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A recent report from researchers at Birmingham Women's and Children's NHS Foundation Trust discusses the diagnosis and treatment of progressive familial intrahepatic cholestasis (PFIC), a rare liver disorder that primarily affects children. The researchers aimed to provide recommendations for the management of PFIC in clinical practice. They developed an algorithm for the diagnosis and treatment of children with suspected PFIC, which includes the use of licensed inhibitors of ileal bile acid transporters as the first-line treatment. The authors hope that these recommendations will help standardize the management of PFIC and raise awareness of current developments in the field. [Extracted from the article]

The regulatory uses are real: In 2021, more than 100 drug and biologic applications submitted to the FDA included AI/ML components. Keywords: Algorithms; Artificial Intelligence; Bioengineering; Biologics; Biotechnology; Cybersecurity; Cyborgs; Drug Development; Drug Manufacturing; Drugs and Therapies; Emerging Technologies; FDA; Genetic Engineering; Genetically-Engineered Proteins; Government Agencies Offices and Entities; Health and Medicine; Machine Learning; Office of the FDA Commissioner; Public Health; Technology; U.S. Food and Drug Administration EN Algorithms Artificial Intelligence Bioengineering Biologics Biotechnology Cybersecurity Cyborgs Drug Development Drug Manufacturing Drugs and Therapies Emerging Technologies FDA Genetic Engineering Genetically-Engineered Proteins Government Agencies Offices and Entities Health and Medicine Machine Learning Office of the FDA Commissioner Public Health Technology U.S. Food and Drug Administration 497 497 1 05/22/23 20230523 NES 230523 2023 MAY 22 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Trials Week -- By: Patrizia Cavazzoni, M.D., Director of the Center for Drug Evaluation and Research Artificial intelligence (AI) and machine learning (ML) are no longer futuristic concepts; they are now part of how we live and work. [Extracted from the article]

Keywords: Acid-Base Imbalance; Algorithms; Computers; Diabetes Complications; Diabetes Mellitus; Diabetic Ketoacidosis; Glucose Metabolism Disorders; Health and Medicine; Hospitals; Nutritional and Metabolic Diseases and Conditions; Peptide Hormones; Peptide Proteins; Proinsulin EN Acid-Base Imbalance Algorithms Computers Diabetes Complications Diabetes Mellitus Diabetic Ketoacidosis Glucose Metabolism Disorders Health and Medicine Hospitals Nutritional and Metabolic Diseases and Conditions Peptide Hormones Peptide Proteins Proinsulin 53 53 1 04/17/23 20230420 NES 230420 2023 APR 17 (NewsRx) -- By a News Reporter-Staff News Editor at Diabetes Week -- New study results on diabetic ketoacidosis have been published. Acid-Base Imbalance, Algorithms, Computers, Diabetes Complications, Diabetes Mellitus, Diabetic Ketoacidosis, Glucose Metabolism Disorders, Health and Medicine, Hospitals, Nutritional and Metabolic Diseases and Conditions, Peptide Hormones, Peptide Proteins, Proinsulin. [Extracted from the article]

A study conducted by researchers at Sichuan University of Science and Engineering in China explores the design of smart senior care (SSC) product app interfaces for the elderly. The researchers use deep learning (DL) technology to optimize the app interface design, taking into consideration the cognitive characteristics and habits of the elderly. The study finds that the proposed model based on the deep-Q-network (DQN) algorithm significantly improves the user experience and satisfaction of the elderly, enhancing their ability to utilize smart products and improve their quality of life. The research provides valuable insights for further research and practice in related fields. [Extracted from the article]

A paper published in the journal Green Energy and Intelligent Transportation proposes a hybrid data-driven framework for battery state of health (SOH) estimation and remaining useful life (RUL) prediction. The framework combines the Variational Mode Decomposition (VMD) method, the improved Sparrow Search Algorithm (ISSA), and the Multi-Kernel Support Vector Regression (MKSVR) model. The researchers use eight features obtained through feature extraction and apply VMD to stabilize the capacity data. They also use an elite chaotic opposition-learning strategy and adaptive weights to optimize the SSA algorithm and improve prediction accuracy. Experimental verification using a dataset from NASA shows that the VMD-ISSA-MKSVR framework has the smallest errors in SOH estimation and RUL prediction, demonstrating high accuracy and stability. [Extracted from the article]

A report from the University of Oslo discusses the use of machine learning algorithms and heuristic stopping criteria in systematic reviews in education and educational psychology. The study found that using machine learning algorithms for abstract screening reduced the workload of irrelevant records by an average of 58% and saved an estimated 1.66 days of time. The random forests algorithm with sentence bidirectional encoder representations from transformers performed the best. The study also found that heuristic stopping rules could retrieve 95% of relevant abstracts, with the best results achieved by stopping the screening process after classifying 20% of records and consecutively classifying 5% of irrelevant papers. The research provides empirical evidence on the performance of machine learning screening algorithms in this field. [Extracted from the article]

Algorithms, Diagnostics and Screening, Emerging Technologies, Fibromyalgia, Health and Medicine, Linear Discriminant Analysis, Machine Learning, Muscular Diseases and Conditions, Musculoskeletal Diseases and Conditions, Neuromuscular Diseases and Conditions, Rheumatic Diseases and Conditions Keywords: Algorithms; Diagnostics and Screening; Emerging Technologies; Fibromyalgia; Health and Medicine; Linear Discriminant Analysis; Machine Learning; Muscular Diseases and Conditions; Musculoskeletal Diseases and Conditions; Neuromuscular Diseases and Conditions; Rheumatic Diseases and Conditions EN Algorithms Diagnostics and Screening Emerging Technologies Fibromyalgia Health and Medicine Linear Discriminant Analysis Machine Learning Muscular Diseases and Conditions Musculoskeletal Diseases and Conditions Neuromuscular Diseases and Conditions Rheumatic Diseases and Conditions 158 158 1 04/10/23 20230413 NES 230413 2023 APR 13 (NewsRx) -- By a News Reporter-Staff News Editor at Hematology Week -- Research findings on fibromyalgia are discussed in a new report. [Extracted from the article]

Keywords: Chicago; State:Illinois; United States; North and Central America; Algorithms; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning EN Chicago State:Illinois United States North and Central America Algorithms Cyborgs Emerging Technologies Health and Medicine Machine Learning 380 380 1 05/22/23 20230526 NES 230526 2023 MAY 28 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Devices & Surgical Technology Week -- Fresh data on Machine Learning are presented in a new report. Machine learning algorithms may be used to compare the risk of failure of specific patient-procedure combinations in the treatment of cartilage defects of the knee. [Extracted from the article]

A recent study conducted by researchers at Hamad Bin Khalifa University in Doha, Qatar, focuses on the use of machine learning and digital signal processing to improve the analysis of electroencephalogram (EEG) signals for medical diagnosis. EEG is a safe and common test that records electrical signals from the brain and has various applications in diagnosing conditions such as epilepsy, Alzheimer's, brain tumors, sleep disorders, and stroke. The study provides a comprehensive review of recent approaches for EEG preprocessing, feature extraction, and diagnosis of brain disorders, aiming to identify reliable algorithms for improving the accuracy of automatic diagnostic systems. The research concludes that the paper can be a valuable resource for researchers and practitioners in the field, offering insights into recent developments and future research directions. [Extracted from the article]

A paper published in JAMA Network Open discusses the issue of bias in healthcare algorithms and provides guiding principles to address this problem. The paper supports the Biden Administration's Executive Order on advancing racial equity and preventing discrimination. The technical expert panel, supported by various healthcare organizations, developed a conceptual framework to address structural racism and discrimination in algorithms. The paper emphasizes the importance of understanding and mitigating algorithmic bias to promote health equity. [Extracted from the article]

A new report from the University of Florence in Italy provides an extensive review of monitoring methods for electrical power lines, with a focus on high-voltage and medium-voltage systems. The objective of these techniques is to prevent catastrophic failures by detecting partial damage or deterioration of components and allowing for organized maintenance operations. The paper discusses the coordination of protection devices and the implementation of artificial intelligence algorithms to improve the reliability of the network. It also highlights diagnostic techniques, protection devices, and prognostic methods, emphasizing the role of artificial intelligence and offering guidelines for choosing between different approaches. [Extracted from the article]

A study conducted by researchers at Henan Normal University in China has proposed a new method for monitoring the training load of athletes using sensor technology and motion image analysis. The researchers combined the advantages of vision-based motion monitoring and wearable sensor-based motion monitoring to create a method that can be used for motion monitoring anytime and anywhere. They also developed a two-stage neural network motion monitoring algorithm to identify different types of motions separately, which improves the accuracy of recognition. Additionally, the study introduced a data enhancement algorithm based on acceleration data to increase the number of samples without the need for re-collecting data. The results of simulation experiments showed that this method can be applied to real-life situations. [Extracted from the article]

A new report discusses research conducted at Kunming University of Science and Technology in China on applied sciences. The research focuses on the design of a multi-UAV collaborative distribution scheme to address the challenges of rescue channel blocking and a shortage of emergency materials during significant disasters. The study proposes a multi-objective, improved brainstorming optimization algorithm to solve the problem of multi-UAV task assignment and routing. The research concludes that the algorithm has good performance and feasibility in convergence and distribution of reconciliation, providing practical and theoretical support for rescue tasks in sudden significant disasters. [Extracted from the article]

A recent study conducted by researchers at the ECE Department in Vijayawada, India, examined the effectiveness of machine learning techniques for the early detection of type 2 diabetes. The researchers analyzed various papers and methodologies, assessing the accuracy of these methods. They proposed a comprehensive framework that utilizes current machine learning algorithms and incorporates novel technologies to enhance the accuracy and reliability of diabetes prediction. The study emphasizes the socio-economic impact of early diabetes detection and highlights the importance of timely interventions in reducing healthcare expenses and improving patient outcomes. The research contributes to global efforts to combat diabetes and improve public health. [Extracted from the article]

A study conducted by researchers at the University of Salerno in Italy has developed a user-friendly algorithm to assist surgeons in pedicle arthrodesis operations for spine surgery. The algorithm uses 3D scans of a patient's vertebra to identify the optimal direction for screw insertion and select the best screws to maximize bone occupation without intersecting each other or going through the walls of the pedicle and vertebral body. The algorithm has been tested on six vertebrae and allows surgeons to freely choose the entry point for the screw. This research represents a novel approach in the field and provides surgeons with a tool for planning their surgeries and creating customized surgical templates. [Extracted from the article]

The diagnostic-related genes and diagnostic genes obtained in this paper can be utilized as a reference for clinical sepsis diagnosis. Keywords: Algorithms; Blood Diseases and Conditions; Bloodstream Infection; Cyborgs; Diagnostics and Screening; Emerging Technologies; Genetics; Health and Medicine; Machine Learning; Sepsis; Septicemia EN Algorithms Blood Diseases and Conditions Bloodstream Infection Cyborgs Diagnostics and Screening Emerging Technologies Genetics Health and Medicine Machine Learning Sepsis Septicemia 399 399 1 10/16/23 20231019 NES 231019 2023 OCT 19 (NewsRx) -- By a News Reporter-Staff News Editor at Blood Weekly -- Data detailed on sepsis have been presented. [Extracted from the article]

A research paper from the Kalasalingam Academy of Research and Education in Tamil Nadu, India, introduces a model called Taylor-Tunicate Swarm Algorithm-based Generative Adversarial Networks (Taylor-TSA-based GANs) for cancer prediction. The model incorporates the Taylor series with the Tunicate Swarm Algorithm and uses the Yeo-Johnson transformation for data transformation. The feature fusion is evaluated by Deep Stacked Autoencoder, and the fused feature is given as input to the cancer prediction done by GAN trained by Taylor-TSA. The research concludes that the developed model is effective and efficient, with high accuracy, a low false positive rate, and a high true positive rate. [Extracted from the article]

A recent study conducted at the Central University of Haryana in India has proposed a privacy-preserving collaborative model for the classification of brain tumors using magnetic resonance imaging (MRI) images. The study utilized deep learning and transfer learning algorithms to analyze an open-source dataset of MRI images, which included four types of tumors and no tumor. The researchers found that federated deep learning models, involving multiple clients, outperformed conventional pretrained models in terms of accuracy. The proposed framework offers a solution for early diagnosis of brain tumors while ensuring data privacy for edge devices with limited resources. [Extracted from the article]

A researcher from Renmin Hospital of Wuhan University in China has published new data on the use of machine learning and natural language processing algorithms in the remote mobile medical diagnosis system of internet hospitals. The study aimed to integrate internet technology into the medical field and improve the accuracy of medical diagnoses. The research found that the remote mobile medical diagnosis system based on machine learning achieved an accuracy of 80% or higher compared to traditional systems. The study suggests that this system has a broad application prospect and can accommodate more than 10,000 people simultaneously. [Extracted from the article]

A recent study conducted at the University of California Riverside has developed an algorithm for searching optimal variance component estimators in linear mixed models. The study focuses on the estimation of variance components, which is crucial in data analysis based on a linear mixed model. The algorithm aims to derive strictly positive unbiased estimators with minimum variance by modifying the method-of-moments estimator using a class of square non-singular regularization matrices. The proposed method allows for the convenient study of the theoretical and numerical properties of the estimator, even in small samples. The research concludes by illustrating the use of the algorithm in two illustrative examples. [Extracted from the article]

A report from Kerman University of Medical Sciences discusses the use of natural language processing (NLP) to extract cancer concepts from clinical notes automatically. The study systematically reviewed papers that used NLP methods to identify cancer concepts from clinical notes. The researchers found that rule-based algorithms were the most commonly used for concept extraction, and the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) and Unified Medical Language System (UMLS) were the most commonly used terminologies. The study concluded that the use of NLP for extracting cancer concepts has increased in recent years, and suggested that future studies use rule-based algorithms for extracting concepts of other diseases as well. [Extracted from the article]

Bones of mice with osteoporosis that received the treatment were similar to those of the control group, and the treatment showed no evidence of toxicity. Keywords: Algorithms; American Chemical Society; Antiinfectives; Antimalarial Agents; Antiprotozoal Agents; Asia; Bone Research; Chemistry; China; Drugs and Therapies; Genetics; Health and Medicine; Metabolic Bone Diseases and Conditions; Mosquito-Borne Diseases; Musculoskeletal Diseases and Conditions; Osteoporosis EN Algorithms American Chemical Society Antiinfectives Antimalarial Agents Antiprotozoal Agents Asia Bone Research Chemistry China Drugs and Therapies Genetics Health and Medicine Metabolic Bone Diseases and Conditions Mosquito-Borne Diseases Musculoskeletal Diseases and Conditions Osteoporosis 357 357 1 10/30/23 20231103 NES 231103 2023 NOV 3 (NewsRx) -- By a News Reporter-Staff News Editor at Zika & Mosquito Week -- Correction (): The paper's title has been corrected to "Deep Learning-Predicted Dihydroartemisinin Rescues Osteoporosis by Maintaining Mesenchymal Stem Cell Stemness through Activating Histone 3 Lys 9 Acetylation Artificial intelligence has exploded in popularity and is being harnessed by some scientists to predict which molecules could treat illnesses, or to quickly screen existing medicines for new applications. [Extracted from the article]

To this end, this paper proposes an agent-based algorithm for the protection for user privacy health topics based on identity replacement." Keywords: Zhejiang; People's Republic of China; Asia; Engineering; Algorithms; Cybersecurity; Health and Medicine; Public Health EN Zhejiang People's Republic of China Asia Engineering Algorithms Cybersecurity Health and Medicine Public Health 8478 8478 1 10/30/23 20231103 NES 231103 2023 NOV 3 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- New research on Engineering is the subject of a report. [Extracted from the article]

Jammu Kashmir, India, Asia, Algorithms, Cancer, Cervical Cancer, Diagnostics and Screening, Health and Medicine, Oncology, Women's Health Keywords: Jammu Kashmir; India; Asia; Algorithms; Cancer; Cervical Cancer; Diagnostics and Screening; Health and Medicine; Oncology; Women's Health EN Jammu Kashmir India Asia Algorithms Cancer Cervical Cancer Diagnostics and Screening Health and Medicine Oncology Women's Health 1014 1014 1 10/03/23 20231006 NES 231006 2023 OCT 3 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- A new study on Oncology - Cervical Cancer is now available. [Extracted from the article]

The three stages of the QIP algorithm include: (1) detection of line segments, (2) unraveling of the interlocking pentagons, and (3) quantification of areas. "In this paper, we present a novel three-stage algorithm, called Quantification of Interlocking Pentagons (QIP), which quantifies PCT performance by computing the areas of individual pentagons and their intersection areas, and a balance ratio between the areas of the two individual pentagons. [Extracted from the article]

To address this challenge, the paper introduces an accurate binary prostate cancer classification algorithm that integrates linear support vector machines (SVM) and ridge regression-based four-feature selection algorithms. Keywords: Algorithms; Cancer; Diagnostics and Screening; Health and Medicine; Machine Learning; Oncology; Prostate Cancer; Prostatic Neoplasms; Support Vector Machines EN Algorithms Cancer Diagnostics and Screening Health and Medicine Machine Learning Oncology Prostate Cancer Prostatic Neoplasms Support Vector Machines 1492 1492 1 09/11/23 20230912 NES 230912 2023 SEP 12 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Investigators publish new report on prostate cancer. [Extracted from the article]

Keywords: Algorithms; Computers; Endoscopy; Health and Medicine; Minimally Invasive Surgical Procedures; Surgery EN Algorithms Computers Endoscopy Health and Medicine Minimally Invasive Surgical Procedures Surgery 686 686 1 08/28/23 20230903 NES 230903 2023 SEP 3 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Devices & Surgical Technology Week -- Research findings on endoscopy are discussed in a new report. Algorithms, Computers, Endoscopy, Health and Medicine, Minimally Invasive Surgical Procedures, Surgery. [Extracted from the article]

Researchers from the University of Electronic Science and Technology of China have developed an intelligent inspection guidance system for urethral endoscopy. The system combines image processing, simultaneous localization and mapping (SLAM), and intelligent navigation technologies to assist doctors in conducting a quick and comprehensive examination. The system uses a generative adversarial network-based deblurring algorithm and a vascular attention-based feature extraction algorithm to improve accuracy in identifying urethral vascular features. The research concludes that the proposed method is more accurate and comprehensive, resulting in a 4.34% accuracy improvement. [Extracted from the article]

A recent study conducted by researchers from the Icahn School of Medicine at Mount Sinai in New York City explores the use of advanced deep learning algorithms for magnetic resonance imaging (MRI) segmentation. Accurate MRI segmentation is crucial for diagnosing abnormalities and planning treatments, but traditional supervised learning techniques require a large amount of annotated data. The study reviews state-of-the-art algorithms that utilize a limited number of annotated samples, including self-supervised learning, generative models, few-shot learning, and semi-supervised learning. The researchers also discuss future directions in the field, such as contrastive language-image pretraining, to further improve image segmentation with limited labels. [Extracted from the article]

A report from the China University of Petroleum (East China) discusses the need for modern technology, specifically artificial intelligence, to support the diagnosis of breast cancer. The existing computer-aided diagnostic systems for breast cancer are costly and limited in their capabilities. The report proposes the use of improved algorithms and a simple integrated system for pre-diagnosis of breast cancer based on CNN and Pyqt5. The system utilizes tensorflow and pytorch frameworks and includes a human-computer interaction GUI system interface. The research has been peer-reviewed and more information can be obtained from the China University of Petroleum (East China). [Extracted from the article]

Researchers from Brigham and Women's Hospital in Boston, Massachusetts have published a report on algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation (TMS). TMS is a device-based neuromodulation technique used to treat brain diseases. The report discusses modern algorithms for real-time TMS electric field modeling, including basis representation techniques and deep neural-network-based methods. It also reviews software tools that integrate electric field modeling with navigated TMS. The research was supported by the National Institutes of Health (NIH) in the United States. [Extracted from the article]

This article discusses the use of machine learning algorithms, specifically the K-Nearest Neighbors (KNN) algorithm, in predicting vaccine targets. Traditional methods for identifying suitable antigen targets for vaccines can be time-consuming and lack scalability. The authors present an implementation of the KNN algorithm for vaccine target prediction and demonstrate its effectiveness in accurately identifying vaccine targets. The study highlights the potential of the KNN algorithm in this field and provides insights for future research. Please note that this article has not been peer-reviewed. [Extracted from the article]

Researchers from Linkoping University in Sweden have developed an enhanced chameleon swarm algorithm (ICSA) for global optimization and multi-level thresholding medical image segmentation. Medical image segmentation is crucial for disease diagnosis and analysis, but selecting optimal thresholds can be computationally intensive. The ICSA algorithm improves precision and efficiency by introducing the concept of the "best random mutation strategy" and utilizing three distribution functions for mutating search individuals. Comparative analysis shows that ICSA outperforms other metaheuristic algorithms and significantly improves image segmentation performance. This research has been peer-reviewed and published in Neural Computing and Applications. [Extracted from the article]

A study conducted at Velagapudi Ramakrishna Siddhartha Engineering College explores the use of a genetic algorithm-based decision tree to detect dementia through MRI analysis. The researchers emphasize the importance of early and accurate detection of dementia for optimal therapy and management. By analyzing a large collection of MRI images and associated clinical data, the decision tree algorithm achieved an impressive accuracy rate of 96.67%. This research contributes to the development of a non-invasive diagnostic tool for dementia detection. [Extracted from the article]

A new study conducted by researchers at Quantum University focuses on hydrocephalus, a central nervous system disorder that primarily affects infants and toddlers. The study aims to improve the accuracy of hydrocephalus diagnosis by developing an algorithm called H-detect. This algorithm utilizes image processing techniques and the CatBoost algorithm to predict hydrocephalus more effectively than traditional methods. The research concludes that the H-detect strategy outperforms conventional approaches. For more information, readers can refer to the journal article "H-Detect: an Algorithm for Early Detection of Hydrocephalus" published in Informatika i avtomatizacia. [Extracted from the article]

A study conducted by researchers at Prince Sattam bin Abdulaziz University in Saudi Arabia explores the issue of balancing generation and demand in interconnected microgrids (MGs) to address security, power quality, and reliability concerns. The study proposes an optimized fractional order load frequency control (LFC) scheme with cascaded outer and inner control loops. The proposed controller, based on a modified version of the liver cancer optimization algorithm (MLCA), achieves better disturbance rejection compared to traditional LFC methods. The study also considers the contribution of electric vehicles and the high penetration of renewables to test the stability of the proposed LFC technique. The results confirm the superior response and improved performance of the proposed controller. [Extracted from the article]

Researchers at the University of Maribor in Slovenia have developed a recycling procedure for rapid antigen tests, which were widely used during the Covid-19 epidemic. The tests, which consist of various materials including gold nanoparticles, become mass waste after use. The researchers developed an algorithm of characterization techniques to successfully identify and describe the key materials in the tests, including the gold nanoparticles. The next step is to validate the recycling process so that the gold nanoparticles and plastic can be reused in new products. [Extracted from the article]

A study conducted by researchers at Zhengzhou University of Aeronautics explores the optimization of overseas surgical warehouse locations in cross-border medical supply chain networks using parallel big data mining. The researchers propose an algorithm that takes into account factors such as cross-border transportation cost, local transportation cost, warehouse operation cost, and market value to determine the best location for overseas warehouses. By introducing the concept of time window and customer satisfaction, a multi-objective location model is established. The study concludes that the proposed algorithm effectively improves user satisfaction, optimal probability, and reduces system costs. [Extracted from the article]

A recent study conducted by researchers at Sichuan University in Chengdu, China explores the use of computer algorithms to mimic the route choices made by human players in wargames. The researchers developed a fuzzy dynamic route choice model and algorithm that takes into account the uncertain and dynamic decision-making environment of wargaming. The model was applied to a practical case and demonstrated its effectiveness in handling fuzzy and uncertain information while simulating the decision-making psychology of wargame players. This research provides a new method for automatic route choice in wargame simulations. [Extracted from the article]

Spatial Omics is a new field that is revolutionizing biological and biomedical research by analyzing biological data-based spatial information. This technology allows researchers to study the structure and function of biological systems by uncovering the spatial distribution characteristics in cells, tissues, and organs. However, the potential of Spatial Omics is hindered by the lack of available techniques and computational tools to handle the enormous multi-dimensional datasets. To address this, GigaScience Press has published a series of articles that provide open-source algorithms, tools, and applications for analyzing and interpreting spatial omics data. These resources aim to democratize the use of this technology and facilitate further research in the field. [Extracted from the article]

Researchers at Chitkara University in Punjab, India have developed an improved lung cancer segmentation technique using nature-inspired optimization approaches. The study focused on the precise identification of tumor nodules in lung cancer Computed Tomography (CT) images. The researchers integrated four nature-inspired swarm intelligent techniques with the K-means algorithm to address the visual challenges of segmentation. The results showed that the K-means algorithm with Cuckoo Search Algorithm (CSA) significantly improved the quality of lung cancer segmentation compared to other optimization approaches. This research provides a valuable tool for researchers to improve the segmentation of medical image datasets for identifying the targeted region of interest. [Extracted from the article]