Immediate effect of dynamic oscillatory stretching vs. neurodynamic sliding technique on stretch tolerance, popliteal angle range and hamstring flexibility in apparently healthy individuals with hamstring tightness: a pre-post clinical trail

Background: The hamstrings being postural muscles are prone to tightness which leads to muscular imbalances and inefficiency of daily living activities. Hence, the present study aims to compare two competent techniques Dynamic Oscillatory Stretching (DOS)vs. neurodynamic sliding (NDS) technique.Methods: A Total of 60 subjects were recruited (31 males, 29 females). passive 90-90 knee extension test, modified v sit to reach test and NPRS scale were used to evaluate the range, flexibility and stretch tolerance in participants pre intervention and were allotted into Group A (DOS) and Groups B. The subjects were then re-assessed immediately post intervention.Results: The results were obtained using the independent and dependent t-tests. Post intervention results were suggestive of a significant within group result with a p=0.0001 under all the parameters. Subjects in Group a showed a greater increase in the ROM while, Group B showed a better result in flexibility and stretch tolerance.Conclusions: Both the techniques are efficient and can be incorporated in sports rehabilitation to prevent on site injury thereby improving athlete’s performance

Sparse Gaussian Process Hyperparameters: Optimize or Integrate?

The kernel function and its hyperparameters are the central model selection choice in a Gaussian proces (Rasmussen and Williams, 2006). Typically, the hyperparameters of the kernel are chosen by maximising the marginal likelihood, an approach known as Type-II maximum likelihood (ML-II). However, ML-II does not account for hyperparameter uncertainty, and it is well-known that this can lead to severely biased estimates and an underestimation of predictive uncertainty. While there are several works which employ a fully Bayesian characterisation of GPs, relatively few propose such approaches for the sparse GPs paradigm. In this work we propose an algorithm for sparse Gaussian process regression which leverages MCMC to sample from the hyperparameter posterior within the variational inducing point framework of Titsias (2009). This work is closely related to Hensman et al. (2015b) but side-steps the need to sample the inducing points, thereby significantly improving sampling efficiency in the Gaussian likelihood case. We compare this scheme against natural baselines in literature along with stochastic variational GPs (SVGPs) along with an extensive computational analysis.Comment: NeurIPS 202

Cyp2c44 Gene Disruption Exacerbated Pulmonary Hypertension and Heart Failure in Female but Not Male Mice

Epoxyeicosatrienoicacids (EETs), synthesized from arachidonic acid by epoxygenases of the CYP2C and CYP2J gene subfamilies, contribute to hypoxic pulmonary vasoconstriction (HPV) in mice. Despite their roles in HPV, it is controversial whether EETs mediate or ameliorate pulmonary hypertension (PH). A recent study showed that deficiency of Cyp2j did not protect male and female mice from hypoxia-induced PH. Since CYP2C44 is a functionally important epoxygenase, we hypothesized that knockout of the Cyp2c44 gene would protect both sexes of mice from hypoxia-induced PH. We tested this hypothesis in wild-type (WT) and Cyp2c44 knockout (Cyp2c44 (-/-)) mice exposed to normoxia (room air) and hypoxia (10% O2) for 5 weeks. Exposure of WT and Cyp2c44 (-/-) mice to hypoxia resulted in pulmonary vascular remodeling, increased pulmonary artery resistance, and decreased cardiac function in both sexes. However, in female Cyp2c44 (-/-) mice, compared with WT mice, (1) pulmonary artery resistance and right ventricular hypertrophy were greater, (2) cardiac index was lower, (3) left ventricular and arterial stiffness were higher, and (4) plasma aldosterone levels were higher, but (5) there was no difference in levels of EET in lungs and heart. Paradoxically and unexpectedly, we found that Cyp2c44 disruption exacerbated hypoxia-induced PH in female but not male mice. We attribute exacerbated PH in female Cyp2c44 (-/-) mice to elevated aldosterone and as-yet-unknown systemic factors. Therefore, we suggest a role for the human CYP2C genes in protecting women from severe PH and that this could be one of the underlying causes for a better 5-year survival rate in women than in men

Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study

BACKGROUND: Patients with precursors to multiple myeloma are dichotomised as having monoclonal gammopathy of undetermined significance or smouldering multiple myeloma on the basis of monoclonal protein concentrations or bone marrow plasma cell percentage. Current risk stratifications use laboratory measurements at diagnosis and do not incorporate time-varying biomarkers. Our goal was to develop a monoclonal gammopathy of undetermined significance and smouldering multiple myeloma stratification algorithm that utilised accessible, time-varying biomarkers to model risk of progression to multiple myeloma. METHODS: In this retrospective, multicohort study, we included patients who were 18 years or older with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma. We evaluated several modelling approaches for predicting disease progression to multiple myeloma using a training cohort (with patients at Dana-Farber Cancer Institute, Boston, MA, USA; annotated from Nov, 13, 2019, to April, 13, 2022). We created the PANGEA models, which used data on biomarkers (monoclonal protein concentration, free light chain ratio, age, creatinine concentration, and bone marrow plasma cell percentage) and haemoglobin trajectories from medical records to predict progression from precursor disease to multiple myeloma. The models were validated in two independent validation cohorts from National and Kapodistrian University of Athens (Athens, Greece; from Jan 26, 2020, to Feb 7, 2022; validation cohort 1), University College London (London, UK; from June 9, 2020, to April 10, 2022; validation cohort 1), and Registry of Monoclonal Gammopathies (Czech Republic, Czech Republic; Jan 5, 2004, to March 10, 2022; validation cohort 2). We compared the PANGEA models (with bone marrow [BM] data and without bone marrow [no BM] data) to current criteria (International Myeloma Working Group [IMWG] monoclonal gammopathy of undetermined significance and 20/2/20 smouldering multiple myeloma risk criteria). FINDINGS: We included 6441 patients, 4931 (77%) with monoclonal gammopathy of undetermined significance and 1510 (23%) with smouldering multiple myeloma. 3430 (53%) of 6441 participants were female. The PANGEA model (BM) improved prediction of progression from smouldering multiple myeloma to multiple myeloma compared with the 20/2/20 model, with a C-statistic increase from 0·533 (0·480-0·709) to 0·756 (0·629-0·785) at patient visit 1 to the clinic, 0·613 (0·504-0·704) to 0·720 (0·592-0·775) at visit 2, and 0·637 (0·386-0·841) to 0·756 (0·547-0·830) at visit three in validation cohort 1. The PANGEA model (no BM) improved prediction of smouldering multiple myeloma progression to multiple myeloma compared with the 20/2/20 model with a C-statistic increase from 0·534 (0·501-0·672) to 0·692 (0·614-0·736) at visit 1, 0·573 (0·518-0·647) to 0·693 (0·605-0·734) at visit 2, and 0·560 (0·497-0·645) to 0·692 (0·570-0·708) at visit 3 in validation cohort 1. The PANGEA models improved prediction of monoclonal gammopathy of undetermined significance progression to multiple myeloma compared with the IMWG rolling model at visit 1 in validation cohort 2, with C-statistics increases from 0·640 (0·518-0·718) to 0·729 (0·643-0·941) for the PANGEA model (BM) and 0·670 (0·523-0·729) to 0·879 (0·586-0·938) for the PANGEA model (no BM). INTERPRETATION: Use of the PANGEA models in clinical practice will allow patients with precursor disease to receive more accurate measures of their risk of progression to multiple myeloma, thus prompting for more appropriate treatment strategies. FUNDING: SU2C Dream Team and Cancer Research UK

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 580,869 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies