavy metal concentrations were measured in eleven edible marine fish, four shellfish, two mollusks, and a common seaweed sample collected from a local sea fish market and sea beach area of the northern part of the Bay of Bengal, Kuakata, Bangladesh. Using HPGe gamma spectrometry, the activity concentrations of 238U, 232Th, and 40K were measured and found to be 19.7 ± 1.5 Bq/kg, 12.2 ± 0.9 Bq/kg, and 188 ± 15 Bq/kg, respectively, with the 232Th concentration surpassing that of 238U. The ZnS scintillation detector was used to assess the gross alpha and gross beta activity. The average gross alpha activity and gross beta activity were found to be 9.4 ± 1.4 Bq/kg and 26 ± 4 Bq/kg, respectively, with the latter attributed to beta emitting radionuclides's abundance. The activity concentrations of 238U, 232Th, and 40K in the analyzed samples varied in the order of Shellfish > Seaweed > Fish > Mollusks, Seaweed > Shellfish > Mollusks > Fish, and Seaweed > Shellfish > Fish > Mollusks. The annual effective dose due to consumption of analyzed seafood was found to be within the world limit of 2400 μSv/y recommended by UNSCEAR. The main contributor to the annual effective dose was 238U. The excess lifetime cancer risk (ELCR) results were below the permissible threshold of 10−3 for radiological risks. Furthermore, average concentrations of Zn and Mn were higher than WHO/FAO recommended values, and carcinogenic Pb, Cd, Cr, and Hg concentrations were below detection limits, according to heavy metal analysis performed by AAS. The average concentration of heavy metals in all of the seafood samples under investigation was in the order of Zn > Fe > Mn. The calculated hazard index (HI), target hazard quotient (THQ), and estimated daily intake (EDI) were compared to the permissible safety limits.

The study assessed the quality of four different edible fats and oils using standard analytical techniques. The presence of potentially toxic elements was determined using atomic absorption spectrometry. This study reveals that edible oils function admirably in terms of physical traits such as moisture content, boiling point, melting point, density, and specific gravity. Some edible fats and oils exceeded the standard limit of moisture, acid value, and peroxide value and these values were found in the range of 0.120–0.760 %, 0.220–2.45 mg KOH/g, and 1.23–21.7 meq/kg respectively. The iodine value for fats showed satisfactory results but for oils observed lower than the standard value varied from 68.2 to 104 g/100 g. The results of saponification value for most of the oils and fats were found satisfactory but others were lower than recommended limits and detected results were in the range of 167–224 mg KOH/g. Trace metals viz. Fe, Mn, Ni, Pb, Cd, Cu, and Co were measured in all samples and the concentration ranged from 0.070 to 47.0, 0.120–2.44, 0.540–27.1, 0.030–1.87, 0.010–4.63 and 0.060–8.39 ppm for iron, manganese, nickel, lead, copper, and cobalt respectively. The study found high levels of Fe, Mn, Ni, Cu, and Co in edible fats and oils in Bangladesh. No Cd was found, and Pb was not present in over half of the samples, which included the majority of mustard oils. The levels of Fe and Ni were higher than advised, but there was no discernible toxicological danger from Cd or Pb. The results of the health risk assessment indicated that there was no risk to children's health and possible hazards to adults' health.

Penelitian ini bertujuan untuk mengoptimalkan klasifikasi Decision Tree menggunakan teknik pruning untuk mengurangi overfitting pada dataset penyakit jantung Kaggle. Overfitting adalah masalah umum dalam pembelajaran mesin, ketika model terlalu cocok dengan data pelatihan dan kehilangan kemampuannya untuk menggeneralisasi data baru dengan baik. Teknik pruning, termasuk prepruning dan postpruning, diterapkan untuk membatasi kompleksitas model dan meningkatkan kemampuannya dalam mengklasifikasikan data baru. Hasilnya menunjukkan bahwa model dengan postpruning memiliki performa terbaik, dengan akurasi 0,8841, recall 0,8571, presisi 0,8571, dan skor F1 0,8571. Sebagai perbandingan, model dengan prepruning memiliki akurasi sebesar 0,8333, recall sebesar 0,8304, presisi sebesar 0,8304, dan skor F1 sebesar 0,7434. Peningkatan metrik ini menegaskan bahwa postpruning lebih efektif dalam mengurangi overfitting dan meningkatkan kemampuan generalisasi model. Dengan demikian, teknik postpruning dapat dianggap sebagai metode unggulan dalam mengoptimalkan kinerja Decision Tree Classifier untuk klasifikasi penyakit jantung. Penelitian ini diharapkan dapat berkontribusi pada pengembangan model prediksi yang lebih akurat dalam diagnosis penyakit jantung, sehingga membantu upaya pencegahan dan pengobatan yang lebih baik. Kata Kunci: Decision Tree, Pruning, Prepruning, Postpruning, Overfitting, Heart Disease Dataset, Kaggle, Machine Learning, Classification, Model Optimization.

Background: Hepatocarcinogenesis is a multifactorial process that arises from a integration of genetic and epigenetic anomalies leading to abnormal gene expression and function. It is difficult to characterize HCC with a single biomarker. Our study aimed at detecting the expression of a panel of 8 methylated genes (SOCS1, APC, Gadd45b, CDKN1B, P15, PAX6, STAT1 and MSH2) as regulatory factors among Egyptian patients with HCC. Methods: This study was conducted on HCC tissue samples of 30 Egyptian patients in comparison with their non-cancerous adjacent cirrhotic tissue as a control. Tissue samples were obtained from patients who have undergone living donor liver transplantation (LDLT) or liver resection at El Sahel Teaching Hospital (Cairo, Egypt). A special Custom designed PCR Arrays was used to analyze the expression profiles of chosen methylated genes associated with HCC. Results: Expression of SOCS1, APC, Gadd45b, CDKN1B, P15, PAX6, STAT1 and MSH2 were lower in the HCC tissue compared to the cirrhotic tissue (pvalue = 0.015, 0.081, 0.004, 0.027, 0.211, 0.015, 0.025 and 0.0001 respectively). 5 genes (SOCS1, APC, GAdd45b, CDKN1B, and MSH2) showed the ability to be used as diagnostic biomarkers for HCC with high sensitivity and specificity values at cut off values: 1.05, 1.17, 0.995, 0.546, and 0.125 respectively. As for the other 3 genes (P15, PAX6, STAT1), PAX6 gene has the highest sensitivity at a cut off value of 0.3364. A significant negative correlation was shown between alpha fetoprotein (AFP) and 5 of the studied genes (SOCS1, APC, Gadd45b, STAT1, and MSH2). Conclusions: Expression of the selected hypermethylated genes (SOCS1, APC, Gadd45b, CDKN1B, P15, PAX6, STAT1 and MSH2) in HCC tissue samples was lower than adjacent tissue. Their role should be further studied to solve the mystery that surrounds the pathogenesis of HCC.