This study, utilizing data from NHANES 2017-2018, sought to explore the association of mercury exposure with allostatic load and cardiovascular disease risk among a representative sample of the United States noninstitutionalized civilian population, involving 9,254 participants. The investigation provided critical insights including statistically significant relationships between mercury levels and diastolic blood pressure, total cholesterol and LDL Cholesterol in linear regression and triglycerides in logistic regression. These findings highlight the potential influence of environmental factors on cardiovascular health and the importance of integrating such factors into cardiovascular disease prevention and management strategies.

This study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA’s Land Information System (LIS). Soil moisture and crop biomass simulations from AquaCrop were updated using Sentinel-1 synthetic aperture radar (SAR) observations, over three regions in Europe. This was done in two separate DA experiments. The first experiment concerned updating soil moisture using VV-polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH-polarized backscatter. The assimilation showed mixed results, with slight overall improvements in the (anomaly) correlations of soil moisture simulations relative to independent satellite and in situ data. By contrast, the biomass estimates obtained with backscatter data assimilation did not agree better with reference datasets from independent optical and microwave satellite data. Given the complex backscatter response over agricultural areas, more research is required to optimally perform DA to improve crop growth estimation.

Gas extraction from the Groningen gas field resulted in significant induced seismicity. We analyze the magnitude-frequency distribution of these earthquakes in space, time and in view of stress changes calculated based on gas production and reservoir properties. Previous studies suggested variations related to reservoir geometry and decreasing b-value with increasing Coulomb stress. While we confirm the spatial variations, we do not detect a clear sensitivity of b-value to Coulomb stress. However, we find that b-value correlates positively with Coulomb stress rate. This correlation is statistically significant and robust to uncertainties related to stress calculation. This study thus points to a possible influence of stress rate on the magnitude probability of induced earthquakes.

AbstractThis project focuses on enhancing fault detection and location on a 240V distribution network to minimize power outages. It introduces a system consisting of a main unit mounted on the distribution line, interfaced with a GSM SIM800L model to relay fault information to a cloud service acting as the substation. The main unit, equipped with current and voltage sensors, gathers real-time data and analyzes it to detect faults. In case of an overcurrent fault, additional parameters such as the fault’s location relative to the main unit are reported. A hardware implementation confirmed the system’s ability to identify overcurrent faults and estimate their location on the distribution line. It features a liquid crystal display (LCD) to show voltage, current, and line status, along with a buzzer to alert of detected faults.IntroductionPower systems encompass power generation, transmission, and distribution systems. Distribution systems play a vital role in transmitting power from generating points to distribution points [1]. However, distribution lines incur significant losses compared to other power system classes. The vulnerability of modern power infrastructure to various natural and malicious events affects overall performance and stability. With growing demand for power, faults in distribution lines remain a challenge, affecting supply to consumers. Traditional fault detection methods are slow and laborious, relying on circuit indicators and manual processes [2]. Engineers face difficulty in precisely locating faults, necessitating a shift towards automated fault detection systems interfaced with high-performance data communication networks. This modern approach aims to expedite fault detection, reduce downtime, and ensure uninterrupted power supply to consumers amidst increasing demand and extensive distribution line networks [1]. Problem StatementThe demand for power across various sectors in Ghana is rapidly increasing, leading to a corresponding rise in problems associated with distribution lines. Detecting the exact location of faults in these lines has proven challenging, resulting in prolonged downtime and damage to infrastructure [3]. To address this issue, a reliable and efficient fault detection system is essential to maintain power system stability. This project introduces an Internet of Things (IoT) based fault detection and location system designed to accurately detect and locate faults in distribution lines. By optimizing performance procedures, this system aims to preserve the lifespan of distribution lines, protect household devices, and reduce the frequency of power outages experienced by consumers [3]. Implementing an IoT-based fault detection system, integrated with a GSM model, will address the crucial challenge of delivering real-time information promptly. This system will send overcurrent fault status or normal operation status in real-time to a cloud service, including information about the faulted side of the distribution system. By eliminating the need for manual fault identification processes, engineers can avoid trial-and-error methods, leading to quicker fault resolution. Ultimately, reducing the duration of power outages endured by consumers.ObjectiveThe below outline the objectives of this project:1.      To detect and locate over-current faults in distribution lines with the implementation of an IoT mounted system.2.      To intimate fault information to a cloud service.3.      To protect loads from the effects of over-current by disconnecting the loads from the faulted line.Scope of WorkImplementing an IoT-based fault detection system, integrated with a GSM model, will address the crucial challenge of delivering real-time information promptly. This system will send overcurrent fault status or normal operation status in real-time to a cloud service, including information about the faulted side of the distribution system. By eliminating the need for manual fault identification processes, engineers can avoid trial-and-error methods, leading to quicker fault resolution. Ultimately, reducing the duration of power outages endured by consumers.Literature ReviewIn [4], an algorithm was presented for detecting single-phase ground faults (SPGF) in resonant ground (RG) systems. This method combines a neutral voltage displacement technique with a mathematical morphology algorithm to identify SPGF. By comparing pre-fault and post-fault voltages and analyzing the slope of neutral voltage displacement, faulty phases and feeders can be determined. This technique successfully localized faults on long feeders and detected faults within 15ms using local voltage and current information.The paper described in [5] proposes a Fault Detection and Localization (FDL) system comprising a master device located in a control room and slave devices installed on poles for fault detection. The master device communicates with slave devices via GSM using short message service (SMS). Equipped with a graphical user interface (GUI), the master device consists of a personal computer with a modem to facilitate communication.The equipment described in [6] consists of a transmitter and a current transformer (CT) detector designed for fault detection. Signals detected by the CT detector are transmitted to the distribution lines, while current signals are monitored through the fault loop. If a signal is detected and relayed to the transmitter, it indicates an earth fault in the distribution line; otherwise, no earth fault is present. When the CT detector identifies a fault, both an LED and a buzzer are activated to emit light and sound respectively, signalling an earth fault. By gradually determining the fault location using this principle, the faulty feeder can be successfully detected.The method discussed in [7] determines the fault position by analyzing the voltage and current of the faulted line, relying on the impedance of the distribution line per unit length. Two impedance-based methods are employed: the single-ended impedance method and the double-ended impedance method. The single-ended method offers a simple and rapid fault location process without requiring extensive communication. It finds applications in power systems due to its solid sequence value and high resistance to faults, despite challenges posed by non-homogeneous loads and tapping.Electric Faults in AC Distribution SystemsA fault-free electrical system is an ideal that does not exist in reality due to the inevitability of electrical faults. These faults, including overcurrent, over-voltages, and short circuits, pose significant threats to power systems’ continuous operation. Overcurrent faults, in particular, involve abnormally high currents flowing through conductors due to various causes such as mechanical effects, insulation failures, or atmospheric conditions. These currents can lead to thermal damage and mechanical stresses on system components, resulting in irreversible damage and safety hazards. Protective systems comprising relays and automatic circuit breakers are crucial for detecting faults and isolating faulted parts to minimize service disruptions. Planning and operating power systems must account for short circuits by estimating worst-case currents to design system components to withstand overcurrent and define circuit breaker breaking capacities. Adjusting relay settings ensures efficient detection of fault conditions, highlighting the importance of protective devices in maintaining power system reliability [3]. Brief Introduction of Proposed WorkThis project proposes a technique for recording voltage and current values of a distribution line using voltage and current sensors respectively. A predetermined threshold current value is set, and if the measured current exceeds this threshold, the microcontroller classifies the fault as an overcurrent fault, triggering a buzzer to indicate its presence. If the measured current falls within a specified range, the system is classified as being in a normal operating state. Additionally, the project involves interfacing a microcontroller with a GSM SIM800L module, which will be embedded in the circuitry to send the distribution line’s state to a cloud service. The block diagram for the proposed system for fault detection, location, and protection is illustrated in Figure 2.7.METHODOLOGYThe objective of this project is to design a system for detecting overcurrent in distribution systems, comprising two main components: the fault detection system and the centralized processing system. The fault detection system, positioned at a specific distance from the distribution line, simultaneously measures current and voltage levels in real-time. This setup enables the centralized processing system to accurately estimate the side of the line where the fault occurred. When a fault occurs, the fault detection system reports abnormal current and voltage to the centralized processing system for analysis. The main unit then analyzes this data to determine the type of fault and identify the side of the line where the fault occurred. Block DiagramThe figure below shows a block diagram for the proposed system.

C'è una piccola grande Italia nascosta, borghi sparsi da Nord a Sud. Territori dove è sempre più difficile incontrare bambini che giocano e giovani che passeggiano. Il cuore dell'Italia che ha un battito sempre più lento e affaticato, ma dal temperamento determinato e tenace, con tanta voglia di riscatto. Quando si parla di codesti luoghi, lo si fa con la nostalgia del passato, ma è doveroso raccontarli con la complessità del presente e la necessità del futuro. Si potrebbe coniare un termine ricco di significato che ben sintetizzare l'essenza dei piccoli territori: "locanza". Cosa significa?

Background: Accurate and early detection of neuroblastoma (NB) metastases is an urgent requirement for clinical diagnosis and treatment. This study aimed to explore the value of a new diagnostic model combining 123I -Metaiodobenzylguanidine (MIBG) scintigraphy, minimal residual disease (MRD) examination and clinical parameters in detecting metastases of NB. Methods: A total of 76 pediatric patients (41 males and 35 females) with both 123I-MIBG scintigraphy and bone marrow (BM) MRD were retrospectively analyzed. MRD were detected using Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) targeting PHOX2B gene and disseminated NB cells in BM were detected by flow cytometry (FCM). The diagnostic efficacy was assessed by comparing 123I-MIBG scintigraphy and diagnostic model with the gold standard which included pathology, other relevant imaging examinations, and follow-up more than 18 months. The diagnostic efficacy was evaluated using the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and receiver operating characteristic (ROC) curve. The Delong test was used to evaluate the improvement in diagnostic efficacy. Results: Regarding the diagnosis of metastases, 123I-MIBG scintigraphy exhibited high diagnostic value (sensitivity: 57.5%, specificity: 94.4%, PPV: 92.0%, NPV: 66.7%, are under the curve [AUC]: 0.760, with a 95% confidence interval [CI]: 0.649-0.870). After logistic regression analysis, 123I-MIBG scintigraphy, MYCN gene amplification status, chromosome 11q23 aberration, radiotherapy, and BM-MRD based on PHOX2B gene were included in the multi-parameter diagnostic model. The model demonstrated an AUC=0.907 (95% CI: 0.834 - 0.979), with sensitivity, specificity, PPV and NPV of 82.1%, 87.9%, 88.9% and 80.6%, respectively. Conclusion: In the assessment of metastases in pediatric NB patients, the multi-parameter diagnostic model demonstrated superior diagnostic efficacy compared to 123I-MIBG scintigraphy and had the potential to be a promising tool for monitoring residual disease and diagnosing the presence of metastases.

In this letter, we proposed and experimentally demonstrated a simple and efficient design method for high-voltage Floating Field Ring (FFR). Three floating FFRs is firstly used to determine the influence of rings’ distance (d) on the voltage drops of the highest voltage to second ring (Vn+1) and the second to the first ring (Vn). Then, two voltage curves (curve for Vn+1 and Vn) based on d are drawn. Then, for a target breakdown voltage, by iteration between the curves for Vn+1 and Vn, number of FFRs and corresponding distances can be found. Based on this method, an 8.0 kV PN diode with 110 FFRs is designed and fabricated. The measured breakdown voltage is 7840 V, which reaches 93% of the designed value. This method provides a simple and efficient design for high-voltage SiC devices, especially for ultra-high voltage applications where the number of rings exceeds dozens.

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large-scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, we provide insights into the transformative potential of ML in shaping the future of power system asset management.

A document by Yang Zong. Click on the document to view its contents.

Serious concerns have been raised over safety of vaginal mesh tapes for stress urinary incontinence (SUI). Autologous rectus fascial sling and the more recent “sling on a string” through a laparotomy are gaining popularity as native tissue options for SUI. We describe a novel technique of laparoscopic mid-urethral autologous rectus fascial sling for SUI. Ten patients underwent this new technique safely. At six months, all subjects reported cure of SUI with normal voiding. The advantages of this technique include the minimal access approach, introduction of the sutures under laparoscopic guidance, and avoidance of over tightening of the sling.

Background and objective Exposure to inorganic dust and particulate matter (PM) disrupts the integrity and function of the airway epithelial barrier (AEB). Herein, the immune response and epithelial barrier integrity disruption effect of respirable ex vivo airway tissue models. Methods Particles at an average size of 1 µm, referred to as ex vivo. Computational fluid dynamics simulations were performed to assess shear stress profiles under different flow conditions. WST-1, LDH, SEM, ELISA, IF and qRT-PCR analyses were carried out to evaluate cell survivability, morphology, barrier integrity and inflammation. Results In AEB-on-a-chip platform, the exposure to PM2.5 dusts disrupted AEB integrity via decreasing cell adhesion-barrier markers such as ZO-1, Vinculin, ACE2 and CD31, impaired cell viability and increased the expression levels of proinflammatory markers; IFNs, IL-6, IL-1s, TNF-α, CD68, CD80, and Inos, mostly under dynamic conditions. Besides, decreased tissue viability, impaired tissue integrity via decreasing of Vinculin, ACE2, β-catenin and E-cadherin, and also proinflammatory response with elevated CD68, IL-1α , IL-6, IFN-Ɣ, Inos and CD80 markers, were observed after PM2.5 dust exposure in ex vivo tissue. Conclusion The disruption of the barrier integrity and subacute immune response upon silica PM2.5 exposures is nicely demonstrated by both AEB-on-a-chip platform emulating dynamic forces in the body and resident immune cells containing ex vivo bronchial biopsy slices. Airborne dust pollutants pose a significant future respiratory hazard for human airways under climate change conditions and Lung-on-a-Chip Models will serve as reliable substitutes in this context.

A document by Pinglin Cao. Click on the document to view its contents.

The swift melting of glaciers and thawing of permafrost in mountainous regions has increased the danger of rock-ice avalanches, which poses a severe threat due to their potential transition into water-saturated debris flows. The devastating event in Chamoli, India, on February 7th, 2021, exemplifies the catastrophic consequences of such cascades of processes. Developing a model capable of predicting the dynamics and extent of these events is imperative for natural hazard science and disaster mitigation. In response, we propose a depth-averaged rock-ice avalanche model encompassing four distinct materials: rock, ice, snow, and water. The model integrates crucial physical processes, including frictional heating, phase changes, ground material entrainment, and air-blast hazards. Through a system of mass and momentum balance equations extended with grain flow and internal energy equations, the model captures heat exchanges and resulting phase changes as the fragmented material flows. Focusing on identifying the primary water source in the flow and testing the model on the Chamoli event, we quantify water’s influence on flow dynamics and regime transitions. However, uncertainties persist in heat transfer physics and quantifying the hydro-meteorological state of the flow path. Our thermo-mechanical model enables the simulation of complex avalanches and identifies key flow transitions: powder cloud formation, potential debris flow transformation. The study underscores the pivotal role of water in avalanche dynamics and the challenge of accurately quantifying water content within the flow, necessitating comprehensive ground assessments for effective disaster management.

The Solar Geoengineering (SG) is an interim solution to combat global warming, which involves scattering back a tiny fraction of the incoming sunlight. Hence, SG and its potential impacts are important to study for the identification of changing weather patterns over regions of climate vulnerable South Asia. This study explores the projected spatio-temporal patterns of two meteorological parameters, temperature, and precipitation, under SG numerical experiment (stratospheric aerosol injection), relative to projected climate change. Furthermore, future projections of same meteorological parameters without SG under a representative concentration pathway (RCP 4.5) will also be studied for comparative analysis. Offsetting climate parameters are associated with multiple risk factors. Thus, Both SG and non-SG scenarios will be studied for the future time period. The results indicate that the temperature reduces by -0.62 °C under the SG G4 scenario and spatial distribution patterns of temperature also depicts an overall cooling effects during the G4 implementation (2020-2029) and continuation (2030-2069) phase. Moreover, on a regional scale, a cold bias (less severe) is projected as compared to projected climate under RCP 4.5. Our findings show that, precipitation is also projected to be decreased by -0.02 mmday-1. Dry bias pattern is projected during implementation phase only. The G4 based SG continuation and termination (2070-2090) phases depict no drastic change in precipitation over South Asia.

A document by Lilli J. Freischem. Click on the document to view its contents.

The exploration of heterostructures composed of two-dimensional (2D) transition metal dichalcogenide (TMDc) materials has garnered significant research attention due to the distinctive properties of each individual component and their phase-dependent unique properties. Using the plasma-enhanced chemical vapor deposition (PECVD) method, we analyze the fabrication of heterostructures consisting of two phases of molybdenum disulfide (MoS2) in four different cases. The initial hydrogen evolution reaction (HER) polarization curve indicates that the activity of the heterostructure MoS2 is consistent with that of the underlying MoS2, rather than the surface activity of the upper MoS2. This behavior can be attributed to an energy barrier arising from the physical contact resistance between the two different phases of MoS2 layers, which is mediated by van der Waals bonds. Remarkably, the energy barrier at the junction dissipates upon reaching a certain electrochemical potential, indicating surface activation from the top phase of MoS2 in the heterostructure. Notably, the 1T/2H MoS2 heterostructure demonstrates enhanced electrochemical stability compared to its metastable 1T-MoS2. This fundamental understanding paves the way for the creation of phase-controllable heterostructures through an experimentally viable PECVD, offering significant promise for a wide range of applications.

This is the project that criticize about the need of Ambulance. We all used ambulance whenever we are facing emergency health issue to get medical support in quick time. Despite the ambulance siren actively making emergency sound so that the cars would give way, the ambulance gets stuck. This is because people in the car are unaware of the ambulance siren due to closed windows or because they hear the siren sol late that there is no space to move the car. Another factor is that siren sound makes the patient and others more anxious contributing to increasing noise pollution. To solve this issue, we introduce a new ambulance system without siren sound. In this system all the ambulances will attach a transmitter and all the private and commercial vehicles will attach a receiver so that they can catch the signal transmitted by the ambulance. Our target is to initiate contact i.e., to send signal to a minimum range of 100 meter . This will help us to get quick response from a farther distance to move the cars away for the ambulance, also reducing sound pollution.

Species abundance distributions, meaning counts of individuals apportioned among species, are fundamental patterns in ecology. Numerous distribution models have been proposed, and most suffer from poor fit to data, complex formulation, excessive parameterisation, or unrealistic modelling of processes. I discuss three that meet all the basic criteria, are easily distinguished, and stem from simple and distinct population dynamics. The log series can be produced by assuming taxonomically and temporally fixed turnover rates. A model derived from scaled odds ratios assumes highly variable dynamics, and one derived from exponential variates assumes taxonomically variable but temporally fixed rates. Mathematical derivations are elementary. Maximum likelihood fits to published empirical data suggest that the two new distributions are more common in nature. Saturated models are rarely better. Ecological communities may be assembled by processes that are easily discerned, instead of being as mysterious as many have thought.

SummaryPrompt Programming is a new paradigm in software development, where programs are created through the use of text-based instructions, known as prompts. Instead of writing code in a programming language, you describe to the model in natural language what you want it to do. The model then generates the corresponding code. History of Prompt ProgrammingThe idea of Prompt Programming has been around for a few years, but it has only become a viable tool with the development of Large Language Models (LLMs) like GPT-3 and Bard. These LLMs are capable of understanding and generating complex natural language, making them ideal for use with Prompt Programming. Applications of Prompt ProgrammingPrompt Programming can be used in a variety of application areas, for example:Rapid prototyping: With Prompt Programming, you can quickly and easily create prototypes of software without needing to write code.Task automation: You can use prompts to automate tasks that would otherwise be laborious and time-consuming.Creation of creative content: You can use prompts to create creative content such as poems, stories, or scripts.Code explanation: You can use prompts to explain and document code. INTRODUCTION The concept of Prompt Programming has established itself as a transformative force in the world of artificial intelligence (AI), particularly in the development and application of AI language models. With the advent of advanced language models like GPT (Generative Pretrained Transformer), a new era of human-machine interaction has opened up, where the ability to control AI through targeted, text-based input prompts—known as prompts—has become central. This method of Prompt Programming allows for the immense capabilities of AI technology to be accessible in an intuitive and direct manner, which is crucial for both developers and end-users. This paper focuses on exploring this method, its applications, challenges, and the implications for the future of AI-supported interaction.Prompts act as an interface between the user and the AI, with the quality and precision of the prompt formulation directly influencing the relevance and usefulness of the AI responses. This way of interaction requires a deep understanding of both the technical mechanisms of the AI model and the contextuality and nuances of human language. Thus, Prompt Programming is not just a technical skill but also an art form that demands a high degree of creativity and insight.The increasing proliferation of AI language models in a variety of application areas—from automated customer support to content generation and decision-making support—highlights the importance of effective prompt design. This raises both ethical and practical questions, particularly regarding transparency, bias, and accountability in the generated responses. Timely programming, a concept that refers to the rapid and adaptive development of prompts, is crucial to keep pace with constant changes in technology, society, and language.This paper aims to provide a comprehensive overview of the field of Prompt Programming, discuss its key techniques, present practical examples and case studies, and finally, venture a look into the future development of this exciting area. By exploring the possibilities and limits of Prompt Programming, we can not only maximize the capacity of AI language models but also gain deeper insights into the potential and direction of human-AI collaboration. Chapter 1: Prompt ProgrammingPrompt programming is an interactive method for designing and controlling Artificial Intelligence (AI), especially AI language models, through the use of specifically formulated text-based input prompts. This technique allows for the targeted use of the diverse capabilities of AI models by forming a bridge between human intentions and the AI's ability to interpret. Through precise, creative, or strategically crafted prompts, the AI can be stimulated to deliver specific performances, responses, or creative outputs, ranging from simple information requests to generating complex content.My definition of prompt programming:Prompt programming is the craft process through which users tailor their interactions with AI systems by the artful design of text-based input prompts. It combines linguistic precision, psychological understanding, and creative thinking to achieve specific, often nuanced outputs from an AI model. This method goes beyond mere command input and views the prompt as a creative act aimed at navigating and shaping the 'thought process' of the AI model to generate accurate, relevant, and often innovative responses. Not only does this maximize the AI's capabilities, but it also establishes a new form of human-machine interaction that promotes creative and analytical exchange between humans and AI.The difference between traditional programming and prompt programming fundamentally lies in how interactions with the computer or AI system are designed and conducted. Each approach has its specific characteristics, areas of application, and advantages.Traditional Programming:Fundamental Principle: In traditional programming, developers write code in a programming language that defines specific algorithms and command sequences to perform certain tasks. The focus is on the logic and structure of the code to solve problems or perform specific functions.Interaction: Interaction with the system is indirect through the written code. The user does not directly control the system but creates programs that the machine then executes.Flexibility: Changes in the system's functionality or behavior require code adjustments and, depending on the complexity, recompilation of the program.Areas of Application: Designed for a wide range of applications, from simple scripts to complex systems like operating systems, databases, and web applications.Prompt Programming:Fundamental Principle: Prompt programming uses text-based input prompts to interact with AI systems, particularly with AI language models. Instead of writing rigid lines of code, users enter specific prompts that guide the desired behavior or response of the AI.Interaction: The interaction is direct and in natural language, making it more intuitive and accessible for non-programmers. Users can control the AI by formulating questions, commands, or instructions in natural language.Flexibility: Prompt programming allows for high flexibility and adaptability. By changing the prompts, users can generate different outputs or improve the quality of the AI's response without technical effort.Areas of Application: Especially suitable for applications requiring natural language processing, such as text generation, conversational systems, automated content summarization, and more.Advantages of Prompt Programming:1. Accessibility: It is more accessible to people without deep programming knowledge, as the interaction occurs in natural language.2. Speed: Rapid iterations and adjustments are possible by simply changing the prompt, without the need to rewrite or recompile code.3. Creativity: The method encourages creative thinking, as users can experiment with how different formulations affect the AI's responses.4. Personalization: Prompts can be easily customized to generate personalized responses tailored to the user's specific context or needs.5. Interdisciplinary Application: It offers the opportunity to use AI tools in a variety of fields, from education to art to science and technology, without requiring specific technical knowledge.Prompt programming thus represents a paradigm shift in human-machine interaction, advancing the democratization of AI technologies and opening up new possibilities for creative and interactive applications.Prompt programming changes the traditional approach to coding and programming through a more direct and intuitive interaction with AI systems, especially with advanced AI language models. Instead of classical programming languages and logic, "programming" or control here is achieved through skillfully formulated text-based inputs, the so-called prompts. This method is a form of coding that relies heavily on natural language and less on syntactic or logical programming constructs.Coding through PromptsIn prompt programming, one "codes" by designing the input prompts (prompts) in such a way that they guide the AI model to produce a certain type of response or performance. This can be considered a form of coding because one is effectively communicating with the AI model in a manner that generates specific outputs. The "programming language" in this case is natural language, enriched by a deep understanding of how the model works and how different formulations can influence its responses.Programming through GuidanceControlling an AI model through prompts can be seen as programming through guidance. Instead of formulating explicit instructions in a programming language, one guides the AI model by providing context, objectives, and sometimes even the style or structure of the desired response. By fine-tuning the prompts, one can steer the model's output in a certain direction, which is akin to a form of programming where the AI model is the "execution engine."Examples of "Programming" with Prompts 1.           Generating Specific Content: By providing an AI model with a prompt that precisely describes what one wants to generate (e.g., a blog post about renewable energy), the model is utilized to create specific content. Here, the prompt acts as a programming instruction that the AI model executes.2.           Data Preparation: Prompts can be used to instruct an AI model to convert or analyze raw data into a specific format. For example, a prompt might read: "Analyze the following dataset and provide a summary of the main trends."3.           Interactive Bots: By formulating prompts that guide a conversation model, one can create interactive bots that respond to specific user requests in a predetermined manner. Advantages and LimitationsThe advantages of this method include its flexibility, accessibility, and speed. It does not require deep technical knowledge of programming languages and allows for rapid iteration and customization. However, it requires a good understanding of how different formulations can affect the responses of the AI model, as well as awareness of the model's limitations and biases.I will offer a few illustrated examples in text format for "programming" using prompts. These examples are intended to illustrate how prompts can be used for coding to obtain specific results from an AI language model. Each example will include an Input (Prompt) and the expected Output (Response). Example 1: Generating a Business EmailInput (Prompt): "Write a professional email to a supplier explaining a delay in the delivery of a crucial product. Ask for understanding and propose a solution."Expected Output (Response): Subject: Delay in Delivery of Product XYZ – Request for Understanding and Suggested SolutionDear Sir/Madam,I hope this message finds you well. Unfortunately, we must inform you that there has been an unexpected delay in the delivery of Product XYZ. Due to [specific reason for the delay], the delivery will be delayed by [period].We are aware of the importance of this product for your processes and would like to express our deepest regret for the inconvenience caused. As a proposed solution, we offer [solution, e.g., a temporary replacement or a discount on the next order].Thank you for your understanding and patience in this matter. Please let us know how you feel about our proposal or if there are any other ways we can resolve this situation to your satisfaction.Kind regards, …Your Name… Example 2: Creating a Short PoemInput (Prompt): "Write a short poem about autumn that touches on the changing colors of the leaves and the cool air."Expected Output (Response): Autumn leaves dance in the wind, in red, yellow, and brown they swiftly form a blanket on the ground, heralding the coolness, winter's bound.The air, it wears a fresh coat, mixing scents from afar and wide, from chimneys that now again ignite, and gently warm us from inside.Thus, autumn brings its splendor forth, with colors that soften the heart, and cool air that softly laughs, until winter finally starts. Example 3: Data Analysis InstructionInput (Prompt): "Analyze the following fictional dataset for last quarter's sales figures and identify the two products with the highest sales. Explain possible reasons for their success."Expected Output (Response): After analyzing the dataset for last quarter's sales figures, the two products with the highest sales were identified: Product A and Product B. Product A saw a significant increase in sales, possibly due to a successful marketing campaign and positive customer reviews. Product B benefited from its positioning as a sustainable and eco-friendly option in its segment, aligning with current consumer trends. Both products are characterized by strong brand perception and adaptation to the needs and desires of the target audience. These examples illustrate how targeted prompts can generate specific responses and content from an AI model, akin to a coding process using natural language.Sure, here's the translation of the provided text into English: Use of Prompt Programming to Write Actual Code Let's illustrate this with some examples where the user uses prompts to generate complete and functional code: Example 1: Generating a Simple Python ScriptInput (Prompt): "Write a Python script that prints the numbers from 1 to 10 and indicates next to each number whether it is even or odd."Expected Output (Code):```pythonfor i in range(1, 11):    if i % 2 == 0:        print(f"{i} is even")    else:        print(f"{i} is odd")``` Example 2: HTML and CSS Code for a Simple WebpageInput (Prompt): "Create the HTML and CSS code for a simple webpage with a header that says 'Welcome to my website', and a paragraph under the header that explains this is a demo page. Make sure the header is centered and displayed in a large, blue font."Expected Output (Code):```html<!DOCTYPE html><html><head>    <title>Welcome Page</title>    <style>        h1 {            color: blue;            text-align: center;        }        p {            text-align: center;        }    </style></head><body>    <h1>Welcome to my Website</h1>    <p>This is a demo page.</p></body></html>``` Example 3: SQL Query to Retrieve DataInput (Prompt): "Write a SQL query that selects all entries from the 'Customers' table where the country is 'Germany', sorted by the customer's last name in ascending order."Expected Output (Code):```sqlSELECT * FROM Customers WHERE Country = 'Germany' ORDER BY LastName ASC;```These examples show how specific prompts can be used not only to generate textual content but also complete and executable code in various programming languages. The ability of AI models to respond to such requests with precise and functional code opens extensive possibilities for automating development tasks and supporting developers in code creation.Prompt Programming, in the world of artificial intelligence and especially in the context of language models, is an innovative method of interaction and programming where users use carefully formulated text-based queries or "prompts" to generate specific outputs, responses, or even executable code from an AI system. These queries serve as direct means of communication through which the user guides, informs, and requests specific tasks or problem solutions from the AI, without needing to use traditional programming languages in the conventional sense.At its core, Prompt Programming allows users to bypass the complexity of coding and instead use natural language to communicate with the AI. This method expands the accessibility of AI technologies to a broader user base that may not have technical knowledge in programming, promoting a new era of interaction with AI where the barrier between human and machine is further reduced.Prompt Programming represents a paradigm shift in the digital world that drives the democratization of technology use and lays the groundwork for future innovations in the field of artificial intelligence. Prompt Engineering and Prompt ProgrammingPrompt Engineering and Prompt Programming are two concepts often mentioned in connection with the use of artificial intelligence (AI), especially advanced language models. Although they sound similar and are closely related, they refer to different aspects of interacting with AI systems. Prompt EngineeringPrompt Engineering focuses on the art and science of formulating the most effective and precise "prompts" (text-based input prompts) to elicit the desired responses or outcomes from an AI model. It is a process of experimenting, testing, and refining how questions or commands are posed to maximize the performance of the AI model. The quality of the prompts can significantly influence the quality of the responses, and thus, Prompt Engineering is dedicated to optimizing these inputs for specific use cases or goals. It involves techniques and strategies to subtly steer the AI to produce nuanced, relevant, and accurate outputs. Prompt Programming On the other hand, Prompt Programming extends the concept of Prompt Engineering by involving the creation of more complex instructions or sequences of prompts to automate specific tasks or directly generate executable code from AI interactions. It's not just about asking the right question but also using AI to write code, process data, perform analyses, or execute other programmatic functions that go beyond simple question-and-answer interactions. Prompt Programming can be considered a method where users with little to no traditional programming experience can perform complex tasks by directing the AI through natural language instructions. Key DifferencesFocus: Prompt Engineering concentrates on optimizing the input to achieve the best possible output. Prompt Programming deals with creating instructions that prompt AI models to perform specific tasks, including generating code.Goal: The goal of Prompt Engineering is to improve the quality of interaction between human and AI. Prompt Programming aims to make AI usable for broader applications, including developing and executing programs or code.Application Area: Prompt Engineering is relevant in any context where AI models are used to answer questions or generate content. Prompt Programming opens up more specific use cases where AI is used for task automation or programming itself.In summary, both concepts play important roles in the current landscape of AI interaction and use, with Prompt Engineering laying the groundwork upon which Prompt Programming builds to enable more complex and specific applications of AI technology. Prompt Engineering: Focus on InputsIn my interpretation, the focus of prompt engineering lies on optimizing the inputs—the text-based prompts or queries given to the AI. The primary goal is to obtain effective and targeted responses from the AI through precise and strategically formulated prompts. It's a creative and analytical process that hinges on asking the AI in the best possible way to yield the desired information or outcomes. The choice of words, context, structure of the question, and clarity of the instruction play a crucial role here. Prompt Programming: Focus on OutputsFollowing my approach, prompt programming concentrates more on the outputs—that is, the results, actions, or code generated by the AI in response to the prompts. While effective inputs remain central, the emphasis here is on utilizing the AI to accomplish specific tasks, automate processes, or generate code that finds application outside the AI environment. The art of prompt programming is to design prompts that not only elicit precise responses but also action-oriented outcomes or directly usable outputs. Integrative ViewpointIn an integrative viewpoint, both approaches complement each other: Effective prompt engineering forms the basis for successful prompt programming. A deep understanding of how to elicit the desired responses from the AI through carefully constructed prompts enables more sophisticated and targeted programming tasks. Simultaneously, prompt programming requires an advanced application of prompt engineering to not just obtain answers but to compel the AI to generate outputs that can directly serve as solutions or code.This perspective underscores the significance of both competencies in dealing with advanced AI systems and highlights how through skillful input formulation (inputs), purposeful and useful results (outputs) can be achieved.In my opinion, distinguishing between the focus on inputs in prompt engineering and the focus on outputs in prompt programming offers a clear and meaningful perspective on the different goals and approaches in interacting with AI systems. This emphasis on how both areas interact to facilitate effective and useful engagements with AI is an important aspect that accentuates the complementary nature of these two disciplines. This perspective broadens the understanding of how interactions with AI technologies can be carefully designed to generate both precise answers and specific, action-oriented outcomes. Chapter 2: Text-Based Prompts as a Control MethodDefinition and Explanation of Text-Based Prompts Text-based prompts, also known as prompts, are instructions or questions formulated in natural language intended to elicit a specific response or action from an AI language model. Prompts are the primary means of interaction between the user and the model, where the quality and precision of prompt formulation directly influence the relevance and usefulness of AI responses. They can range from simple commands, like "Tell me a story," to complex queries that request the model to conduct in-depth analyses or generate creative content. How Prompts Enable and Control Interaction with AI Language ModelsPrompts serve as an interface that translates human intent into a form understandable by AI. They guide the model not just in terms of the "what" of the request—the content of the response—but also the "how," including tone, style, and structure. This enables extraordinarily flexible and nuanced interaction with AI systems, going far beyond traditional, form-based user inputs.The mechanics behind AI language models processing prompts are based on extensive training data drawn from a wide array of sources. These models, often trained on billions of words, develop a deep understanding of language and its structures. When a prompt is received, the model uses this understanding to generate a response that matches the requirements implied in the query.Examples of Effective Prompts and Their Impact on AI Model Performance1.           Precise vs. Vague Prompts: A precisely formulated prompt, like "Write an informative paragraph about the history of quantum physics," generally leads to a more direct and focused response than a vague prompt, e.g., "Tell me something about science." The precision in the prompt helps the AI model better understand the scope and depth of the expected answer.To illustrate the concept and impact of effective prompts in controlling AI language models, let's consider a fictional but realistic example demonstrating the transformation of a general prompt into a specific, targeted prompt. This is supplemented with visual representations and a detailed explanation to highlight the differences and effects on the quality of the AI model's response.Example: Creating an Informative ArticleGeneral Prompt: "Write an article."Result: The AI model generates generic text with a randomly chosen subject. This could lead to a general discourse on any topic, lacking specific information or a clear focus.Illustration: An image depicting a desk scattered with unlabeled books and papers symbolizes the disorder and lack of direction.Improved, Specific Prompt: "Write an informative article on the history of quantum physics, including key individuals and breakthroughs, for an audience without a background in physics."Result: The model delivers a well-structured, informative article summarizing the complex history of quantum physics in easily understandable language. It highlights significant scientists like Planck, Einstein, and Schrödinger and explains their contributions without using difficult technical terms.Illustration: A graphic showing a timeline of quantum physics, with portraits of the mentioned scientists and symbolic representations of their discoveries interspersed. Each element on the timeline is annotated with simple icons or brief descriptions to enhance understandability.The Power of the Specific Prompt:Targetedness: The precise instruction focuses the AI model on a specific topic, generating relevant and detailed content.Understandability: The requirement to write the article for an audience without a physics background guides the model to explain complex concepts in simple language.Engagement: Including key individuals and breakthroughs makes the article not only informative but also narratively engaging, helping to maintain the reader's interest in the topic.Visualization and Illustration as Amplification:The accompanying illustrations serve not only to visually break up the text and make it more attractive but also to make complex information more accessible and understandable. Visualizations, like the timeline of quantum physics, offer a quick overview of the subject and facilitate the understanding of historical development and key moments. They are a powerful tool to increase attention and support learning by translating abstract concepts into tangible representations.This example clearly shows how the skillful formulation of prompts and the use of complementary visualizations can significantly enhance the effectiveness of AI-generated texts. It underscores the importance of interaction between humans and AI, where exceptional results can be achieved through precise communication and creative thinking. 2. Creative Instructions: Prompts that require creative performance, such as "Invent a short story about an astronaut who lands on an unknown planet," demonstrate the capability of AI models to generate original content. The specificity and creativity of the prompt encourage a correspondingly unique response.For the second example, we consider how creative prompts can unleash the imaginative power of an AI language model to generate unique and captivating content. This time, we illustrate the process and outcomes through the development of a short story inspired by a creative prompt.Creative Prompt: "Invent a short story about an astronaut who lands on an unknown planet and discovers a hidden civilization. The story should contain elements of adventure and mystery and be told from the perspective of the astronaut."Step 1: Setting and IntroductionText: The AI model begins by describing the unknown planet, painting a vivid scene that piques curiosity about the unknown. The narrative kicks off as the astronaut, Captain Alex Rivera, steps onto the planet's surface and discovers the first signs of a hidden civilization.Illustration: An image showing the astronaut stepping out of his landing module, looking onto an exotic landscape filled with strange plants and alien structures in the distance.Step 2: Development of the PlotText: The story delves into Alex's journey of discovery, encountering mysterious artifacts that hint at the advanced technology and rich culture of the hidden civilization. His sense of wonder and curiosity drives him further into the heart of the deserted city.Illustration: A sequence of images depicting Alex navigating through a series of ancient, technologically advanced ruins, with a focus on the mysterious artifacts and hieroglyphs telling stories of a bygone era.Step 3: Climax and RevelationText: The climax of the story is reached when Alex finally encounters a group of survivors, descendants of the original inhabitants. Through a mixture of sign language and AI-assisted translation, he begins to communicate with them, unveiling the tragic history of their people and their hopes for the future.Illustration: An emotional depiction of the encounter between Alex and the survivors, focusing on the exchange of gifts and information that builds a bridge between the two worlds.Step 4: Conclusion and AftermathText: The story concludes with Alex returning to his spaceship, laden with knowledge and artifacts, ready to share his amazing discoveries with Earth. Yet, he leaves a part of his heart with the new friends, promises to return, and reflects on the lessons learned about the importance of discovery, understanding, and interculturality.Illustration: A touching farewell image showing Alex looking up at the sky, with the silhouette of the planet and his new friends waving goodbye in the background.The Power of the Creative Prompt:This example demonstrates how a creative prompt can inspire the AI language model to create a story with depth, emotion, and imagination. By specifying the inclusion of adventure and mystery elements and setting the perspective, the prompt enables a focused and engaging narrative. The accompanying illustrations enhance the impact of the text by visually immersing the readers into the world of the story and deepening the emotional resonance of the narrative. 3. Teaching and Learning Scenarios: In educational contexts, prompts like "Explain the theory of relativity in simple terms for an eight-year-old" can guide the model to convey complex concepts in an accessible manner. This shows how prompts can be used to tailor the presentation of information to the needs of specific target groups.For the third example, we focus on teaching and learning scenarios enhanced by the use of AI language models and creative prompts. We explore how this technology can be utilized in various educational contexts to deepen learning, increase engagement, and foster the creative abilities of learners. Each example is supplemented with detailed descriptions and illustrations to make the concepts vivid and tangible.Example 1: Interactive History LessonsScenario: A history teacher wants to bring the French Revolution closer to his students by immersing them directly into the events.Prompt: "Create an interactive narrative that transports a student to 18th-century France. The student should experience key historical events through the eyes of a contemporary."Implementation: The AI model generates a series of "diary entries" that students read and to which they can write their own responses or continuations. This activity combines creative writing with historical learning.Illustration: Digital drawings of historical scenes and characters that visually underpin the diary entries, as well as interactive timelines that contextualize the events.Example 2: Understanding Mathematics Through PuzzlesScenario: To spark interest in mathematics, a teacher creates a puzzle that applies mathematical concepts in real-life situations.Prompt: "Develop a puzzle that uses the principles of geometry to find a treasure. Each clue leads to the next, until students discover the virtual treasure location."Implementation: The AI model designs an adventure where students must solve geometric problems to decipher clues. This not only promotes logical thinking but also teamwork.Illustration: An interactive map where students can track their progress, with embedded mathematical puzzles and clues visually presented.Example 3: Learning Languages Through Immersive StorytellingScenario: A language teacher wants to help her students learn a new language by immersing them in stories told in the target language.Prompt: "Tell an engaging story in the target language that introduces cultural aspects and everyday vocabulary. The story should be divided into episodes, each episode including a quiz and exercises for language application."Implementation: The AI model creates a story that is presented over several weeks in parts. Each episode ends with interactive exercises and a quiz that students can apply immediately.Illustration: Characters and scenes from the story, animated or as comic strips, that set the language learning content within a cultural and contextual framework.Example 4: Exploring Science through ExperimentsScenario: A science teacher wants to introduce complex concepts to students through practical experiments that can be conducted with everyday objects.Prompt: "Create a guide for a simple yet enlightening scientific experiment that explains the principles behind chemical reactions, which can safely be conducted at home."Implementation: The AI generates a step-by-step guide for an experiment using everyday materials such as baking soda and vinegar to create a chemical reaction. Additionally, the scientific principles are explained.Illustration: Detailed illustrations that show each step of the experiment, supplemented by animated GIFs or videos that show the reaction in action and visually highlight safety precautions. These examples demonstrate how AI-supported teaching and learning scenarios can enhance engagement and understanding across different educational fields through creative prompts and visual aids. By making learning content lively and interactive, they help to spark learners' curiosity and enthusiasm. 4. Interactive Scenarios: Through sequential prompts that build on previous responses, users can engage in a dialogue with AI models. An initial prompt might be: "What steps are required to start a small business?" Following the response, a subsequent prompt could request more specific information based on the first answer. This allows for a deeper exploration of the topic, similar to a natural conversation.For the fourth example, we consider how AI-supported scenarios can be used for professional training and development in companies. We illustrate how tailored prompts and AI technologies can promote the development of soft skills, technical skills, and industry-specific knowledge. Through practical examples and the integration of visual elements, a deep, interactive learning experience is created. Example 1: Improving Communication SkillsScenario: A company wants to improve the communication skills of its employees to make both internal and external communication more effective.- Prompt: "Simulate a series of business situations where effective communication is crucial, such as negotiating with customers, resolving team conflicts, or presentations. Provide feedback and tips for improvement."- Implementation: The AI model creates interactive modules that place employees in various role-playing scenarios, followed by an analysis of their performance with constructive feedback and suggestions for improvement.- Illustration: Comic-style scenarios depicting typical business situations, supplemented by video examples of 'good' and 'areas for improvement' communication. Example 2: Developing Technical SkillsScenario: A technology company wants to train its employees in the latest programming languages to support innovative projects.- Prompt: "Create an interactive tutorial for the Python programming language that focuses on developing AI applications. Integrate exercises based on real projects."- Implementation: The AI model offers a step-by-step learning path from basics to advanced concepts, with exercises that can be completed directly in the browser.- Illustration: Step-by-step guides and code examples visually prepared, interactive coding environments, and diagrams to explain complex concepts. Example 3: Industry-Specific KnowledgeScenario: A financial services company wants to train its team on the latest financial regulations to ensure compliance.Prompt: "Develop a modular training program that introduces the latest international financial regulations and explains their impact on different business areas. Include assessments to check understanding."Implementation: The AI model generates a comprehensive training program that explains legislative changes, provides practical examples of their application, and integrates tests to monitor learning progress.Illustration: Infographics summarizing the regulations and highlighting their significance, plus interactive case studies simulating real scenarios. Example 4: Encouraging Creativity and InnovationScenario: An advertising company wants to foster creativity among its teams to develop innovative campaigns.Prompt: "Host a virtual idea workshop that teaches techniques of creative thinking and challenges participants to design unconventional advertising campaigns."Implementation: The AI suggests creative exercises, such as "reversing" advertising ideas or inventing products for fictional worlds, to stimulate creative thinking.Illustration: Examples of outstanding and unusual advertising campaigns as inspiration, creative templates for brainstorming sessions, and tools for visualizing ideas. These examples show how AI and targeted prompts can revolutionize professional training by providing tailored, interactive, and visually appealing learning experiences. They demonstrate the versatility of AI-supported learning scenarios, from developing hard and soft skills to fostering creativity and innovation across a variety of industries. The effectiveness of prompts and their impact on the performance of AI models underscore the importance of careful prompt design. A well-formulated prompt can guide the AI towards precise, creative, or in-depth responses, while a poorly formulated prompt may lead to irrelevant or inaccurate outputs. Understanding how to create effective prompts is thus crucial for anyone looking to leverage the full potential of AI language models. Dr.o.B Milaim DelijaPrompt Engineer and Data Scientist, Doctor of  BlockchainDirector of Engineering at Neuronium Engineers Frankfurt

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IntroductionAptamers, single-stranded DNA or RNA sequences, fold into distinctive three-dimensional (3D) structures, enabling them to selectively recognize targets akin to monoclonal antibodies. These molecules are exclusively obtained in vitro through a process termed SELEX (S ystematic E volution of L igands byEx ponential enrichment), initially proposed independently by two research groups (1, 2). The SELEX methodology was patented in 1993 by one of its originators and remained under protection for two decades. However, in 2010, the patent expired, rendering it openly accessible to the scientific community (3).Aptamers exhibit remarkable recognition capabilities across a broad spectrum of targets, ranging from small molecules like ions, antibiotics, and dyes, to larger entities such as proteins and entire cells. This diversity in target recognition translates into a wide array of applications, typically falling into five main categories: 1) Antineoplastic agents, 2) Drugs for specific purposes, 3) Mixtures of active ingredients without chemical characterization, 4) Combinations with antibodies, and 5) Regulation of gene expression.In recent years, aptamer research has witnessed exponential growth, driven by advancements in SELEX techniques, bioinformatics, and nanotechnology. These advancements have led to the development of aptamers with enhanced specificity, stability, and binding affinity, opening new avenues for their application in various fields.For instance, aptamers have shown promising results as therapeutic agents in cancer treatment, demonstrating the potential to specifically target tumor cells while minimizing off-target effects. Additionally, aptamers have been utilized in the development of biosensors for the rapid and sensitive detection of pathogens, toxins, and biomarkers in clinical samples, food safety, and environmental monitoring.Moreover, the versatility of aptamers extends to their integration with other technologies, such as nanoparticles, to enhance drug delivery systems and imaging agents. This interdisciplinary approach has paved the way for innovative solutions in personalized medicine and targeted therapy.Distinct from conventional scientific literature and news articles, patents offer both technical specifications and insights into potential future applications. Therefore, the objective of this review is to delineate the current applications of aptamers, pinpoint the most promising avenues for commercial utilization, and deliberate on the prospective trajectories of these molecular entities. Through a comprehensive analysis of patents, scientific literature, and technological trends, this review aims to provide valuable insights into the evolving landscape of aptamer-based technologies and their impact on various fields of research and industry.

Background: This study aims to investigate whether immune dysregulation and gut microbiome alteration are exacerbated in atopic dermatitis (AD) with food allergy (ADFA) and potential treatment strategies. Methods: Total 159 children with AD (tAD) were divided into two groups: AD without-food allergy (ADNFA) and with food allergy (ADFA); 100 children without AD were included as control. Eosinophil counts and total serum IgE levels were measured by routine methods, serum food-specific IgE levels by quantitative fluorescence immunoassay, and serum cytokine levels by multi-microsphere flow immunofluorescence. The intestinal microbiota was evaluated in fecal specimens using metagenomic sequencing. A novel ADFA mouse model was generated to evaluate whether probiotic candidates identified from human fecal samples contributed to the improvement in ADFA pathology. Results: The levels of eosinophils, IgE, IL-2, TNF-α, IL-4, IL-5, IL-6, IL-10, IL-17, IL-12P70 and IFN-α were elevated in tAD compared to normal controls. Compared with ADNFA, the levels of eosinophils, IgE and IL-5 were persistently increased, while IFN-γ was decreased, the species of Lactococcus lactis (L. lactis) was reduced in ADFA. Compared with AD, the ADFA model had more severe skin lesions on the back and significantly higher serum OVA-specific IgE, IL-4 and IL-5. Following oral administration of L. lactis ( L. lactis 1.1936+1.3992), skin lesions in ADFA mice was significantly improved. The levels of OVA-specific IgE, IL-4 and IL-5 decreased in a dose-dependent manner. Conclusions: Food allergy aggravates immune dysregulation and gut microbiome dysbiosis in children with AD. L. lactis could be a candidate probiotic for the treatment of ADFA.

Background and Purpose: In diabetes, elevated blood sugar (BS) levels contribute to the overproduction of reactive oxygen species (ROS), leading to endothelial progenitor cell (EPC) dysfunction. 2-hydroxy hispolon (2HH), a derivative of Hispolon (HIS), shows potential in reversing high glucose-induced EPC dysfunction. Experimental Approach: In vitro, we inspected the effect of 2HH on three different angiogenic cells, including outgrowth endothelial cell (OEC), circulating angiogenic cell (CAC) and endothelial cell (EC). In vivo, High-fat diet and streptozotocin induced diabetic mice and hindlimb ischemic model were utilized to evaluate the effect of 2HH on angiogenesis and CAC mobilization. Key Results: Treatment with 2HH significantly improved the proliferation, migration, and tube formation, NO synthesis and ROS diminishing of OEC under high glucose conditions. Moreover, 2HH administration potentiates the AMP-activated protein kinase (AMPK)/heme oxygenase-1 (HO-1) and phosphoinositide 3-kinases (PI3K)/protein kinase B (AKT)/endothelial nitric oxide synthase (eNOS) signaling pathways in OECs exposed to high glucose levels. Additionally, 2HH reverses high glucose-induced impairment of CAC function but fails to restore EC dysfunction under high glucose conditions. Hindlimb ischemic assay demonstrated that 2HH treatment enhances blood flow recovery in ischemic hindlimbs and increases the number of circulating CACs in diabetic mice after femoral ligation. Conclusion and Implications: Our study has demonstrated that 2HH effectively reduces oxidative stress, thereby preventing EPC dysfunction induced by high glucose levels, primarily through the PI3K/AKT/eNOS and AMPK/HO-1 pathways. These findings offer a promising therapeutic avenue for attenuating the susceptibility to critical limb ischemia in patients with diabetes.

Background and Purpose: This study investigated the potential antioxidant effects of minocycline on sperm parameters and hormonal receptor expression in male rats. Experimental Approach: A total of 32 male Wistar rats were randomly divided into four groups: Sham, Varicocele induced (VC) group, VC+Mino group (Varicocele induction with minocycline treatment), and Mino group (only treated with minocycline). The researchers measured serum levels of “testosterone”, “luteinizing hormone” (LH), and “follicle-stimulating hormone” (FSH) and examined “sperm parameters”, “histopathological” analysis, and expressions of “hormonal receptor genes” among the different groups. Results: The outcomes showed that VC meaningfully improved serum levels of FSH and LH compared to the sham group (P < 0.05). Additionally, VC led to a reduction in sperm parameters, serum “testosterone” level, number of germ cells, and expression of hormonal receptor genes (P < 0.05). However, treatment with minocycline improved sperm quality and prevented testis tissue damage caused by VC. Conclusion: These findings suggest that minocycline treatment can be protective against testis tissue damage induced by VC.