Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring population HIV incidence trends from surveillance data of recent HIV infection among HIV testing clients

Supplemental Digital Content is available in the text Background: Measuring recent HIV infections from routine surveillance systems could allow timely and granular monitoring of HIV incidence patterns. We evaluated the relationship of two recent infection indicators with alternative denominators to true incidence patterns. Methods: We used a mathematical model of HIV testing behaviours, calibrated to population-based surveys and HIV testing services programme data, to estimate the number of recent infections diagnosed annually from 2010 to 2019 in Côte d’Ivoire, Malawi, and Mozambique. We compared two different denominators to interpret recency data: those at risk of HIV acquisition (HIV-negative tests and recent infections) and all people testing HIV positive. Sex and age-specific longitudinal trends in both interpretations were then compared with modelled trends in HIV incidence, testing efforts and HIV positivity among HIV testing services clients. Results: Over 2010–2019, the annual proportion of the eligible population tested increased in all countries, while positivity decreased. The proportion of recent infections among those at risk of HIV acquisition decreased, similar to declines in HIV incidence among adults (≥15 years old). Conversely, the proportion of recent infections among HIV-positive tests increased. The female-to-male ratio of the proportion testing recent among those at risk was closer to 1 than the true incidence sex ratio. Conclusion: The proportion of recent infections among those at risk of HIV acquisition is more indicative of HIV incidence than the proportion among HIV-positive tests. However, interpreting the observed patterns as surrogate measures for incidence patterns may still be confounded by different HIV testing rates between population groups or over time.
Mechanisms of Antiviral Cytotoxic CD4 T Cell Differentiation

While it is well established that cytotoxic CD4 T cells (CD4-CTLs) directly contribute to viral clearance, it remains unclear how CD4-CTL are induced. We now show that CD4-CTLs require sustained antigen presentation and are induced by CD11c-expressing antigen-presenting cells. ABSTRACT Cytotoxic CD4 T lymphocytes (CD4-CTL) are important in antiviral immunity. For example, we have previously shown that in mice, CD4-CTL are important to control ectromelia virus (ECTV) infection. How viral infections induce CD4-CTL responses remains incompletely understood. We demonstrate here that not only ECTV but also vaccinia virus and lymphocytic choriomeningitis virus induce CD4-CTL, though the response to ECTV is stronger. Using ECTV, we also demonstrate that in contrast to CD8-CTL, CD4-CTL differentiation requires constant virus replication and ceases once the virus is controlled. We also show that major histocompatibility complex class II molecules on CD11c+ cells are required for CD4-CTL differentiation and for mousepox resistance. Transcriptional analysis indicated that antiviral CD4-CTL and noncytolytic T helper 1 (Th1) CD4 T cells have similar transcriptional profiles, suggesting that CD4-CTL are terminally differentiated classical Th1 cells. Interestingly, CD4-CTL and classical Th1 cells expressed similar mRNA levels of the transcription factors ThPOK and GATA-3, necessary for CD4 T cell linage commitment, and Runx3, required for CD8 T cell development and effector function. However, at the protein level, CD4-CTL had higher levels of the three transcription factors, suggesting that further posttranscriptional regulation is required for CD4-CTL differentiation. Finally, CRISPR/Cas9-mediated deletion of Runx3 in CD4 T cells inhibited CD4-CTL but not classical Th1 cell differentiation in response to ECTV infection. These results further our understanding of the mechanisms of CD4-CTL differentiation during viral infection and the role of posttranscriptionally regulated Runx3 in this process. IMPORTANCE While it is well established that cytotoxic CD4 T cells (CD4-CTLs) directly contribute to viral clearance, it remains unclear how CD4-CTL are induced. We now show that CD4-CTLs require sustained antigen presentation and are induced by CD11c-expressing antigen-presenting cells. Moreover, we show that CD4-CTLs are derived from the terminal differentiation of classical T helper 1 (Th1) subset of CD4 cells. Compared to Th1 cells, CD4-CTLs upregulate protein levels of the transcription factors ThPOK, Runx3, and GATA-3 posttranscriptionally. Deletion of Runx3 in differentiated CD4 T cells prevents induction of CD4-CTLs but not classical Th1 cells. These results advance our knowledge of how CD4-CTLs are induced during viral infection.
The neural representation of abstract words may arise through grounding word meaning in language itself

In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words, which lack a physical referent. Hebbian formalism and optimization are basic principles of brain function, and they provide an appealing approach for modeling word meanings based on word co‐occurrences. We provide proof of concept that a statistical model of the semantic space can account for neural representations of both concrete and abstract words, using MEG. Here, we built a statistical model using word embeddings extracted from a text corpus. This statistical model was used to train a machine learning algorithm to successfully decode the MEG signals evoked by written words. In the model, word abstractness emerged from the statistical regularities of the language environment. Representational similarity analysis further showed that this salient property of the model co‐varies, at 280–420 ms after visual word presentation, with activity in regions that have been previously linked with processing of abstract words, namely the left‐hemisphere frontal, anterior temporal and superior parietal cortex. In light of these results, we propose that the neural encoding of word meanings can arise through statistical regularities, that is, through grounding in language itself.
Lung ultrasound predicts non-invasive ventilation outcome in COVID-19 acute respiratory failure: a pilot study.

BACKGROUND To determine relationships between lung aeration assessed by lung ultrasound (LUS) with Non-Invasive Ventilation (NIMV) outcome, intensive care unit (ICU) admission and mechanical ventilation (MV) needs in COVID-19 respiratory failure. METHODS A cohort of adult patients with COVID-19 respiratory failure underwent LUS during initial assessment. A simplified LUS protocol consisting in scanning six areas, three for each side, was adopted. A score from 0 to 3 was assigned to each area. Comprehensive LUS score (LUSsc) was calculated as the sum of the score in all areas. LUSsc, the amount of involved sonographic lung areas (LUSq), the number of lung quadrants radiographically infiltrated and the degree of oxygenation impairment at admission (SpO2/FiO2 ratio) were compared to NIMV Outcome, MV needs and ICU admission. RESULTS Among 85 patients prospectively included in the analysis, 49 of 61 needed MV. LUSsc and LUSq were higher in patients who required MV (median 12 [IQR 8-14] and median 6 [IQR 4-6], respectively) than in those who did not (6 [IQR 2-9] and 3 [IQR 1-5], respectively), both p < 0.001. NIMV trial failed in 26 patients out 36. LUSsc and LUSq were significantly higher in patients who failed NIMV than in those who did not. From ROC analysis, LUSsc ≧ 12 and LUSq ≧5 gave the best cut-off values for NIMV failure prediction (AUC=0.95, 95%CI 0.83-0.99 and AUC=0.81, 95%CI 0.65-0.91, respectively). CONCLUSIONS Our data suggest LUS as a possible tool for identifying patients who are likely to require MV and ICU admission or to fail a NIMV trial.
Long-Term Care Nurses' Experiences With Patient Safety Incident Management

Background: Global trends in the aging population will increase the demands for long-term care (LTC) resources. Due to recent pressures to deliver more complex care, there is further risk to resident safety in LTC. Emphasis on the management and the delivery in safe and quality resident care in LTC is required. Purpose: The purpose of this study was to describe nurses' experiences with patient safety incident (PSI) management involving residents living in LTC. Methods: Using a qualitative descriptive approach, 9 nurses were recruited in 3 LTC homes. Semistructured interviews were conducted, and data were analyzed using inductive content analysis. Results: Three main categories emerged: commitment to resident safety, workplace culture, and emotional reaction. Conclusions: Providing nurses with an opportunity to share their PSI management experiences highlights the current factors influencing frontline resident safety in LTC. Study results can inform nursing practice and policy development to support PSI identification and management.
Divergent responses of primary production to increasing precipitation variability in global drylands

Interannual variability in precipitation has increased globally as climate warming intensifies. The increased variability impacts both terrestrial plant production and carbon (C) sequestration. However, mechanisms driving these changes are largely unknown. Here, we examined mechanisms underlying the response of aboveground net primary production (ANPP) to interannual precipitation variability in global drylands with mean annual precipitation (MAP) <500 mm year−1, using a combined approach of data synthesis and process‐based modeling. We found a hump‐shaped response of ANPP to precipitation variability along the MAP gradient. The response was positive when MAP < ~300 mm year−1 and negative when MAP was higher than this threshold, with a positive peak at 140 mm year−1. Transpiration and subsoil water content mirrored the response of ANPP to precipitation variability; evaporation responded negatively and water loss through runoff and drainage responded positively to precipitation variability. Mean annual temperature, soil type, and plant physiological traits all altered the magnitude but not the pattern of the response of ANPP to precipitation variability along the MAP gradient. By extrapolating to global drylands (<500 mm year−1 MAP), we estimated that ANPP would increase by 15.2 ± 6.0 Tg C year−1 in arid and hyper‐arid lands and decrease by 2.1 ± 0.5 Tg C year−1 in dry sub‐humid lands under future changes in interannual precipitation variability. Thus, increases in precipitation variability will enhance primary production in many drylands in the future.
Predictive Risk Models for Wound Infection-Related Hospitalization or ED Visits in Home Health Care Using Machine-Learning Algorithms

ABSTRACT OBJECTIVE Wound infection is prevalent in home healthcare (HHC) and often leads to hospitalizations. However, none of the previous studies of wounds in HHC have used data from clinical notes. Therefore, the authors created a more accurate description of a patient’s condition by extracting risk factors from clinical notes to build predictive models to identify a patient’s risk of wound infection in HHC. METHODS The structured data (eg, standardized assessments) and unstructured information (eg, narrative-free text charting) were retrospectively reviewed for HHC patients with wounds who were served by a large HHC agency in 2014. Wound infection risk factors were identified through bivariate analysis and stepwise variable selection. Risk predictive performance of three machine learning models (logistic regression, random forest, and artificial neural network) was compared. RESULTS A total of 754 of 54,316 patients (1.39%) had a hospitalization or ED visit related to wound infection. In the bivariate logistic regression, language describing wound type in the patient’s clinical notes was strongly associated with risk (odds ratio, 9.94; P < .05). The areas under the curve were 0.82 in logistic regression, 0.75 in random forest, and 0.78 in artificial neural network. Risk prediction performance of the models improved (by up to 13.2%) after adding risk factors extracted from clinical notes. CONCLUSIONS Logistic regression showed the best risk prediction performance in prediction of wound infection-related hospitalization or ED visits in HHC. The use of data extracted from clinical notes can improve the performance of risk prediction models.
EFFECT OF SUBSTANCE-P ON GALACTOPOIETICS HORMONES SECRETIONS AND MILK PRODUCTION AND COMPOSITIONS

It has been well established that the secretions of thyroid hormones and growth hormone (GH) is under the regulation of different neurotransmitters. The goal of this study was to determine whether substance-P, as a neurotransmitter, regulates the mean plasma concentrations of thyroxin (T4), triiodothyronine (T3), GH, milk amount and constituents in Sanan goats. Nine Sanan goats were randomly divided into 3 groups. Each group received a daily infusion of either 1, 2 or 4 Ug substance-P for 7 days into the third ventricle. Blood samples were collected daily for 11 days, and assayed for plasma T3, T4 and GH concentrations by double-antibody RIA. Milk samples were collected daily for each of the 11 days. Milk samples were assayed for protein, fat and lactose constituents. The daily amount of milk was determined throughout the experiment. Infusions of 1 and 2 Ug substance-P did not change the plasma concentrations of the T3, T4 and GH throughout the experiment. Infusions of 4 Ug substance-P significantly (P<0.01) increased the plasma concentrations of the T3, T4 and GH among all the animals, however, different dosages of substance-P did not change the amount, the protein, fat and lactose constituents of milk among all the animals in the different groups. The result of this experiment indicated that substance-P may increase the mean plasma concentrations of GH, T3, and T4 in Sanan goats.
A genome‐wide analysis of the molecular alterations occurring in the adenomatous and carcinomatous components of the same tumor based on the adenoma–carcinoma sequence

Identification of molecular alterations occurring in the adenomatous and carcinomatous components within the same tumor would greatly enhance understanding of the neoplastic progression of colorectal cancer. We examined somatic copy number alterations (SCNAs) and mRNA expression at the corresponding loci involved in the adenoma–carcinoma sequence in the isolated adenomatous and cancer glands of the same tumor in 15 cases of microsatellite‐stable “carcinoma in adenoma,” using genome‐wide SNP and global gene expression arrays. Multiple copy‐neutral loss of heterozygosity events were detected at 4q13.2, 15q15.1, and 14q24.3 in the adenomatous component and at 4q13.2, 15q15.1, and 14q24.3 in the carcinomatous component. There were significant differences in the copy number (CN) gain frequencies at 20q11.21–q13.33, 8q13.3, 8p23.1, and 8q21.2–q22.2 between the adenomatous and carcinomatous components. Finally, we found a high frequency of five genotypes involving CN gain with upregulated expression of the corresponding gene (RPS21, MIR3654, RSP20, SNORD54, or ASPH) in the carcinomatous component, whereas none of these genotypes were detected in the adenomatous component. This finding is interesting in that CN gain with upregulated gene expression may enhance gene function and play a crucial role in the progression of an adenoma into a carcinomatous lesion.
Treatment adherence and persistence of seven commonly prescribed biologics for moderate to severe psoriasis and psoriatic arthritis in a U.S. commercially insured population

Abstract Background Adalimumab (ADA), certolizumab pegol (CER), etanercept (ETA), guselkumab (GUS), ixekizumab (IXE), secukinumab (SEC), and ustekinumab (UST) are biologics approved for the treatment of psoriasis (PsO) and psoriatic arthritis (PsA). We examined adherence and persistence among PsO patients with comorbid PsA who initiated treatment with any of these biologics. Methods Adult patients with ≥1 pharmacy/medical claim for any of these seven biologics, and ≥1 diagnosis of both PsO and PsA were selected from the MarketScan Commercial database (July 2014–June 2019). Adherence and persistence rates were examined among the seven study cohorts during fixed follow-up periods (3, 6, 9, and 12 months). Results Among patients with ≥9 months of continuous enrollment, 3,251 initiated ADA, 418 CER, 1,563 ETA, 126 GUS, 422 IXE, 1,596 SEC, and 1,267 UST. During the 9-month follow-up period, the proportions of adherent patients were numerically highest among those treated with GUS (59.5%) and UST (57.0%), followed by SEC (47.9%), IXE (47.6%), ADA (46.8%), ETA (37.4%), and CER (22.0%); persistence rates were also numerically highest among those treated with GUS (65.9%) and UST (65.7%). Limitations Adjustment for potential confounders was not conducted. Conclusions Adherence and persistence rates were numerically highest among patients who initiated GUS and UST.
COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity

Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To gain a better understanding of the wide variety of SARS-CoV-2 strains and their associated symptoms, thousands of SARS-CoV-2 genomes have been sequenced in dozens of countries. In this article, we introduce COVIDomic, a multi-omics online platform designed to facilitate the analysis and interpretation of the large amount of health data collected from patients with COVID-19. The COVIDomic platform provides a comprehensive set of bioinformatic tools for the multi-modal metatranscriptomic data analysis of COVID-19 patients to determine the origin of the coronavirus strain and the expected severity of the disease. An integrative analytical workflow, which includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows to analyze the presence of the most common microbial organisms, their antibiotic resistance, the severity of the infection and the set of the most probable geographical locations from which the studied strain could have originated. The online platform integrates a user friendly interface which allows easy visualization of the results. We envision this tool will not only have immediate implications for management of the ongoing COVID-19 pandemic, but will also improve our readiness to respond to other infectious outbreaks.
Age Related Changes of Superior Orbicularis Oris Muscle in Terms of Tone and Viscoelastic Properties

Supplemental Digital Content is available in the text Objective: To examine the age-related changes in the viscoelastic properties and tone of the superior orbicularis oris muscle by a portablehand-held myotonometer. Methods: A total of 128 individuals (65 female, 63 male) who met the inclusion criteria were evaluated. Individuals were divided into 2 groups as under 40 years old (Group 1), and above (Group 2). The viscoelastic properties and muscle tone of orbicularis oris muscle were evaluated bilaterally in supine position with the Myoton PRO (Myoton AS, Estonia) device from the skin overlying the orbicularis oris. The reference point is accepted as the right and left paramedial philtrum dimple. The statistical analysis was performed. Results: There was no statistically significant difference in the elasticity values of the right and left orbicularis oris muscles of the Group 1 and Group 2 individuals. There was a difference between the groups when the tone and stiffness values of both groups were compared. It was determined that individuals over the age of 40 had higher muscle tone and stiffness. All mechanical properties of the superior orbicularis oris muscle between the male and female individuals in Group 1 were different. The values are higher in the female gender. The elasticity values in both genders in Group 2 were similar. Tone and stiffness parameters were different between genders. Conclusions: Myoton PRO can be recommended as an alternative device in the quantitative evaluations of post-operative follow-ups, and pre/post rehabilitation in terms of viscoelastic parameters of the muscle.
COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes

To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.
Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation

Abstract Background Manual delineation of gross tumor volume (GTV) is essential for radiotherapy treatment planning, but it is time-consuming and suffers inter-observer variability (IOV). In clinics, CT, PET, and MRI are used to inform delineation accuracy due to their different complementary characteristics. This study aimed to investigate deep learning to assist GTV delineation in head and neck squamous cell carcinoma (HNSCC) by comparing various modality combinations. Materials and methods This retrospective study had 153 patients with multiple sites of HNSCC including their planning CT, PET, and MRI (T1-weighted and T2-weighted). Clinical delineations of gross tumor volume (GTV-T) and involved lymph nodes (GTV-N) were collected as the ground truth. The dataset was randomly divided into 92 patients for training, 31 for validation, and 30 for testing. We applied a residual 3 D UNet as the deep learning architecture. We independently trained the UNet with four different modality combinations (CT-PET-MRI, CT-MRI, CT-PET, and PET-MRI). Additionally, analogical to post-processing, an average fusion of three bi-modality combinations (CT-PET, CT-MRI, and PET-MRI) was produced as an ensemble. Segmentation accuracy was evaluated on the test set, using Dice similarity coefficient (Dice), Hausdorff Distance 95 percentile (HD95), and Mean Surface Distance (MSD). Results All imaging combinations including PET provided similar average scores in range of Dice: 0.72-0.74, HD95: 8.8-9.5 mm, MSD: 2.6-2.8 mm. Only CT-MRI had a lower score with Dice: 0.58, HD95: 12.9 mm, MSD: 3.7 mm. The average of three bi-modality combinations reached Dice: 0.74, HD95: 7.9 mm, MSD: 2.4 mm. Conclusion Multimodal deep learning-based auto segmentation of HNSCC GTV was demonstrated and inclusion of the PET image was shown to be crucial. Training on combined MRI, PET, and CT data provided limited improvements over CT-PET and PET-MRI. However, when combining three bimodal trained networks into an ensemble, promising improvements were shown.