Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Ancestry Adjusted Allelic Association Analysis Robustly Captures Tuberculosis Susceptibility Loci

Pulmonary tuberculosis (TB), caused by Mycobacterium tuberculosis, is a complex disease. The risk of developing active TB is in part determined by host genetic factors. Most genetic studies investigating TB susceptibility fail to replicate association signals particularly across diverse populations. South African populations arose because of multi-wave genetic admixture from the indigenous KhoeSan, Bantu-speaking Africans, Europeans, Southeast Asian-and East Asian populations. This has led to complex genetic admixture with heterogenous patterns of linkage disequilibrium and associated traits. As a result, precise estimation of both global and local ancestry is required to prevent both false positive and false-negative associations. Here, 820 individuals from South Africa were genotyped on the SNP-dense Illumina Multi-Ethnic Genotyping Array (∼1.7M SNPs) followed by local and global ancestry inference using RFMix. Local ancestry adjusted allelic association (LAAA) models were utilized owing to the extensive genetic heterogeneity present in this population. Hence, an interaction term, comprising the identification of the minor allele that corresponds to the ancestry present at the specific locus under investigation, was included as a covariate. One SNP (rs28647531) located on chromosome 4q22 was significantly associated with TB susceptibility and displayed a SNP minor allelic effect (G allele, frequency = 0.204) whilst correcting for local ancestry for Bantu-speaking African ancestry (p-value = 5.518 × 10−7; OR = 3.065; SE = 0.224). Although no other variants passed the significant threshold, clear differences were observed between the lead variants identified for each ancestry. Furthermore, the LAAA model robustly captured the source of association signals in multi-way admixed individuals from South Africa and allowed the identification of ancestry-specific disease risk alleles associated with TB susceptibility that have previously been missed.
Predicting Mortality of COVID-19 Patients based on Data Mining Techniques

If Coronavirus (COVID-19) is not predicted, managed, and controlled timely, the health systems of any country and their people will face serious problems. Predictive models can be helpful in health resource management and prevent outbreak and death caused by COVID-19. The present study aimed at predicting mortality in patients with COVID-19 based on data mining techniques. To do this study, the mortality factors of COVID-19 patients were first identified based on different studies. These factors were confirmed by specialist physicians. Based on the confirmed factors, the data of COVID-19 patients were extracted from 850 medical records. Decision tree (J48), MLP, KNN, random forest, and SVM data mining models were used for prediction. The models were evaluated based on accuracy, precision, specificity, sensitivity, and the ROC curve. According to the results, the most effective factor used to predict the death of COVID-19 patients was dyspnea. Based on ROC (1.000), accuracy (99.23%), precision (99.74%), sensitivity (98.25%) and specificity (99.84%), the random forest was the best model in predicting of mortality than other models. After the random forest, KNN5, MLP, and J48 models were ranked next, respectively. Data analysis of COVID-19 patients can be a suitable and practical tool for predicting the mortality of these patients. Given the sensitivity of medical science concerning maintaining human life and lack of specialized human resources in the health system, using the proposed models can increase the chances of successful treatment, prevent early death and reduce the costs associated with long treatments for patients, hospitals and the insurance industry.
Effect of left atrial functions upon the functional capacity in patients with systolic heart failure

Objective To assess left atrial (LA) function and its effect upon the functional capacity in patients with systolic heart failure (HF) using the two-dimensional (2D) speckle tracking echocardiography (STE). Patients and methods This study included 44 patients with systolic HF, ejection fraction less than 40%, sinus rhythm, and New York Heart Association (NYHA) class II–IV. All patients underwent six-minute walk test and conventional, tissue Doppler imaging (TDI), and 2D-STE echo. LA measures included LA dimensions and volumes for calculation of reservoir, conduit, and contractile functions. Peak atrial longitudinal strain (PALS) and peak atrial contractile strain were measured by 2D-STE. Based on NYHA class, patients were divided into group 1 (patients with NYHA II) and group 2 (patients with NYHA III–IV). Results There were no significant differences between both groups regarding demographic data. The 2D ejection fraction was significantly lower in group 2 compared with group I (P<0.05). Group 2 had significantly lower average LA peak systolic velocity (S′) and average LA late diastolic velocity (a′) by TDI compared with group 1, and also group 2 had significantly lower average PALS (16.3±7.2 vs. 11.3±4.4) and average peak atrial contractile strain (10.7±5.4 vs. 5.8±3.3, respectively; P<0.001), compared with group I. There were negative correlations between NYHA class and PALS (r=−0.436, P=0.003), whereas a positive correlation for PALS with six-minute walk distance (r=0.632, P=0.000). PALS was the most significant parameter that can predict limited exercise capacity. Conclusion LA mechanical functions were decreased in patients with systolic HF either measured by STE or TDI. PALS measured by 2D-STE is the most significant parameter correlated with functional capacity.
Accuracy of computer-aided chest X-ray screening in the Kenya National Tuberculosis Prevalence Survey

Background: Community-based screening for tuberculosis (TB) could improve detection but is resource intensive. We set out to evaluate the accuracy of computer-aided TB screening using digital chest X-ray (CXR) to determine if this approach met target product profiles (TPP) for community-based screening. Methods: CXR images from participants in the 2016 Kenya National TB Prevalence Survey were evaluated using CAD4TBv6 (Delft Imaging), giving a probabilistic score for pulmonary TB ranging from 0 (low probability) to 99 (high probability). We constructed a Bayesian latent class model to estimate the accuracy of CAD4TBv6 screening compared to bacteriologically-confirmed TB across CAD4TBv6 threshold cut-offs, incorporating data on Clinical Officer CXR interpretation, participant demographics (age, sex, TB symptoms, previous TB history), and sputum results. We compared model-estimated sensitivity and specificity of CAD4TBv6 to optimum and minimum TPPs. Results: Of 63,050 prevalence survey participants, 61,848 (98%) had analysable CXR images, and 8,966 (14.5%) underwent sputum bacteriological testing; 298 had bacteriologically-confirmed pulmonary TB. Median CAD4TBv6 scores for participants with bacteriologically-confirmed TB were significantly higher (72, IQR: 58-82.75) compared to participants with bacteriologically-negative sputum results (49, IQR: 44-57, p<0.0001). CAD4TBv6 met the optimum TPP; with the threshold set to achieve a mean sensitivity of 95% (optimum TPP), specificity was 83.3%, (95% credible interval [CrI]: 83.0%-83.7%, CAD4TBv6 threshold: 55). There was considerable variation in accuracy by participant characteristics, with older individuals and those with previous TB having lowest specificity. Conclusions: CAD4TBv6 met the optimal TPP for TB community screening. To optimise screening accuracy and efficiency of confirmatory sputum testing, we recommend that an adaptive approach to threshold setting is adopted based on participant characteristics.
Neural correlates of interoceptive accuracy: Beyond cardioception

Interoceptive accuracy (IAc), the precision with which one assesses the signals arising from one's own body, is receiving increasing attention in the literature. IAc has mainly been approached as an individual trait and has been investigated through the cardiac modality using mostly non‐ecological methods. Such studies consensually designate the anterior insular cortex as the main brain correlate of IAc. However, there is a lack of brain imaging studies investigating IAc in a broader and more ecological way. Here, we used a novel ecological task in which participants monitored their general bodily reactions to external events and investigated brain regions subtending intraindividual (i.e. trial‐by‐trial) variations of IAc. At each trial, participants had to rate the intensity of their bodily reactions to an emotional picture. We recorded participants' skin conductance response (SCR) to the picture as an indicator of actual physiological response intensity. We fitted a functional magnetic resonance imaging (fMRI) model using, as regressors, the SCR value, the rating and the product of the two (as a proxy of participants' IAc) obtained trial per trial. We observed that activity in the dorsomedial prefrontal cortex (dmPFC) increased when individuals' IAc decreased. This result reveals general mechanism of error processing in intraindividual variations of IAc, which are unspecific to interoception. Our result has a practical impact in the clinical domain. Namely, it supports the predictive coding framework whereby IAc deficits may reflect impairments in processing a mismatch between actual interoceptive signals and predictions.
The mediational role of trust in the healthcare system in the association between generalized trust and willingness to get COVID-19 vaccination in Iran

ABSTRACT For some individuals, there appears to be some level of unwillingness in getting a COVID-19 vaccine which may be due to trust issues. The present study used a mediation model to investigate how trust is associated with an individual’s willingness to get COVID-19 vaccination among Iranians. A total of 10,843 Iranian adults were recruited in Qazvin province using a multistage stratified cluster sampling method. The survey was completed between February 19 and April 9, 2021. The findings showed that generalized trust was positively associated with trust in the healthcare system, trust in the healthcare system was positively associated with willingness to get COVID-19 vaccination, and generalized trust was positively associated with willingness to get COVID-19 vaccination. Also, trust in the healthcare system mediated the association between generalized trust and willingness to get COVID-19 vaccination. There were some significant demographic differences in COVID-19 vaccination willingness. The findings suggest that generalized trust plays a significant role in directly or indirectly influencing individuals’ willingness to get COVID-19 vaccine. Therefore, government bodies and health officials may utilize these findings to appeal in a more transparent and professional manner in encouraging individuals to get a COVID-19 vaccine. However, for those with lower trust levels (in general and in the healthcare system), the focus may be to re-build and/or regain the individuals’ trust through carefully planned transparent communication, information dissemination, and ethical education to help increase the uptake of COVID-19 vaccination.
Seasonal genotype dynamics of a marine dinoflagellate: Pelagic populations are homogeneous and as diverse as benthic seed banks

Genetic diversity is the basis for evolutionary adaptation and selection under changing environmental conditions. Phytoplankton populations are genotypically diverse, can become genetically differentiated within small spatiotemporal scales and many species form resting stages. Resting stage accumulations in sediments (seed banks) are expected to serve as reservoirs for genetic information, but so far their role in maintaining phytoplankton diversity and in evolution has remained unclear. In this study we used the toxic dinoflagellate Alexandrium ostenfeldii (Dinophyceae) as a model organism to investigate if (i) the benthic seed bank is more diverse than the pelagic population and (ii) the pelagic population is seasonally differentiated. Resting stages (benthic) and plankton (pelagic) samples were collected at a coastal bloom site in the Baltic Sea, followed by cell isolation and genotyping using microsatellite markers (MS) and restriction site associated DNA sequencing (RAD). High clonal diversity (98%–100%) combined with intermediate to low gene diversity (0.58–0.03, depending on the marker) was found. Surprisingly, the benthic and pelagic fractions of the population were equally diverse, and the pelagic fraction was temporally homogeneous, despite seasonal fluctuation of environmental selection pressures. The results of this study suggest that continuous benthic–pelagic coupling, combined with frequent sexual reproduction, as indicated by persistent linkage equilibrium, prevent the dominance of single clonal lineages in a dynamic environment. Both processes harmonize the pelagic with the benthic population and thus prevent seasonal population differentiation. At the same time, frequent sexual reproduction and benthic–pelagic coupling maintain high clonal diversity in both habitats.
Effects of nitrogen and phosphorus fertilization on the ratio of activities of carbon-acquiring to nitrogen-acquiring enzymes in a primary lowland tropical rainforest in Borneo, Malaysia

ABSTRACT Previous meta-analyses revealed that the ratio of activities of carbon (C)-acquiring enzyme to nitrogen (N)-acquiring enzymes in tropical forest ecosystems was nearly identical to those in other ecosystems, despite of the N-rich condition in tropical forests. This could be explained by microbes in tropical forest soils, which require a large amount of N to produce N-rich acid phosphatase (AP) for catalyzation of the organic form of phosphorus (P) and compensation for poor P availability in soils. Based on this idea, we hypothesized that experimental P fertilization would reduce the allocation to N-acquiring enzymes compared with that of C-acquiring enzymes, i.e. that it would increase the ratios of activities of β-1,4-glucosidase (BG) to β-1,4-acetylglucosaminidase (NAG) and leucine aminopeptidase (LAP). We tested this hypothesis using an experimental fertilization site with factorial N (100 kg ha−1 yr−1) and P (50 kg ha−1 yr−1) addition in a primary tropical lowland forest in Bornean Malaysia, where our earlier work demonstrated that P fertilization reduced AP activity. Contrary to our hypothesis, the BG:NAG and BG:(NAG + LAP) ratios were not altered by either N or P fertilizations. This result indicated that AP production was not a reason for the maintenance of a relatively high investment in N-acquiring enzyme at our study site. Rather, NAG and LAP production was likely driven by C acquisition, rather than N acquisition, as the target substrates contained C as well as N. This idea was supported by the fact that neither the BG:NAG ratio nor the BG:(NAG + LAP) ratio was elevated by N addition. We propose that the ratios of activities of BG to NAG and LAP do not necessarily indicate the ratio of C:N acquisition, at least in our N-rich tropical forest ecosystem.
Adaptive Output Feedback Fuzzy Fault-Tolerant Control for Nonlinear Full-State-Constrained Switched Systems

In this article, an output feedback adaptive fuzzy tracking control method for a class of switched uncertain nonlinear systems with actuator failures and full-state constraints is proposed under an arbitrary switching signal combining the dynamic surface technique. Since the state variables of the system under study are not measurable, a fuzzy observer is constructed to identify the unmeasured states. The actuator failures are considered in the system. To compensate this failure, a fault-tolerant controller is proposed. Moreover, each state needs to be kept within the constraints, so the tangent Barrier Lyapunov function is selected to solve the full-state constraint problem, and the unknown nonlinear function is approximated by fuzzy-logic systems (FLSs). We also proved that all signals in the closed-loop system are bounded. Furthermore, the states can be kept within the predetermined range even if the actuator fails. Finally, a simulation example is given to verify the effectiveness of the proposed control strategy.
Clinical Activity of Single-Agent Cabozantinib (XL184), a Multi-receptor Tyrosine Kinase Inhibitor, in Patients with Refractory Soft-Tissue Sarcomas

Purpose: Soft-tissue sarcomas (STS) are a rare, heterogeneous group of mesenchymal tumors. For decades the mainstay of treatment for advanced, unresectable STS has been palliative chemotherapy. High levels of activated MET receptor have been reported in various sarcoma cell lines, together with elevated vascular endothelial growth factor (VEGF) levels in patients with STS, suggesting that dual targeting of the VEGF and MET pathways with the multi-receptor tyrosine kinase inhibitor cabozantinib would result in clinical benefit in this population. Patients and Methods: We performed an open-label, multi-institution, single-arm phase II trial of single-agent cabozantinib in adult patients with advanced STS and progressive disease after at least 1 standard line of systemic therapy. Patients received 60 mg oral cabozantinib once daily in 28-day cycles, and dual primary endpoints of overall response rate and 6-month progression-free survival (PFS) were assessed. Changes in several circulating biomarkers were assessed as secondary endpoints. Results: Six (11.1%; 95% CI, 4.2%–22.6%) of the 54 evaluable patients enrolled experienced objective responses (all partial responses). Six-month PFS was 49.3% (95% CI, 36.2%–67.3%), with a median time on study of 4 cycles (range, 1–99). The most common grade 3/4 adverse events were hypertension (7.4%) and neutropenia (16.7%). Patients' levels of circulating hepatocyte growth factor (HGF), soluble MET, and VEGF-A generally increased after a cycle of therapy, while soluble VEGFR2 levels decreased, regardless of clinical outcome. Conclusions: Cabozantinib single-agent antitumor activity was observed in patients with selected STS histologic subtypes (alveolar soft-part sarcoma, undifferentiated pleomorphic sarcoma, extraskeletal myxoid chondrosarcoma, and leiomyosarcoma) highlighting the biomolecular diversity of STS.
Heterogeneous treatment effect analysis based on machine‐learning methodology

Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment effects for individuals or subgroups in a population. For example, an HTE‐informed understanding can critically guide physicians to individualize the medical treatment for a certain disease. However, HTE analysis has not been widely recognized and used, even given the explosive increase of data availability attributed to the arrival of the Big Data era. Part of the reason behind its underuse is that data are often of high dimension and high complexity, which pose significant challenges for applying conventional HTE analysis methods. To meet these challenges, a newly developed causal forest HTE method has been derived from the random forest machine‐learning algorithm. We conducted a systematic performance evaluation for the causal forest method against the conventional two‐step method by simulating scenarios with different levels of complexity for the analysis. Our results show that causal forest outperforms the conventional HTE method in assessing treatment effect, especially when data are complex (e.g., nonlinear) and high dimensional, suggesting that causal forest is a promising tool for real‐world applications of HTE analysis.
Elevated cytokines and chemokines in peripheral blood of patients with SARS-CoV-2 pneumonia treated with high-titer convalescent plasma

The global SARS-CoV-2 coronavirus pandemic continues to be devastating in many areas. Treatment options have been limited and convalescent donor plasma has been used by many centers to transfer passive neutralizing antibodies to patients with respiratory involvement. The results often vary by institution and are complicated by the nature and quality of the donor plasma itself, the timing of administration and the clinical aspects of the recipients. SARS-CoV-2 infection is known to be associated with an increase in the blood concentrations of several inflammatory cytokines/chemokines, as part of the overall immune response to the virus and consequential to mediated lung pathology. Some of these correlates contribute to the cytokine storm syndrome and acute respiratory distress syndrome, often resulting in fatality. A Phase IIa clinical trial at our institution using high neutralizing titer convalescent plasma transfer gave us the unique opportunity to study the elevations of correlates in the first 10 days after infusion. Plasma recipients were divided into hospitalized COVID-19 pneumonia patients who did not (Track 2) or did (Track 3) require mechanical ventilation. Several cytokines were elevated in the patients of each Track and some continued to rise through Day 10, while others initially increased and then subsided. Furthermore, elevations in MIP-1α, MIP-1β and CRP correlated with disease progression of Track 2 recipients. Overall, our observations serve as a foundation for further study of these correlates and the identification of potential biomarkers to improve upon convalescent plasma therapy and to drive more successful patient outcomes.
Chlamydia and HPV induce centrosome amplification in the host cell through additive mechanisms

Based on epidemiology studies, Chlamydia trachomatis has been proposed as a co‐factor for human papillomavirus (HPV) in the development of cervical cancer. These two intracellular pathogens have been independently reported to induce the production of extra centrosomes, or centrosome amplification, which is a hallmark of cancer cells. We developed a cell culture model to systematically measure the individual and combined effects of Chlamydia and HPV on the centrosome in the same host cell. We found that C. trachomatis caused centrosome amplification in a greater proportion of cells than HPV and that the effects of the two pathogens on the centrosome were additive. Furthermore, centrosome amplification induced by Chlamydia, but not by HPV, strongly correlated with multinucleation and required progression through mitosis. Our results suggest that C. trachomatis and HPV induce centrosome amplification through different mechanisms, with the chlamydial effect being largely due to a failure in cytokinesis that also results in multinucleation. Our findings provide support for C. trachomatis as a co‐factor for HPV in carcinogenesis and offer mechanistic insights into how two infectious agents may cooperate to promote cancer.