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Devant son souhait de poursuivre l’entreprise de Pierre Boaistuau, en répondant aux goûts du public de l’époque, François de Belleforest ne se contente pas de traduire les nouvelles de l’Italien Bandello ; il produit une oeuvre d’une grande originalité, en privilégiant la portée exemplaire de ses récits. Paradoxalement, tout en affichant ses préoccupations morales, l’auteur met en scène des personnages emportés par des passions destructrices et complètement gouvernés par le mal. Dans cette étude, on montrera que le conteur opte pour l’authenticité de la représentation, en puisant ses histoires tragiques dans l’actualité ou dans l’Histoire. En outre, il semble primordial, tout en s’interrogeant sur l’efficacité du projet d’édification du lecteur, de mettre en relief l’aspect novateur de l’oeuvre narrative de Belleforest.

Hikmat Abdel-Razeq,1,2 Baha Sharaf,1 Suhaib Khater,1 Huda Jafar Baidoun,2 Hira Bani Hani,1 Ayat Taqash,3 Osama El Khatib,1 Sarah Edaily,1 Mahmoud Abunasser,1 Faris Tamimi,1 Yosra Nabeel Al-Masri,1 Tamer Moh’d Waleed Al-Batsh,1 Anas Zayed,1 Tala Ghatasheh,1 Tala Radaideh1 1Department of Internal Medicine, Section of Hematology and Medical Oncology, King Hussein Cancer Center, Amman, Jordan; 2School of Medicine, the University of Jordan, Amman, Jordan; 3Office of Scientific Affairs and Research. King Hussein Cancer Center, Amman, JordanCorrespondence: Hikmat Abdel-Razeq, Department of Internal Medicine, Section of Hematology and Medical OncologyKing Hussein Cancer Center, 202 Queen Rania Al Abdullah Street, P.O. Box: 1269, Amman, 11941, Jordan, Tel: +962-6 5300460, Email habdelrazeq@khcc.joBackground: Cyclin-dependent kinase (CDK) 4/6 inhibitors have revolutionized the treatment landscape of hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2 −) metastatic breast cancer (MBC). Here, we present the real-world clinical outcomes and toxicity data of patients treated at a single cancer center.Methods: A retrospective analysis was conducted on patients with HR+/HER2− MBC treated with ribociclib plus endocrine therapy (ET). Outcomes measured included progression-free survival (PFS), overall survival (OS), and adverse events.Results: A total of 356 patients (median age 52, range 27– 91 years) were enrolled, all with metastatic disease; 204 (57.5%) had de novo metastasis, and 183 (51.4%) had visceral metastasis. Ribociclib was combined with aromatase inhibitors in 321 patients (90.2%) and with fulvestrant in 35 patients (9.8%). Dose reduction was needed in 101 patients (28.4%), primarily due to neutropenia (21.3%) and abnormal liver enzymes (5.9%). After a median follow-up of 36.3 months, median PFS was 27.3 months (95% CI: 21.3– 31.7). PFS was significantly better in patients receiving ribociclib as first-line therapy (32.1 months, 95% CI: 27.7– 42.1, p < 0.0001) and those with non-visceral metastasis (38.6 months, 95% CI: 29.8–NR, p < 0.0001). Similarly, OS was significantly better in first-line treatment (48.6 months, 95% CI: 39.1–NR) and non-visceral metastasis cases (NR, 95% CI: 40.6–NR, p < 0.0001). No significant differences in 3-year PFS and OS were found between patients with and without dose reductions.Conclusion: In real-world settings, and away from the stringency of controlled clinical trials, endocrine therapy in combination with ribociclib in patients with HR-positive/HER2-negative MBC is an effective and well-tolerated therapy with a manageable toxicity profile and a low drug discontinuation rate. Dose reduction due to toxicity did not worsen the outcome.Keywords: metastatic breast cancer, MBC, CDK4/6 inhibitors, ribociclib, aromatase inhibitors, fulvestrant

Abstract Salinity stress negatively affects the growth and yield of crops worldwide. Onion (Allium cepa L.) is moderately sensitive to salinity. Beneficial microorganisms can potentially confer salinity tolerance. This study investigated the effects of endomycorrhizal fungi (M), Pseudomonas putida (Ps) and their combination (MPs) on onion growth under control (0 ppm), moderate (2000 ppm) and high (4000 ppm) NaCl salinity levels. A pot experiment was conducted with sandy loam soil and onion cultivar Giza 20. Results showed that salinity reduced growth attributes, leaf pigments, biomass and bulb yield while increasing oxidative stress markers. However, individual or combined inoculations significantly increased plant height, bulb diameter and biomass production compared to uninoculated plants under saline conditions. MPs treatment provided the highest stimulation, followed by Pseudomonas and mycorrhizae alone. Overall, dual microbial inoculation showed synergistic interaction, conferring maximum benefits for onion growth, bulbing through integrated physiological and biochemical processes under salinity. Bulb yield showed 3.5, 36 and 83% increase over control at 0, 2000 and 4000 ppm salinity, respectively. In conclusion, combined application of mycorrhizal-Pseudomonas inoculations (MPs) effectively mitigate salinity stress. This approach serves as a promising biotechnology for ensuring sustainable onion productivity under saline conditions.

Abstract Background Abamectin (ABA) is considered a powerful insecticidal and anthelmintic agent. It is an intracellular product of Streptomyces avermitilis; is synthesized through complicated pathways and can then be extracted from mycelial by methanol extraction. ABA serves as a biological control substance against the root-knot nematode Meloidogyne incognita. This investigation is intended to reach a new strain of S. avermitilis capable of producing ABA effectively. Results Among the sixty actinobacterial isolates, Streptomyces St.53 isolate was chosen for its superior nematicidal effectiveness. The mycelial-methanol extract of isolate St.53 exhibited a maximum in vitro mortality of 100% in one day. In the greenhouse experiment, the mycelial-methanol extract demonstrated, for the second-stage juveniles (J2s), 75.69% nematode reduction and 0.84 reproduction rate (Rr) while for the second-stage juveniles (J2s), the culture suspension demonstrated 75.38% nematode reduction and 0.80 reproduction rate (Rr). Molecular identification for St.53 was performed using 16 S rRNA gene analysis and recorded in NCBI Genbank as S. avermitilis MICNEMA2022 with accession number (OP108264.1). LC-MS was utilized to detect and identify abamectin in extracts while HPLC analysis was carried out for quantitative determination. Both abamectin B1a and abamectin B1b were produced and detected at retention times of 4.572 and 3.890 min respectively. Conclusion Streptomyces avermitilis MICNEMA2022 proved to be an effective source for producing abamectin as a biorational agent for integrated nematode management.

Abstract Background The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and accuracy remains a significant challenge due to variations in data recording and reporting standards. Methods We developed lab2clean, a novel algorithm aimed at automating and standardizing the cleaning of retrospective clinical laboratory results data. lab2clean was implemented as two R functions specifically designed to enhance data conformance and plausibility by standardizing result formats and validating result values. The functionality and performance of the algorithm were evaluated using two extensive electronic medical record (EMR) databases, encompassing various clinical settings. Results lab2clean effectively reduced the variability of laboratory results and identified potentially erroneous records. Upon deployment, it demonstrated effective and fast standardization and validation of substantial laboratory data records. The evaluation highlighted significant improvements in the conformance and plausibility of lab results, confirming the algorithm’s efficacy in handling large-scale data sets. Conclusions lab2clean addresses the challenge of preprocessing and cleaning clinical laboratory data, a critical step in ensuring high-quality data for research outcomes. It offers a straightforward, efficient tool for researchers, improving the quality of clinical laboratory data, a major portion of healthcare data. Thereby, enhancing the reliability and reproducibility of clinical research outcomes and clinical machine learning models. Future developments aim to broaden its functionality and accessibility, solidifying its vital role in healthcare data management. Graphical Abstract

Abstract Introduction Landslides pose significant geological hazards, necessitating advanced prediction techniques to protect vulnerable populations. Research Gap Reviewing landslide time series analysis predictions is found to be missing despite the availability of numerous reviews. Methodology Therefore, this paper systematically reviews time series analysis in landslide prediction, focusing on physically based causative models, highlighting data preparation, model selection, optimizations, and evaluations. Key Findings The review shows that deep learning, particularly the long-short-term memory (LSTM) model, outperforms traditional methods. However, the effectiveness of these models hinges on meticulous data preparation and model optimization. Significance While the existing literature offers valuable insights, we identify key areas for future research, including the impact of data frequency and the integration of subsurface characteristics in prediction models.

Non-coding RNAs (ncRNAs) are a diverse group of functional RNA molecules that lack the ability to code for proteins. Despite missing this traditional role, ncRNAs have emerged as crucial regulators of various biological processes and have been implicated in the development and progression of many diseases, including cancer. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two prominent classes of ncRNAs that have emerged as key players in cancer pathophysiology. In particular, miR-21 has been reported to exhibit oncogenic roles in various forms of human cancer, including prostate, breast, lung, and colorectal cancer. In this context, miR-21 overexpression is closely associated with tumor proliferation, growth, invasion, angiogenesis, and chemoresistance, whereas miR-21 inactivation is linked to the regression of most tumor-related processes. Accordingly, miR-21 is a crucial modulator of various canonical oncogenic pathways such as PTEN/PI3K/Akt, Wnt/β-catenin, STAT, p53, MMP2, and MMP9. Moreover, interplays between lncRNA and miRNA further complicate the regulatory mechanisms underlying tumor development and progression. In this regard, several lncRNAs have been found to interact with miR-21 and, by functioning as competitive endogenous RNAs (ceRNAs) or miRNA sponges, can modulate cancer tumorigenesis. This work presents and discusses recent findings highlighting the roles and pathophysiological implications of the miR-21-lncRNA regulatory axis in cancer occurrence, development, and progression. The data collected indicate that specific lncRNAs, such as MEG3, CASC2, and GAS5, are strongly associated with miR-21 in various types of cancer, including gastric, cervical, lung, and glioma. Indeed, these lncRNAs are well-known tumor suppressors and are commonly downregulated in different types of tumors. Conversely, by modulating various mechanisms and oncogenic signaling pathways, their overexpression has been linked with preventing tumor formation and development. This review highlights the significance of these regulatory pathways in cancer and their potential for use in cancer therapy as diagnostic and prognostic markers.

We present a novel framework for introducing generalized 3-variable 1-parameter Hermite-based Appell polynomials. These polynomials are characterized by generating function, series definition, and determinant definition, elucidating their fundamental properties. Moreover, utilizing a factorization method, we established recurrence relations, shift operators, and various differential equations, including differential, integrodifferential, and partial differential equations. Special attention is given to exploring the specific cases of 3-variable 1-parameter generalized Hermite-based Bernoulli, Euler, and Genocchi polynomials, offering insights into their unique features and applications.

Abstract Post-transplantation primary central nervous system lymphoma (PT-PCNSL) is a rare neoplasm that occurs in immunocompromised patients. It can manifest months or years after transplantation, presenting with various neurological symptoms. A 64-year-old woman, who had received a liver transplant due to autoimmune hepatitis, presented with generalized weakness, headache, and confusion. Further investigation revealed multiple ring-enhancing lesions in the right frontal and temporoparietal regions on brain MRI. A brain biopsy confirmed the diagnosis of PT-PCNSL. This case underscores the importance of considering PT-PCNSL in the differential diagnosis of contrast-enhancing brain lesions in post-transplant patients. Timely recognition of PT-PCNSL is crucial for appropriate management and improved outcomes. To the best of our knowledge, this report describes the first instance of isolated CNS lymphoma in a liver transplant recipient, due to autoimmune hepatitis, successfully brought to complete remission with a rituximab-methotrexate regimen.

Abstract The current study introduces groundbreaking insights into how organic matter (OM) of the black phosphate (RB-Ph) uniquely influences phosphorus (P) solubility during acetic acid (AA) leaching, expanding our understanding in this crucial area. To highlight such role, the OM of the RB-Ph was treated separately by different procedures including calcination at 550 ℃/4 h (CB-Ph), 30% hydrogen peroxide (HB-Ph) and intensive grinding to nano-sizes (NB-Ph). The mineralogical, chemical and morphological characteristics of phosphatic and non-phosphatic components of these phosphatic materials were carefully examined pre- and post-treatment via different techniques. The P dissolution of the precursor RB-Ph and its modified derivatives all over the applied experimental parameters traced the following trend: NB-Ph > RB-Ph > CB-Ph > HB-Ph. Intensive grinding to nanoscale resulted in amorphous components with conspicuous OM content (TOC, 0.410%), significantly enhanced P dissolution rate of NB-Ph (730–980 ppm), despite the noticeable reduction in its P2O5 content to 22.34 wt.%. The precursor RB-Ph, thanks to its high OM content (TOC, 0.543%), also displayed a sufficient P dissolution rate (470–750 ppm) compared to the two other modified derivatives, CB-Ph (410–700 ppm) and HB-Ph (130–610 ppm). Such deep and conspicuous impact of OM on P solubility can be tied to their decomposition, releasing not only organic acids but also the adsorbed P by the OM’s surficial binding sites to the solution. Finally, the optimum conditions of P leaching were attained at 2:1 acid/solid (w/w) ratio and 2 h of retention time of all investigated samples.

Abstract The failure of water pipes in Water Distribution Networks (WDNs) is associated with environmental, economic, and social consequences. It is essential to mitigate these failures by analyzing the historical data of WDNs. The extant literature regarding water pipe failure analysis is limited by the absence of a systematic selection of significant factors influencing water pipe failure and eliminating the bias associated with the frequency distribution of the historical data. Hence, this study presents a new framework to address the existing limitations. The framework consists of two algorithms for categorical and numerical factors influencing pipe failure. The algorithms are employed to check the relevance between the pipe’s failure and frequency distributions in order to select the most significant factors. The framework is applied to Hong Kong WDN, selecting 10 out of 21 as significant factors influencing water pipe failure. The likelihood feature method and Bayes’ theorem are applied to estimate failure probability due to the pipe materials and the factors. The results indicate that galvanized iron and polyethylene pipes are the most susceptible to failure in the WDN. The proposed framework enables decision-makers in the water infrastructure industry to effectively prioritize their networks’ most significant failure factors and allocate resources accordingly.