ElSohly Laboratories (United States)

The article outlines the conceptual foundations of a unified system-synergetic theory of information, which represents a significant step forward compared to classical approaches. This theory is the result of a formal synthesis of two leading Russian scientific schools: the synergetic theory of information, which describes information through the prism of dynamic self-organization processes, and the systems theory of information, which defines information through the structural-hierarchical and emergent properties of systems. Unlike classical theory, which views information as a measure of diversity within a set of unrelated elements, the proposed approach introduces the concept of a «system», where interconnections between elements play a pivotal role. This allows for the quantitative measurement of such previously purely qualitative concepts as complexity and emergence. The central result of the theory is the formulation of the universal information variational principle. This principle postulates that the development of any open system — from physical and biological to economic and social — occurs in such a way as to maximize the rate of information increment. It is proposed that this principle be viewed as one of the key regularities of evolution, determining the direction of system development toward increasing complexity and order. The theory offers specific metrics for measuring systemicity and complexity, such as the coefficient of emergence, which indicates how many times the information capacity of a system exceeds the information capacity of a simple set of its elements. In conclusion, this work offers an approach to overcoming a number of limitations inherent in classical information theory and formulates a fruitful program for future research. It opens new horizons for understanding and modeling complex systems by offering a unified explanatory mechanism for a wide range of phenomena. Practical applications of the theory include the development of new methods for big data analysis, the creation of more adaptive and self-learning artificial intelligence systems, and the forecasting of market and social network development. Thus, the proposed theory lays the foundations for the creation of a unified science of complexity, uniting the efforts of scientists from various fields.

Chronic Kidney Disease (CKD) remains a major contributor to global morbidity, often progressing unnoticed until advanced stages when treatment options become limited and costly. Recent advances in deep learning have reshaped early CKD assessment by enabling the analysis of complex imaging, clinical, and longitudinal laboratory datasets. This mini-review synthesizes findings from studies published between 2020 and 2025, highlighting models that report diagnostic accuracies ranging from 88% to 99.96%, AUC values reaching 0.93, and ensemble architectures capable of forecasting CKD 6 to12 months before clinical diagnosis with up to 99.31% accuracy. These systems spanning Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), hybrid CNN–LSTM designs, and transfer-learning frameworks have demonstrated clear advantages over conventional diagnostic markers such as serum creatinine and eGFR. Despite impressive numerical performance, key limitations persist: class imbalance in early-stage CKD, restricted generalizability due to single-centre datasets, variability in imaging quality, and the limited interpretability of high-capacity neural networks. As deep learning continues to advance, robust external validation, transparent model explanations, and multi-institutional datasets will be essential to support safe and reliable clinical integration.

Introduction: Intraocular lens (IOL) mislabeling is an exceptionally rare yet consequential manufacturing error. In this case, it was identified as the cause of a significant postoperative myopic shift. The diagnostic approach, integrating anterior segment optical coherence tomography (AS-OCT) and ex vivo lens analysis, proved essential in reconciling the discrepancy between predicted and observed refractive outcomes. Patient and Clinical Findings: A 57-year-old highly myopic patient with a history of retinal detachment in the right eye underwent uneventful cataract surgery with the implantation of a +6.00 diopters (D) enhanced monofocal IOL (TECNIS Eyhance) in the right eye. Postoperatively, the patient exhibited an unexpected refraction of −13.00 D. Diagnosis, Intervention, and Outcomes: Surgical records confirmed correct IOL selection and implantation. AS-OCT imaging revealed a steeper optic profile than that expected for a low-diopter lens. Optical modeling estimated the lens power to be +18.43 D. The lens was explanted, and ex vivo lensometry confirmed its true power to be +21.00 D. Implantation of a correctly labeled +6.00 D IOL restored the intended refractive target, achieving a corrected distance visual acuity of logMAR 0.0. Conclusions: Although this case involved an extreme refractive surprise, IOL mislabeling should be considered in any unexpected postoperative refractive outcome, particularly when clinical findings and surgical records do not align. Advanced imaging and optical analyses may be key to confirming the diagnosis.

This article provides a critical survey of the essential considerations for applying deep learning models within the financial domain, particularly in asset management. While deep learning has shown immense promise, its direct application is hindered by formidable challenges unique to finance, including low signal-to-noise ratios, pervasive nonstationarity in time series data, and the adversarial and adaptive nature of markets where discovered patterns quickly decay. This work serves as a guide for both researchers and practitioners, navigating the gap between the idealized performance reported in academic studies and the significant hurdles to robust, real-world deployment. Rather than developing a rigid classification or forecasting system, the authors survey the crucial points to consider across the entire deep learning pipeline. They begin by examining the inherent model–data mismatch that arises when standard architectures such as CNNs, RNNs, and transformers are applied to financial time series. They then review key application areas not to provide an exhaustive list, but to highlight the domain-specific adaptations and practical trade-offs required for success. The survey further addresses a suite of practical considerations often overlooked in theoretical research, including the challenges of data acquisition and quality, rigorous model validation to prevent overfitting, the impact of transaction costs, and the operational hurdles of integrating these technologies into legacy systems. Finally, the authors outline a forward-looking perspective on the most significant open challenges and argue that the future of the field depends on moving beyond a narrow focus on predictive accuracy toward a more holistic emphasis on causality, robustness, and economic grounding.

Cannabidiol (CBD) is a major phytocannabinoid from Cannabis sativa. It is currently widely available and widely used in the USA, but despite its rapid progress to market, the pharmacology and toxicology of both CBD and cannabidiol-rich cannabis extracts (CRCE) remain largely unknown. The goals of this study were to investigate the potential of a novel human microphysiological system to emulate CRCE-induced hepatotoxicity and pharmacological properties demonstrated in animal models. For this purpose, C57BL6/J male mice were subjected to dosing with either 0, 61.5, 184.5, or 615 mg/kg of CRCE for 10 days. The liver-on-chip system, incorporating human primary hepatocytes, sinusoidal endothelial cells, as well as Kupffer and stellate cells was subjected to 0, 300, 1,200, or 4,400 ng/mL of CRCE (8 h exposure followed by 16 h washout) for 5 days. Administration of CRCE in mice resulted in nearly 4-fold elevations of plasma ALT at 615 mg/kg (p < 0.01) and a dose-dependent decrease in intrahepatic miR-122. Elevated levels of ALT, paralleled by decreased intrahepatic and increased effluent levels of miR-122, were also observed in the liver-on-chip, although these results were not statistically significant. Exposure to CRCE resulted in a robust and dose-dependent induction of key cytochrome P450 enzymes, namely Cyp1a2, Cyp2b6 (CYP2B10), Cyp2e1, and Cyp2c9 (CYP2C19) in both mouse livers and liver-on-chip. The results of this study demonstrate the congruence between the responses observed in mouse and human liver-on-chip experimental systems and provide evidence of the potential microphysiological systems hold for translating animal data into clinical practice.

Nos centraremos en las realizaciones de la vibrante en posición posnuclear de hablantes de nivel de instrucción bajo, analizando asimismo la

Peer review, a cornerstone of scholarly communications, is often acknowledged as a vital yet flawed process in academia. Among its well-known limitations—sluggishness, wastefulness, inconsistency and bias—its lack of transparency has arguably most impeded efforts to reform research assessment and promote open science. The recent boom in preprints, however, may now provide the impetus and opportunity to reimagine peer review for the better. eLife’s new ""preprint-first"" publishing model is a response to this challenge and opportunity. Since January 2023, the open-access journal has eliminated ""accept/reject"" decisions after peer review. Instead, they’ve redesigned their processes to provide public assessments for every preprint reviewed. In doing so, peer review at eLife makes better use of the peer reviewers’ time and can offer readers an understanding of a specific work's strengths and weaknesses, much more nuanced and representative than any individual metric could ever hope to provide. Drawing on data and lessons from the first year of the new model, this presentation will delve into the transformative potential of preprints and reimagined peer review to positively influence both career development opportunities and diversity within the researcher community. The talk made the case that the remaining barrier to the wider adoption of new models of transparent peer review is not technological but cultural. Transforming research culture for the better now hinges on uptake by other publishers, as well as support from institutions and funders. While this kind of change will take time, the potential resulting shift in research culture could see academia finally break free from its obsession with journal titles and impact factors, enabling researchers to be judged on what they publish and not where they publish.

Fonds Bohumil Holas, bobine 16mm E203097 « Haute-Volta », 1971©Andrès_Diaz_2024.The audiovisual collection of the Musée du quai Branly media library preserves the amateur films of Bohumil Holas (1909–1979), the former director of the Abidjan Museum (Ivory Coast). These unique reels were shown to the public for the first time at the Jean Rouch International Festival of 2023. Their screening raised the question of private film stocks in museum collections: what heritage value should be assigne...