Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing and Piloting of a Mobile Learning Curriculum for Quality Point-Of-Care Diagnostics Services in Rural Clinics of KwaZulu-Natal, South Africa

Background The use of mobile technology has been reported to help improve access to education for people in remote areas. However, there is limited evidence of its adoption in resource-limited settings. The aim of this study was to utilize stakeholder generated ideas to design and pilot a mobile learning curriculum, with the purpose of facilitating training to improve the quality of point-of-care diagnostics services in KwaZulu-Natal (KZN) rural clinics. Methods Nominal Group Technique was employed to enable collaboration with stakeholders in designing and piloting of a POC diagnostics curriculum. Stakeholders were selected from 11 KZN districts to participate in a clinic-based piloting of the curriculum using an online application. The application was designed in collaboration with a teacher training institute in Durban. Moodle was used as an established reliable online learning management system. During piloting, quantitative and qualitative data were generated and analyzed using descriptive statistics and content analysis. Findings and Conclusion Guided by the Nominal Group Technique results, five delivery modes for curriculum content through Mobile Learning were generated. An interactive course page was created on the Moodle site, titled: Quality HIV Point of Care Diagnostics Curriculum Delivery for Nurses in Rural Areas. The course content consisted of three teaching units, activities, an online quiz and an online survey. An analytic-algorithm built into the online course enabled monitoring of participation and assessment outcomes automatically. At piloting, 64% of the invited representative clinics were able to access the course, with 47% meeting the course completion requirements. All the participants achieved the set pass mark of 75% with an average of 87%. The activity completion report showed that topics presented through images, videos and simple text were accessed more than those presented as attachments of national documents. Despite poor network coverage and limited access to mobile technology, exacerbated by Covid-19 related restrictions, Point of care diagnostics Mobile Learning curriculum was well-received in participating rural clinics. Recommendations relating to course improvement and access, included extending collaboration with specialists in eHealth systems development and with South African cell phone network providers.
Impact of Diabetes Mellitus on Lower Urinary Tract Symptoms in Benign Prostatic Hyperplasia Patients: A Meta-Analysis

Background Benign prostatic hyperplasia (BPH) is a disease that causes lower urinary tract symptoms (LUTS), which are the most common urological problem in approximately one-third of the male population aged over 50 years. Some studies have suggested that diabetes may be a risk factor for the development of BPH. However, whether diabetes aggravates the LUTS of BPH patients is still controversial. Aim To investigate the impact of diabetes mellitus on LUTS in BPH patients. Methods A literature search was conducted using Web of Science, Embase, PubMed, and China National Knowledge Infrastructure literature databases. This meta-analysis was registered in PROSPERO (registration number: CRD 42020200794). Fixed- or random-effects models were used for analysis according to heterogeneity. The results of the systematic analysis are presented as weighted mean difference (WMD) with the corresponding 95% confidence intervals (CI). Results In total, 1308 studies were retrieved from databases and 18 articles comprising 1685 cases and 4653 controls were selected for meta-analysis. The results of the meta-analysis showed that the International Prostate Symptom Score (IPSS) value and prostate volume of BPH patients with diabetes was significantly higher than that of BPH patients without diabetes. Conclusions This systematic review is the first to evaluate the impact of diabetes mellitus on LUTS in BPH patients. The results of our meta-analysis support the hypothesis that LUTS in BPH patients is increased in patients with diabetes mellitus compared with controls, which suggests that physicians should pay more attention to BPH patients with diabetes mellitus. Systematic Review Registration PROSPERO [https://www.crd. york.ac. uk/PROSPERO/display_record. php? RecordID=200794], identifier CRD 42020200794.
A Random Regression Model Based on a Single-Step Method for Improving the Genomic Prediction Accuracy of Residual Feed Intake in Pigs

Residual feed intake (RFI) is considered as a measurement of feed efficiency, which is greatly related to the growth performance in pigs. Daily feeding records can be obtained from automatic feeders. In general, RFI is usually calculated from the total measurement records during the whole test period. This measurement cannot reflect genetic changes in different growth periods during the test. A random regression model (RRM) provides a method to model such type of longitudinal data. To improve the accuracy of genetic prediction for RFI, the RRM and regular animal models were applied in this study, and their prediction performances were compared. Both traditional pedigree-based relationship matrix (A matrix) and pedigree and genomic information-based relationship matrix (H matrix) were applied for these two models. The results showed that, the prediction accuracy of the RRM was higher than that of the animal model, increasing 24.2% with A matrix and 40.9% with H matrix. Furthermore, genomic information constantly improved the accuracy of evaluation under each evaluation model. In conclusion, longitudinal traits such as RFI can describe feed efficiency better, and the RRM with both pedigree and genetic information was superior to the animal model. These results provide a feasible method of genomic prediction using longitudinal data in animal breeding.
Negative or Positive? Loading Area Dependent Correlation Between Friction and Normal Load in Structural Superlubricity

Structural superlubricity (SSL), a state of ultra-low friction between two solid contacts, is a fascinating phenomenon in modern tribology. With extensive molecular dynamics simulations, for systems showing SSL, here we discover two different dependences between friction and normal load by varying the size of the loading area. The essence behind the observations stems from the coupling between the normal load and the edge effect of SSL systems. Keeping normal load constant, we find that by reducing the loading area, the friction can be reduced by more than 65% compared to the large loading area cases. Based on the discoveries, a theoretical model is proposed to describe the correlation between the size of the loading area and friction. Our results reveal the importance of loading conditions in the friction of systems showing SSL, and provide an effective way to reduce and control friction.
The Effect of Intraoperative Cerebral Oximetry Monitoring on Postoperative Cognitive Dysfunction and ICU Stay in Adult Patients Undergoing Cardiac Surgery: An Updated Systematic Review and Meta-Analysis

Aim Determining whether intraoperative cerebral oximetry monitoring-guided intervention reduces the risk of postoperative cognitive dysfunction remains controversial. The objective of this study was to conduct an up-to-date meta-analysis to comprehensively assess the effects of regional cerebral oxygen saturation (rSO2) monitoring-guided intervention on cognitive outcomes after cardiac surgery. Methods PubMed, EMBASE, Ovid, and Cochrane Library databases were systematically searched using the related keywords for cardiac surgical randomized-controlled trials (RCTs) published from their inception to July 31, 2021. The primary outcome was postoperative delirium (POD). The secondary outcomes were postoperative cognitive decline (POCD) and other major postoperative outcomes. The odds ratio (OR) or weighted mean differences (WMDs) with 95% confidence interval (CI) were used to pool the data. The random-effect model was used for the potential clinical inconsistency. We performed meta-regression and subgroup analyses to assess the possible influence of rSO2 monitoring-guided intervention on clinical outcomes. Results In total, 12 RCTs with 1,868 cardiac surgical patients were included. Compared with controls, the incidences of POD (n = 6 trials; OR, 0.28; 95% CI, 0.09–0.84; p = 0.02; I2 = 81%) and POCD (n = 5 trials; OR, 0.38; 95% CI, 0.16–0.93; p = 0.03; I2 = 78%) were significantly lower in the intervention group. Cerebral oximetry desaturation also showed a positive association with the incidence of POD (n = 5 trials; OR, 2.02; 95% CI, 1.25–3.24; p = 0.004; I2 = 81%). The duration of intensive care unit (ICU) stay was markedly shorter in the intervention group than in the control group (n = 10 trials; WMD, −0.22 days; 95% CI, −0.44 to −0.00; p = 0.05; I2 = 74%). Univariate meta-regression analyses showed that the major sources of heterogeneity were age (p = 0.03), body mass index (BMI, p = 0.05), and the proportion of congenital heart disease (CHD, p = 0.02) for POD, age (p = 0.04) for POCD, diabetes mellitus (DM, p = 0.07), cerebrovascular accident (CVA, p = 0.02), and chronic obstructive pulmonary disease (COPD, p = 0.09) for ICU stay. Subsequent subgroup analyses also confirmed these results. Conclusion Available evidence from the present study suggests that an intraoperative cerebral oximetry desaturation is associated with an increased POD risk, and the rSO2 monitoring-guided intervention is correlated with a lower risk of POD and POCD, and a shorter ICU stay in adults undergoing cardiac surgery. These clinical benefits may be limited in patients with older age, diabetes status, high BMI, non-CHD, non-COPD, or a previous cardiovascular accident. Systematic Review Registration: [PROSPREO], identifier: [CRD42021252654].
Health Care Professionals' Perception of Stress During COVID-19 Pandemic in Iran: A Qualitative Study

Background The health care professionals have a unique role in controlling the pandemic of COVID-19 and decreasing its mortality and morbidity. The burden of care and psychological impact of working in this circumstance can be unfavorable for many caregivers. In this qualitative study, the health care professionals' perception of stress during COVID-19 pandemic in Iran was assessed and several implications were proposed. Materials and Methods The participants were selected among staff who were providing medical services to patients with COVID-19 infection at the largest teaching hospital in Iran. Quota sampling was used to include physicians, nurses, and other paramedics. The grounded theory was selected to develop interview questions. Moreover, the thematic approach was applied to analyze the data content and data analysis was performed based on open and axial coding following the implementation of codes in MAQDA software. Results A wide range of psychological reactions including anxiety, feelings of guilt, depression, and anger were detected in the staff. Uncertainty accompanied by the pandemic of COVID-19 and shortcomings in preparation for crisis management were recognized as the two main sources of stress among health care professionals. Conclusion Based on the findings of the study, it is important to identify and evaluate the mental health needs of healthcare professionals. To reduce stress among health staff at COVID-19 care centers, it seems that the optimal strategy is simultaneous improvement in equipment and crisis management.
Quantification of Fractional Dynamical Stability of EEG Signals as a Bio-Marker for Cognitive Motor Control

Assessing the stability of biological system models has aided in uncovering a plethora of new insights in genetics, neuroscience, and medicine. In this paper, we focus on analyzing the stability of neurological signals, including electroencephalogram (EEG) signals. Interestingly, spatiotemporal discrete-time linear fractional-order systems (DTLFOS) have been shown to accurately and efficiently represent a variety of neurological and physiological signals. Here, we leverage the conditions for stability of DTLFOS to assess a real-world EEG data set. By analyzing the stability of EEG signals during movement and rest tasks, we provide evidence of the usefulness of the quantification of stability as a bio-marker for cognitive motor control.
Integrated Metabolomics and Network Pharmacology Revealed Hong-Hua-Xiao-Yao Tablet’s Effect of Mediating Hormone Synthesis in the Treatment of Mammary Gland Hyperplasia

Hong-Hua-Xiao-Yao Tablet (HHXYT) is a traditional Chinese medicine (TCM) formula that has been approved for the treatment of mammary gland hyperplasia (MGH), but its mechanism of action is unclear. In this study, a strategy that integrated metabolomics and network pharmacology was applied to systemically reveal the mechanism of HHXYT in the treatment of MGH. Our pharmacodynamic study indicated that the proliferation of mammary gland was inhibited in rats, and serum-level disorder of estradiol and progesterone was reversed after HHXYT treatment. 54 compounds absorbed in rat plasma were identified after administration of HHXYT. The serum metabolome revealed 58 endogenous differential metabolites, of which 31% were steroid lipids metabolites, with steroid hormone biosynthesis being the most significant metabolic module. 7 targets, 6 herbs, and 17 ingredients were found to play key roles in HHXYT’s treatment of MGH. 3 of the 7 key targets (CYP11A1, HSD3B2, and CYP17A1) were directly involved in androgen synthesis, while 2 targets (AR and ESR1) were receptors for the direct action of androgens and estrogens. Molecular docking was utilized to confirm the bindings between the 5 targets and their corresponding compounds. In an in vitro test, HHXYT (50 µg/ml) and its ingredient formononetin (3.2, 6.3, and 12.5 µM) were found to significantly reduce the increase of testosterone level induced by dexamethasone (10 µM) in thecal cells. In summary, this study illustrated that the mechanism of HHXYT’s treatment of MGH was to regulate hormone disorder. HHXYT could reduce estrogen-stimulated hyperplasia by inhibiting the production of its precursor androgen.
Antecedents and Consequences of Endorsing Prescriptive Views of Active Aging and Altruistic Disengagement

In this study, we investigated endorsement of two types of prescriptive views of aging, namely active aging (e.g., prescriptions for older adults to stay fit and healthy and to maintain an active and productive lifestyle) and altruistic disengagement (e.g., prescriptions for older adults to behave altruistically toward the younger generation by granting young people access to positions and resources). The study comprised a large international sample of middle-aged and older adults (N = 2,900), covering the age range from 40 to 90 years. Participants rated their personal endorsement of prescriptive views of active aging and altruistic disengagement targeting older adults in general (i.e., “In my personal opinion, older adults should…”). Findings showed that endorsement was higher for prescriptions for active aging than for prescriptions for altruistic disengagement. Age groups in the sample differed regarding their endorsement of both prescriptive views of active aging and altruistic disengagement with older adults showing higher endorsement than middle-aged adults did. Prescriptive views of active aging and altruistic disengagement related positively to each other and to the superordinate social belief that older adults should not become a burden, which attests to their functional similarity. In contrast, prescriptive views of active aging and altruistic disengagement were associated with psychological adjustment in opposite ways, with endorsement of active aging (vs. altruistic disengagement) being related to better (vs. worse) adjustment outcomes such as life satisfaction and subjective health. Our findings highlight the internalization of prescriptive views of aging in older people and their implications for their development and well-being.
The “Neural Shift” of Sleep Quality and Cognitive Aging: A Resting-State MEG Study of Transient Neural Dynamics

Sleep quality changes dramatically from young to old age, but its effects on brain dynamics and cognitive functions are not yet fully understood. We tested the hypothesis that a shift in brain networks dynamics relates to sleep quality and cognitive performance across the lifespan. Network dynamics were assessed using Hidden Markov Models (HMMs) in resting-state MEG data from a large cohort of population-based adults (N = 564, aged 18–88). Using multivariate analyses of brain-sleep profiles and brain-cognition profiles, we found an age-related “neural shift,” expressed as decreased occurrence of “lower-order” brain networks coupled with increased occurrence of “higher-order” networks. This “neural shift” was associated with both increased sleep dysfunction and decreased fluid intelligence, and this relationship was not explained by age, sex or other covariates. These results establish the link between poor sleep quality, as evident in aging, and a behavior-related shift in neural dynamics.
The Effect of Virtual Reality on Emotional Response and Symptoms Provocation in Patients With OCD: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis aimed to evaluate the effectiveness of virtual reality (VR)-based technology on emotional response and symptoms in patients with obsessive–compulsive disorder (OCD). We systematically searched major electronic databases, including PubMed/Medline, Scopus, Embase, ISI Web of Science, PsycINFO, and Cochrane central, up to April 14, 2021, with no data or language limits. We performed reference, related articles, and citation searches to find additional articles. We included original articles comparing and studying VR-based technology in patients with OCD against the control group. We observed that VR significantly increases in anxiety (SMD = 2.92; 95% CI 1.89–3.94, p < 0.0001; I2 = 95%), disgust (SMD = 2.52; 95% CI 1.36–3.68, p < 0.0001; I2 = 95%), urge to wash (SMD = 3.12; 95% CI 1.92–4.32, p < 0.0001; I2 = 94%), checking time (SMD = 1.06; 95% CI 0.71–1.4, p < 0.0001; I2 = 44%), number of checking behavior (SMD = 1.45; 95% CI 0.06–2.83, p = 0.04; I2 = 93%), and uncertainty (SMD = 2.59; 95% CI 0.90–4.27, p = 0.003; I2 = 70%) in OCD patients compared with healthy controls using a random-effect model. This meta-analysis found that this environment has a moderate enhancement in emotional response and symptoms test scores of patients with OCD. However, our findings should be generalized with caution due to the lack of standardized methods and high heterogeneity among included evidence. The appropriate mode of integrating VR-based technology for patients with OCD requires more exploration.
Don’t Let It Get Under Your Skin! – Vaccination Protects the Skin Barrier of Common Carp From Disruption Caused by Cyprinid Herpesvirus 3

Vaccination is the best form of protecting fish against viral diseases when the pathogen cannot be contained by biosecurity measures. Vaccines based on live attenuated viruses seem to be most effective for vaccination against challenging pathogens like Cyprinid herpesvirus 3. However, there are still knowledge gaps how these vaccines effectively protect fish from the deadly disease caused by the epitheliotropic CyHV-3, and which aspects of non-direct protection of skin or gill integrity and function are important in the aquatic environment. To elucidate some elements of protection, common carp were vaccinated against CyHV-3 using a double deletion vaccine virus KHV-T ΔDUT/TK in the absence or presence of a mix of common carp beta-defensins 1, 2 and 3 as adjuvants. Vaccination induced marginal clinical signs, low virus load and a minor upregulation of cd4, cd8 and igm gene expression in vaccinated fish, while neutralisation activity of blood serum rose from 14 days post vaccination (dpv). A challenge infection with CyHV-3 induced a severe disease with 80-100% mortality in non-vaccinated carp, while in vaccinated carp, no mortality was recorded and the virus load was >1,000-fold lower in the skin, gill and kidney. Histological analysis showed strongest pathological changes in the skin, with a complete destruction of the epidermis in non-vaccinated carp. In the skin of non-vaccinated fish, T and B cell responses were severely downregulated, inflammation and stress responses were increased upon challenge, whereas vaccinated fish had boosted neutrophil, T and B cell responses. A disruption of skin barrier elements (tight and adherence junction, desmosomes, mucins) led to an uncontrolled increase in skin bacteria load which most likely exacerbated the inflammation and the pathology. Using a live attenuated virus vaccine, we were able to show that increased neutrophil, T and B cell responses provide protection from CyHV-3 infection and lead to preservation of skin integrity, which supports successful protection against additional pathogens in the aquatic environment which foster disease development in non-vaccinated carp.
Detecting Presence of PTSD Using Sentiment Analysis From Text Data

Rates of Post-traumatic stress disorder (PTSD) have risen significantly due to the COVID-19 pandemic. Telehealth has emerged as a means to monitor symptoms for such disorders. This is partly due to isolation or inaccessibility of therapeutic intervention caused from the pandemic. Additional screening tools may be needed to augment identification and diagnosis of PTSD through a virtual medium. Sentiment analysis refers to the use of natural language processing (NLP) to extract emotional content from text information. In our study, we train a machine learning (ML) model on text data, which is part of the Audio/Visual Emotion Challenge and Workshop (AVEC-19) corpus, to identify individuals with PTSD using sentiment analysis from semi-structured interviews. Our sample size included 188 individuals without PTSD, and 87 with PTSD. The interview was conducted by an artificial character (Ellie) over a video-conference call. Our model was able to achieve a balanced accuracy of 80.4% on a held out dataset used from the AVEC-19 challenge. Additionally, we implemented various partitioning techniques to determine if our model was generalizable enough. This shows that learned models can use sentiment analysis of speech to identify the presence of PTSD, even through a virtual medium. This can serve as an important, accessible and inexpensive tool to detect mental health abnormalities during the COVID-19 pandemic.