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The Global Globin Network (GGN) is a project-wide initiative of the Human Variome/Global Variome Project (HVP) focusing on haemoglobinopathies to build the capacity for genomic diagnosis, clinical services, and research in low- and middle-income countries. At present, there is no framework to evaluate the improvement of care, treatment, and prevention of thalassaemia and other haemoglobinopathies globally, despite thalassaemia being one of the most common monogenic diseases worldwide. Here, we propose a universally applicable system for evaluating and grouping countries based on qualitative indicators according to the quality of care, treatment, and prevention of haemoglobinopathies. We also apply this system to GGN countries as proof of principle. To this end, qualitative indicators were extracted from the IthaMaps database of the ITHANET portal, which allowed four groups of countries (A, B, C, and D) to be defined based on major qualitative indicators, supported by minor qualitative indicators for countries with limited resource settings and by the overall haemoglobinopathy carrier frequency for the target countries of immigration. The proposed rubrics and accumulative scores will help analyse the performance and improvement of care, treatment, and prevention of haemoglobinopathies in the GGN and beyond. Our proposed criteria complement future data collection from GGN countries to help monitor the quality of services for haemoglobinopathies, provide ongoing estimates for services and epidemiology in GGN countries, and note the contribution of the GGN to a local and global reduction of disease burden.

Aims: This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results. Method: An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied. Results: Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs. Conclusion: It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs. [ABSTRACT FROM AUTHOR]

Stroke is a significant disability and death cause worldwide and is conventionally defined as a neurological impairment relating to the intense focal harm of the central nervous system (CNS) by vascular causative components. Although the applicability of robotic rehabilitation is a topic with considerable practical significance because it has produced noticeably higher improvements in motor function than regular (physical and occupational) therapy and exempted the therapists, most of the existing bibliometric papers were not focused on stroke survivors. Additionally, a modular system is designed by joining several medical end-effector devices to a single limb segment, which addresses the issue of potentially dangerous pathological compensatory motions. Searching the Web of Science database, 31,930 papers were identified, and using the VOSviewer software and science mapping technology, data were extracted on the most prolific countries, the connections between them, the most valuable journals according to certain factors, their average year of publication, the most influential papers, and the most relevant topical issues (bubble map of term occurrence). The most prolific country in the analyzed field and over the entire period evaluated (1975–2022) is the United States, and the most prolific journal is Neurorehabilitation and Neural Repair, observing a marked increase in the three periods of scientific interest for this field. The present paper assesses numerous scientific publications to provide, through statistical interpretation of the data, a detailed description of the use of robotic rehabilitation in stroke survivors. The findings may aid scientists, academics, and clinicians in establishing precise goals in the optimization of the management of stroke survivors via robotic rehabilitation, but also through easier access to scientifically validated literature. [ABSTRACT FROM AUTHOR]

Introduction: Uniportal video-assisted thoracoscopic surgery (uVATS) is becoming popular for major lung resections, even for more complex procedures. The technique initially described for minor procedures seems more difficult to reproduce and has a longer learning curve. This review aims to describe the evolution from multiportal to uVATS and to explore its feasibility and reproducibility by identifying its drawbacks and limitations. Methods: Research from PubMed was obtained with the terms [uniportal] AND [surgery] OR [single-port] AND [thoracic surgery] OR [VATS]. Papers concerning pediatric cases and non-English papers were excluded. Individual case reports were also excluded. Discussion: uVATS seems to be widely adopted and performed for minor procedures. The applicability of uVATS for different indications is discussed, even though practically all thoracic surgical interventions can be performed through a single incision. Conclusions: The transition from conventional three-port VATS to uVATS is described in this paper. An increasing number of thoracic surgeons worldwide have adopted this approach, even for major complex anatomical lung resections. Regarding the performance of minor thoracic interventions, we believe this technique is easily reproducible with a short learning curve because the instruments do not cross each other, and intraoperative movements remain intuitive. It is therefore a feasible, safe, and efficacious technique. For these reasons, we believe uVATS should be offered to all patients undergoing minor thoracoscopic procedures. [ABSTRACT FROM AUTHOR]

Communication and cooperation are fundamental for the correct deployment of P5 medicine, and this can be achieved only by correct comprehension of semantics so that it can aspire to medical knowledge sharing. There is a hierarchy in the operations that need to be performed to achieve this goal that brings to the forefront the complete understanding of the real-world business system by domain experts using Domain Ontologies, and only in the last instance acknowledges the specific transformation at the pure information and communication technology level. A specific feature that should be maintained during such types of transformations is versioning that aims to record the evolution of meanings in time as well as the management of their historical evolution. The main tool used to represent ontology in computing environments is the Ontology Web Language (OWL), but it was not created for managing the evolution of meanings in time. Therefore, we tried, in this paper, to find a way to use the specific features of Common Terminology Service—Release 2 (CTS2) to perform consistent and validated transformations of ontologies written in OWL. The specific use case managed in the paper is the Alzheimer's Disease Ontology (ADO). We were able to consider all of the elements of ADO and map them with CTS2 terminological resources, except for a subset of elements such as the equivalent class derived from restrictions on other classes. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper argues the importance of considering domain knowledge when dealing with missing data in healthcare. We identify fundamental missingness scenarios in healthcare facilities and show how they impact the missing data analysis methods. Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into the missing data methods. In this paper, we argue that the remedy is to identify the key scenarios that lead to data missingness and investigate their theoretical implications. Based on this proposal, we first introduce an analysis framework where we investigate how different observation agents, such as physicians, influence the data availability and then scrutinize each scenario with respect to the steps in the missing data analysis. We apply this framework to the case study of observational data in healthcare facilities. We identify ten fundamental missingness scenarios and show how they influence the identification step for missing data graphical models, inverse probability weighting estimation, and exponential tilting sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis: complete-case analysis, Missforest imputation, and inverse probability weighting estimation. The experiments are conducted for two objectives: variable mean estimation and classification accuracy. We advocate for our analysis approach as a reference for the observational health data analysis. Beyond that, we also posit that the proposed analysis framework is applicable to other medical domains. [ABSTRACT FROM AUTHOR]

This document is a special issue of the Journal of Personalized Medicine titled "Personalized Medicine in Blood Disease of Children." The issue focuses on the current state of research and practice in personalized medicine for non-malignant blood disorders in children. The collection of seven articles covers topics such as hemoglobinopathies, genetic and molecular basis of hematological diseases, predictive value of patients' genotype, age-related specificities of sickle cell disease, screening programs worldwide, and the importance of interdisciplinary collaboration. The papers highlight the importance of genetic research in enhancing diagnostic capacity and clinical management, and the need for further implementation of clinical studies and appropriate policies in the future. [Extracted from the article]

Background: Perineal pain is a painful neuropathic condition, which does not have a standard diagnostic or treatment approach. As such, we sought to evaluate the global scientific output of research into perineal pain and explore trends from 1981 to 2021 using bibliometric methods. Methods: Articles on perineal pain were retrieved from the Web of Science (WoS) database. We analyzed the content and quality of publications from within the specified timeframe. We also utilized VOSviewer to mine and cluster data from retrieved articles. Results: A total of 1917 articles were collected. The number of related papers published increased year by year. Articles were most frequently published by authors in the United States and France. Although the US remains at the center of this field, publications from China have become more frequent in recent years. We also found that French academic institutions dominate the field of perineal pain, and Jean-Jacques Labat from Nantes Universite is the most published author in the field. "Episiotomy", "pain", "management", "prostatectomy", "pelvic pain", and "complication" were frequently cited as keywords. Conclusion: The increasing number of publications each year indicates that perineal pain has gained more attention as an important research topic. [ABSTRACT FROM AUTHOR]

Background: The nasal microbiome represents the main environmental factor of the inflammatory process in chronic rhinosinusitis (CRS). Antibiotics and steroids constitute the mainstay of CRS therapies. However, their impact on microbial communities needs to be better understood. This systematic review summarizes the evidence about antibiotics' and steroids' impact on the nasal microbiota in patients with CRS. Methods: The search strategy was conducted in accordance with the PRISMA guidelines for systematic reviews. The authors searched all papers in the three major medical databases (PubMed, Scopus, and Cochrane Library) using the PICO tool (population, intervention, comparison, and outcomes). The search was carried out using a combination of the key terms "Microbiota" or "Microbiome" and "Chronic Rhinosinusitis". Results: Overall, 402 papers were identified, and after duplicate removal (127 papers), excluding papers off-topic (154) and for other structural reasons (110), papers were assessed for eligibility; finally, only 11 papers were included and summarized in the present systematic review. Some authors used only steroids, other researchers used only antibiotics, and others used both antibiotics and steroids. With regard to the use of steroids as exclusive medical treatment, topical mometasone and budesonide were investigated. With regard to the use of antibiotics as exclusive medical treatments, clarithromycin, doxycycline, roxithromycin, and amoxicillin clavulanate were investigated. Regarding the use of both antibiotics and steroids, two associations were investigated: systemic prednisone combined with amoxicillin clavulanate and topical budesonide combined with azithromycin. Conclusions: The impact that therapies can have on the nasal microbiome of CRS patients is very varied. Further studies are needed to understand the role of the nasal microbiome, prevent CRS, and improve therapeutic tools for personalized medicine tailored to the individual patient. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases. [ABSTRACT FROM AUTHOR]

The Emergency Department (ED) is a facility meant to treat patients in need of medical assistance. The choice of triage system hugely impactsed the organization of any given ED and it is important to analyze them for their effectiveness. The goal of this review is to briefly describe selected triage systems in an attempt to find the perfect one. Papers published in PubMed from 1990 to 2022 were reviewed. The following terms were used for comparison: "ED" and "triage system". The papers contained data on the design and function of the triage system, its validation, and its performance. After studies comparing the distinct means of patient selection were reviewed, they were meant to be classified as either flawed or non-ideal. The validity of all the comparable segregation systems was similar. A possible solution would be to search for a new, measurable parameter for a more accurate risk estimation, which could be a game changer in terms of triage assessment. The dynamic development of artificial intelligence (AI) technologies has recently been observed. The authors of this study believe that the future segregation system should be a combination of the experience and intuition of trained healthcare professionals and modern technology (artificial intelligence). [ABSTRACT FROM AUTHOR]

Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine. [ABSTRACT FROM AUTHOR]

Background: Non-specific back pain is a global concern. Exercise and health education are crucial components in its management. The Back School is a theoretical practical program that integrates both elements. The objective of this study is to determine if Back School-based programs are effective in reducing pain, disability, and kinesiophobia in patients with non-specific back pain. Methods: A systematic review of research involving participants with non-specific back pain was carried out on databases such as PubMed, Scopus, Web of Science, and Medline. Results: In total, 25 papers were chosen for review. All of these papers focused on the effects on the lumbar area, with the exception of one paper that specifically targeted the cervical region. The pain variable showed statistically significant results with standardized mean differences of −1.01 (950 confidence interval = −1.39 to −0.63; p < 0.001), and the disability variable had standardized mean differences of −0.98 (95% confidence interval = −1.38 to −0.58; p < 0.001), and only one study analysed the kinesiophobia variable and concluded that Back School programs have a positive effect on kinesiophobia between the baseline and post-intervention levels. Conclusions: Back School programs have shown effectiveness in reducing non-specific back pain and lowering disability rates. [ABSTRACT FROM AUTHOR]

Background: Genetic data play a crucial role in diagnosing and treating various diseases, reflecting a growing imperative to integrate these data into clinical care. However, significant barriers such as the structure of electronic health records (EHRs), insurance costs for genetic testing, and the interpretability of genetic results impede this integration. Methods: This paper explores solutions to these challenges by combining recent technological advances with informatics and data science, focusing on the diagnostic potential of artificial intelligence (AI) in cancer research. AI has historically been applied in medical research with limited success, but recent developments have led to the emergence of large language models (LLMs). These transformer-based generative AI models, trained on vast datasets, offer significant potential for genetic and genomic analyses. However, their effectiveness is constrained by their training on predominantly human-written text rather than comprehensive, structured genetic datasets. Results: This study reevaluates the capabilities of LLMs, specifically GPT models, in performing supervised prediction tasks using structured gene expression data. By comparing GPT models with traditional machine learning approaches, we assess their effectiveness in predicting cancer subtypes, demonstrating the potential of AI models to analyze real-world genetic data for generating real-world evidence. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this paper is to describe our experience in the embolization of hypervascular giant thoracic tumors before surgical excision. Methods: A single-center retrospective review of five trans-arterial preoperative embolization procedures executed between October 2020 and July 2024. Patients' demographics, anatomical aspects, feasibility, technique, and outcomes were reviewed. Results: In all cases, accurate targeting and safe embolization was achieved, with satisfactory devascularization evaluated with post-procedural angiography and with minimal blood loss during subsequent surgical operation. Conclusions: In our experience, preoperative embolization of giant thoracic masses has been technically feasible, safe, and effective in reducing tumor vascularization, thus facilitating surgical treatment. This approach should be evaluated as an option, especially in patients with hypervascular thoracic tumors. [ABSTRACT FROM AUTHOR]

This paper aims to conduct a statistical analysis of different components of nnU-Net models to build an optimal pipeline for lung nodule segmentation in computed tomography images (CT scan). This study focuses on semantic segmentation of lung nodules, using the UniToChest dataset. Our approach is based on the nnU-Net framework and is designed to configure a whole segmentation pipeline, thereby avoiding many complex design choices, such as data properties and architecture configuration. Although these framework results provide a good starting point, many configurations in this problem can be optimized. In this study, we tested two U-Net-based architectures, using different preprocessing techniques, and we modified the existing hyperparameters provided by nnU-Net. To study the impact of different settings on model segmentation accuracy, we conducted an analysis of variance (ANOVA) statistical analysis. The factors studied included the datasets according to nodule diameter size, model, preprocessing, polynomial learning rate scheduler, and number of epochs. The results of the ANOVA analysis revealed significant differences in the datasets, models, and preprocessing. [ABSTRACT FROM AUTHOR]

Background: Optic neuropathies represent one of the most frequent causes of vision loss, and they can manifest alone or in conjunction with neurological or systemic symptoms and signs. In recent years, the diagnostic techniques used to detect optic neuropathies have significantly improved, facilitating diagnosis and improving treatment. Among these, ocular ultrasound has assumed a fundamental role, although with conflicting results in the published scientific literature. For this reason, the aim of this review is to analyze the role of ocular ultrasound in the precise and targeted diagnosis of optic neuropathies to better understand the presumed potential of this precious diagnostic tool in the management of these ocular and neurological disorders. Methods: We carried out a search on PubMed and Scopus utilizing terms related to optic neuropathies and ocular ultrasound, including only relevant English full-length research articles, case reports, or case series. Results: Most of the papers published in the scientific literature use only the B-scan ultrasound technique without considering the more precise and objective standardized A-scan technique that allows for performing more accurate diagnostic tests, such as the "30-degree test" and the "optic nerve exercise test". Conclusions: Future clinical trials and research on optic neuropathies should also consider the use of the standardized A-scan technique in order to compare clinical findings not only with B-scan ultrasonography but also with other noninvasive procedures that could be helpful in reaching the correct diagnosis. [ABSTRACT FROM AUTHOR]

Background: Mental and physical health are both important for overall health. Mental health includes emotional, psychological, and social well-being; however, it is often difficult to monitor remotely. The objective of this scoping review is to investigate studies that focus on mental health and stress detection and monitoring using PPG-based wearable sensors. Methods: A literature review for this scoping review was conducted using the PRISMA (Preferred Reporting Items for the Systematic Reviews and Meta-analyses) framework. A total of 290 studies were found in five medical databases (PubMed, Medline, Embase, CINAHL, and Web of Science). Studies were deemed eligible if non-invasive PPG-based wearables were worn on the wrist or ear to measure vital signs of the heart (heart rate, pulse transit time, pulse waves, blood pressure, and blood volume pressure) and analyzed the data qualitatively. Results: Twenty-three studies met the inclusion criteria, with four real-life studies, eighteen clinical studies, and one joint clinical and real-life study. Out of the twenty-three studies, seventeen were published as journal-based articles, and six were conference papers with full texts. Because most of the articles were concerned with physiological and psychological stress, we decided to only include those that focused on stress. In twelve of the twenty articles, a PPG-based sensor alone was used to monitor stress, while in the remaining eight papers, a PPG sensor was used in combination with other sensors. Conclusion: The growing demand for wearable devices for mental health monitoring is evident. However, there is still a significant amount of research required before wearable devices can be used easily and effectively for such monitoring. Although the results of this review indicate that mental health monitoring and stress detection using PPG is possible, there are still many limitations within the current literature, such as a lack of large and diverse studies and ground-truth methods, that need to be addressed before wearable devices can be globally useful to patients. [ABSTRACT FROM AUTHOR]

Background: Orthognathic surgery is a multidisciplinary surgery in which the aesthetic results have become increasingly important, and consequently, also the predictability of the surgical outcomes. In this paper, we analyzed the volumetric distribution of the lower two-thirds of the face, in patients operated by orthognathic surgery and selected for their attractiveness. Our goal was to analyze the aesthetic volumetric distribution for gender and to propose our operating philosophy, that a normative distribution of facial volumes could be used like a new 3D aesthetic guide in orthognathic planning. Methods: A group of 46 orthognathic patients (26 females, 20 males) with the best postoperative aesthetic score was selected by a jury of plastic surgeons, orthodontists, and journalists. The mean soft tissue volumes of the malar, maxillary, mandibular, and chin regions were analyzed. Results: Overall, we measured a mean female facial volume distribution of 38.7%, 29%, 27.6%, and 4.7%, respectively, in the malar, maxillary, mandibular, and chin regions, while in males, it was 37%, 26%, 30%, and 6%, respectively. Conclusions: In this paper, the expansion of facial volumes in orthognathic surgery is proposed as a key point for facial harmonization. Beauty could be scientifically interpreted as a balanced distribution of facial volumes, and the virtual study of this distribution can become an important part of the preoperative analysis, such as a "volumetric" 3D cephalometry, where the surgeon could use average values of aesthetic volumetric distribution as preoperative surgical references. [ABSTRACT FROM AUTHOR]

This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, utilizing machine learning to distill meaningful insights from the expansive datasets they capture. Within the bio-electrical category, these devices employ biosignal data, such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc., to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals, including glucose levels and electrolytes, offering a holistic understanding of the wearer's physiological state. Lastly, the electro-mechanical category encompasses devices designed to capture motion and physical activity data, providing valuable insights into an individual's physical activity and behavior. This review critically evaluates the integration of machine learning algorithms within these wearable devices, illuminating their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and the provision of personalized lifestyle recommendations, the paper outlines how the amalgamation of advanced machine learning techniques with wearable devices can pave the way for more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being. [ABSTRACT FROM AUTHOR]

This review delves into the challenge of pediatric anesthesia, underscoring the necessity for tailored perioperative approaches due to children's distinctive anatomical and physiological characteristics. Because of the vulnerability of pediatric patients to critical incidents during anesthesia, provider skills are of primary importance. Yet, almost equal importance must be granted to the adoption of a careful preanesthetic mindset toward patients and their families that recognizes the interwoven relationship between children and parents. In this paper, the preoperative evaluation process is thoroughly examined, from the first interaction with the child to the operating day. This evaluation process includes a detailed exploration of the medical history of the patient, physical examination, optimization of preoperative therapy, and adherence to updated fasting management guidelines. This process extends to considering pharmacological or drug-free premedication, focusing on the importance of preanesthesia re-evaluation. Structural resources play a critical role in pediatric anesthesia; components of this role include emphasizing the creation of child-friendly environments and ensuring appropriate support facilities. The results of this paper support the need for standardized protocols and guidelines and encourage the centralization of practices to enhance clinical efficacy. [ABSTRACT FROM AUTHOR]

Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine. [ABSTRACT FROM AUTHOR]

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies. [ABSTRACT FROM AUTHOR]

Given the increasingly important role that the use of artificial intelligence algorithms is taking on in the medical field today (especially in oncology), the purpose of this systematic review is to analyze the main reports on such algorithms applied for the prognostic evaluation of patients with head and neck malignancies. The objective of this paper is to examine the currently available literature in the field of artificial intelligence applied to head and neck oncology, particularly in the prognostic evaluation of the patient with this kind of tumor, by means of a systematic review. The paper exposes an overview of the applications of artificial intelligence in deriving prognostic information related to the prediction of survival and recurrence and how these data may have a potential impact on the choice of therapeutic strategy, making it increasingly personalized. This systematic review was written following the PRISMA 2020 guidelines. [ABSTRACT FROM AUTHOR]

(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords "prostate cancer", "MRI", "radiomics", "textural analysis", "artificial intelligence", "computer assisted diagnosis", out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies. [ABSTRACT FROM AUTHOR]

The effect of multiple general anesthesia (mGA) procedures administered in early life is a critical theme and has led the Food and Drug Administration (FDA) to issue an alert. This systematic review seeks to explore the potential effects on neurodevelopment of mGA on patients under 4 years. The Medline, Embase and Web of Science databases were searched for publications up to 31 March 2021. The databases were searched for publications regarding "children multiple general anesthesia OR pediatric multiple general anesthesia". Case reports, animal studies and expert opinions were excluded. Systematic reviews were not included, but they were screened to identify any possible additional information. A total of 3156 studies were identified. After removing the duplicates, screening the remaining records and analyzing the systematic reviews' bibliography, 10 studies were considered suitable for inclusion. Comprehensively, a total cohort of 264.759 unexposed children and 11.027 exposed children were assessed for neurodevelopmental outcomes. Only one paper did not find any statistically significant difference between exposed and unexposed children in terms of neurodevelopmental alterations. Controlled studies on mGA administered before 4 years of age support that there might be a greater risk of neurodevelopmental delay in children receiving mGA, warranting the need for careful risk/benefit considerations. [ABSTRACT FROM AUTHOR]

Background: The benefit of hearing rehabilitation is often measured using audiological tests or subjective questionnaires/interviews. It is important to consider both aspects in order to evaluate the overall benefits. Currently, there is no standardized method for reporting combined audiological and patient reported subjective outcome measures in clinical practice. Therefore, this study focuses on showing the patient's audiological, as well as subjective outcomes in one graph using data from an existing study. Method: The present paper illustrated a graph presenting data on four quadrants with audiological and subjective findings. These quadrants represented speech comprehension in quiet (unaided vs. aided) as WRS% at 65 dB SPL, speech recognition in noise (unaided vs. aided) as SRT dB SNR, sound field threshold (unaided vs. aided) as PTA4 in dB HL, wearing time and patient satisfaction questionnaire results. Results: As an example, the HEARRING graph in this paper represented audiological and subjective datasets on a single patient level or a cohort of patients for an active bone conduction hearing implant solution. The graph offered the option to follow the user's performance in time. Conclusion: The HEARRING graph allowed representation of a combination of audiological measures with patient reported outcomes in one single graph, indicating the overall benefit of the intervention. In addition, the correlation and consistency between some results (e.g., aided threshold and aided WRS) can be better visualized. Those users who lacked performance benefits on one or more parameters and called for further insight could be visually identified. [ABSTRACT FROM AUTHOR]

Introduction: Considering the increasing number of conservative surgeries (quadrantectomies) for primary breast carcinoma, especially in the early stages, often followed by adjuvant radiotherapy, the incidence of radiation-associated angiosarcoma (RAS) is expected to rise in the coming decades, and it will represent a clinical and therapeutic challenge, as limited data are available due to the rarity and heterogeneity of the disease. Though the prognosis of these patients is poor, a number of clinical and pathological factors can be evaluated to better understand the course of RAS. The aim of this systematic review is to explore the available clinical-pathological, therapeutic, and prognostic data regarding RAS to evaluate its occurrence, diagnosis, treatment, and outcomes. Method: RAS clinical data were identified by a systematic review conducted in five different databases (Medline, Web of Knowledge, Google Scholar, Scopus, and Cochrane). Only RAS works published in English, with access to the full manuscript text, and with clear results, were considered as eligible. Results: We considered 52 papers comprising 319 RAS cases. The patient age at diagnosis ranged from 37 to 93 years, with most cases occurring from 5 to 10 years after breast irradiation. The most common clinical presentation was an aggressive development of macules, papules, or erythematous-violaceous skin discoloration at the site of previous radiation treatment for primary breast carcinoma. Complete surgical excision appeared to be the first-line treatment of the disease, in some cases followed by adjuvant local and/or systemic therapies. Despite different treatments, local recurrence rates ranged from 40% to 90%, leading to negative outcomes and poor prognosis for patients. Conclusion: Although the literature is limited and the data are heterogeneous and contentious, our review aims to highlight the importance of early diagnosis, multimodal treatment, and long-term follow-up of RAS in order to limit and prevent the aggressiveness of this neoplasm. [ABSTRACT FROM AUTHOR]

Early detection of breast cancer is essential for increasing survival rates, as it is one of the primary causes of death for women globally. Mammograms are extensively used by physicians for diagnosis, but selecting appropriate algorithms for image enhancement, segmentation, feature extraction, and classification remains a significant research challenge. This paper presents a computer-aided diagnosis (CAD)-based hybrid model combining convolutional neural networks (CNN) with a pruned ensembled extreme learning machine (HCPELM) to enhance breast cancer detection, segmentation, feature extraction, and classification. The model employs the rectified linear unit (ReLU) activation function to enhance data analytics after removing artifacts and pectoral muscles, and the HCPELM hybridized with the CNN model improves feature extraction. The hybrid elements are convolutional and fully connected layers. Convolutional layers extract spatial features like edges, textures, and more complex features in deeper layers. The fully connected layers take these features and combine them in a non-linear manner to perform the final classification. ELM performs classification and recognition tasks, aiming for state-of-the-art performance. This hybrid classifier is used for transfer learning by freezing certain layers and modifying the architecture to reduce parameters, easing cancer detection. The HCPELM classifier was trained using the MIAS database and evaluated against benchmark methods. It achieved a breast image recognition accuracy of 86%, outperforming benchmark deep learning models. HCPELM is demonstrating superior performance in early detection and diagnosis, thus aiding healthcare practitioners in breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Background: The prediction of patients' outcomes is a key component in personalized medicine. Oftentimes, a prediction model is developed using a large number of candidate predictors, called high-dimensional data, including genomic data, lab tests, electronic health records, etc. Variable selection, also called dimension reduction, is a critical step in developing a prediction model using high-dimensional data. Methods: In this paper, we compare the variable selection and prediction performance of popular machine learning (ML) methods with our proposed method. LASSO is a popular ML method that selects variables by imposing an L 1 -norm penalty to the likelihood. By this approach, LASSO selects features based on the size of regression estimates, rather than their statistical significance. As a result, LASSO can miss significant features while it is known to over-select features. Elastic net (EN), another popular ML method, tends to select even more features than LASSO since it uses a combination of L 1 - and L 2 -norm penalties that is less strict than an L 1 -norm penalty. Insignificant features included in a fitted prediction model act like white noises, so that the fitted model will lose prediction accuracy. Furthermore, for the future use of a fitted prediction model, we have to collect the data of all the features included in the model, which will cost a lot and possibly lower the accuracy of the data if the number of features is too many. Therefore, we propose an ML method, called repeated sieving, extending the standard regression methods with stepwise variable selection. By selecting features based on their statistical significance, it resolves the over-selection issue with high-dimensional data. Results: Through extensive numerical studies and real data examples, our results show that the repeated sieving method selects far fewer features than LASSO and EN, but has higher prediction accuracy than the existing ML methods. Conclusions: We conclude that our repeated sieving method performs well in both variable selection and prediction, and it saves the cost of future investigation on the selected factors. [ABSTRACT FROM AUTHOR]

Standard endovascular aortic repair (EVAR) has become the standard of care for treating infrarenal abdominal aortic aneurysms (AAAs) in patients with favorable anatomies, while patients with challenging AAA anatomies, and those with suprarenal or thoraco-abdominal aneurysms, still need alternative, more complex, solutions, including custom-made branched or fenestrated grafts, which are constrained by production delay and costs. To address urgent needs and complex cases, physicians have proposed modifying standard endografts by manually creating graft fenestrations. This allows for effective aneurysm exclusion and satisfactory patency of visceral vessels. Although physician-modified grafts (PMEGs) have demonstrated high technical success, standardized creation processes and long-term safety data are still lacking, necessitating further study to validate their clinical and legal standing. The aim of this article is to illustrate the state of the art with regard to this surgical technique, summarizing its origin, evolution, and the main clinical evidence supporting its effectiveness. The paper also aims to discuss the main medico-legal issues related to the use of PMEGs, with particular reference to the issue of safety related to the standardization of the surgical technique, medical liability profiles, and informed consent. [ABSTRACT FROM AUTHOR]

Fluid restriction has long been believed to benefit patients with heart failure by counteracting the activated renin–angiotensin aldosterone system and sympathetic nervous activity. However, its effectiveness remains controversial. In this paper, we summarized the current recommendations and reviewed the scientific evidence on fluid restriction in the setting of both acute decompensated heart failure and compensated heart failure. While a recent meta-analysis demonstrated the beneficial effects of fluid restriction on both all-cause mortality and hospitalization compared to usual care, several weaknesses were identified in the assessment of the methodological quality of the meta-analysis using AMSTAR 2. Further randomized controlled trials with larger sample sizes are needed to elucidate the benefits of fluid restriction for both clinical outcomes and patient-reported outcomes in patients with heart failure. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) of the knee is a prevalent cause of chronic pain and disability, particularly affecting women. While traditionally attributed to chronic wear and tear, recent evidence highlights multifactorial pathogenesis involving low-grade inflammation and neoangiogenesis. Current therapeutic options include physical therapy, pharmacotherapy, and total knee arthroplasty (TKA). However, a subset of patients remain symptomatic despite conservative measures, necessitating the development of minimally invasive interventions. Genicular artery embolization (GAE) emerges as a promising option, targeting neovascularization and inflammatory processes in OA. This paper reviews the pathophysiological basis, patient selection criteria, procedural details, and outcomes of GAE. Notably, GAE demonstrates efficacy in relieving knee pain and improving function in patients refractory to conventional therapy. While further research is warranted to elucidate its long-term outcomes and compare it with existing modalities, GAE represents a novel approach in the management of symptomatic knee OA, potentially delaying or obviating the need for surgical intervention. Here, we synthesize the relevant literature, technical details of the procedure, and future perspectives. Moreover, the success of GAE prompts the exploration of transarterial embolization in other musculoskeletal conditions, underscoring the evolving role of interventional radiology in personalized pain management strategies. [ABSTRACT FROM AUTHOR]

Background/Objectives: Temporomandibular disorder (TMD) is the term used to describe a pathology (dysfunction and pain) in the masticatory muscles and temporomandibular joint (TMJ). There is an apparent upward trend in the publication of dental research and a need to continually improve the quality of research. Therefore, this study was conducted to analyse the use of sample size and effect size calculations in a TMD randomised controlled trial. Methods: The period was restricted to the full 5 years, i.e., papers published in 2019, 2020, 2021, 2022, and 2023. The filter article type—"Randomized Controlled Trial" was used. The studies were graded on a two-level scale: 0–1. In the case of 1, sample size (SS) and effect size (ES) were calculated. Results: In the entire study sample, SS was used in 58% of studies, while ES was used in 15% of studies. Conclusions: Quality should improve as research increases. One factor that influences quality is the level of statistics. SS and ES calculations provide a basis for understanding the results obtained by the authors. Access to formulas, online calculators and software facilitates these analyses. High-quality trials provide a solid foundation for medical progress, fostering the development of personalized therapies that provide more precise and effective treatment and increase patients' chances of recovery. Improving the quality of TMD research, and medical research in general, helps to increase public confidence in medical advances and raises the standard of patient care. [ABSTRACT FROM AUTHOR]

The aim of this paper is to identify factors associated with interstitial lung disease (ILD) in patients with systemic sclerosis (SSc) and build an algorithm to better define this association for a personalised application in clinical practice. Methods. A total of 78 SSc patients underwent HRCT to assess ILD. Demographic, clinical and laboratory variables were collected, focusing on those associated either directly or indirectly with lung involvement. The discriminant value of each variable was determined using the operating characteristic curves (ROC) and included in a model to estimate the strength of ILD association in SSc. Results. Thirty-three (42.31%) patients showed ILD on HRCT. DLco, M-Borg, GERD-Q and capillary density were significantly associated with the presence of ILD-SSc. A model including these variables had a coefficient of determination (R2) of 0.697. DLco had an AUC of 0.861 (p < 0.001) with a cut-off of ≤72.3% (sensitivity 78.8%, specificity 91.1%, +LR 8.86). The m-Borg Scale showed an AUC of 0.883 (p < 0.001) with a cut-off >2 (sensitivity 84.8%, specificity 82.2%, +LR 4.77), GERD-Q had an AUC of 0.815 (p < 0.001) with a cut-off >7 (sensitivity 72.7%, specificity 86.7%, +LR 5.45). The capillary density showed an AUC of 0.815 (p < 0.001) with a cut-off of ≤4.78 (sensitivity 87.9%, specificity 68.9%, +LR 2.82). Based on the pre-test probability values, these four variables were applied to Fagan's nomogram to calculate the post-test probability of this association. Conclusions. Our study identified four associated clinical factors of ILD in SSc patients. Moreover, their inclusion in an algorithm for the post-test probability, tailored to the specific patients' characteristics, significantly increases the ability to find out the presence of SSc-ILD. [ABSTRACT FROM AUTHOR]

Purpose: Imaging of the delicate inner ear morphology has become more and more precise owing to the rapid progress in magnetic resonance imaging (MRI). However, in clinical practice, the interpretation of imaging findings is hampered by a limited knowledge of anatomical details which are frequently obscured by artifacts. Corresponding review articles are as rare in journals as they are in reference books. This shortness prompted us to perform a direct comparison of imaging with anatomical whole-mount sections as a reference. It was the intention of this paper to compare the microscopic anatomy of a human inner ear as shown on anatomical whole-mount sections with high-resolution MRI and cone beam computed tomography (CBCT). Both are available in clinical routine and depict the structures with maximum spatial resolution. It was also a goal of this work to clarify if structures that were observed on MRI in a regular manner correlate with factual inner ear anatomy or correspond with artifacts typical for imaging. Methods: A fresh human anatomical specimen was examined on a clinical 3-Tesla MRI scanner using a dedicated surface coil. The same specimen was then studied with CBCT. In each imaging modality, high-resolution 3D data sets which enabled multiplanar reformatting were created. In the second step, anatomical whole-mount sections of the specimen were cut and stained. This process enabled a direct comparison of imaging with anatomical conditions. Results: Clinical MRI was able to depict the inner ear with remarkable anatomical precision. Strongly T2-weighted imaging protocols are exquisitely capable of showing the fluid-filled components of the inner ear. The macular organs, ampullar crests and cochlear aqueduct were clearly visible. Truncation artifacts are prone to be confused with the delicate membrane separating the endolymphatic from the perilymphatic compartment. However, it was not possible to directly depict this borderline. Conclusions: With the maximum resolution of magnetic resonance tomography, commonly used in everyday clinical practice, even the smallest details of the inner ear structures can be reliably displayed. However, it is important to distinguish between truncation artifacts and true anatomical structures. Therefore, this study can be useful as a reference for image analysis. [ABSTRACT FROM AUTHOR]

Sarcoidosis is a granulomatous disease of unknown etiology that can affect almost any organ. Although the acute form can have spontaneous regression, a certain number of patients can have a chronic form, which leads to an increase in mortality and a decrease in the quality of life. Considering that the risk factors are still unknown, we wanted to compare the characteristics of patients with acute and chronic forms of sarcoidosis in Serbia in order to determine significant differences between them with hopes of contributing to everyday clinical practice. A total of 2380 patients treated in our clinic were enrolled in this study. They were separated into the following two groups: 1126 patients with acute form and 1254 patients with chronic form. They were further compared by gender, smoking status, radiological status, exposition, biomarkers for sarcoidosis, organ involvement, and other comorbidities; the distribution of patients according to regions of Serbia was also noted. Statistical significance was found in radiological findings (p < 0.001), biomarkers (calcium in 24 h urine p < 0.001; chitotriosidase p = 0.001), and the affliction of organs (p < 0.001). The differences noted in this paper could help improve our understanding of this disease. [ABSTRACT FROM AUTHOR]

Background: Duration of untreated illness (DUI)—defined as the time period between the onset of a mental disorder and its first adequate treatment—should influence patients' long-term prognosis and outcome. In patients with obsessive–compulsive disorder (OCD), DUI lasts on average from 87.5 up to 94.5 months, being significantly longer compared with data available from patients affected by other severe mental disorders, such as schizophrenia and bipolar disorder. We carried out a systematic review in order to assess the impact of DUI on long-term outcomes in OCD patients. Methods: A systemic review has been implemented, searching from inception to April 2023; only papers written in English were included. Results: Seventy-one articles were initially identified; only eight papers were included in the review. The DUI ranged from 7.0 ± 8.5 to 20.9 ± 11.2 years. Patients reporting a longer DUI have a poor long-term outcome in terms of lower level of treatment response and greater symptom severity. Conclusions: The present review confirms that longer DUI has a negative impact on the long-term outcome of patients with OCD. It should be useful to promote the dissemination of early interventions with a specific focus on OCD symptoms. [ABSTRACT FROM AUTHOR]

Background: Compliance with rehabilitative physiotherapeutic measures leads to an improvement in outcomes in patients suffering from a variety of musculoskeletal conditions. To date, a tool for assessing the parameters that lead to non-adherence to physical therapy does not exist in the German language. The objective of this paper is to cross-culturally adapt a non-compliance questionnaire to German. Methods: In reference to the "Guidelines for the Process of Cross-Cultural Adaption of Self-Reported Measures", the questionnaire was translated into German followed by a back-translation into the original language. An expert committee met and refined the pre-final version. A preliminary version was handed out to patients for evaluation of the quality of the resulting German version. Results: After the forward- and back-translation of the questionnaire, some discrepancies were discovered between the translators on the one hand and between the back-translations and the original document on the other. The statistical analysis showed satisfactory results regarding the quality of the questionnaire. Conclusion: The translation and adaption of the items proved to have a high degree of reliability. The German version will be made available for German-speaking researchers and used for evaluating a mobile-application-based physical therapy regimen by the authors of the paper. [ABSTRACT FROM AUTHOR]

Neural tube defects (NTDs) are congenital abnormalities in the central nervous system. The exact etiology of NTDs is still not determined, but several genetic and epigenetic factors have been studied. Folate supplementation during gestation is recommended to reduce the risk of NTDs. In this review we examine single nucleotide polymorphisms (SNPs) of the genes in the folate pathway associated with NTD. We reviewed the literature for all papers discussing both NTDs and SNPs in the folate pathway. Data were represented through five different genetic models. Quality assessment was performed using the Newcastle–Ottawa Scale (NOS) and Cohen's Kappa inter-rater coefficient assessed author agreement. Fifty-nine papers were included. SNPs in MTHFR, MTRR, RFC genes were found to be highly associated with NTD risk. NOS showed that high quality papers were selected, and Kappa Q-test was 0.86. Our combined results support the notion that SNPs significantly influence NTDs across the population, particularly in Asian ethnicity. Additional high-quality research from diverse ethnicities is needed and meta-regression analysis based on a range of criteria may provide a more complete understanding of the role of folate metabolism in NTDs. [ABSTRACT FROM AUTHOR]

Background: The protocol described in this paper is part of a research project coordinated between three Spanish universities, where a technology aimed at improving the quality of life of people with cerebral palsy will be developed. Part of the proposed technology will consist of an interface and a series of applications to increase motivation for daily physical activity. The basis of these developments is the measurement of the emotional state of the subjects. Methods: The experimental protocol is designed with two research objectives, on the one hand to identify the emotional state through physiological signals, and on the other to determine whether music can be a motivating factor to promote physical activity. It is specifically designed for subjects with cerebral palsy, taking into account the special characteristics of this population. These are people with whom it is difficult to use questionnaires to have a basis to contrast with the measured physiological signals, so measurements must be taken in carefully chosen daily-life situations. Discussion: We hope our findings show which physiological parameters are the most robust to measure the emotional state and how to design rehabilitation and physical activity promotion routines that are motivating, in addition to being able to avoid risk factors during the performance of these routines. Trial registration: NCT05621057. [ABSTRACT FROM AUTHOR]

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is one of the most aggressive, heterogeneous, and fatal types of human cancer; therefore, more effective therapeutic drugs are urgently needed. Human epidermal growth factor receptor 2 (HER2) overexpression and amplification have been identified as a cornerstone in this pathology. The aim of this review is to identify HER2 membrane overexpression in relation to pancreatic cancer pathways that can be used in order to develop a targeted therapy. After searching the keywords, 174 articles were found during a time span of 10 years, between 2013 and 2023, but only twelve scientific papers were qualified for this investigation. The new era of biomolecular research found a significant relationship between HER2 overexpression and pancreatic cancer cells in 25–30% of cases. The variables are dependent on tumor-derived cells, with differences in receptor overexpression between PDAC (pancreatic ductal adenocarcinoma), BTC (biliary tract cancer), ampullary carcinoma, and PNETs (pancreatic neuroendocrine tumors). HER2 overexpression is frequently encountered in human pancreatic carcinoma cell lines, and the ERBB family is one of the targets in the near future of therapy, with good results in phase I, II, and III studies evaluating downregulation and tumor downstaging, respectively. [ABSTRACT FROM AUTHOR]

This article aims to present cryoanalgesia as an inventive strategy for pain alleviation among pediatric patients. It underlines the tremendous need to align pain management with the principles of the enhanced recovery after surgery (ERAS) approach. The aim of the study was to review the patient outcomes of nerve cryoanalgesia during surgery reported with regard to ERAS in the literature. The literature search was performed using PubMed and Embase to identify articles on the use of cryoanalgesia in children. It excluded editorials, reviews, meta-analyses, and non-English articles. The analysis focused on the study methods, data analysis, patient selection, and patient follow-up. This review includes a total of 25 articles. Three of the articles report the results of cryoanalgesia implemented in ERAS protocol in children. The research outcome indicates shortened hospital stay, potential reduction in opioid dosage, and significant progress in physical rehabilitation. This paper also describes the first intraoperative utilization of intercostal nerve cryoanalgesia during the Nuss procedure in Poland, highlighting its effectiveness in pain management. Adding the cryoanalgesia procedure to multimodal analgesia protocol may facilitate the implementation of the ERAS protocol in pediatric patients. [ABSTRACT FROM AUTHOR]