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While families have a central role in shaping individual choices and behaviors, healthcare largely focuses on treating individuals or supporting self-care. However, a family is also a health unit. We argue that family informatics is a necessary evolution in scope of health informatics. To deal with the needs of individuals, we must ensure technologies account for the role of their families and may require new classes of digital service. Social networks can help conceptualize the structure, composition, and behavior of families. A family network can be seen as a multiagent system with distributed cognition. Digital tools can address family needs in (1) sensing and monitoring; (2) communicating and sharing; (3) deciding and acting; and (4) treating and preventing illness. Family informatics is inherently multidisciplinary and has the potential to address unresolved chronic health challenges such as obesity, mental health, and substance abuse, support acute health challenges, and to improve the capacity of individuals to manage their own health needs.

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The use of computerized decision support systems (DSS) in nursing practice is increasing. However, research about who uses DSS, where are they implemented, and how they are linked with standards of nursing is limited. This paper presents evidence on users and settings of DSS implementation, along with specific nursing standards of practice that are facilitated by such DSS. We searched six bibliographic databases using relevant terms and identified 28 studies, each evaluating a unique DSS. Of these, 24 were used by registered nurses and 19 were implemented in short-term care units. Most of the DSS were found to facilitate nursing standards of assessment and intervention, however, outcome identification and evaluation were the least included standards. These findings not only highlight gaps in systems but also offer opportunities for further research development in this area.

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OBJECTIVE: The study sought to summarize research literature on nursing decision support systems (DSSs ); understand which steps of the nursing care process (NCP) are supported by DSSs, and analyze effects of automated information processing on decision making, care delivery, and patient outcomes. MATERIALS AND METHODS: We conducted a systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, CINAHL, Cochrane, Embase, Scopus, and Web of Science were searched from January 2014 to April 2020 for studies focusing on DSSs used exclusively by nurses and their effects. Information about the stages of automation (information acquisition, information analysis, decision and action selection, and action implementation), NCP, and effects was assessed. RESULTS: Of 1019 articles retrieved, 28 met the inclusion criteria, each studying a unique DSS. Most DSSs were concerned with two NCP steps: assessment (82%) and intervention (86%). In terms of automation, all included DSSs automated information analysis and decision selection. Five DSSs automated information acquisition and only one automated action implementation. Effects on decision making, care delivery, and patient outcome were mixed. DSSs improved compliance with recommendations and reduced decision time, but impacts were not always sustainable. There were improvements in evidence-based practice, but impact on patient outcomes was mixed. CONCLUSIONS: Current nursing DSSs do not adequately support the NCP and have limited automation. There remain many opportunities to enhance automation, especially at the stage of information acquisition. Further research is needed to understand how automation within the NCP can improve nurses' decision making, care delivery, and patient outcomes.

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Food processing bacteria play important role in providing flavors, ingredients and other beneficial characteristics to the food but at the same time some bacteria are responsible for food spoilage. Therefore, quick and reliable identification of these food processing bacteria is very necessary for the differentiation between different species which may help in the development of more useful food processing methodologies. In this study, analysis of different bacterial species (Lactobacillus fermentum, Fructobacillus fructosus, Pediococcus pentosaceus and Halalkalicoccus jeotgali) was performed with our in-house developed Ag NPs-based surface-enhanced Raman spectroscopy (SERS) method. The SERS spectral data was analyzed by multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). Bacterial species were differentiated on the basis of SERS spectral features and potential of SERS was compared with the Raman spectroscopy (RS). SERS along with PCA and PLS-DA was found to be an efficient technique for identification and differentiation of food processing bacterial species. Differentiation with accuracy of 99.5% and sensitivity of 99.7% was depicted by PLS-DA model using leave one out cross validation.

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Tigecycline (TGC) is recognised as last resort of drugs against several antibiotic-resistant bacteria. Bacterial resistance to tigecycline due to presence of plasmid-mediated mobile TGC resistance genes (tet X3/X4) has broken another defense line. Therefore, rapid and reproducible detection of tigecycline-resistant E. coli (TREC) is required. The current study is designed for the identification and differentiation of TREC from tigecycline-sensitive E. coli (TSEC) by employing SERS by using Ag NPs as a SERS substrate. The SERS spectral fingerprints of E. coli strains associated directly or indirectly with the development of resistance against tigecycline have been distinguished by comparing SERS spectral data of TSEC strains with each TREC strain. Moreover, the statistical analysis including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were employed to check the diagnostic potential of SERS for the differentiation among TREC and TSEC strains. The qualitative identification and differentiation between resistant and sensitive strains and among individual strains have been efficiently done by performing both PCA and HCA. The successful discrimination among TREC and TSEC at the strain level is performed by PLS-DA with 98% area under ROC curve, 100% sensitivity, 98.7% specificity and 100% accuracy.

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BACKGROUND: Surface-enhanced Raman spectroscopy (SERS) of body fluids is considered a quick, simple and easy to use method for the diagnosis of disease. OBJECTIVES: To evaluate rapid, reliable, and non-destructive SERS-based diagnostic tool with multivariate data analysis including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) for classification of different stages of typhoid on the basis of characteristic SERS spectral features. METHODS: SERS has been used for analysis of serum samples of different stages of typhoid including early acute stage and late acute stage in comparison with healthy samples, in order to investigate capability of this technique for diagnosis of typhoid. SERS spectral features associated with the biochemical changes taking place during the development of the typhoid fever were analyzed and identified. RESULTS: The value of area under the receiver operating characteristics (AUROC) for early acute stage versus healthy is 0.87 and that for healthy versus late acute stage is 0.52. PLS-DA classifier model gives values of 100 % for accuracy, sensitivity and specificity, respectively for the SERS spectral data sets of healthy versus early acute stage. Moreover, this classifier model gives values of 91 %, 89 % and 97 % for accuracy, sensitivity and specificity, respectively for the SERS spectral data sets of healthy versus late acute stage. CONCLUSIONS: Based on preliminary work it is concluded that SERS has potential to diagnose various stages of typhoid fever including early acute and late acute stage in comparison with healthy samples.

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In the current study, for a qualitative and quantitative study of Polymerase Chain Reaction (PCR) products of viral RNA of Hepatitis C virus (HCV) infection, surface-enhanced Raman spectroscopy (SERS) methodology has been developed. SERS was used to identify the spectral features associated with the PCR products of viral RNA of Hepatitis C in various samples of HCV-infected patients with predetermined viral loads. The measurements for SERS were performed on 30 samples of PCR products, which included three PCR products of RNA of healthy individuals, six negative controls, and twenty-one HCV positive samples of varying viral loads (VLs) using Silver nanoparticles (Ag NPs) as a SERS substrates. Additionally, on SERS spectral data, the multivariate data analysis methods including Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) were also carried out which help to illustrate the diagnostic capabilities of this method. The PLSR model is designed to predict HCV viral loads based on biochemical changes observed as SERS spectral features which can be associated directly with HCV RNA. Several SERS characteristic features are observed in the RNA of HCV which are not detected in the spectra of healthy RNA/controls. PCA is found helpful to differentiate the SERS spectral data sets of HCV RNA samples from healthy and negative controls. The PLSR model is found to be 99% accurate in predicting VLs of HCV RNA samples of unknown samples based on SERS spectral changes associated with the Hepatitis C development.

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Hepatitis B is a contagious liver disorder caused by hepatitis B virus and if not treated at an early stage, it becomes chronic and results in liver cirrhosis and hepatocellular carcinoma which can even lead to death. In present study, surface-enhanced Raman spectroscopy (SERS) is employed for the analysis of polymerase chain reaction (PCR) products of DNA extracted from hepatitis B virus (HBV) infected patients in comparison with healthy individuals. SERS spectral features are identified which are solely present in the HBV positive samples and consistently increase in intensities with increase in viral load which can be considered as a SERS spectral marker for HBV infection. For sake of understanding, these various levels of viral loads in this study are classified as low (1-1000 IU), medium (1000-10,000 IU), high (above 10,000 IU) and negative control (>1). In order to explore the efficiency of SERS for discrimination of SERS spectral datasets of different samples of varying viral loads and healthy individuals, principal component analysis (PCA) is applied. PCA is used for comparison of these classes including low, medium and high levels of viral loads with each other and with healthy class. Moreover, partial least square discriminant analysis and partial least square regression analysis are employed for the classification of different levels of viral loads in the HBV positive samples and prediction of viral loads in the unknown samples, respectively. PLS-DA is applied for validity of classification and its sensitivity and specificity was found to be 89% and 98% respectively. PLSR model was constructed for prediction of viral loads on the bases of SERS spectral markers of HBV infection with goodness value of 0.9031 and value of root means square error (RMSE) 0.2923. PLSR model also proved to be valid for prediction of blind sample.

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OBJECTIVE: To summarize the research literature about safety concerns with consumer-facing health apps and their consequences. MATERIALS AND METHODS: We searched bibliographic databases including PubMed, Web of Science, Scopus, and Cochrane libraries from January 2013 to May 2019 for articles about health apps. Descriptive information about safety concerns and consequences were extracted and classified into natural categories. The review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement. RESULTS: Of the 74 studies identified, the majority were reviews of a single or a group of similar apps (n = 66, 89%), nearly half related to disease management (n = 34, 46%). A total of 80 safety concerns were identified, 67 related to the quality of information presented including incorrect or incomplete information, variation in content, and incorrect or inappropriate response to consumer needs. The remaining 13 related to app functionality including gaps in features, lack of validation for user input, delayed processing, failure to respond to health dangers, and faulty alarms. Of the 52 reports of actual or potential consequences, 5 had potential for patient harm. We also identified 66 reports about gaps in app development, including the lack of expert involvement, poor evidence base, and poor validation. CONCLUSIONS: Safety of apps is an emerging public health issue. The available evidence shows that apps pose clinical risks to consumers. Involvement of consumers, regulators, and healthcare professionals in development and testing can improve quality. Additionally, mandatory reporting of safety concerns is needed to improve outcomes.

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In the title compound, C15H19NO4, the cyclo-hexyl ring adopts a chair conformation with both exocyclic C-C bonds in equatorial orientations. The dihedral angle between the basal plane of cyclo-hexyl ring and the 2,4-di-hydroxy-benzaldehyde moiety is 84.13 (13)°. An intra-molecular O-H⋯N hydrogen bonds closes an S(6) ring. In the crystal, Oc-H⋯Op (c = carb-oxy-lic acid, p = phenol) hydrogen bonds link the mol-ecules into [100] C(13) chains whereas an Op-H⋯Oc hydrogen bond generates [101] C(15) chains. Together, these bonds generate (010) sheets incorporating R 2 (2)(20) loops. Weak C-H⋯O and C-H⋯π inter-actions also occur.