Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.

BACKGROUND: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows. OBJECTIVE: The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS. METHODS: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics. RESULTS: Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework. CONCLUSIONS: Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.

Phylogeography and genetic structure of Papaver bracteatum populations in Iran based on genotyping-by-sequencing (GBS).

Papaver bracteatum, known for its high thebaine content and absence of morphine, has emerged as a promising alternative to opium poppy for codeine production. In this study, our objective was to create a diverse panel representing the natural variation of this species in Iran. To achieve this, we employed genotyping-by-sequencing to obtain genome-wide distributed single-nucleotide polymorphisms (SNPs) for phylogeographic analysis, population structure assessment, and evaluation of genetic diversity within P. bracteatum populations. A total of 244 P. bracteatum individuals from 13 distinct populations formed seven genetic groups, along with one highly admixed population. We observed a clear split between the populations inhabiting the Alborz Mts. in the east and Zagros Mts. in the west. In between these mountain ranges, the population of Kachal Mangan exhibited a high degree of genetic admixture between both genetic groups. At or after the end of the last glacial maximum, when climate conditions rapidly changed, all P. bracteatum populations experienced a strong demographic bottleneck reducing the already small effective population sizes further before they increased to their recent strengths. Our results suggest that the ongoing climate change together with human pressure on the species' habitats and limited seed-dispersal ability are potential factors contributing today to rising genetic isolation of P. bracteatum populations. Our results provide genetic data that can be used for conservation measures to safeguard the species' genetic diversity as a resource for future breeding approaches in this medicinally important species.

Nanobodies against the myelin enzyme CNPase as tools for structural and functional studies.

2',3'-cyclic nucleotide 3'-phosphodiesterase (CNPase) is an abundant constituent of central nervous system non-compact myelin, frequently used as a marker antigen for myelinating cells. The catalytic activity of CNPase, the 3'-hydrolysis of 2',3'-cyclic nucleotides, is well characterised in vitro, but the in vivo function of CNPase remains unclear. CNPase interacts with the actin cytoskeleton to counteract the developmental closure of cytoplasmic channels that travel through compact myelin; its enzymatic activity may be involved in adenosine metabolism and RNA degradation. We developed a set of high-affinity nanobodies recognizing the phosphodiesterase domain of CNPase, and the crystal structures of each complex show that the five nanobodies have distinct epitopes. One of the nanobodies bound deep into the CNPase active site and acted as an inhibitor. Moreover, the nanobodies were characterised in imaging applications and as intrabodies, expressed in mammalian cells, such as primary oligodendrocytes. Fluorescently labelled nanobodies functioned in imaging of teased nerve fibers and whole brain tissue sections, as well as super-resolution microscopy. These anti-CNPase nanobodies provide new tools for structural and functional biology of myelination, including high-resolution imaging of nerve tissue.

Combined Effect of Biological and Organic Fertilizers on Agrobiochemical Traits of Corn (Zea mays L.) under Wastewater Irrigation.

Corn (Zea mays L.) is an important annual grain that is cultivated as a food staple around the world. The current study examined the effect of wastewater and a combination of biological and organic fertilizers on the morphological and phytochemical traits of corn, using a factorial experiment based on a randomized complete block design with three replications. The first factor was biological and organic fertilizers at seven levels, including the control (no fertilization), bacterial biological fertilizers (NPK) along with iron and zinc Barvar biofertilizers, fungal biofertilizers made from Mycorrhiza and Trichoderma, biochar, a combination of bacterial and fungal biofertilizers, and a combination of bacterial and fungal biofertilizers with biochar. The second factor was irrigation at two levels (conventional irrigation and irrigation with wastewater). The traits studied included the morphological yield, phenols, flavonoids, polyphenols, glomalin, cadmium content in plant parts, and translocation factor (TF). The results disclosed that the best treatment in regard to the morphological traits was related to conventional water + biochar + mycorrhiza + Trichoderma + NPK. The highest phenol and flavonoid content were observed when biochar + mycorrhiza + Trichoderma + NPK treatments were used in both water treatments. Also, the wastewater + biochar + mycorrhiza + Trichoderma + NPK treatment demonstrated the highest total glomalin and phenylalanine ammonia-lyase (PAL) activity. The obtained results demonstrate that combined biological and organic fertilizer use on corn plants can effectively alleviate the deleterious effects of cadmium present in wastewater.

Medical interns' training in family medicine at a district hospital and primary health care clinics in Middelburg, Mpumalanga.

Training of medical interns at the Middelburg district hospital has been introduced as part of the mandatory 6 months' rotation in Family Medicine department since 2021. This report provides an overview of what has been attained in 2021 and 2022. It covers various aspects of the activities medical interns have been exposed to in the Middelburg hospital and the surrounding primary health care clinics.Contribution: Sharing experiences of family medicine training for medical interns in district hospitals is essential because the 6 months' rotation is new for most family physician trainers, especially those in small hospitals and primary health care clinics. Taking into account the paucity of evidence on the topic, the report brings current information that supports that training medical interns in district hospitals and primary health care clinics prepares them to be comfortable and competent clinicians for the generalist work during the community service year ahead.

The Role of Stress Modifier Biostimulants on Adaptive Strategy of Oregano Plant for Increasing Productivity under Water Shortage.

To investigate the influence of stress modulators on the adaptive physiological responses and biomass traits of oregano under water stress conditions, a two-year (2018 and 2019) randomized complete block-designed factorial research was performed. In this study, oregano plants were treated with five stress modulators levels (CHN: chitosan, AMA: amino acids, SEW: seaweed, ASA: ascorbic acid, SAA: salicylic acid, and CON: control) at three levels of irrigation regimes (Irr40 (40), Irr60 (60) and Irr75 (75) % field capacity). The effects of water shortage and biostimulant application were evaluated on total dry weight (TDW), relative water content (RWC), essential oil production, chlorophyll, nutrient (N, K, and P), proline, total soluble sugar, polyphenol and flavonoid content, and activity of antioxidant enzymes. The result showed that under optimal irrigation conditions, oregano plants sprayed with CHN exhibited the highest dry weight (141.23 g m-2) as a morphological trait, the highest relative water content (79.34%), the most consistent concentrations of nitrogen, phosphorus and potassium (3.14, 0.39, and 1.69%, respectively), chlorophylls a and b (3.02 and 1.95 mg g-1 FW, respectively), and total phenols and total flavonoids (30.72 and 3.17 mg g-1 DW, respectively). The water deficit increased the proline content, with the greatest amount (4.17 µg g-1 FW) observed in control plants. Moreover, under moisture shortage stress conditions, the application of CHN and SEW increased the soluble sugar (27.26 µmol g-1 FW) and essential oil yield (1.80%) production, the catalase, ascorbate peroxidase, and superoxide dismutase activities (3.17, 1.18, and 63.89 µmol min-1 g-1 FW, respectively) compared to control plants. In summary, the study demonstrated that oregano plants respond positively to stress modulator treatments when subjected to moisture shortage stress, especially when treated with chitosan. The results offer promising insights for developing sustainable adaptative strategies aimed at enhancing the oregano's tolerance to water shortage, ultimately improving its productivity and biochemical traits.

Machine learning clinical prediction models for acute kidney injury: the impact of baseline creatinine on prediction efficacy.

BACKGROUND: There are many Machine Learning (ML) models which predict acute kidney injury (AKI) for hospitalised patients. While a primary goal of these models is to support clinical decision-making, the adoption of inconsistent methods of estimating baseline serum creatinine (sCr) may result in a poor understanding of these models' effectiveness in clinical practice. Until now, the performance of such models with different baselines has not been compared on a single dataset. Additionally, AKI prediction models are known to have a high rate of false positive (FP) events regardless of baseline methods. This warrants further exploration of FP events to provide insight into potential underlying reasons. OBJECTIVE: The first aim of this study was to assess the variance in performance of ML models using three methods of baseline sCr on a retrospective dataset. The second aim was to conduct an error analysis to gain insight into the underlying factors contributing to FP events. MATERIALS AND METHODS: The Intensive Care Unit (ICU) patients of the Medical Information Mart for Intensive Care (MIMIC)-IV dataset was used with the KDIGO (Kidney Disease Improving Global Outcome) definition to identify AKI episodes. Three different methods of estimating baseline sCr were defined as (1) the minimum sCr, (2) the Modification of Diet in Renal Disease (MDRD) equation and the minimum sCr and (3) the MDRD equation and the mean of preadmission sCr. For the first aim of this study, a suite of ML models was developed for each baseline and the performance of the models was assessed. An analysis of variance was performed to assess the significant difference between eXtreme Gradient Boosting (XGB) models across all baselines. To address the second aim, Explainable AI (XAI) methods were used to analyse the XGB errors with Baseline 3. RESULTS: Regarding the first aim, we observed variances in discriminative metrics and calibration errors of ML models when different baseline methods were adopted. Using Baseline 1 resulted in a 14% reduction in the f1 score for both Baseline 2 and Baseline 3. There was no significant difference observed in the results between Baseline 2 and Baseline 3. For the second aim, the FP cohort was analysed using the XAI methods which led to relabelling data with the mean of sCr in 180 to 0 days pre-ICU as the preferred sCr baseline method. The XGB model using this relabelled data achieved an AUC of 0.85, recall of 0.63, precision of 0.54 and f1 score of 0.58. The cohort size was 31,586 admissions, of which 5,473 (17.32%) had AKI. CONCLUSION: In the absence of a widely accepted method of baseline sCr, AKI prediction studies need to consider the impact of different baseline methods on the effectiveness of ML models and their potential implications in real-world implementations. The utilisation of XAI methods can be effective in providing insight into the occurrence of prediction errors. This can potentially augment the success rate of ML implementation in routine care.

Expression of the Hepatitis C Virus core-NS3 Fusion Protein on the Surface of Bacterial Ghosts: Prospects for Vaccine Production.

Background: Antigen presentation using bacterial surface display systems, on one hand, has the benefits of bacterial carriers, including low-cost production and ease of manipulation. On the other hand, the bacteria can help in stimulating the immune system as an adjuvant. For example, using bacterial surface display technology, we developed a hepatitis C virus (HCV) multiple antigens displaying bacteria's surface and then turned it into a bacterial ghost. Methods: The HCV core and NS3 proteins' conserved epitopes were cloned into the AIDA gene plasmid as an auto transporter. The recombinant plasmid was then transformed into Escherichia coli (E. coli) Bl21 (DE3). Recombinant bacteria were then turned into a bacterial ghost, an empty cell envelope. Whole-cell ELISA, flow cytometry, and Western blot techniques were used for monitoring the expression of proteins on the surface of bacteria. Results: A fusion protein of HCV core-NS3-AIDA was successfully expressed on the E. coli Bl21 (DE3) surface and confirmed by western blotting, Enzyme-Linked Immunosorbent Assay (ELISA), and flow cytometry detection techniques. Conclusion: The presence of HCV antigens on non-pathogen bacteria surfaces holds promise for developing safe and cost-benefit-accessible vaccines with optimal intrinsic adjuvant effects and exposure of heterologous antigens to the immune system.

A Versatile Synaptotagmin-1 Nanobody Provides Perturbation-Free Live Synaptic Imaging And Low Linkage-Error in Super-Resolution Microscopy.

Imaging of living synapses has relied for over two decades on the overexpression of synaptic proteins fused to fluorescent reporters. This strategy alters the stoichiometry of synaptic components and ultimately affects synapse physiology. To overcome these limitations, here a nanobody is presented that binds the calcium sensor synaptotagmin-1 (NbSyt1). This nanobody functions as an intrabody (iNbSyt1) in living neurons and is minimally invasive, leaving synaptic transmission almost unaffected, as suggested by the crystal structure of the NbSyt1 bound to Synaptotagmin-1 and by the physiological data. Its single-domain nature enables the generation of protein-based fluorescent reporters, as showcased here by measuring spatially localized presynaptic Ca2+ with a NbSyt1- jGCaMP8 chimera. Moreover, the small size of NbSyt1 makes it ideal for various super-resolution imaging methods. Overall, NbSyt1 is a versatile binder that will enable imaging in cellular and molecular neuroscience with unprecedented precision across multiple spatiotemporal scales.

Evaluating the Typing Power of Six Isoenzymatic Systems for Differentiation of Clinical and Standard Isolates of Candida Species.

Background: Due to the increasing prevalence of candidiasis, early detection of the causative agents may pave the way for the management of this infection. The present study aimed to assess the discriminative power of the six isoenzymatic systems for differentiating the Candida species. Materials and Methods: Sixteen standard Candida albicans and Candida dubliniensis strains and 30 fluconazole-sensitive and fluconazole-resistant clinical strains of Candida albicans were analyzed using a Multilocus Enzyme Electrophoresis (MLEE) method, including six enzymatic systems consisting of malate dehydrogenase (MDH), phosphoglucomutase (PGM), glucose-phosphate isomerase (GPI), glucose-6-phosphate dehydrogenase (G6PDH), 6-phosphogluconate dehydrogenase (6PGD), and malic enzyme (ME). Results: Among the six enzymatic systems, ME showed no diagnostic activity, whereas MDH provided the best species-specific pattern for species discrimination. In addition, the MDH and G6PD systems provided a discriminatory pattern for differentiating C. dubliniensis from C. albicans isolates. The same isoenzymatic activity was detected in all 36 standard and clinical isolates. Moreover, the results showed no correlation between the isoenzymatic profiles and drug resistance. Conclusion: Among the investigated MLEE systems, MDH was able to differentiate between Candida albicans and Candida dubliniensis. Although no association was detected between isoenzyme patterns and fluconazole resistance in this investigation, isoenzyme patterns are likely correlated with virulence factors between species and even within species. To answer these questions, additional studies should be done on more strains.

Impact of Different Fertilizer Sources under Supplemental Irrigation and Rainfed Conditions on Eco-Physiological Responses and Yield Characteristics of Dragon's Head (Lallemantia iberica).

The effects of the irrigation regime and different fertilizer sources on the eco-physiological responses and yield characteristics of dragon's head were explored in a factorial experiment based on a randomized complete block design with 12 treatments and 3 replications in the 2019 growing season. The treatments included six different fertilizer sources (animal manure, vermicompost, poultry manure, biofertilizer, chemical fertilizer, and control) and two irrigation regimes (rainfed and supplemental irrigation). The results indicated the positive effects of supplementary irrigation and the application of vermicompost, poultry manure, and animal manure by increasing the absorption of nutrients (phosphorus and potassium) and improving relative water contents, chlorophyll and carotenoid contents, and the fixed oil percentage of dragon's head. The activities of catalase, ascorbate peroxidase, and superoxide dismutase decreased in the rainfed plants, whereas organic fertilizer application increased the antioxidant enzyme activity. The highest grain yield (721 kg ha-1), biological yield (5858 kg ha-1), total flavonoids (1.47 mg g-1 DW), total phenol (27.90 mg g-1 DW), fixed oil yield (200.17 kg ha-1), and essential oil yield (1.18 kg ha-1) were noted in plants that were treated with vermicompost under supplemental irrigation. Therefore, it is recommended that organic fertilizers such as vermicompost and poultry manure be used to substitute chemical fertilizers. These practices can help popularize organic crops using rainfed and supplementary irrigation.

Risk of mortality in COVID-19 patients: a meta- and network analysis.

Understanding the most relevant hematological/biochemical characteristics, pre-existing health conditions and complications in survivors and non-survivor will aid in predicting COVID-19 patient mortality, as well as intensive care unit (ICU) referral and death. A literature review was conducted for COVID-19 mortality in PubMed, Scopus, and various preprint servers (bioRxiv, medRxiv and SSRN), with 97 observational studies and preprints, consisting of survivor and non-survivor sub-populations. This meta/network analysis comprised 19,014 COVID-19 patients, consisting of 14,359 survivors and 4655 non-survivors. Meta and network analyses were performed using META-MAR V2.7.0 and PAST software. The study revealed that non-survivors of COVID-19 had elevated levels of gamma-glutamyl transferase and creatinine, as well as a higher number of neutrophils. Non-survivors had fewer lymphocytes and platelets, as well as lower hemoglobin and albumin concentrations. Age, hypertension, and cerebrovascular disease were shown to be the most influential risk factors among non-survivors. The most common complication among non-survivors was heart failure, followed by septic shock and respiratory failure. Platelet counts, creatinine, aspartate aminotransferase, albumin, and blood urea nitrogen levels were all linked to ICU admission. Hemoglobin levels preferred non-ICU patients. Lower levels of hemoglobin, lymphocytes, and albumin were associated with increased mortality in ICU patients. This meta-analysis showed that inexpensive and fast biochemical and hematological tests, as well as pre-existing conditions and complications, can be used to estimate the risk of mortality in COVID-19 patients.

Hypertensive patients' knowledge and practices on lifestyle modification in Extension 6, Middelburg.

BACKGROUND: Hypertension (HTN) is one of the most common chronic diseases affecting the majority of patients worldwide, including in South Africa. The control of HTN and prevention of complications are major challenges for patients and healthcare workers. The proper control of the disease requires a multifactorial approach consisting of medical treatment, as well as lifestyle modification, with the assistance of healthcare workers. Addressing patients' beliefs, the practice of lifestyle and acceptance of change are some of the ways of aiding control. METHODS: The researchers used a cross-sectional and descriptive survey to establish the knowledge and practices of lifestyle modifications in patients with hypertension. A validated questionnaire was adopted. A total of 250 participants from the Extension 6 Clinic in Middelburg, Mpumalanga, constituted the study population. RESULTS: Most of the participants had borderline high blood pressure (37.2%) or uncontrolled high blood pressure (46%). The participants' knowledge of HTN and its complications was not adequate. The study established that increased age and long duration of HTN were associated with high numbers of uncontrolled HTN. Most of the participants (88.8%) had difficulty exercising. Also, most of the participants (90.8%) did not have a place or facility for exercises. Fifty per cent of the participants were unable to maintain a balanced healthy diet which included fruits and vegetables. CONCLUSION: The study explored the participants' knowledge and practice of lifestyle modification. The participants lacked knowledge regarding the definition of HTN and the meaning of controlled HTN. To assist the patients and improve on the identified pitfalls, each consultation session should include some methods of education, and motivation for healthy behaviours and lifestyle modification. This should be extended to all the people visiting the health facilities for them to adopt a healthier diet, greater intake of vegetables and availability of fitness facilities for the community.

Heterogeneity of the NIH3T3 Fibroblast Cell Line.

The embryonic mouse fibroblast cell line NIH3T3 is widely used in life science research, including the study of cell cycle control and primary cilia. Fibroblasts are the most important cell type in connective tissue, as they produce components of the extracellular matrix and determine tissue architecture. However, they are very heterogeneous and consist of subtypes specific to their organ of residence, among others. The NIH3T3 cell line was derived from whole mouse embryos that developed to pre-birth and is therefore most likely composed of different fibroblast subtypes. Furthermore, prolonged proliferation may have influenced their cellular composition. A heterogeneous cell population is unsuitable for any sophisticated research project. We found that the proportion of ciliated cells in the total NIH3T3 cell population was highly variable and asked whether this was a consequence of cellular heterogeneity and what molecular signatures were associated with it. We have established sub-cell lines by clonal expansion of single cells and characterized them morphologically and molecularly. Eventually, a myofibroblast-like and a fibroblast-like cell line were generated that differ in ciliation and proliferation. These homogeneous cell lines are valuable for a more detailed study of their molecular signatures, not least to uncover further the molecular pathways that contribute to the formation of the primary cilium.

Suggesting Tissue-Specific MSMB Gene Promoter as a Novel Approach for Prostate Targeted Gene Therapy.

BACKGROUND AND AIM: Prostate cancer is the second most common cancer among men that has affected their quality of life. This study aimed to find prostate tissue-specific genes using bioinformatics methods to specifically target prostate cells in case of metastasis to other tissues. MATERIALS AND METHODS: In this study, after finding a specific gene (MSMB)  that is highly expressed in cancer, the optimal promoter region of this gene was isolated and inserted in an expression vector. Then, this vector was transfected into two prostate cancer cell lines (DU145 and LNCaP) and three non-prostate cell lines  (LX-2, MRC-5, and U87) using the PEI chemical method. The expression of this vector in these cells was examined using fluorescent microscopy and flow cytometry. RESULTS: We observed that the expression of MSMB promoter in DU145 cell line has a much higher activity than the CMV promoter, which is a ubiquitous promoter. The MSMB promoter didn't show any activity in cells other than that of prostate derived cell lines. CONCLUSION: MSMB  gene promoter with specific expression and high efficiency in prostate tissue compared to CMV promoter can play an essential role in gene therapy of prostate cancer.

Machine learning models for diabetes management in acute care using electronic medical records: A systematic review.

BACKGROUND: Machine learning (ML) is a subset of Artificial Intelligence (AI) that is used to predict and potentially prevent adverse patient outcomes. There is increasing interest in the application of these models in digital hospitals to improve clinical decision-making and chronic disease management, particularly for patients with diabetes. The potential of ML models using electronic medical records (EMR) to improve the clinical care of hospitalised patients with diabetes is currently unknown. OBJECTIVE: The aim was to systematically identify and critically review the published literature examining the development and validation of ML models using EMR data for improving the care of hospitalised adult patients with diabetes. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines were followed. Four databases were searched (Embase, PubMed, IEEE and Web of Science) for studies published between January 2010 to January 2022. The reference lists of the eligible articles were manually searched. Articles that examined adults and both developed and validated ML models using EMR data were included. Studies conducted in primary care and community care settings were excluded. Studies were independently screened and data was extracted using Covidence® systematic review software. For data extraction and critical appraisal, the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was followed. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). Quality of reporting was assessed by adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. The IJMEDI checklist was followed to assess quality of ML models and the reproducibility of their outcomes. The external validation methodology of the studies was appraised. RESULTS: Of the 1317 studies screened, twelve met inclusion criteria. Eight studies developed ML models to predict disglycaemic episodes for hospitalized patients with diabetes, one study developed a ML model to predict total insulin dosage, two studies predicted risk of readmission, and one study improved the prediction of hospital readmission for inpatients with diabetes. All included studies were heterogeneous with regard to ML types, cohort, input predictors, sample size, performance and validation metrics and clinical outcomes. Two studies adhered to the TRIPOD guideline. The methodological reporting of all the studies was evaluated to be at high risk of bias. The quality of ML models in all studies was assessed as poor. Robust external validation was not performed on any of the studies. No models were implemented or evaluated in routine clinical care. CONCLUSIONS: This review identified a limited number of ML models which were developed to improve inpatient management of diabetes. No ML models were implemented in real hospital settings. Future research needs to enhance the development, reporting and validation steps to enable ML models for integration into routine clinical care.

Toward a Learning Health Care System: A Systematic Review and Evidence-Based Conceptual Framework for Implementation of Clinical Analytics in a Digital Hospital.

OBJECTIVE: A learning health care system (LHS) uses routinely collected data to continuously monitor and improve health care outcomes. Little is reported on the challenges and methods used to implement the analytics underpinning an LHS. Our aim was to systematically review the literature for reports of real-time clinical analytics implementation in digital hospitals and to use these findings to synthesize a conceptual framework for LHS implementation. METHODS: Embase, PubMed, and Web of Science databases were searched for clinical analytics derived from electronic health records in adult inpatient and emergency department settings between 2015 and 2021. Evidence was coded from the final study selection that related to (1) dashboard implementation challenges, (2) methods to overcome implementation challenges, and (3) dashboard assessment and impact. The evidences obtained, together with evidence extracted from relevant prior reviews, were mapped to an existing digital health transformation model to derive a conceptual framework for LHS analytics implementation. RESULTS: A total of 238 candidate articles were reviewed and 14 met inclusion criteria. From the selected studies, we extracted 37 implementation challenges and 64 methods employed to overcome such challenges. We identified common approaches for evaluating the implementation of clinical dashboards. Six studies assessed clinical process outcomes and only four studies evaluated patient health outcomes. A conceptual framework for implementing the analytics of an LHS was developed. CONCLUSION: Health care organizations face diverse challenges when trying to implement real-time data analytics. These challenges have shifted over the past decade. While prior reviews identified fundamental information problems, such as data size and complexity, our review uncovered more postpilot challenges, such as supporting diverse users, workflows, and user-interface screens. Our review identified practical methods to overcome these challenges which have been incorporated into a conceptual framework. It is hoped this framework will support health care organizations deploying near-real-time clinical dashboards and progress toward an LHS.

PyProtModel: An easy to use GUI for comparative protein modeling.

A deep insight into the 3D structures of the proteins is essential to understand their functions and will be helpful in drug design and making decisions for the treatment of diseases. The 3D structure for less than one-thousandth of the protein sequences has been determined so far due to the slow structure elucidation procedures with experimental methods, such as X-ray. For this reason, different computational methods were used to determine the structure of the proteins, including template-based and de novo approaches. Much software has been developed to facilitate the computational approaches in protein modeling, some of which need user expertise and extensive knowledge in bioinformatics. The present study attempted to provide a user-friendly environment to motivate the researchers in computational biology, a Python-based interface based on MODELLER software. PyProtModel can be used as a toolbox in structural bioinformatics for protein modeling from sequence to the prediction of 3D structure. In addition, the users can take benefits of PyProtModel in order to prepare and evaluate protein PDB files prior to other in silico experiments. PyProtModel allows the user to apply different MODELLER options, automates, and speeds up time-consuming homology modeling steps. Different aspects of PyProtModel and a comprehensive view of the application will be discussed in this paper. The application is available for download and use at: https://github.com/msisakht/pyprotmodel.git.

Influence of high wave sound pretreatment on drying quality parameters of echinacea root with infrared drying.

BACKGROUND: Echinacea is a perennial and herbaceous herb containing the most active phenolic compounds and valuable antimicrobial and antioxidant content, such as chicoric acid, which increases the body immune function. The root of this plant is mainly used in pharmaceutical fields to treat inflammation, kidney infections and colds. In the present study, we investigated the drying of echinacea root in an infrared dryer with airborne ultrasonic pretreatment aiming to determine shrinkage, color changes, extraction, total phenolic content, flavonoid content and radical inhibition of 2,2-diphenyl-1-picrylhydrazyl (DPPH). The experiments were performed in an infrared dryer at three temperature levels of 40, 45 and 50 °C, with ultrasonic pretreatment at frequencies of 2000, 3000 and 4000 Hz and a time duration of 20, 40 and 60 min in three replications. RESULTS: Shrinkage and color changes of the samples varied in the range 27.2-6.3% and 39.82-39.64%, respectively. According to all of the evaluated physical and chemical factors, the optimal conditions for echinacea root processing were a drying temperature of 40 °C, a frequency of 3000 Hz and an ultrasound duration of 60 min, in which case shrinkage was 3.35% and color changes were 38.25%. Total phenolic content was 27.41 mg g-1 , flavonoid content was 39.36 mg g-1 and DPPH radical inhibition was 34.87%. CONCLUSION: Therefore, the use of airborne ultrasound pretreatment demonstrates increased chemical properties compared to the optimal state when drying the echinacea root with an infrared dryer by 54/21-81/64%. Hence, ultrasound pretreatment preserves the quality and quantity of active ingredients in the therapeutic root of Echinacea. © 2021 Society of Chemical Industry.

Increasing medicinal and phytochemical compounds of coneflower (Echinacea purpurea L.) as affected by NO3-/NH4+ ratio and perlite particle size in hydroponics.

Medicinal plants are considered as one of the most important sources of chemical compounds, so preparing a suitable culture media for medicinal plant growth is a critical factor. The present study is aimed to improve the caffeic acid derivatives and alkylamides percentages of Echinacea purpurea root extract in hydroponic culture media with different perlite particle size and NO3-/NH4+ ratios. Perlite particle size in the growing media was varied as very coarse perlite (more than 2 mm), coarse perlite (1.5-2 mm), medium perlite (1-1.5 mm), fine perlite (0.5-1 mm), and very fine perlite (less than 0.5 mm) in different ratios to peat moss (including pure perlite, 50:50 v/v, 30:70 v/v, and pure peat moss). Two NO3-/NH4+ ratios (90:10 and 70:30) were tested in each growing media. All phytochemical analyses were performed according to standard methods using high performance liquid chromatography (HPLC). It was found that the E. purpurea grown in the medium containing very fine-grade perlite with 50:50 v/v perlite to peat moss ratio had the maximum caffeic acid derivatives, including chicoric acid (17 mg g-1 DW), caftaric acid (6.3 mg g-1 DW), chlorogenic acid (0.93 mg g-1 DW), cynarin (0.84 mg g-1 DW), and echinacoside (0.73 mg g-1 DW), as well as, alkylamides (54.21%). The percentages of these phytochemical compounds increased by decreasing perlite particle size and increasing of NO3-/NH4+ ratio. The major alkylamide in the E. purpurea root extract was dodeca-2E, 4E, 8Z-10 (E/Z)-tetraenoic acid isobutylamide in all treatments, ranging from 31.12 to 54.21% of total dry weight. It can be concluded that optimizing hydroponic culture media and nutrient solution has significant effects on E. purpurea chemical compounds.