Kinematic and Kinetic Gait Parameters Can Distinguish between Idiopathic and Neurologic Toe-Walking

The differentiation between mild forms of toe-walking (equinus) in cerebral palsy (CP) and idiopathic toe-walking (ITW) is often clinically challenging. This study aims to define kinematic and kinetic parameters using 3D gait analysis to facilitate and secure the diagnosis of “idiopathic toe-walking”. We conducted a retrospective controlled stratified cohort study. 12 toe-walking subjects per group diagnosed as ITW or CP were included and stratified according to age, gender and maximal dorsiflexion in stance. We collected kinematic and kinetic data using a three-dimensional optical motion analysis system with integrated floor force plates. Pairwise comparison between ITW and CP gait data was performed, and discriminant factor analysis was conducted. Both groups were compared with typically developing peers (TD). We found kinematic and kinetic parameters having a high discriminatory power and sensitivity to distinguish between ITW and CP groups (e.g., knee angle at initial contact (91% sensitivity, 73% specificity) and foot progression angle at midstance (82% sensitivity, 73% specificity)). The strength of this study is a high discriminatory power between ITW and CP toe-walking groups. Described kinematic parameters are easy to examine even without high-tech equipment; therefore, it is directly transferable to everyday praxis.

Screening and selection of 21 novel microhaplotype markers for ancestry inference in ten Chinese Subpopulations

Genetic findings suggested that ethnolinguistically diverse populations in China harbor differentiated genetic structure and complex evolutionary admixture history, which provide the genetic basis and theoretical foundation for forensic biogeographical ancestry inference (BGAI). Forensic assays for BGAI among intracontinental eastern Eurasians were previously conducted mainly based on the SNPs or InDels. Microhaplotypes, as a set of closely linked SNPs within 200 base pairs, possess the advantages of both STR and SNP and have great potential in forensic ancestry inference. However, the developed forensic assay based on the ancestry informative microhaplotypes in the BGAI remained to be comprehensively explored, especially in China with enriching genetic diversity. Here, we described a new BGAI panel based on 21 novel identified ancestry informative microhaplotypes that focused on dissected finer-scale ancestry composition of Chinese populations. We initially screened all possible microhaplotypes with high Fst values among five East Asian populations and finally employed 21 candidate microhaplotypes in two multiplex SNaPshot assays. Forensic amplification efficiency and statistically/physically phased haplotypes of the 21 microhaplotypes were validated using both SNaPshot and massively parallel sequencing (MPS) platforms. Followingly, we validated the efficiency of these microhaplotypes for BGAI in 764 individuals from ten Chinese populations. Fine-scale ancestry source and ancestry proportion estimated by the principal component analysis (PCA), multidimensional scaling (MDS), phylogenetic tree and model-based STRUCTURE among worldwide populations and East Asians showed that our customized panel could provide a higher discrimination resolution in both continental population stratification and East Asian regional substructure. East Asian populations could be further classified into linguistically/geographically different intracontinental subpopulations (Tibeto-Burman, Tai-Kadai and others). Finally, we obtained a higher estimated accuracy using training and tested datasets in the microhaplotype-based panel than traditional SNP-based panels. Generally, the above results demonstrated that this microhaplotype panel was robust and suitable for forensic BGAI in Chinese populations, which not only provided a high discriminatory power for continental populations but also discriminated East Asians into linguistically restricted subpopulations.

Configuring a federated network of real-world patient health data for multimodal deep learning prediction of health outcomes.

Vast quantities of electronic patient medical data are currently being collated and processed in large federated data repositories. For instance, TriNetX, Inc., a global health research network, has access to more than 300 million patients, sourced from healthcare organizations, biopharmaceutical companies, and contract research organizations. As such, pipelines that are able to algorithmically extract huge quantities of patient data from multiple modalities present opportunities to leverage machine learning and deep learning approaches with the possibility of generating actionable insight. In this work, we present a modular, semi-automated end-to-end machine and deep learning pipeline designed to interface with a federated network of structured patient data. This proof-of-concept pipeline is disease-agnostic, scalable, and requires little domain expertise and manual feature engineering in order to quickly produce results for the case of a user-defined binary outcome event. We demonstrate the pipeline's efficacy with three different disease workflows, with high discriminatory power achieved in all cases.

Neutrophils and Lymphocytes Can Help Distinguish Asymptomatic COVID-19 From Moderate COVID-19

IntroductionAsymptomatic coronavirus disease 2019 (COVID-19) and moderate COVID-19 may be the most common COVID-19 cases. This study was designed to develop a diagnostic model for patients with asymptomatic and moderate COVID-19 based on demographic, clinical, and laboratory variables.MethodsThis retrospective study divided the subjects into 2 groups: asymptomatic COVID-19 (without symptoms, n = 15) and moderate COVID-19 (with symptoms, n = 57). Demographic characteristics, clinical data, routine blood tests, other laboratory tests, and inpatient data were collected and analyzed to compare patients with asymptomatic COVID-19 and moderate COVID-19.ResultsComparison of the asymptomatic COVID-19 group with the moderate COVID-19 group yielded the following results: the patients were younger (P = 0.045); the cluster of differentiation (CD)8+ (cytotoxic) T cell level was higher (P = 0.017); the C-reactive protein (CRP) level was lower (P = 0.001); the white blood cell (WBC, P < 0.001), neutrophil (NEU, P = 0.036), lymphocyte (LYM, P = 0.009), and eosinophil (EOS, P = 0.036) counts were higher; and the serum iron level (P = 0.049) was higher in the asymptomatic COVID-19 group. The multivariate analysis showed that the NEU count (odds ratio [OR] = 2.007, 95% confidence interval (CI): 1.162 - 3.715, P = 0.014) and LYM count (OR = 9.380, 95% CI: 2.382 - 36.934, P = 0.001) were independent factors for the presence of clinical symptoms after COVID-19 infection. The NEU count and LYM count were diagnostic predictors of asymptomatic COVID-19. This diagnostic prediction model showed high discriminatory power, consistency, and net clinical benefits.ConclusionsThe proposed model can distinguish asymptomatic COVID-19 from moderate COVID-19, thereby helping clinicians identify and distinguish patients with potential asymptomatic COVID-19 from those with moderate COVID-19.

SqueezeFace: Integrative Face Recognition Methods with LiDAR Sensors

In this paper, we propose a robust and reliable face recognition model that incorporates depth information such as data from point clouds and depth maps into RGB image data to avoid false facial verification caused by face spoofing attacks while increasing the model’s performance. The proposed model is driven by the spatially adaptive convolution (SAC) block of SqueezeSegv3; this is the attention block that enables the model to weight features according to their importance of spatial location. We also utilize large-margin loss instead of softmax loss as a supervision signal for the proposed method, to enforce high discriminatory power. In the experiment, the proposed model, which incorporates depth information, had 99.88% accuracy and an F 1 score of 93.45%, outperforming the baseline models, which used RGB data alone.

Early antiretroviral therapy initiation effect on metabolic profile in vertically HIV-1-infected children

Background Early combined antiretroviral treatment (cART) in perinatally acquired HIV-1 children has been associated with a rapid viral suppression, small HIV-1 reservoir size and reduced mortality and morbidity. Immunometabolism has emerged as an important field in HIV-1 infection offering both relevant knowledge regarding immunopathogenesis and potential targets for therapies against HIV-1. Objectives To characterize the proteomic, lipidomic and metabolomic profile of HIV-1-infected children depending on their age at cART initiation. Patients and methods Plasma samples from perinatally HIV-1-infected children under suppressive cART who initiated an early cART (first 12 weeks after birth, EARLY, n = 10) and late cART (12–50 weeks after birth, LATE, n = 10) were analysed. Comparative plasma proteomics, lipidomics and metabolomics analyses were performed by nanoLC-Orbitrap, UHPLC-qTOF and GC-qTOF, respectively. Results Seven of the 188 proteins identified exhibited differences comparing EARLY and LATE groups of HIV-1-infected children. Despite no differences in the lipidomic (n = 115) and metabolomic (n = 81) profiles, strong correlations were found between proteins and lipid levels as well as metabolites, including glucidic components and amino acids, with clinical parameters. The ratio among different proteins showed high discriminatory power of EARLY and LATE groups. Conclusions Protein signature show a different proinflammatory state associated with a late cART introduction. Its associations with lipid levels and the relationships found between metabolites and clinical parameters may potentially trigger premature non-AIDS events in this HIV-1 population, including atherosclerotic diseases and metabolic disorders. Antiretroviral treatment should be started as soon as possible in perinatally acquired HIV-1-infected children to prevent them from future long-life complications.

First core microsatellite panel identification in Apennine brown bears (Ursus arctos marsicanus): a collaborative approach

Background The low cost and rapidity of microsatellite analysis have led to the development of several markers for many species. Because in non-invasive genetics it is recommended to genotype individuals using few loci, generally a subset of markers is selected. The choice of different marker panels by different research groups studying the same population can cause problems and bias in data analysis. A priority issue in conservation genetics is the comparability of data produced by different labs with different methods. Here, we compared data from previous and ongoing studies to identify a panel of microsatellite loci efficient for the long-term monitoring of Apennine brown bears (Ursus arctos marsicanus), aiming at reducing genotyping uncertainty and allowing reliable individual identifications overtimes. Results We examined all microsatellite markers used up to now and identified 19 candidate loci. We evaluated the efficacy of 13 of the most commonly used loci analyzing 194 DNA samples belonging to 113 distinct bears selected from the Italian national biobank. We compared data from 4 different marker subsets on the basis of genotyping errors, allelic patterns, observed and expected heterozygosity, discriminatory powers, number of mismatching pairs, and probability of identity. The optimal marker set was selected evaluating the low molecular weight, the high discriminatory power, and the low occurrence of genotyping errors of each primer. We calibrated allele calls and verified matches among genotypes obtained in previous studies using the complete set of 13 STRs (Short Tandem Repeats), analyzing six invasive DNA samples from distinct individuals. Differences in allele-sizing between labs were consistent, showing a substantial overlap of the individual genotyping. Conclusions The proposed marker set comprises 11 Ursus specific markers with the addition of cxx20, the canid-locus less prone to genotyping errors, in order to prevent underestimation (maximizing the discriminatory power) and overestimation (minimizing the genotyping errors) of the number of Apennine brown bears. The selected markers allow saving time and costs with the amplification in multiplex of all loci thanks to the same annealing temperature. Our work optimizes the available resources by identifying a shared panel and a uniform methodology capable of improving comparisons between past and future studies.

CRISPR Typing Increases the Discriminatory Power of Streptococcus agalactiae Typing Methods

We explored the relevance of a Clustered regularly interspaced short palindromic repeats (CRISPR)-based genotyping tool for Streptococcus agalactiae typing and we compared this method to current molecular methods [multi locus sequence typing (MLST) and capsular typing]. To this effect, we developed two CRISPR marker schemes (using 94 or 25 markers, respectively). Among the 255 S. agalactiae isolates tested, 229 CRISPR profiles were obtained. The 94 and 25 markers made it possible to efficiently separate isolates with a high diversity index (0.9947 and 0.9267, respectively), highlighting a high discriminatory power, superior to that of both capsular typing and MLST (diversity index of 0.9017 for MLST). This method has the advantage of being correlated with MLST [through analysis of the terminal direct repeat (TDR) and ancestral spacers] and to possess a high discriminatory power (through analysis of the leader-end spacers recently acquired, which are the witnesses of genetic mobile elements encountered by the bacteria). Furthermore, this “one-shot” approach presents the benefit of much-reduced time and cost in comparison with MLST. On the basis of these data, we propose that this method could become a reference method for group B Streptococcus (GBS) typing.

Whole genome survey of big cats (Genus: Panthera) identifies novel microsatellites of utility in conservation genetic study

Big cats (Genus: Panthera) are among the most threatened mammal groups of the world, owing to hunting, habitat loss, and illegal transnational trade. Conservation genetic studies and effective curbs on poaching are important for the conservation of these charismatic apex predators. A limited number of microsatellite markers exists for Panthera species and researchers often cross-amplify domestic cat microsatellites to study these species. We conducted data mining of seven Panthera genome sequences to discover microsatellites for conservation genetic studies of four threatened big cat species. A total of 32 polymorphic microsatellite loci were identified in silico and tested with 152 big cats, and were found polymorphic in most of the tested species. We propose a set of 12 novel microsatellite markers for use in conservation genetics and wildlife forensic investigations of big cat species. Cumulatively, these markers have a high discriminatory power of one in a million for unrelated individuals and one in a thousand for siblings. Similar PCR conditions of these markers increase the prospects of achieving efficient multiplex PCR assays. This study is a pioneering attempt to synthesise genome wide microsatellite markers for big cats.

Development of a Simple and Robust Whole Blood Assay with Dual Co-Stimulation to Quantify the Release of T-Cellular Signature Cytokines in Response to Aspergillus fumigatus Antigens

Deeper understanding of mold-induced cytokine signatures could promote advances in the diagnosis and treatment of invasive mycoses and mold-associated hypersensitivity syndromes. Currently, most T-cellular immunoassays in medical mycology require the isolation of mononuclear cells and have limited robustness and practicability, hampering their broader applicability in clinical practice. Therefore, we developed a simple, cost-efficient whole blood (WB) assay with dual α-CD28 and α-CD49d co-stimulation to quantify cytokine secretion in response to Aspergillus fumigatus antigens. Dual co-stimulation strongly enhanced A. fumigatus-induced release of T-cellular signature cytokines detectable by enzyme-linked immunosorbent assay (ELISA) or a multiplex cytokine assay. Furthermore, T-cell-dependent activation and cytokine response of innate immune cells was captured by the assay. The protocol consistently showed little technical variation and high robustness to pre-analytic delays of up to 8 h. Stimulation with an A. fumigatus lysate elicited at least 7-fold greater median concentrations of key T-helper cell signature cytokines, including IL-17 and the type 2 T-helper cell cytokines IL-4 and IL-5 in WB samples from patients with Aspergillus-associated lung pathologies versus patients with non-mold-related lung diseases, suggesting high discriminatory power of the assay. These results position WB-ELISA with dual co-stimulation as a simple, accurate, and robust immunoassay for translational applications, encouraging further evaluation as a platform to monitor host immunity to opportunistic pathogens.