PurposeThe National Health Insurance Service-Health Screening Cohort (NHIS-HEALS) is a cohort of participants who participated in health screening programmes provided by the NHIS in the Republic of ...Korea. The NHIS constructed the NHIS-HEALS cohort database in 2015. The purpose of this cohort is to offer relevant and useful data for health researchers, especially in the field of non-communicable diseases and health risk factors, and policy-maker.ParticipantsTo construct the NHIS-HEALS database, a sample cohort was first selected from the 2002 and 2003 health screening participants, who were aged between 40 and 79 in 2002 and followed up through 2013. This cohort included 514 866 health screening participants who comprised a random selection of 10% of all health screening participants in 2002 and 2003.Findings to dateThe age-standardised prevalence of anaemia, diabetes mellitus, hypertension, obesity, hypercholesterolaemia and abnormal urine protein were 9.8%, 8.2%, 35.6%, 2.7%, 14.2% and 2.0%, respectively. The age-standardised mortality rate for the first 2 years (through 2004) was 442.0 per 100 000 person-years, while the rate for 10 years (through 2012) was 865.9 per 100 000 person-years. The most common cause of death was malignant neoplasm in both sexes (364.1 per 100 000 person-years for men, 128.3 per 100 000 person-years for women).Future plansThis database can be used to study the risk factors of non-communicable diseases and dental health problems, which are important health issues that have not yet been fully investigated. The cohort will be maintained and continuously updated by the NHIS.
Purpose of Review
Artificial intelligence (AI) technology holds both great promise to transform mental healthcare and potential pitfalls. This article provides an overview of AI and current ...applications in healthcare, a review of recent original research on AI specific to mental health, and a discussion of how AI can supplement clinical practice while considering its current limitations, areas needing additional research, and ethical implications regarding AI technology.
Recent Findings
We reviewed 28 studies of AI and mental health that used electronic health records (EHRs), mood rating scales, brain imaging data, novel monitoring systems (e.g., smartphone, video), and social media platforms to predict, classify, or subgroup mental health illnesses including depression, schizophrenia or other psychiatric illnesses, and suicide ideation and attempts. Collectively, these studies revealed high accuracies and provided excellent examples of AI’s potential in mental healthcare, but most should be considered early proof-of-concept works demonstrating the potential of using machine learning (ML) algorithms to address mental health questions, and which types of algorithms yield the best performance.
Summary
As AI techniques continue to be refined and improved, it will be possible to help mental health practitioners re-define mental illnesses more objectively than currently done in the DSM-5, identify these illnesses at an earlier or prodromal stage when interventions may be more effective, and personalize treatments based on an individual’s unique characteristics. However, caution is necessary in order to avoid over-interpreting preliminary results, and more work is required to bridge the gap between AI in mental health research and clinical care.
This study of loneliness across adult lifespan examined its associations with sociodemographics, mental health (positive and negative psychological states and traits), subjective cognitive ...complaints, and physical functioning.
Analysis of cross-sectional data.
340 community-dwelling adults in San Diego, California, mean age 62 (SD = 18) years, range 27-101 years, who participated in three community-based studies.
Loneliness measures included UCLA Loneliness Scale Version 3 (UCLA-3), 4-item Patient-Reported Outcomes Measurement Information System (PROMIS) Social Isolation Scale, and a single-item measure from the Center for Epidemiologic Studies Depression (CESD) scale. Other measures included the San Diego Wisdom Scale (SD-WISE) and Medical Outcomes Survey- Short form 36.
Seventy-six percent of subjects had moderate-high levels of loneliness on UCLA-3, using standardized cut-points. Loneliness was correlated with worse mental health and inversely with positive psychological states/traits. Even moderate severity of loneliness was associated with worse mental and physical functioning. Loneliness severity and age had a complex relationship, with increased loneliness in the late-20s, mid-50s, and late-80s. There were no sex differences in loneliness prevalence, severity, and age relationships. The best-fit multiple regression model accounted for 45% of the variance in UCLA-3 scores, and three factors emerged with small-medium effect sizes: wisdom, living alone and mental well-being.
The alarmingly high prevalence of loneliness and its association with worse health-related measures underscore major challenges for society. The non-linear age-loneliness severity relationship deserves further study. The strong negative association of wisdom with loneliness highlights the potentially critical role of wisdom as a target for psychosocial/behavioral interventions to reduce loneliness. Building a wiser society may help us develop a more connected, less lonely, and happier society.
The number of geriatric patients who undergo surgery has been increasing, but there are insufficient tools to predict postoperative outcomes in the elderly.
To design a predictive model for adverse ...outcomes in older surgical patients.
From October 19, 2011, to July 31, 2012, a single tertiary care center enrolled 275 consecutive elderly patients (aged ≥65 years) undergoing intermediate-risk or high-risk elective operations in the Department of Surgery.
The primary outcome was the 1-year all-cause mortality rate. The secondary outcomes were postoperative complications (eg, pneumonia, urinary tract infection, delirium, acute pulmonary thromboembolism, and unplanned intensive care unit admission), length of hospital stay, and discharge to nursing facility.
Twenty-five patients (9.1%) died during the follow-up period (median interquartile range, 13.3 11.5-16.1 months), including 4 in-hospital deaths after surgery. Twenty-nine patients (10.5%) experienced at least 1 complication after surgery and 24 (8.7%) were discharged to nursing facilities. Malignant disease and low serum albumin levels were more common in the patients who died. Among the geriatric assessment domains, Charlson Comorbidity Index, dependence in activities of daily living, dependence in instrumental activities of daily living, dementia, risk of delirium, short midarm circumference, and malnutrition were associated with increased mortality rates. A multidimensional frailty score model composed of the above items predicted all-cause mortality rates more accurately than the American Society of Anesthesiologists classification (area under the receiver operating characteristic curve, 0.821 vs 0.647; P = .01). The sensitivity and specificity for predicting all-cause mortality rates were 84.0% and 69.2%, respectively, according to the model's cutoff point (>5 vs ≤5). High-risk patients (multidimensional frailty score >5) showed increased postoperative mortality risk (hazard ratio, 9.01; 95% CI, 2.15-37.78; P = .003) and longer hospital stays after surgery (median interquartile range, 9 5-15 vs 6 3-9 days; P < .001).
The multidimensional frailty score based on comprehensive geriatric assessment is more useful than conventional methods for predicting outcomes in geriatric patients undergoing surgery.
Acute liver failure is an infrequent yet fatal condition marked by rapid liver function decline, leading to abnormalities in blood clotting and cognitive impairment among individuals without prior ...liver ailments. The primary reasons for liver failure are infection with hepatitis virus or overdose of certain medicines, such as acetaminophen. Phaeodactylum tricornutum (PT), a type of microalgae known as a diatom species, has been reported to contain an active ingredient with anti-inflammatory and anti-obesity effects. In this study, we evaluated the preventive and therapeutic activities of PT extract in acute liver failure. To achieve our purpose, we used two different acute liver failure models: acetaminophen- and D-GalN/LPS-induced acute liver failure. PT extract showed protective activity against acetaminophen-induced acute liver failure through attenuation of the inflammatory response. However, we failed to demonstrate the protective effects of PT against acute liver injury in the D-GalN/LPS model. Although the PT extract did not show protective activity against two different acute liver failure animal models, this study clearly demonstrates the importance of considering the differences among animal models when selecting an acute liver failure model for evaluation.
We designed and prepared the imidazoline‐2‐thione containing OCl− probes, PIS and NIS, which operate through specific reactions with OCl− that yield corresponding fluorescent imidazolium ions. ...Importantly, we demonstrated that PIS can be employed to image OCl− generation in macrophages in a co‐culture system. We have also employed two‐photon microscopy and PIS to image OCl− in live cells and tissues, indicating that this probe could have wide biological applications.
Imaging in a co‐culture system: The first two‐photon fluorescence probes for hypochlorite were developed. The imidazoline‐2‐thione probes operate through specific reactions with OCl− that produce fluorescent products. Using the probes, imaging OCl− generation in macrophages in a co‐culture system was demonstrated.
The U.S. population over 65 years of age is increasing. Most older adults prefer to age in place, and technologies, including Internet of things (IoT), Ambient/Active Assisted Living (AAL) robots and ...other artificial intelligence (AI), can support independent living. However, a top-down design process creates mismatches between technologies and older adults' needs. A user-centered design approach was used to identify older adults' perspectives regarding AAL and AI technologies and gauge interest in participating in a co-design process. A survey was used to obtain demographic characteristics and assess privacy perspectives. A convenience sample of 31 retirement community residents participated in one of two 90-min focus group sessions. The semi-structured group interview solicited barriers and facilitators to technology adoption, privacy attitudes, and interest in project co-design participation to inform technology development. Focus group sessions were audiotaped and professionally transcribed. Transcripts were reviewed and coded to identify themes and patterns. Descriptive statistics were applied to the quantitative data. Identified barriers to technology use included low technology literacy, including lack of familiarity with terminology, and physical challenges, which can make adoption difficult. Facilitators included an eagerness to learn, interest in co-design, and a desire to understand and control their data. Most participants identified as privacy pragmatics and fundamentalists, indicating that privacy is important to older adults. At the same time, they also reported a willingness to contribute to the design of technologies that would facilitate aging independently. There is a need to increase technology literacy of older adults along with aging literacy of technologists.
With evaluation for physical performance, measuring muscle mass is an important step in detecting sarcopenia. However, there are no methods to estimate muscle mass from blood sampling.
To develop a ...new equation to estimate total-body muscle mass with serum creatinine and cystatin C level, we designed a cross-sectional study with separate derivation and validation cohorts. Total body muscle mass and fat mass were measured using dual-energy x-ray absorptiometry (DXA) in 214 adults aged 25 to 84 years who underwent physical checkups from 2010 to 2013 in a single tertiary hospital. Serum creatinine and cystatin C levels were also examined.
Serum creatinine was correlated with muscle mass (P < .001), and serum cystatin C was correlated with body fat mass (P < .001) after adjusting glomerular filtration rate (GFR). After eliminating GFR, an equation to estimate total-body muscle mass was generated and coefficients were calculated in the derivation cohort. There was an agreement between muscle mass calculated by the novel equation and measured by DXA in both the derivation and validation cohort (P < .001, adjusted R2 = 0.829, β = 0.95, P < .001, adjusted R2 = 0.856, β = 1.03, respectively).
The new equation based on serum creatinine and cystatin C levels can be used to estimate total-body muscle mass.
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