Miracle-dz/UniFAR-FTUs huggingface dataset:
Dog visits in nursing homes – increase complexity or keep it simple? A randomised controlled study

Objective To compare the immediate response of nursing home residents to dog visits with or without an activity, and the impact of cognitive ability. Methods In a randomly controlled trial, 174 nursing home residents were allocated to 12 bi-weekly 10-minute visits: either ordinary dog visits (D, n = 57, 49 analysed), dog visits with an activity (DA, n = 56, 48 analysed), or visits with activity but no dog (A, n = 61, 54 analysed). We recorded frequency and duration of residents’ verbal and physical interactions with the dog and persons. Data were analysed in three periods of four visits (period 1–3) as binomial variables (generalised linear models) or durations (non-parametric statistics). Results Both visit type and impairment level affected the likelihood of interacting with the dog (D and DA). In some periods increased cognitive impairment lowered odds of touching the dog in DA visits (period 1: F1,85 = 5.17, P < 0.05) and talking to it directly (period 1: F1,90 = 4.60, P < 0.05; period 3: F1,87 = 5.34, P < 0.05). Throughout, residents talked less to persons during DA visits compared to D and A (P = 0.01–0.05), and level of cognitive impairment correlated negatively with talk duration (P < 0.001). Generally, high cognitive impairment level lowered odds of interacting with (period 1: F1,89 = 7.89, P < 0.01; period 2: F1,97 = 6.76, P = 0.01; period 3: F1,92 = 13.57, P < 0.001) and talking about the activities (period 1: F1,89 = 13.78, P <0.001; period 2: F1,88 = 3.27, P = 0.07; period 3: F1,86 = 3.88, P = 0.05). Conclusion Visits without specific activities stimulated residents to interact with the dog, whereas increasing the complexity of dog visits by adding activities resulted in less interaction with the dog for severely impaired residents. The optimal dog visit for the less cognitively impaired residents could include activities and thereby a possibility to interact with the dog in different ways, whereas for severely impaired residents, just being with the dog seems more appropriate.
Differential Clinical Features in Colombian Patients With Rolandic Epilepsy and Suggestion of Unlikely Association With GRIN2A, RBFOX1, or RBFOX3 Gene Variants

Purpose: Our purpose was to describe the phenotypic features and test for association of genes GRIN2A, RBFOX1 and RBFOX3 with rolandic epilepsy in patients from Colombia. Methods: Thirty patients were enrolled. A structured interview was applied. In addition, saliva samples were collected from the patients and their parents. One polymorphism in each of GRIN2A, RBFOX1 and RBFOX3 genes was tested. Results: The average age at onset was 5.3 years. Almost half the sample presented prolonged seizures (>5 minutes); although the majority of the patients presented their seizures only while asleep, over a quarter presented them only while awake. The most frequent comorbidity was the presence of symptoms compatible with attention-deficit hyperactivity disorder (ADHD). Personal history of febrile seizures and parasomnias were equally frequent (20%). Family history of any type of epilepsy was reported in 80% of the patients, followed by migraine (73.3%) and poor academic performance (63.3%). About half the sample reported sleepwalking in parents or sibs. Most patients had received pharmacologic treatment. We found no association of rolandic epilepsy with the single nucleotide polymorphisms tested. Conclusions: Our rolandic epilepsy cohort presents clinical features clearly different from other cohorts. For instance, age at onset is much earlier in our set of patients, and personal and family history of febrile seizures as well as parasomnias are highly prevalent in our sample. No association of rolandic epilepsy with variants at the 3 genes tested was found. This lack of association may reflect the high genetic heterogeneity of the epilepsies.
The Akt pathway mediates the protective effects of myeloid differentiation protein 1 in pathological cardiac remodelling

Myeloid differentiation protein 1 (MD1) was shown to ameliorate pressure overload‐induced cardiac hypertrophy and fibrosis by negatively regulating the MEK–ERK1/2 and NF‐κB pathways. However, whether MD1 modulates cardiac function and whether the Akt pathway mediates the benefits of MD1 in pressure overload‐induced cardiac remodelling remain unclear.
Systematic reviews in spinal cord injury: A step-by-step guide for rehabilitation science learners and clinicians

Background The COVID-19 pandemic has created opportunity for multiple rehabilitation science learners and clinicians to critically evaluate and synthesize published research in the field of spinal cord injury (SCI) rehabilitation. Objective To provide a step-by-step guide for rehabilitation science learners and clinicians outlining how to conduct rigorous systematic reviews in the field of SCI. Results Steps for conducting a systematic review (SR) include: (1) formation of the SR team consisting of interprofessional experts; (2) formulation of the research question(s) with patient/population/problem, Intervention, Comparison, and Outcome (PICO) specification; (3) determination of inclusion and exclusion criteria; (4) development of SR protocol and registration; (5) development of the search strategies (database specific); (6) screening of titles and abstracts (level 1 screening), and full-texts (level 2 screening); (7) quality assessment of the included studies; (8) data extraction; (9) summary of findings and discussion; and, (10) dissemination of results. Conclusions The enclosed ten steps for conducting SRs in SCI rehabilitation research have the potential to significantly improve the quality of evidence synthesis and the associated inferences. The importance of assembling team with diverse expertise is emphasized to assure a quality product with the potential to influence practice and inform the content of clinical practice recommendations.
Transcriptome analysis unveils survival strategies of Streptococcus parauberis against fish serum

Streptococcus parauberis is an important bacterial fish pathogen that causes streptococcosis in a variety of fish species including the olive flounder. Despite its importance in the aquaculture industry, little is known about the survival strategy of S. parauberis in the host. Therefore, the objective of this study was to produce genome-wide transcriptome data and identify key factors for the survival of S. parauberis SPOF3K in its host. To this end, S. parauberis SPOF3K was incubated in olive flounder serum and nutrient-enriched media as a control. Although S. parauberis SPOF3K proliferated in both culture conditions, the transcriptomic patterns of the two groups were very different. Interestingly, the expression levels of genes responsible for the replication of an S. parauberis plasmid in the presence of olive flounder serum were higher than those in the absence of olive flounder serum, indicating that this plasmid may play an important role in the survival and proliferation of S. parauberis in the host. Several ATP-binding cassette transporters known to transport organic substrates (e.g., biotin and osmoprotectants) that are vital for bacterial survival in the host were significantly up-regulated in S. parauberis cultured in serum. In addition, groEL, dnaK operon, and members of the clp protease family, which are known to play important roles in response to various stressors, were up-regulated in S. parauberis incubated in serum, thus limiting damage and facilitating cellular recovery. Moreover, important virulence factors including the hyaluronic acid capsule (has operon), sortase A (srtA), C5a peptidase (scp), and peptidoglycan O-acetyltransferase (oatA) were significantly upregulated in S. paraubers in serum. These results indicate that S. paraubers can resist and evade the humoral immune responses of fish. The transcriptomic data obtained in this study provide a better understanding of the mode of action of S. parauberis in fish.
Integrating an Electronic Health Record System into a Regional Health Information System: An HL7 FHIR Architecture

This paper presents an architecture which has been developed in order to integrate a routinely used cardiologic EHR system into the health information infrastructure system of the region where the EHR is used. A Service Oriented Approach based on HL7 and FHIR was used, achieving interoperability, security and confidentiality at all levels. The system has been used for about a year and has brought about significant improvements in the provision of care.
Comparative Analysis of Public Data Sets to Identify Stemness Markers That Differentiate Liver Cancer Stem Cells

Cancer stem cells (CSCs) represent an important field in translational medicine for generating novel cancer treatments. To identify important stemness markers in liver CSCs that potentially explain their resistance to treatment, we analyzed 10865 single-cell RNA-seq samples across 42684 coding and non-coding genes. Our results show that CSCs have significantly increased expression of two Yamanaka factors (Oct4, 2.14X and SOX2, 1.13X) and three stemness factors (CD44, 3.25X; KRT7, 2.2X; SOX9, 1.71X), relative to liver progenitor cells. Our study demonstrates the potential power of harnessing shared big data for driving translational medicine for novel hypothesis generation.
Effect of disinfection agents and quantification of potentially viable Leptospira in fresh water samples using a highly sensitive integrity-qPCR assay

Leptospirosis is an emerging worldwide zoonotic disease, but the general biology of the causative agents is still poorly understood. Humans are an occasional host. The main risk factors are water-associated exposure during professional or recreational activities or during outbreaks in endemic areas. Detecting the presence of pathogenic bacteria in aquatic environments and their capacity to resist various inactivation processes are research fields that need to be further developed. In addition, the methods used for detecting and enumerating Leptospira still need to be improved. We aimed to describe a new quantitative polymerase chain reaction coupled to propidium monoazide treatment (PMAqPCR) that targets not only total Leptospira but also discriminates pathogenic from non-pathogenic Leptospira while also addressing PCR inhibitors, a frequently encountered problem when studying environmental water. In a second step, the killing efficiency of Leptospira to different treatments was tested and PMAqPCR compared to culture-based enumeration. This provided information about the effects of temperature, as well as ultraviolet and chlorine disinfection, that are both related to water treatment processes, in particular for the production of drinking water, on the persistence of both saprophytic and pathogenic Leptospira. Finally, PMAqPCR was used for the detection of Leptospira in freshwater samples for a proof-of-concept. In conclusion, our method could be used for routine freshwater monitoring and allows better evaluation of the presence of Leptospira, allowing evaluation of the bacterial dynamics in a designated area or assessment of the efficacy of water disinfection processes.
A Deep Learning Framework for Automated ICD-10 Coding

The International Statistical Classification of Diseases and Related Health Problems (ICD) is one of the widely used classification system for diagnoses and procedures to assign diagnosis codes to Electronic Health Record (EHR) associated with a patient's stay. The aim of this paper is to propose an automated coding system to assist physicians in the assignment of ICD codes to EHR. For this purpose, we created a pipeline of Natural Language Processing (NLP) and Deep Learning (DL) models able to extract the useful information from French medical texts and to perform classification. After the evaluation phase, our approach was able to predict 346 diagnosis codes from heterogeneous medical units with an accuracy average of 83%. Our results were finally validated by physicians of the Medical Information Department (MID) in charge of coding hospital stays.
High expression of USP18 is associated with the growth of colorectal carcinoma.

AIM To investigate whether USP18 can be used as a predictive marker for the diagnosis and development of colorectal cancer. METHODS The Gene Expression Omnibus (GEO) Dataset and the Cancer Genome Atlas (TCGA) database were used to select differential proteins for the ubiquitin-specific peptidases (USPs). The extensive target prediction and network analysis methods were used to assess the association with the USP18 interacting proteins, as well as the statistical correlation between USP18 and the clinical pathology parameters. The effects of USP18 on the proliferation of colorectal cancer were examined using CCK8. The effects of USP18 on the migration of colorectal cancer were examined using wound healing assays. Immunohistochemistry (IHC) was performed on the tissue microarray. RESULTS The results showed that the expression of USP18 was related to age (P=0.014). The positive rates of the USP18 protein in T1, T2, T3, and T4 were 0.00%, 22.92%, 78.38%, and 95.35%, respectively (P<0.00). The positive rates of the USP18 protein in I, II, III, and IV were 47.43%, 83.12%, 66.67%, and 100.00%, respectively (P<0.00). The Western blot assay showed that the expression of USP18 in colorectal cancer tissues was significantly higher than that in matched paracancerous tissues (P<0.05). The CCK8 experiments suggested that USP18 promoted the migration of CRC cells. Wound healing assays suggested that USP18 promoted the proliferation of CRC cells. CONCLUSION This study showed that USP18 can promote the proliferation of colorectal cancer cells and might be a potential biomarker for the diagnosis of CRC.
Developing a Prognostic Model to Predict Mortality in Patients with Acute Bacterial Meningitis

Bacterial meningitis is one of the harmful and deadly infectious diseases, and any delay in its treatment will lead to death. In this paper, a prognostic model was developed to predict the risk of death amongst probable cases of bacterial meningitis. Our prognostic model was developed using a decision tree algorithm on the national meningitis registry of the Iranian Center for Disease and Prevention (ICDCP) containing 3,923 records of meningitis suspected cases in 2018-2019. The most important features have been selected for the model construction. This model can predict the mortality risk for the meningitis probable cases with 78% accuracy, 84% sensitivity, and 73% specificity. The identified variables in prognosis the death included age and CSF protein level. CSF protein level (mg/dl) <= 65 versus > 65 provided the first branch of our decision tree. The highest mortality risk (85.8%) was seen in the patients >65 CSF protein level with 30 years < of age. For the patients <=30 year of age with CSF protein level >137 (mg/dl), the mortality risk was 60%. The prognostic factors identified in the present study draw the attention of clinicians to provide early specific measures, such as the admission of patients with a higher risk of death to intensive care units (ICU). It could also provide a helpful risk score tool in decision-making in the early phases of admission in pandemics, decrease mortality rate and improve public health operations efficiently in infectious diseases.
Reputation-Aware Data Fusion for Quantifying Hand Tremor Severity Form Interaction with a Smartphone

In this paper, we present an approach to improve the accuracy of hand tremor severity in Parkinson's patients in real-life unconstrained environments. The system leverages data achieved from daily interaction people with their smartphones and uses technologies for classifying and combining data. We describe the basic concept of data fusion and demonstrate how different combination techniques can improve the accuracy of tremor detection. The fusion enable to achieve the 23.5% improvement with respect to the average of individual classification models.
Sepsis in two hospitals in Rwanda: A retrospective cohort study of presentation, management, outcomes, and predictors of mortality

Purpose Few studies have assessed the presentation, management, and outcomes of sepsis in low-income countries (LICs). We sought to characterize these aspects of sepsis and to assess mortality predictors in sepsis in two referral hospitals in Rwanda. Materials and methods This was a retrospective cohort study in two public academic referral hospitals in Rwanda. Data was abstracted from paper medical records of adult patients who met our criteria for sepsis. Results Of the 181 subjects who met eligibility criteria, 111 (61.3%) met our criteria for sepsis without shock and 70 (38.7%) met our criteria for septic shock. Thirty-five subjects (19.3%) were known to be HIV positive. The vast majority of septic patients (92.7%) received intravenous fluid therapy (median = 1.0 L within 8 hours), and 94.0% received antimicrobials. Vasopressors were administered to 32.0% of the cohort and 46.4% received mechanical ventilation. In-hospital mortality for all patients with sepsis was 51.4%, and it was 82.9% for those with septic shock. Baseline characteristic mortality predictors were respiratory rate, Glasgow Coma Scale score, and known HIV seropositivity. Conclusions Septic patients in two public tertiary referral hospitals in Rwanda are young (median age = 40, IQR = 29, 59) and experience high rates of mortality. Predictors of mortality included baseline clinical characteristics and HIV seropositivity status. The majority of subjects were treated with intravenous fluids and antimicrobials. Further work is needed to understand clinical and management factors that may help improve mortality in septic patients in LICs.
Experiencing COVID-19 symptoms without the disease: The role of nocebo in reporting of symptoms

Background: A nocebo effect occurs when inactive factors lead to worsening of symptoms or reduce treatment outcomes. Believing that one is or has been infected with COVID-19 may act as a nocebo. However, not much is known about potential nocebo effects associated with the reporting of COVID-19 symptoms. Aim: An online survey investigated whether certainty of being infected with COVID-19, age, sex, cognitive, emotional and personality factors were associated with perceived severity of COVID-19 symptoms. Methods: Participants (N=375) filled out an online survey containing 57 questions asking about symptoms resembling COVID-19, certainty of being infected with COVID-19, anxiety, stress and personality dimensions. Results: Certainty of being infected with COVID-19 and anxiety predicted 27% of the variance in reporting of COVID-like symptoms. The mediation analysis showed that both higher certainty of being infected and anxiety independently predicted increased reports of COVID-like symptom. Females had higher anxiety and stress levels, and reported more COVID-like symptoms than males did. Older age was not associated with reporting COVID-like symptoms. Conclusions: Believing to be infected with COVID-19, along with anxiety, can enhance the severity of COVID-like symptoms. Thus, the nocebo effect was due to both cognitive and emotional factors and was higher in females.
Temperate phage-antibiotic synergy eradicates bacteria through depletion of lysogens.

There is renewed interest in bacterial viruses (phages) as alternatives to antibiotics. All phage treatments to date have used virulent phages rather than temperate ones, as these can integrate into the genome of the bacterial host and lie dormant. However, temperate phages are abundant and easier to isolate. To make use of these entities, we leverage stressors known to awaken these dormant, integrated phages. Co-administration of the temperate phage HK97 with sub-inhibitory concentrations of the antibiotic ciprofloxacin results in bacterial eradication (≥8 log reduction) in vitro. This synergy is mechanistically distinct from phage-antibiotic-synergy described for virulent phages. Instead, the antibiotic specifically selects against bacteria in which the phage has integrated. As the interaction between temperate phages and stressors such as ciprofloxacin are known to be widespread, this approach may be broadly applicable and enable the use of temperate phages to combat bacterial infections.
Safe opioid prescribing: a prognostic machine learning approach to predicting 30-day risk after an opioid dispensation in Alberta, Canada

Objective To develop machine learning models employing administrative health data that can estimate risk of adverse outcomes within 30 days of an opioid dispensation for use by health departments or prescription monitoring programmes. Design, setting and participants This prognostic study was conducted in Alberta, Canada between 2017 and 2018. Participants included all patients 18 years of age and older who received at least one opioid dispensation. Pregnant and cancer patients were excluded. Exposure Each opioid dispensation served as an exposure. Main outcomes/measures Opioid-related adverse outcomes were identified from linked administrative health data. Machine learning algorithms were trained using 2017 data to predict risk of hospitalisation, emergency department visit and mortality within 30 days of an opioid dispensation. Two validation sets, using 2017 and 2018 data, were used to evaluate model performance. Model discrimination and calibration performance were assessed for all patients and those at higher risk. Machine learning discrimination was compared with current opioid guidelines. Results Participants in the 2017 training set (n=275 150) and validation set (n=117 829) had similar baseline characteristics. In the 2017 validation set, c-statistics for the XGBoost, logistic regression and neural network classifiers were 0.87, 0.87 and 0.80, respectively. In the 2018 validation set (n=393 023), the corresponding c-statistics were 0.88, 0.88 and 0.82. C-statistics from the Canadian guidelines ranged from 0.54 to 0.69 while the US guidelines ranged from 0.50 to 0.62. The top five percentile of predicted risk for the XGBoost and logistic regression classifiers captured 42% of all events and translated into post-test probabilities of 13.38% and 13.45%, respectively, up from the pretest probability of 1.6%. Conclusion Machine learning classifiers, especially incorporating hospitalisation/physician claims data, have better predictive performance compared with guideline or prescription history only approaches when predicting 30-day risk of adverse outcomes. Prescription monitoring programmes and health departments with access to administrative data can use machine learning classifiers to effectively identify those at higher risk compared with current guideline-based approaches.