A holistic approach to urinary calculi: crystal formation, analysis, diagnosis and management

Aim. To evaluate the potential of generative artificial intelligence technology in the context of supply chain management.Objectives. To analyze how artificial intelligence is changing traditional management practices and structures in supply chain management; to identify fundamental shifts brought about by AI, including automation, data-driven decision making, and human empowerment; to examine current statistics on the effectiveness of artificial intelligence in management processes and conclude the potential, promise of this technology in the logistics industry; to provide strategic insight and practicalMethods. The research combines qualitative and quantitative methods to provide a deeper understanding of the problems considered. Based on these methods, an array of relevant statistical data and expert opinions in the field of generative artificial intelligence has been studied.Results. In today’s business environment, supply chains face a number of challenges: globalization, market volatility, changing customer expectations, and force majeure (natural disasters, pandemics, or wars). To meet these challenges and strengthen their market position, companies are increasingly turning to new technologies and innovations. These technologies have the potential to revolutionize the approach to supply chain management, increasing transparency, efficiency and responsiveness. All emerging technologies, from artificial intelligence and blockchain to the Internet of Things (IoT) and robotics, potentially offer opportunities to streamline operations, improve decision-making, and enhance supply chain collaboration.Conclusions. In this article, the authors have shown how artificial intelligence is changing traditional governance structures in supply chain management. Current statistical data on the effectiveness of artificial intelligence in management processes are analyzed, and a conclusion is made about the potential and prospects of this technology in the logistics sphere. Strategic understanding is formed and practical recommendations for organizations seeking to implement new tools of generative artificial intelligence are given.

The rapid advancement of technology, particularly artificial intelligence (AI), is transforming the world of management. This chapter delves into the futuristic trends in management, focusing on the integration of AI into various managerial aspects, its potential benefits, challenges, and strategies for successful adoption. AI-driven decision-making aids managers in data-driven processes, identifying patterns, trends, and insights through AI algorithms. AI's impact on human resources management includes AI-enabled talent acquisition and recruitment, enhanced employee experience through AI-driven content, and AI-driven operations management. AI-driven supply chain optimization, process automation, and customer relationship management (CRM) are also explored. However, ethical implications of AI in management include addressing biases and fairness concerns, ensuring transparency and accountability, and navigating privacy and data security challenges. Managing the human-AI collaboration involves building a culture that embraces AI while valuing human expertise, fostering a learning mindset, encouraging continuous skill development, and mitigating potential job displacement and promoting AI-human synergy. The chapter emphasizes the importance of fostering a learning mindset, encouraging continuous skill development, and mitigating potential job displacement. AI's integration into management practices has the potential to revolutionize various aspects of organizations, including data management, resource allocation, personalization, risk assessment, supply chain management, and employee productivity. AI-driven tools enable efficient data management, identification of trends, correlations, and actionable insights from complex datasets. They can optimize financial resources, human capital, or physical assets, enhance operational efficiency, and provide personalized customer experiences. AI-driven decision-making aids managers in making informed decisions by leveraging capabilities such as data processing, pattern recognition, real-time insights, and predictive analytics. These capabilities help managers segment customers, analyze market trends, and predict future demand. AI also enhances predictive and prescriptive analytics by providing recommendations for optimal performance. AI's impact on human resources management includes AI-enabled talent acquisition and recruitment. AI-powered tools can streamline the traditional recruitment process by scanning online platforms, screening resumes, and conducting assessments and skill evaluations. AI helps mitigate bias in the hiring process through blind hiring, objective evaluation, and data-driven decisions. However, challenges and ethical considerations include data privacy and security, transparency and explainability, algorithmic bias, and candidate experience. In conclusion, AI's integration into management practices has the potential to revolutionize various areas, including data management, resource allocation, personalization, risk assessment, supply chain management, and employee performance. However, HR professionals must address ethical concerns such as data privacy, transparency, algorithmic bias, and candidate experience to ensure the success and competitiveness of AI-based recruitment. AI can significantly enhance the employee experience by providing personalized learning and development programs, enhancing performance evaluations, building employee engagement strategies, and predicting potential attrition risks. AI-powered tools can assess employees' existing skills, knowledge gaps, and learning preferences, enabling the creation of personalized development plans. AI-driven learning platforms can adjust the difficulty and content of training materials based on individual progress, fostering a culture of continuous learning. AI-based performance evaluations can provide valuable insights to enhance the performance evaluation process, offering real-time tracking and 360-degree feedback analysis. AI-driven employee engagement strategies can bolster employee satisfaction, tailoring benefits, rewards, and recognition programs to individual needs. Predictive attrition analysis and chatbots for employee support can also help retain valuable employees. AI can also optimize supply chain operations by accurately predicting demand and managing inventory. AI-driven systems can dynamically adjust inventory levels based on real-time demand fluctuations and lead times, maintaining optimal resource utilization and minimizing excess inventory. AI-powered logistics and route optimization can revolutionize logistics and transportation management by optimizing routes and enhancing overall efficiency. AI-driven process automation can transform various business operations by identifying suitable processes, analyzing high-volume data processing, streamlining workflow and resource allocation, and addressing workforce concerns and upskilling needs amid automation. By embracing AI in operations management, businesses can achieve unprecedented levels of efficiency, resilience, and responsiveness to market demands. In conclusion, AI can transform the employee experience, drive productivity, and foster a dynamic workforce. However, careful consideration of ethical principles and continuous monitoring of AI systems are essential for responsible implementation. AI can significantly improve customer satisfaction and loyalty through real-time feedback analysis, chat sentiment analysis, and personalized loyalty programs. AI-powered customer support includes chatbots and virtual assistants, which provide instant and round-the-clock assistance. AI platforms, such as Natural Language Processing (NLP), enable chatbots to understand and respond to customer queries in a conversational manner. However, AI integration also presents challenges, such as addressing biases and fairness concerns in AI algorithms, ensuring transparency and accountability in AI systems, and managing privacy and data security challenges. Adhering to data protection regulations and implementing robust data governance frameworks are essential for safeguarding customer and organizational data. Managing the human-AI collaboration involves building a culture that embraces AI while valuing human expertise. This can be achieved through change management and training, promoting a collaborative environment, fostering a learning mindset, upskilling and reskilling initiatives, emphasizing creativity and critical thinking, and mitigating potential job displacement and promoting AI-human synergy. In conclusion, embracing AI in management offers numerous opportunities to enhance organizational efficiency, productivity, and competitiveness. It is crucial for management professionals and corporate leaders to understand and harness the power of AI responsibly. By being proactive in addressing challenges and aligning AI initiatives with organizational values, businesses can leverage futuristic trends in management to thrive in the dynamic and ever-evolving landscape.

Background Artificial intelligence (AI) has significantly revolutionized the diagnosis and treatment of various medical and surgical conditions, including gastroesophageal reflux disease (GORD). AI has the potential to enhance diagnostic and treatment capabilities, contributing to overall advancements in healthcare. The current study aimed to investigate the medical students’ views regarding the use of AI in GORD management. Methods An anonymous, self-administered questionnaire was distributed among undergraduate medical students of various grades within different national medical institutions. The questionnaire comprised three sections, addressing sociodemographic data, knowledge, and attitudes. Knowledge and attitudes were assessed through 5- and 7-item questionnaires, respectively. The knowledge scores constituted a scale of 0–5. This reflected varying levels of understanding. Categories include poor knowledge (two or less), moderate knowledge (three), and good knowledge (4 and 5). Attitudes were classified as negative, neutral, or positive based on 50% and 75% cutoff points, with scores below 50% indicating negative attitudes, 50–75% considered neutral, and scores above 75% reflecting positive attitudes. Results A total of 506 medical students participated, including 273 females and 233 males, with a ratio of 1.2–1. The majority fell within the age range of 20–26 years. Additionally, 388 participants (76.7%) reported grade point averages (GPAs) higher than 4. Regarding knowledge, 9% demonstrated a poor score of knowledge, while 33% had a moderate knowledge score. However, 65% of the participating students held a neutral attitude toward the role of AI in GORD management, with 6.9% expressing a negative stance on the matter. Conclusion Although AI is highly involved in GORD management, the study revealed that medical students and interns possess a limited perception of this emerging technology. This may highlight the necessity for active involvement in AI education within the medical curricula.

The present study was conducted to evaluate urinary crystals in patients with and without urinary calculi. Three hundred and twenty-two patients were enrolled retrospectively, including 219 stone-formers and 103 cases with non-calculus genitourinary tract diseases. The stones were analyzed with infrared spectrophotometer or scanning electron microscopy. Components of the stones included whewellite, weddelite, carbonated-apatite, struvite, brushite and uric acid. Various kinds of crystals were detected from urinalysis including calcium oxalate, amorphous phosphate, triple phosphate and uric acid in 83 patients (37.9%) of the urinary calculus group and 13 patients (12.6%) of the non-calculus group. Of the 83 cases, 75 (90.4%) showed that urinary crystals corresponded to the components of the urinary calculi. Calcium oxalate was the most common crystal in the urinalysis (44.4%). Concerning the pH values from urinalysis, the urine containing uric acid crystals had the lowest pH value (mean 5.30), and the triple phosphate crystals-containing urine had the highest pH value (mean 7.47). The study showed that approximately two-fifths of the patients with urinary calculi had crystals in urine, and there was good correlation between the urinary crystals and the components of the urinary calculi. Further, urinary crystals were found more frequently in patients with urinary calculus than in those without.

In view of the high incidence rate of urinary stones in the south and southwest of Iran, this paper investigates trace elements content including heavy metals in 39 urinary stones, collected from patients in Fars province, Iran. The mineralogy of the stones is investigated using X-ray diffractometry. The samples are classified into five mineral groups (calcium oxalate, uric acid, cystine, calcium phosphate and mixed stone). Major and trace elements in each group were determined using ICP-MS method. P and Ca constitute the main elements in urinary stones with Ca being more affine to oxalates while other alkali and alkaline earths precipitate with phosphate. Significant amounts of trace elements, especially Zn and Sr, were found in urinary calculi (calcium oxalate and phosphates) relative to biominerals (uric acid and cystine). Among urinary calculi, calcium phosphate contains greater amounts of trace metal than calcium oxalate. Phosphates seem to be the most important metal-bearing phases in urinary stones. Results indicate that concentrations of elements in urinary stones depend on the type of mineral phases. Significant differences in elements content across various mineralogical groups were found by applying statistical methods. Kruskal-Wallis test reveals significant difference between Ca, P, K, Na, Mg, S, Zn, Sr, Se, Cd, and Co content in different investigated mineral groups. Moreover, Mann-Whitney test differentiates Ca, Na, Zn, Sr, Co, and Ni between minerals in oxalate and uric acid stones. This study shows that urinary stone can provide complementary information on human exposure to elements and estimate the environmental risks involved in urinary stones formation.

Using 24-Hour Urinalysis to Predict Stone Type

This study was designed to detect the differences in soluble urinary proteins between 20 normal male individuals and 35 urinary-stone-formers by electrophoresis. Twenty urine samples were obtained from normal male individuals and 35, from male urinary stone formers. In these recurrent stone formers, urine samples were collected at a time they were free of urinary stone and urinary tract infection. The crystalline composition of urinary stones of these 35 stone-formers were calcium oxalate in 13, uric acid in 7, carbonate apatite in 12 and brushite in 3. Two hundred milliliter fresh, and midstream, voiding urine was collected for analysis. The urine was concentrated, dialyzed, frozen and lyophilized. The resulting powder was stored at-70#0C until further use. The powdered samples of the same stone composition catagory were pooled and analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Comparision of protein profiles of stone formers with those of normal individuals identified the following different urinary proteins: seven with calcium oxalate, two with uric acid, five with apatite and two with brushite, for the stone formers. The proteins comprised protein A(61kDa), B(37kDa), C(30kDa), D(26kDa), E(25kDa), F(23kDa) and G(22kDa) for calcium oxalate, protein H(63kDa) and I(59kDa) for uric acid, protein J(65kDa), K(42kDa), C(30.0kDa), F(23kDa) and G(22kDa) for carbonate apatite and protein A(61kDa) and I(59kDa) for brushite. Our results showed that the different soluble urinary proteins existed in urine of stone-formers when compared with normal individuals. Whether these proteins may play a role in the formation of urinary stone, it has been investigated by further studies such as western blotting and crystalllization model to confirm these proteins as potential markers of stone formation.

As algorithms powered by Artificial Intelligence (AI) are increasingly involved in the management of organizations, it becomes imperative to conduct human-centered AI management research and understand people's feelings and behaviors when machines gain power over humans. The two mainstream methods – vignette studies and case studies – reveal important but inconsistent insights. Here we discuss the respective limitations of vignette studies (affective forecasting errors, biased media coverage, and question substitution) and case studies (social desirability biases and lack of random assignment and control conditions), which may lead them to overrate negative and positive reactions to AI management, respectively. We further discuss the advantages of a third method for mitigating these limitations: field experiments on crowdsourced marketplaces. A proof-of-concept study on Amazon Mechanical Turk (Mturk; as a world-leading crowdsourcing platform) showed unique human reactions to AI management, which were not perfectly aligned with those in vignette or case studies. Participants (N = 504) did not differ significantly under AI versus human management, in terms of performance, intrinsic motivation, fairness perception, and commitment. We suggest that crowdsourced marketplaces can go beyond human research subject pools and become models of AI-managed workplaces, facilitating timely behavioral research and robust predictions on human-centered work designs and organizations.

Purpose This paper aims to explore the historical evolution of artificial intelligence (AI) in management and organizational studies and practices, highlighting the cyclical patterns of technological advancements, scholarly contributions and organizational adoptions. Design/methodology/approach This paper proposes a wave metaphor and related framework to capture the dynamic and cyclical nature of AI’s evolution into theory and practice. This paper accessed relevant scholarly sources about AI’s technological and practical development over the decades. Findings This study uncovers the recurring misalignments between technological advancements, scholarly contributions and organizational adoptions by identifying five distinct waves in AI history – symbolic AI, the AI Winter, the machine learning renaissance, the big data era and the emerging phase of human–AI collaboration. Each wave reflects distinct challenges and opportunities, providing insights into how management theory and practices shaped and have been shaped by AI. This framework also highlights the role of theory-practice misalignment – both as a barrier and a driver of progress – in shaping the trajectory of AI’s integration into management and organizational studies. Originality/value This work challenges linear views of technological progress and emphasizes the interplay (and misalignments) between scholarly contributions and practice. For academics, it offers comprehensive research directions for investigating AI in management and organization studies. For practitioners, it provides guidance on navigating technological adoptions.

Various crystals are seen in human urine. Oxalate, Phosphate, Uric acid, and Urate crystals are generally seen in urinary calculi. Calcium stones are most common, comprising 75 % of all urinary calculi. They may be pure calcium oxalate or calcium phosphate or a mixture of both. Many stones are not homogeneous. Low calcium intake increases the intestinal absorption of calcium, thus decreasing the amount of calcium available in the intestinal tract to form insoluble complexes with Oxalate. Consequently, a higher amount of oxalate is available for intestinal absorption and as a result, urinary oxalate excretion increases. Mineral water consumption did not reduce urinary oxalate excretion. High urinary excretion and concentration of magnesium decrease both the nucleation and growth rates of calcium oxalate crystals in urine, because of the higher solubility of magnesium oxalate compared with calcium oxalate. Analytical results show calcium oxalate to be one of the major inorganic components of renal stones and found to be present in almost all kidney and bladder stones. About 39.5 % of the total composition of the calculi is found to contain purely calcium oxalate and also hydroxyl apatite. The ten samples are a mixture of calcium oxalate and phosphate stones. Four samples are calcium oxalate as major composition and the remaining are calcium phosphate as major composition. These kidney stones are taken photographically and size of the stone are measured using optical microscopy. These qualitative analyses are also confirmed by UV, FTIR, DSC, and XRD analysis.

The paper presents the results of studying the features of the mineral composition of urinary stones obtained after spontaneous passage, as well as the use of minimally invasive surgical techniques. Material and methods. The study involved 61 patient with urolithiasis, depending who were examined depending on the sex and age of the patients. Infrared spectroscopy in the spectral range of 4000-400 cm-1 was used as a method for assessing the mineral composition of the stone. The next stage of the study consisted of analyzing the occurrence of stones of various mineral types in groups of patients with different gender and age characteristics. Results and discussion. Infrared spectroscopy identified a wide range of chemical compounds in urinary stones, in which calcium oxalate in the form of monohydrate (vevelite) or dihydrate (vedelite), calcium phosphate in the form of hydroxylapatite and uric acid were most often identified. Calcium carbonate, aragonite, struvite, ammonium urate, and cystine were detected in single samples. 45 patients (73.77%) had a mixed composition of the stone, consisting of two or more mineral components. Depending on the prevailing component, which is more than 50% vol. the sample, urinary stones were classified into 3 mineral types: type 1 – calcium oxalate calculi, the prevailing component of which is calcium oxalate in the form of monohydrate (vevellite) or dihydrate (veddelite); Type 2 – calcium phosphate, in which more than 50% vol. represented by apatite, hydroxylapatite or fluorapatite; Type 3 – stones, the dominant substance of which is uric acid or uric acid dihydrate. Most of the urinary stone samples were represented by type 1 stones, which were significantly more often detected in males. The study of gender dependence in the prevalence of stones of types 2 and 3, corresponding to these minerals, was not identified. Type 1 urinary stones were observed in all age categories, with a predominance in patients aged 30-39 years (41.38%). The age distribution of stones of type 2 had similar characteristics, while the maximum number of patients with this type of stone was noted in the age range of 40-49 years (40%), followed by a decrease in this indicator in older age groups. The minimum number of cases of type 3 stone formation was registered among patients aged 30-39 years (8.33%). In the following age periods, the quantitative presence of this type of stones increased with a maximum value in the age range of 50-59 years (41.67%). Conclusion. The mineral composition of urinary stones has features, depending on the sex and age of patients. The formation of oxalate-calcium urinary stones is most characteristic of men, the peak incidence occurs at the age of 40-49 years. Gender features of the distribution of stones, the dominant components of which are calcium phosphate and uric acid were not detected. The frequency of detection of uric acid stones increases with age, the inverse relationship is characteristic of the age distribution of phosphate-calcium stones

Control of calcium oxalate crystal structure and cell adherence by urinary macromolecules

Urolithiasis is a common urologic disease whose prevalence is about 1-20% and increasing throughout the world. The recurrence rate after treatment is more than 50%. Urinary stone analysis is important in determining the possible etiology and the pathophysiology of stone formation. A better understanding of the stone composition may help prevent urinary stone formation. From March 2007 to December 2008, physical analysis of urolithiasis in patients who lived in eastern China for more than 5 years and underwent surgery or shock wave lithotripsy in our hospital or passed their stones spontaneously was carried out using the Fourier transform infrared spectroscopy (FT-IR). Clinical and demographic findings were evaluated and compared with the stone components. Stone analysis was performed in 625 patients. The FT-IR evaluation showed that 234 (37.4%) were pure, and the most frequent was calcium oxalate (33.9%), followed by calcium phosphate (2.7%), and uric acid (0.8%). 391 (62.6%) were mixed stone, calcium oxalate (43.2%) was the most commonly major component, followed by calcium phosphate (16.3%), cystine (1.3%), uric acid (1.1%), and struvite (0.6%). Uric acid (p = 0.029) was the major component found more frequently in men, while struvite (p = 0.037) was more frequent in women. Uric acid (p = 0.031) was more common in lower urinary tract stones, and its formers with the mean age of 55 years were older than those with other components (p = 0.039). In eastern China, the most commonly found pure stone was calcium oxalate, while the most frequent mixed stone was calcium oxalate and calcium phosphate mixture. Stone location, gender, and age may influence stone component.

ABSTRACT: Objective: To determine chemical analysis of urinary stones of central sindh. Study design : Prospective and randomized study. Setting: Department of Surgery and Pathology of Peoples University of Medical and Health Sciences Nawabshah . Duration of study: Three years from May2008 to May 2011. Material and Methods: Total 106 urolith patients who underwent open stone surgery were included in the study. EDTA Titration used for determination of calcium ions and determination of oxalate, phosphate, magnesium, ammonia, uric acid and cystine stones was carried out using spectrophotometer. These patients were asked to fill out a proforma with parameters of age, sex, radiological location of stone and chemical composition of surgically recovered stones. The stone analysis findings were reviewed and compared with other reported series Results: In this study 75(70.75%) patients were male and 31 (29.25%) female. Male to female ratio was of 2.41:1. The age ranged from 1 to 70 years with the mean of 22.69 years. The peak incidence of upper urinary tract stone in 20-30 years and lower urinary tract stones in both sexes was under 10 years. Anatomical location of stone showed 48(45.29%) renal, 13(12.26%) ureteric and 45(42.45%) bladder calculi. Chemical analysis revealed 56(52.8%) calcium oxalate, 7(6.6%) calcium phosphate, 11(10.3%) ammonium urate, 18(16.9%) uric acid, 13(12.2%) Sturvite and 1(0.9%) cystine calculi. Conclusion: It was concluded that urolithiasis is predominantly male disease. No age group was spared to stone disease. Calcium oxalate, uric acid, ammonium urate and mixed calculi are the main types in our study due to poor nutritional status, poverty and inadequate health facilities. Considering that knowledge of stone composition is of utmost importance to modify the incidence of urolithiasis. Keywords: Chemical composition, Urolithiasis.

FT-IR spectral studies on certain human urinary stones in the patients of rural area