Improve Patient Care Research Articles

Implementation of Obesity Science Into Clinical Practice: A Scientific Statement From the American Heart Association.

Obesity is a recognized public health epidemic with a prevalence that continues to increase dramatically in nearly all populations, impeding progress in reducing incidence rates of cardiovascular disease. Over the past decade, obesity science has evolved to improve knowledge of its multifactorial causes, identifying important biological causes and sociological determinants of obesity. Treatments for obesity have also continued to develop, with more evidence-based programs for lifestyle modification, new pharmacotherapies, and robust data to support bariatric surgery. Despite these advancements, there continues to be a substantial gap between the scientific evidence and the implementation of research into clinical practice for effective obesity management. Addressing barriers to obesity science implementation requires adopting feasible methodologies and targeting multiple levels (eg, clinician, community, system, policy) to facilitate the delivery of obesity-targeted therapies and maximize the effectiveness of guideline-driven care to at-need patient populations. This scientific statement (1) describes strategies shown to be effective or promising for enhancing translation and clinical application of obesity-based research; (2) identifies key gaps in the implementation of obesity science into clinical practice; and (3) provides guidance and resources for health care professionals, health care systems, and other stakeholders to promote broader implementation and uptake of obesity science for improved population-level obesity management. In addition, advances in implementation science that hold promise to bridge the know-do gap in obesity prevention and treatment are discussed. Last, this scientific statement highlights implications for health research policy and future research to improve patient care models and optimize the delivery and sustainability of equitable obesity-related care.

Strengthening the use of artificial intelligence within healthcare delivery organizations: balancing regulatory compliance and patient safety.

Surface the urgent dilemma that healthcare delivery organizations (HDOs) face navigating the US Food and Drug Administration (FDA) final guidance on the use of clinical decision support (CDS) software. We use sepsis as a case study to highlight the patient safety and regulatory compliance tradeoffs that 6129 hospitals in the United States must navigate. Sepsis CDS remains in broad, routine use. There is no commercially available sepsis CDS system that is FDA cleared as a medical device. There is no public disclosure of an HDO turning off sepsis CDS due to regulatory compliance concerns. And there is no public disclosure of FDA enforcement action against an HDO for using sepsis CDS that is not cleared as a medical device. We present multiple policy interventions that would relieve the current tension to enable HDOs to utilize artificial intelligence to improve patient care while also addressing FDA concerns about product safety, efficacy, and equity.

Experiences and perspectives of adults on using opioids for pain management in the postoperative period: A scoping review.

Opioids play an important role in peri-operative pain management. However, opioid use is challenging for healthcare practitioners and patients because of concerns related to opioid crises, addiction and side effects. This review aimed to identify and synthesise the existing evidence related to adults' experiences of opioid use in postoperative pain management. Systematic scoping review of qualitative studies. Inductive content analysis and the Theoretical Domains Framework (TDF) were applied to analyse and report the findings and to identify unexplored gaps in the literature. Ovid MEDLINE, PsycInfo, Embase, CINAHL (EBSCO), Cochrane Library and Google Scholar. All qualitative and mixed-method studies, in English, that not only used a qualitative approach that explored adults' opinions or concerns about opioids and/or opioid reduction, and adults' experience related to opioid use for postoperative pain control, including satisfaction, but also aspects of overall quality of a person's life (physical, mental and social well being). Ten studies were included; nine were qualitative (n = 9) and one used mixed methods. The studies were primarily conducted in Europe and North America. Concerns about opioid dependence, adverse effects, stigmatisation, gender roles, trust and shared decision-making between clinicians and patients appeared repeatedly throughout the studies. The TDF analysis showed that many peri-operative factors formed people's perceptions and experiences of opioids, driven by the following eight domains: Knowledge, Emotion, Beliefs about consequences, Beliefs about capabilities, Self-confidence, Environmental Context and Resources, Social influences and Decision Processes/Goals. Adults have diverse pain management goals, which can be categorised as proactive and positive goals, such as individualised pain management care, as well as avoidance goals, aimed at sidestepping issues such as addiction and opioid-related side effects. It is desirable to understand the complexity of adults' experiences of pain management especially with opioid use and to support adults in achieving their pain management goals by implementing an individualised approach, effective communication and patient-clinician relationships. However, there is a dearth of studies that examine patients' experiences of postoperative opioid use and their involvement in opioid usage decision-making. A summary is provided regarding adults' experiences of peri-operative opioid use, which may inform future researchers, healthcare providers and guideline development by considering these factors when improving patient care and experiences.

Identifying the contributors to nursing caring success stories.

To (a) seek examples of nursing caring success stories and (b) identify the common contributors to these successes. By focusing on the successes of nursing care rather than critically examining failures, this research seeks to provide examples of proven and feasible approaches and processes for improving care. This study used a narrative inquiry design. Data were collected through group interviews. Four interviews were conducted with a total of 20 nurse participants working in inpatient settings in South Australian hospitals. A thematic analysis approach was used to analyse the data. Two dominant themes concerning the contributors to caring success were identified. These contributors were (1) the provision of holistic care and (2) the influence of the caring community, which includes family members and other patients. The findings also indicated that the definition of caring success according to nurses is not aligned with organisational performance indicators but is more closely represented by caring values. Success, according to nurses, is not exclusively defined by patient outcomes but includes the approach to, and process of, care delivery. Nurses value the caring process while working in an environment that primarily values clinical and systems-level outcomes. Nurses want patients and their families, allied health professionals and hospital executives to be involved and invested in the process of care. This study addressed a gap in the current literature to identify commonalities in nursing success stories, the contributors informing these successes and how these contributors can facilitate improved patient care. Understanding nursing definitions of caring success provides an opportunity to expand upon current accepted industry definitions and perspectives such as key performance indicators. Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist. No direct patient or public contribution.

Machine Learning Based X-RAY Prediction Model

This study aimed to develop and evaluate a convolutional neural network (CNN) model for multi-disease classification using a large dataset of 53,000+ chest X-ray images. The CNN architecture was trained to predict the presence of 14 different diseases based on input chest X-ray images. Key findings indicate the model achieves competitive performance with high accuracy, demonstrating potential for automated disease diagnosis. Leveraging the power of deep learning, particularly CNNs, this study shows promising results in improving diagnostic processes in healthcare. Automating disease diagnosis using deep learning methods can significantly enhance the efficiency of healthcare systems, potentially reducing the burden on medical professionals and improving patient outcomes. The success of this CNN model in multi-disease classification based on chest X-ray images highlights the potential of artificial intelligence in revolutionizing diagnostic processes in healthcare, underscoring the importance and effectiveness of deep learning methods, particularly CNNs, in advancing medical diagnostics and improving patient care.

Analysis of the use of electronic medical records and its effect on improving patient care

Analysis of the use of electronic medical records and its effect on improving patient care

Longitudinal assessment of real-world patient adherence: a 12-month electronic patient-reported outcomes follow-up of women with early breast cancer undergoing treatment

BackgroundElectronic patient-reported outcomes (ePROs) assess patients’ health status and quality of life, improving patient care and treatment effects, yet little is known about their use and adherence in routine patient care.AimsWe evaluated the adherence of invasive breast cancer and ductal carcinoma in situ (DCIS) patients to ePROs follow-up and whether specific patient characteristics are related to longitudinal non-adherence.MethodsSince November 2016, the Breast Center at Charité – Universitätsmedizin Berlin has implemented an ongoing prospective PRO routine program, requiring patients to complete ePROs assessments and consent to email-based follow-up in the first 12 months after therapy starts. Frequencies and summary statistics are presented. Multiple logistic regression models were performed to determine an association between patient characteristics and non-adherence.ResultsOut of 578 patients, 239 patients (41.3%, 95%CI: 37.3–45.5%) completed baseline assessment and all five ePROs follow-up during the first 12 months after therapy. On average, above 70% of those patients responded to the ePROs follow-up assessment. Adherence to the ePROs follow-up was higher during the COVID-19 pandemic than in the time periods before (47.4% (111/234) vs. 33.6% (71/211)). Factors associated with longitudinal non-adherence were younger age, a higher number of comorbidities, no chemotherapy, and a low physical functioning score in the EORTC QLQ-C30 at baseline.ConclusionsThe study reveals moderate adherence to 12-month ePROs follow-up assessments in invasive early breast cancer and DCIS patients, with response rates ranging from 60 to 80%. Emphasizing the benefits for young patients and those with high disease burdens might further increase adherence.

ICT in Nursing and Patient Healthcare Management: Scoping Review and Case Studies

Over the past few decades, Information and Communication Technologies (ICT) have revolutionized the fields of nursing and patient healthcare management. This scoping review and the accompanying case studies shed light on the extensive scope and impact of ICT in these critical healthcare domains. The scoping review explores the wide array of ICT tools employed in nursing care and patient healthcare management. These tools encompass electronic health records systems, mobile applications, telemedicine solutions, remote monitoring systems, and more. This article underscores how these technologies have enhanced the efficiency, accuracy, and accessibility of clinical information, contributing to improved patient care. ICT revolution has revitalized nursing care and patient management, improving the quality of care and patient satisfaction. This review and the accompanying case studies emphasize the ongoing potential of ICT in the healthcare sector and call for further research to maximize its benefits.

Anti-oppression pedagogy in health professions: a scoping review.

Health professional learners are increasingly called to learn about health inequity to reduce inequities and improve patient care and health outcomes. Anti-oppression pedagogy (AOP) addresses the need for health professional learners to understand multiple health inequities and the structures and systems that produce inequities. However, the inclusion of AOP in health professions education varies and there is a lack of clarity in its conceptualization and integration. A scoping review was conducted to address this gap and to understand how AOP is conceptualized and integrated in health professions education. Thirty-six articles met the inclusion criteria. The articles demonstrated that AOP is not commonly utilized terminology within health professions education. When AOP is integrated, it is not consistently conceptualized but is generally viewed as a broad concept that focuses on antiracism; decoloniality; intersectionality; and supporting learners to understand, critically reflect on, and act against structural and systemic forms of oppressions. In addition, there is variation in the integration of AOP in health professions education with the most common methods consisting of discussions, cases, reflection, learning through lived experiences, and the incorporation of humanities within a longitudinal curriculum. The results of this scoping review highlight the need for health professions education to develop one clear concept that educators use when teaching about anti-oppression, which may reduce working in silos and allow educators to better collaborate with each other in advancing this work. In addition, this review suggests that health professional programs should consider incorporating AOP in curricula with a broad and longitudinal approach utilizing the common methods of delivery. To better support programs in including AOP in curricula, further research is required to emphasize the benefits, provide clarity on its conceptualization, and determine the most effective methods of integration.

Artificial Intelligence in Medicine: A New Frontier

Artificial intelligence (AI) refers to the engineering and science of making intelligent machines through algorithms or rules, mimicking human cognitive functions, such as learning and problem-solving. AI has several branches, such as machine learning and deep learning, which can add intelligence to applications. Machine learning is the study of algorithms that allow computer programs to improve automatically through experience. Deep learning algorithms learn from an extensive, multi-layered collection of interconnected processes and expose these processors to many examples. In the coming years, the integration of AI in routine medical care is expected to revolutionize Medicine, potentially improving patient care and quality of life. The time required for a diagnosis can be greatly reduced, and diagnostic efficiency can be significantly enhanced when AI assists clinicians. Large language model chatbots are capable of clinical expert-level medical note-taking, consultation, and questionanswering. Chatbotscan generate human-like text, may help diagnose diseases based on medical records, and may suggest treatment options or plans. Artificial intelligence algorithms, particularly deep learning, have demonstrated remarkable progress in radiological image analysis and diagnosis and may improve radiologists’ efficiency. These algorithms may also improve diagnostic accuracy in dermatology, histopathology, fundoscopy, endoscopy, and other medical images. Natural language processing and ambient clinical intelligence automate administrative duties like recording patient visits in electronic health records, streamlining clinical workflow, and freeing up doctors to spend more time with patients. AI may also help with new drug discoveries, precision medicine, and clinical research. AI developments can revolutionize several healthcare-related fields and pave the way for a more individualized, accurate, predictive, and portable future. Bangladesh J Medicine 2024; 35: 54-60

Emotional labour in nursing research: A bibliometric analysis.

This study aims to conduct a comprehensive bibliometric analysis to explore the trajectory and thematic developments of emotional labour research in nursing. Utilizing descriptive and bibliometric analysis techniques. The data analysis and graphical presentation were conducted using the Bibliometrix Package in R software. The Web of Science Core Collection (WoSCC) database was searched on October 20, 2023. From 1992 to 2023, 842 authors published relevant articles, yielding 779 author keywords. There has been a general upward trend in the number of articles published over the past 30 years, with an annual growth rate of 11.71%. Keyword co-occurrence cluster analysis revealed the main focus areas of research on emotional labour antecedents and consequences, regulatory modalities, training and education, as well as research methods and application scenarios. Emotional labour significantly influences nursing staff's well-being and patient care outcomes. Effective management and education regarding emotional labour are crucial for enhancing nursing staff performance and patient care quality. Future research should focus on long-term effects, training efficacy, regulatory strategies across clinical settings, and innovative approaches to address current challenges. This study provides valuable insights into the unique trajectory and thematic developments of emotional labour research in nursing. The findings underscore the importance of addressing emotional labour in nursing practice and education to improve patient care outcomes and nursing staff well-being. Adherence to recognized bibliometric reporting methods, following relevant EQUATOR guidelines. This study is based solely on existing literature and did not involve patients or the public in its design, conduct, analysis, interpretation, or preparation.

Unveiling the mechanisms by which inclusive leadership influences the good soldier syndrome among health-care employees

Purpose The purpose of this study is to investigate the relationship between inclusive leadership (IL), psychological safety (PS), affective commitment (AC) and organizational citizenship behavior (OCB) at the workplace. By understanding the sequential linkages, the research aims to provide insights for fostering a positive organizational culture that promotes employee commitment and employees’ willingness to go the extra mile for the organization. Design/methodology/approach The authors study how IL and OCB are related through PS and AC. For this purpose, cross-sectional data from 384 nursing professionals in India was collected and structural equation modeling was conducted on the same using IBM AMOS. Findings The study found that IL has a major impact on OCB. The study further found that perceived IL leads to PS which is associated with OCB through AC. Research limitations/implications The study has many theoretical and practical implications. This study uses a framework that is based on Affective events theory. In a health-care environment, IL can foster AC by promoting a culture of respect, collaboration and value for diverse perspectives, which enhances health-care professionals’ emotional attachment to their work and the organization. Additionally, by encouraging open communication and a sense of belonging, IL contributes to OCB, as health-care staff are more likely to engage in discretionary behaviors that support the overall effectiveness and positive functioning of the health-care team if PS is improved, ultimately improving patient care outcomes. Originality/value To the best of the authors’ knowledge, this is one of the primary studies that looks into the sequential mechanism through which IL impacts OCB.

Medicine in the 21st Century: How Not to Lose the Art of Healing?

In our time of advanced technologies, the Internet, and the enormous acceleration of all areas of humanitarian activity, medical scientists make outstanding discoveries and innovations that revolutionize diagnostic approaches and treatment options, improve patient care, and enrich the way to a healthier world. Is there still a place for the art of medicine in the era of innovations and artificial intelligence? Although modern technology in the field of medicine with computerization of every aspect (including electronic medical records, laboratory data entry, images, patients' monitoring systems, etc.), young physicians, while embracing that innovation, should be trained in the development of clinical thinking and the establishment of personalized connections to the patients. Young physicians should be taught to adopt the ability to see, hear, and feel a patient's problems, formulate optimal management, and be empathetic to the patient's emotions and suffering. Communication skills are a key to successful medical practice and a part of "the Introduction to Clinic" course in almost all medical schools, but a substantial lack of time in clinical settings creates new challenges for communication between medical students, clinical teachers, and patients in medical schools. In many areas around the world, including Ukraine, this problem has been aggravated by wars, epidemics, pandemics, and natural disasters. At the same time, developing clinical observational skills remains an integral part of medical education and the basis of medical art. The art of a careful physical exam should not be lost behind a computer screen. Dnipro State Medical University has traditions of integrating centuries-old world historical experiences of teaching the art of healing into the philosophical concept of forming the critical clinical thinking of medical students. Our medical school has incorporated "diagnosing the canvas" into its curriculum. A thematic gallery of diagnostic symptoms and signs from great artists of all times and peoples has been created, expanding the young physicians' educational horizons in clinical thinking, empathy, and mercy.

Exploring the Experiences of Health Care Providers and Managers in the COVID-19 Pandemic: A Summative Content Analysiss

Background: The rapid morbidity and high mortality rates associated with COVID-19 have placed significant strain on healthcare systems. Mazandaran, as the third province, has been affected by this infectious pandemic. Objectives: The present study aimed to explore the experiences of healthcare providers and managers during the COVID-19 pandemic. Methods: The present qualitative research study was conducted using the content analysis approach between April 2022 and August 2022 to provide deep insights into the experiences of healthcare providers and managers during the COVID-19 pandemic. The participants included 15 hospital managers, comprising 2 clinical nurse supervisors, 3 matrons, and 10 chief executive officers. The participants were purposefully selected from hospitals affiliated with Mazandaran University of Medical Sciences, Iran. Data were collected through semi-structured individual interviews and the assessment of documents from the vice-chancellors of treatment, food and drug, development, and human resources. Results: The category of "challenges and strategies" emerged during the process of reducing and abstracting the codes. The challenges category evolved from three subcategories, including care and cure challenges, challenges related to providing physical resources and personal protective equipment (PPE), and challenges faced by healthcare providers. Similarly, the strategies category emerged from three subcategories, including strategies to improve patient care and treatment outcomes, strategies for procuring PPE and physical resources, and strategies for human resource management. Conclusions: The results of the present study revealed several challenges and strategies experienced by healthcare managers at Mazandaran University of Medical Sciences and hospital managers. Additionally, based on the existing infrastructure of Mazandaran University and the managerial skills of the managers, successful strategies were employed to tackle these challenges. These experiences can be utilized to inform revisions to health-related policies. Documenting these experiences can aid in better management of future infectious pandemics.

Real-Time Use of SGLT2i Verified in Pre-dialysis: The RSVP Cross-sectional Study.

The use of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in nephrology practice is increasingly becoming standard of care in patients with diabetes or those with proteinuria. The primary outcome was to identify the proportion of pre-dialysis patients with chronic kidney disease (CKD) G3a, G3b, or G4 prescribed an SGLT2i and describe their characteristics. This was a retrospective, multicentric, cross-sectional study of patients with CKD followed at 4 pre-dialysis clinics in the province of Quebec, Canada. We collected data of multiple covariates associated with prescribing SGLT2i in patients over 18 years of age with CKD G3a, G3b, or G4. We then performed a multivariate logistic regression to assess their associations. Of the 874 patients included, 22.7% were prescribed an SGLT2i. Factors most strongly associated included male sex (odds ratio [OR] = 4.88, 95% CI = 2.38-10.03), being prescribed metformin (OR = 4.30, 95% CI = 2.23-8.31), having type 2 diabetes (OR = 4.00, 95% CI = 1.86-8.62), or having an albumin-to-creatinine ratio greater than 300 mg/g (OR = 1.84, 95% CI = 1.08-3.14). The majority of patients (60.4%) had their SGLT2i initiated by the pre-dialysis clinic and the most frequent adverse event was an initial increase in serum creatinine 1 week after starting treatment (33.9%). An increasing number of patients with CKD are being prescribed SGLT2i. Nonetheless, significant disparities in sex, severity of disease, and comorbidities remain. We suggest that specific strategies be put in place to promote prescribing of SGLT2i in women and other at-risk populations, in particular among nephrology teams, to improve patient care.

"Incorporating large language models into academic neurosurgery: embracing the new era".

This correspondence examines how LLMs, such as ChatGPT, have an effect on academic neurosurgery. It emphasises the potential of LLMs in enhancing clinical decision-making, medical education, and surgical practice by providing real-time access to extensive medical literature and data analysis. Although this correspondence acknowledges the opportunities that come with the incorporation of LLMs, it also discusses challenges, such as data privacy, ethical considerations, and regulatory compliance. Additionally, recent studies have assessed the effectiveness of LLMs in perioperative patient communication and medical education, and stressed the need for cooperation between neurosurgeons, data scientists, and AI experts to address these challenges and fully exploit the potential of LLMs in improving patient care and outcomes in neurosurgery.

Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics.

Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care. The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process. A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms "artificial intelligence in obstetrics", "maternal-fetal medicine", and "machine learning" combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload. The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.

Psychometric Analyses of a Food Literacy Behaviors Questionnaire for Adults With Type 2 Diabetes Mellitus.

The purpose of the study was to develop and validate a food literacy instrument specific to individuals with type 2 diabetes mellitus (T2DM). Twelve T2DM-specific, food literacy-related statements underwent content validity and face validity testing, and exploratory factor analysis (EFA) was conducted (phase 1). The 6 statements remaining after EFA testing were added to an existing food literacy questionnaire called the Eating and Food Literacy Behaviors Questionnaire (EFLBQ). Confirmatory factor analysis (CFA) examined the EFLBQ plus the T2DM-specific statements, and the instrument was called the Eating and Food Literacy Questionnaire for Diabetics (EFLBQ-D; phase 2). Both EFA and CFA were administered by third-party provider Centiment via Qualtrics to adults with T2DM residing in the 13 United States Department of Agriculture Southern region states. Reliability was assessed via point estimate reliability testing. The EFA items clustered into 2 factors each with 3 statements: (1) carbohydrate counting and nutrition label reading and (2) healthy food preparation methods. The CFA indicated that the EFLBQ-D had good fit and that the factors had good reliability. The EFLBQ's integrity was maintained with its items loading on the respective constructs. The EFLBQ-D appears to be a valid, reliable instrument for use by researchers and professionals to better understand food behaviors and food literacy skills of those with T2DM. This is an important tool to help individualize medical nutrition therapy, improving patient care in this population.

Cytomorphological spectrum of breast fine needle aspiration cytology using the International Academy of Cytology Yokohama System and evaluation of associated risk of malignancy–A retrospective study in a tertiary care cente

BackgroundFine-needle aspiration cytology (FNAC) of the breast is a well-established technique that aids in the preoperative identification and differentiation between nonneoplastic and neoplastic breast lesions. Breast lesions can be efficiently categorized into different cytological groups using the International Academy of Cytology (IAC) Yokohama System. The aim of this study was to classify breast lesions based on the proposed Yokohama system of reporting breast cytology and identify the need for a unified system of reporting breast lesions.Materials and methodsThis retrospective descriptive study was performed in the Department of Pathology, Government Medical College, Datia, M.P. from January 2019 to June 2023. A total of 762 FNAC slides reported during this period were retrieved, re-examined and reclassified according to the IAC Yokohama System. Histopathological diagnoses were also retrieved and correlated with the cytological findings wherever available.ResultsIn the present study, a total of 762 smears of breast fine needle aspirations were included and categorized using the IAC Yokohama System. There were 3.8% cases (29/762) in the C1 category (insufficient), 71.6% cases (545/762) in the C2 category (benign), 6.3% cases (48/762) in the C3 category (atypical), 3.1% cases (24/762) in the C4 category (suspicious for malignancy) and 15.2% (116/762) cases in the C5 category (malignant).ConclusionFNAC is a reliable diagnostic technique for the evaluation and management of breast lesions prior to surgery. Implementing the IAC Yokohama System for reporting breast cytopathology enhances the accuracy of diagnosing breast lesions, reduces missed diagnoses, and offers reliable estimates of risk of malignancy. Adopting the Yokohama system is a crucial first step in boosting the overall effectiveness of breast cytology reporting among clinicians and pathologists, which will ultimately lead to improved communication and patient care.

Cystic fibrosis management in pediatric population—from clinical features to personalized therapy

Cystic fibrosis (CF) is an autosomal recessive disease caused by mutations of the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR). In 1949, it's been identified as a monogenic disease and was thought to primarily affect individuals of Northern European descent. It was the most prevalent autosomal recessive disease that shortens life. With the availability of multiple testing methodologies nowadays, there is a chance to create novel and enhanced treatment options. Even in the absence of a high sweat chloride test (SCT) result, the discovery of two causal mutations is diagnostic for cystic fibrosis (CF). For a CF diagnosis, however, at least two positive E sweat chloride tests are still required. In order to achieve early and active intervention to manage cystic fibrosis (CF) and its comorbidities, treatment regimens for pediatric patients should be evaluated, improved, and closely monitored. New developments in the treatment of cystic fibrosis (CF) have led to the development of medications derived from molecules that target the pathogenetic pathway of the illness. These options are very efficient and allow pediatric patients to receive individualized care. However, in order to better direct patient care and enhance patient outcomes, it is crucial to research uncommon CF mutations, which can provide crucial information about the prognosis of the disease and the relationships between genotype and phenotype. To ensure the success of creating novel, safer, and more efficient treatment approaches, a deeper understanding of the pathogeny of the illness is required. In the age of customized medicine, genetic research will be essential to improving patient care and quality of life for those with uncommon mutations.