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Nov 24, 2024

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Literature Review Intrinsic Capacity and Healthy Aging In order to conform to the emerging paradigm of healthy aging, Muneera, Muhammad, and Althaf (2022) focus on intrinsic capacity (IC) as a key predictor of functional ability and well- being in older people. The research shows that as people become older, their brainpower naturally declines along with it. Moreover, a number of lifestyle factors have been linked to diminished IQ, including smoking and light alcohol use. On the other hand, vigorous exercise and other forms of physical activity like yoga are positively correlated with higher IC levels. The study emphasizes the significance of these findings in encouraging healthy practices among the elderly, which may lead to improved physical capacities and well-being in old age. Qualitative Study in South Africa South Africa is a low- and middle-income country dealing with a dual burden of disease, and a paper by van Pinxteren et al. (2023) provides a comprehensive overview of the methodologies used in a qualitative study focusing on the complex difficulties faced by people living with HIV and non-communicable diseases (NCDs) in this country. The purpose of this study is to apply the Burden of Treatment Theory (BoTT) and the Cumulative Complexity Model (CuCoM) to the question of how much of an impact patients' medical care has on their capacity to provide for themselves. In addition to providing a thorough summary of the methodology used, this study also sheds light on important choices made throughout the research's design, participant recruiting, and data analysis phases. This study adds considerably to the growing body of literature on qualitative research in complicated health situations in low-resource countries. Functional Profile in Long-Term Care Fonseca et al. (2023) undertook a research to determine the functional profile of Portuguese residents in long-term care facilities. The main objective of this study is to investigate the correlation between the functional profile and demographic factors, including educational attainment, gender, age, and emotional well-being. The outcomes of the investigation show intricate patterns within the functional profile. The study found that within the first 90 days of hospitalization, there was an improvement in activities of daily living and cognitive states, whereas mobility and instrumental activities of daily living showed a reduction. Moreover, a

comprehensive decrease was seen across all domains after a period of 450 days of hospitalizationThis finding highlights the significance of the time frame spanning from 90 to 360 days in long-term care facilities, elucidating the critical impact of education on the functional outcomes of elderly individuals within these environments. AI-Based Embryo Selection in IVF Barnes et al. (2023) provide a novel approach to a problem in in-vitro fertilization (IVF) that has plagued the field for some time now: choosing which embryos will survive the transfer process. The conventional approaches, namely morphological quality evaluation and morphokinetic analysis, are subject to some constraints arising from the inherent heterogeneity in how different observers perceive the data. Another technique, known as preimplantation genetic testing for aneuploidy (PGT-A), although providing valuable information, is characterized by invasiveness and high expenses. In order to tackle this issue, the authors put up the concept of STORK-A, a technique for evaluating embryos that is both automated and non-invasive. This approach utilizes artificial intelligence (AI) to make predictions about the ploidy status of embryos. This work focuses on the creation of STORK-A, a computational model. The model is developed using a dataset consisting of 10,378 embryos. The dataset includes many factors such as static photos, morphokinetic parameters, blastocyst morphological evaluations, maternal age, and ploidy status. The researchers used machine-learning and deep-learning methodologies in order to train the model. The prediction accuracy of STORK-A was evaluated using separate and external datasets, indicating its capacity to generalize beyond the original dataset. The results demonstrate the capabilities of STORK-A. The model demonstrated accuracies ranging from 63.4% to 77.6% across several categorization tasks by including many parameters like as pictures, maternal age, morphokinetics, and blastocyst score. The system driven by artificial intelligence shown encouraging results in non-invasively predicting the ploidy status of embryos, so presenting a potential alternative to current approaches for selecting embryos. This study presents STORK-A as a prospective standardized instrument for assisting in the prioritizing of embryos for implantation or advocating preimplantation genetic testing for aneuploidy (PGT-A), thereby addressing significant obstacles in the area of in vitro fertilization (IVF). Multimorbidity Patterns and Comorbidities

The study conducted by Kuan et al. (2023) aims to fill a notable gap in the existing worldwide statistics on multimorbidity and comorbidity, with a particular focus on minority ethnic groups and younger populations. The objective of this study is to determine the frequency at which common diseases co-occur and identify any non-random relationships between different health disorders across diverse age groups and ethnic backgrounds within a multiethnic community. This study used a population-based approach to examine electronic health records of more than 3.8 million persons in England. The researchers utilize data from the Clinical Practice Research Datalink, which is connected with the Hospital Episode Statistics dataset. The researchers look at demographics like race, gender, and age to see how common comorbidities are and how they are grouped together. In order to examine these trends using different subgroup criteria, the researchers have developed online interactive tools that can be used anywhere with an internet connection. As a result of this advancement, doctors, patients, researchers, politicians, and funders will have easier access to crucial data. Patients, clinicians, researchers, policymakers, and healthcare providers are all mentioned as potential beneficiaries of the study's findings and instruments. Prevention strategies, service delivery, training priorities, policy recommendations, and clinical trial designs may all benefit from the information gleaned from this research. Machine Learning for Interstitial Lung Diseases Barnes et al. (2023) explore the potential of machine learning to address challenges associated with the diagnosis, prognosis, and treatment of interstitial lung diseases (ILDs). Problems exist in diagnosing illnesses at an early stage, making accurate prognostications, and measuring the effectiveness of treatments. With the goal of filling up the current gaps in the treatment of interstitial lung disease (ILD), this paper presents an overview of the application of machine learning algorithms in imaging biomarker research. The authors stress the importance of machine learning algorithms in identifying high-risk groups for interstitial lung disease (ILD), predicting the severity of lung fibrosis, linking radiological abnormalities to declines in lung function, and possibly serving as outcome measures in therapy studies. The use of this technology in enhancing the delivery of care to people with ILD has been shown. Moreover, the incorporation of deep-learning-based image analysis and radiomics within the framework of machine learning offers further opportunities for advancement. The study highlights the value of cooperation and consistency in the development of optimum algorithms, while also emphasizing the need of

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verifying prospective radiological biomarkers against relevant predictors of illness outcomes. The expanding capabilities of machine learning have the potential to significantly transform the role of radiography in the therapy of interstitial lung disorders. Global Threat of Spotted Fever Group Rickettsioses Chinese researchers Zhang et al. (2022) discuss the growing danger posed to global public health by spotted fever group (SFG) rickettsioses. The objective of this research is to provide insight into the distribution and risk associated with rickettsial infections, a group of diseases that have gained recognition but still lack comprehensive understanding. The study utilizes a comprehensive review and modeling analysis to delineate the global distribution of verified species of SFG rickettsiae (SFGR) in animals, vectors, and peopleThe results demonstrate the presence of unique spatial groupings characterized by specific environmental and ecoclimatic attributes. The research emphasizes Rickettsia felis as the prevailing species of spotted fever group rickettsiae (SFGR), posing a significant danger to around 4.4 billion individuals. This is followed by Rickettsia conorii, which puts around 3.7 billion people at risk, and Rickettsia africae, which poses a risk to approximately 3.6 billion individuals. In summary, the study highlights the significant contribution of several vectors to the increasing prevalence of SFGR infections in the human population. The authors emphasize the need of enhancing awareness, diagnosis, and monitoring in places with a high risk of infection, especially in cases when human diseases may be inaccurately reported. This research offers significant contributions to the understanding of the worldwide prevalence and related hazards of SFGR infections, therefore establishing a fundamental basis for the mitigation and control of this matter in the field of public health. Patient-Centered Approach to Multimorbidity Varela et al. (2023) investigate the challenges of implementing sophisticated healthcare treatments, zeroing in on the implementation of a patient-centered approach to dealing with multimorbidity. With funding from the Centro de Innovación en Salud ANCORA UC, this research looks at how the Chilean public healthcare system may benefit from implementing a patient-centered model of treatment for those with multiple chronic conditions. The findings shed light on areas where clinical services demonstrated relatively moderate gains while also highlighting the major achievements achieved in operational settings. The findings underscore

the significance of various factors in determining the outcome of complex therapies, including population size, organizational dynamics, coordination among healthcare teams, further training, and support from decision-makers. This research shows that KPIs can track complicated healthcare procedures. This informs decision-makers and ensures success. The findings may help improve patient-centered healthcare practices statewide and internationally. Multimorbidity Clinic for Complex Disorders Bell et al. (2023) propose a novel way for primary care doctors to manage patients with multiple chronic illnesses. The Silkeborg Regional Hospital in Denmark opened the Multimorbidity Clinic (CM) in 2012. This clinic assists primary care doctors (PCPs) in managing severe multimorbid disorders. This study shows that a wide range of healthcare professionals must collaborate to implement a comprehensive CM strategy. The Clinic for Multimorbidity (CM) brings together a variety of sectors, professions, organizations, and basic and secondary care to provide a new therapy that overcomes the constraints of the conventional healthcare system. Under the case manager's guidance, multiple experts and tests were needed to diagnose patients. The study suggests that integrated treatment may assist people with multiple health conditions. It also provides critical information that may impact healthcare policy and practice. Geriatric Mental Health Care Reynolds et al. (2023) report a rise in senior mental illness. The authors note that treating multimorbidities takes time and that geriatric mental health care is difficult. This research evaluates how neurocognitive diseases, severe depression, schizophrenia, and drug use disorders affect senior impairment. The authors recommend a multidisciplinary approach to patient care that combines diagnosis, treatment, community outreach, and resource management. The authors urge rethinking traditional views about aging and emphasizing mental and physical health. The objective of this approach is to challenge ageism and reframe societal views about aging and mental problems in older individuals. In conclusion, the paper presents a thorough guide for forthcoming clinical practices and research initiatives that address the complex mental health requirements of elderly individuals. Multimorbidity in Epilepsy Patients

In their study, Gaitatzis and Majeed (2023) emphasize the importance of multimorbidity in the healthcare sector, as it is closely associated with several aspects like aging, frailty, polypharmacy, and the requirements of health and social care. The study emphasizes that individuals who have epilepsy and many comorbidities have a heightened susceptibility to depression, suicide, early death, diminished health-related quality of life, as well as escalated healthcare expenses and hospitalizations. The authors propose a paradigm change, whereby the conventional practice of treating individual illnesses and comorbidities in isolation is discouraged. In order to proficiently oversee patients afflicted with epilepsy and many comorbidities, the authors posit a want for enhanced healthcare methodologies that duly acknowledge the onerousness of multimorbidity linked to epilepsy. This entails identifying and analyzing patterns of illness clusters and understanding their influence on health outcomes. The essay proposes a fundamental change in the approach of treating patients with epilepsy who also have several other medical conditions. This aligns with the larger objective of promoting patient-centered treatment and boosting overall well-being.

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References Barnes, H., Humphries, S.M., George, P.M., Assayag, D., Glaspole, I., Mackintosh, J.A., Corte, T.J., Glassberg, M., Johannson, K.A., Calandriello, L. and Felder, F., 2023. Machine learning in radiology: the new frontier in interstitial lung diseases. The Lancet Digital Health . Barnes, J., Brendel, M., Gao, V.R., Rajendran, S., Kim, J., Li, Q., Malmsten, J.E., Sierra, J.T., Zisimopoulos, P., Sigaras, A. and Khosravi, P., 2023. A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: A retrospective model development and validation study. The Lancet Digital Health , 5 (1), pp.e28-e40. Bell, C., Vedsted, P., Kraus, D.G.A., Fredberg, U., Jeffery, L., Dahlgaard, M.B., Aarhus, R. and Appel, C.W., 2023. Clinic for Multimorbidity: An Innovative Approach to Integrate General Practice and Specialized Health Care Services. International Journal of Integrated Care , 23 (2). Fonseca, C., Ramos, A., Morgado, B., Quaresma, P., Garcia-Alonso, J., Coelho, A. and Lopes, M., 2023. Long-term care units: a Portuguese study about the functional profile. Frontiers in Aging , 4 , p.1192718. Gaitatzis, A. and Majeed, A., 2023. Multimorbidity in people with epilepsy. Seizure . Kuan, V., Denaxas, S., Patalay, P., Nitsch, D., Mathur, R., Gonzalez-Izquierdo, A., Sofat, R., Partridge, L., Roberts, A., Wong, I.C. and Hingorani, M., 2023. Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study. The Lancet Digital Health , 5 (1), pp.e16-e27. Muneera, K., Muhammad, T. and Althaf, S., 2022. Socio-demographic and lifestyle factors associated with intrinsic capacity among older adults: evidence from India. BMC geriatrics , 22 (1), pp.1-16. Reynolds 3rd, C.F., Jeste, D.V., Sachdev, P.S. and Blazer, D.G., 2022. Mental health care for older adults: recent advances and new directions in clinical practice and research. World Psychiatry , 21 (3), pp.336-363.

Van Pinxteren, M., Mbokazi, N., Murphy, K., Mair, F.S., May, C. and Levitt, N.S., 2023. Using qualitative study designs to understand treatment burden and capacity for self-care among patients with HIV/NCD multimorbidity in South Africa: A methods paper. Journal of multimorbidity and comorbidity , 13 , p.26335565231168041. Varela, T., Zamorano, P., Muñoz, P., Rain, C., Irazoqui, E., Sapag, J.C. and Tellez, A., 2023. Evaluation of the implementation progress through key performance indicators in a new multimorbidity patient-centered care model in Chile. BMC Health Services Research , 23 (1), p.439. Zhang, Y.Y., Sun, Y.Q., Chen, J.J., Teng, A.Y., Wang, T., Li, H., Hay, S.I., Fang, L.Q., Yang, Y. and Liu, W., 2022. Mapping the global distribution of spotted fever group rickettsiae: a systematic review with modelling analysis. The Lancet Digital Health .

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