Forearm Flexor Tendon Injury in Adolescent Athletes: Risk Factors, Treatment, and Prevention.

ABSTRACT: Injury to the flexor pronator mass is a common condition that is especially prevalent in overhead throwing athletes. The increasing incidence of these injuries has promoted considerable efforts in research to better understand the pathology, risk factors, and potential mechanisms to prevent injury in these athletes. While there are numerous intrinsic and extrinsic factors associated with injury, a common theme involves chronic overuse and microtrauma with inadequate resting intervals between performances. The purpose of this review is to discuss medial elbow injuries in young athletes with a particular focus on the flexor pronator mass.

Distributional bias compromises leave-one-out cross-validation.

Cross-validation is a common method for estimating the predictive performance of machine learning models. In a data-scarce regime, where one typically wishes to maximize the number of instances used for training the model, an approach called 'leave-one-out cross-validation' is often used. In this design, a separate model is built for predicting each data instance after training on all other instances. Since this results in a single test data point available per model trained, predictions are aggregated across the entire dataset to calculate common rank-based performance metrics such as the area under the receiver operating characteristic or precision-recall curves. In this work, we demonstrate that this approach creates a negative correlation between the average label of each training fold and the label of its corresponding test instance, a phenomenon that we term distributional bias. As machine learning models tend to regress to the mean of their training data, this distributional bias tends to negatively impact performance evaluation and hyperparameter optimization. We show that this effect generalizes to leave-P-out cross-validation and persists across a wide range of modeling and evaluation approaches, and that it can lead to a bias against stronger regularization. To address this, we propose a generalizable rebalanced cross-validation approach that corrects for distributional bias. We demonstrate that our approach improves cross-validation performance evaluation in synthetic simulations and in several published leave-one-out analyses.

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models.

Every step in common microbiome profiling protocols has variable efficiency for each microbe. For example, different DNA extraction kits may have different efficiency for Gram-positive and -negative bacteria. These variable efficiencies, combined with technical variation, create strong processing biases, which impede the identification of signals that are reproducible across studies and the development of generalizable and biologically interpretable prediction models. "Batch-correction" methods have been used to alleviate these issues computationally with some success. However, many make strong parametric assumptions which do not necessarily apply to microbiome data or processing biases, or require the use of an outcome variable, which risks overfitting. Lastly and importantly, existing transformations used to correct microbiome data are largely non-interpretable, and could, for example, introduce values to features that were initially mostly zeros. Altogether, processing bias currently compromises our ability to glean robust and generalizable biological insights from microbiome data. Here, we present DEBIAS-M (Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using benchmarks of HIV and colorectal cancer classification from gut microbiome data, and cervical neoplasia prediction from cervical microbiome data, we demonstrate that DEBIAS-M outperforms batch-correction methods commonly used in the field. Notably, we show that the inferred bias-correction factors are stable, interpretable, and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M allows for better modeling of microbiome data and identification of interpretable signals that are reproducible across studies.

The Salivary Microbiome and Predicted Metabolite Production Are Associated with Barrett's Esophagus and High-Grade Dysplasia or Adenocarcinoma.

BACKGROUND: Esophageal adenocarcinoma (EAC) is rising in incidence, and established risk factors do not explain this trend. Esophageal microbiome alterations have been associated with Barrett's esophagus (BE) and dysplasia and EAC. The oral microbiome is tightly linked to the esophageal microbiome; this study aimed to identify salivary microbiome-related factors associated with BE, dysplasia, and EAC. METHODS: Clinical data and oral health history were collected from patients with and without BE. The salivary microbiome was characterized, assessing differential relative abundance of taxa by 16S rRNA gene sequencing and associations between microbiome composition and clinical features. Microbiome metabolic modeling was used to predict metabolite production. RESULTS: A total of 244 patients (125 non-BE and 119 BE) were analyzed. Patients with high-grade dysplasia (HGD)/EAC had a significantly higher prevalence of tooth loss (P = 0.001). There were significant shifts with increased dysbiosis associated with HGD/EAC, independent of tooth loss, with the largest shifts within the genus Streptococcus. Modeling predicted significant shifts in the microbiome metabolic capacities, including increases in L-lactic acid and decreases in butyric acid and L-tryptophan production in HGD/EAC. CONCLUSIONS: Marked dysbiosis in the salivary microbiome is associated with HGD and EAC, with notable increases within the genus Streptococcus and accompanying changes in predicted metabolite production. Further work is warranted to identify the biological significance of these alterations and to validate metabolic shifts. IMPACT: There is an association between oral dysbiosis and HGD/EAC. Further work is needed to establish the diagnostic, predictive, and causal potential of this relationship.

Robustness of cancer microbiome signals over a broad range of methodological variation.

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2-12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.

Patterns of colorectal cancer screening and adherence rates among an average-risk population enrolled in a national health insurance provider during 2009-2018 in the United States.

While colorectal cancer (CRC) is the second leading cause of cancer-related mortality in the United States (US), outcomes can be improved through timely screening. Despite the benefits and widespread availability of screening tests, adherence to recommended screening strategies is low. The study aimed to provide recent evidence regarding screening rates and adherence to screening recommendations among adults at average risk for CRC in a commercially insured and Medicare Advantage population. De-identified administrative data from a large US research database were examined to determine screening rates for the years 2009 through 2018. The study population included adults aged 50-75 years and annual study population counts ranged from 1,390,594 in 2009 to 1,654,544 in 2018. Incident screening rates were found to be relatively stable across the study years (approximately 15 %) with adherence lowest in the youngest age group (ages 50-54 years). Colonoscopies accounted for approximately 50 % of all screening tests performed, while there was a substantial increase in the use of home-based screening tests over the study timeframe. The use of the fecal immunochemical test increased from 17.2 % in 2009 to 28.9 % in 2018 and the multi-target stool DNA test increased from 0.4 % in 2015 to 9.0 % in 2018. Overall though, CRC screening and adherence rates remain relatively low among adults at average risk for CRC in the US. Improving adherence rates with CRC screening recommendations among individuals at average risk for CRC is required to improve health outcomes.

Patterns of initial colorectal cancer screenings after turning 50 years old and follow-up rates of colonoscopy after positive stool-based testing among the average-risk population.

OBJECTIVES: Effective colorectal cancer (CRC) screening requires proper adherence beginning at the recommended screening age. For those with positive results on stool-based tests (SBTs), a follow-up colonoscopy is warranted. The objectives of this study were to 1) examine initial screening rates after turning 50 years old; and 2) assess rates of follow-up colonoscopy after a positive SBT. METHODS: This retrospective study used de-identified administrative claims data from 01/01/2006 to 06/30/2020 for commercially insured and Medicare Advantage enrollees. For objective 1, the index year was the year enrollees turned 50. Rates of CRC screening during and after the index year were captured. For objective 2, the index date was the claim date of a fecal immunochemical test (FIT) or multitarget stool DNA test (mt-sDNA) where linked lab data indicated a positive test result. Rates and time to follow-up colonoscopy after a positive SBT were assessed. RESULTS: Approximately 53% of enrollees initiated CRC screening within five years after turning 50 (50+ cohort N = 718,562). Among enrollees with an available lab result indicating a positive SBT (N = 7329; 2110 FIT and 5219 mt-sDNA), overall follow-up colonoscopy within 6 months of the positive result was less than optimal (65%) and varied by modality; 72% vs 46% (p < .001) among enrollees with a positive mt-sDNA test compared to FIT test, respectively. CONCLUSION: There is potential for improving CRC screening among the eligible average-risk population, both to start screening once they reach the screening-eligible age, and to complete the CRC screening paradigm after a positive stool-based screen.

Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data.

Sequencing-based approaches for the analysis of microbial communities are susceptible to contamination, which could mask biological signals or generate artifactual ones. Methods for in silico decontamination using controls are routinely used, but do not make optimal use of information shared across samples and cannot handle taxa that only partially originate in contamination or leakage of biological material into controls. Here we present Source tracking for Contamination Removal in microBiomes (SCRuB), a probabilistic in silico decontamination method that incorporates shared information across multiple samples and controls to precisely identify and remove contamination. We validate the accuracy of SCRuB in multiple data-driven simulations and experiments, including induced contamination, and demonstrate that it outperforms state-of-the-art methods by an average of 15-20 times. We showcase the robustness of SCRuB across multiple ecosystems, data types and sequencing depths. Demonstrating its applicability to microbiome research, SCRuB facilitates improved predictions of host phenotypes, most notably the prediction of treatment response in melanoma patients using decontaminated tumor microbiome data.

Comparative effectiveness of two versions of a caring contacts intervention in healthcare providers, staff, and patients for reducing loneliness and mental distress: A randomized controlled trial.

BACKGROUND: Caring Contacts can effectively reduce suicide ideation, attempts, and death. In published clinical trials, Caring Contacts were sent by someone who knew the recipient. At scale, Caring Contacts programs rarely introduce the recipient and sender. It is not known whether receiving Caring Contacts from someone unknown is as effective as messages from someone the recipient has met. METHODS: Pragmatic randomized controlled trial comparing Caring Contacts with (CC+) versus without an introductory phone call (CC). Recruitment occurred January-July 2021, with outcomes assessed at 6 months. Participants were primary care patients or healthcare providers/staff reporting adverse mental health outcomes on a qualifying survey. Participants were sent 11 standardized caring text messages over 6 months; when participants replied, they received personalized unscripted responses. CC+ calls were semi-structured. The primary outcome was loneliness (NIH Toolkit). RESULTS: Participants included 331 patients (mean [SD] age: 45.5 [16.4], 78.9 % female) and 335 healthcare providers/staff (mean [SD] age: 40.9 [11.8], 86.6 % female). There were no significant differences in loneliness at 6 months by treatment arm in either stratum. In patients, mean (SD) loneliness was 61.9 (10.7) in CC, and 60.8 (10.3) in CC+, adjusted mean difference of -1.0 (95 % CI: -3.0, 1.0); p-value = 0.31. In providers/staff, mean (SD) loneliness was 61.2 (11) in CC, and 61.3 (11.1) in CC+, adjusted mean difference of 0.2 (95 % CI: -1.8, 2.2); p-value = 0.83. LIMITATIONS: Study population was 93 % white which may limit generalizability. CONCLUSIONS: Including an initial phone call added operational complexity without significantly improving the effectiveness of a Caring Contacts program.

Comparative effectiveness of safety planning intervention with instrumental support calls (ISC) versus safety planning intervention with two-way text message caring contacts (CC) in adolescents and adults screening positive for suicide risk in emergency departments and primary care clinics: Protocol for a pragmatic randomized controlled trial.

BACKGROUND: Suicide is a leading cause of death in adolescents and adults in the US. Follow-up support delivered when patients return home after an emergency department (ED) or primary care encounter can significantly reduce suicidal ideation and attempts. Two follow-up models to augment usual care including the Safety Planning Intervention have high efficacy: Instrumental Support Calls (ISC) and Caring Contacts (CC) two-way text messages, but they have never been compared to assess which works best. This protocol for the Suicide Prevention Among Recipients of Care (SPARC) Trial aims to determine which model is most effective for adolescents and adults with suicide risk. METHODS: The SPARC Trial is a pragmatic randomized controlled trial comparing the effectiveness of ISC versus CC. The sample includes 720 adolescents (12-17 years) and 790 adults (18+ years) who screen positive for suicide risk during an ED or primary care encounter. All participants receive usual care and are randomized 1:1 to ISC or CC. The state suicide hotline delivers both follow-up interventions. The trial is single-masked, with participants unaware of the alternative treatment, and is stratified by adolescents/adults. The primary outcome is suicidal ideation and behavior, measured using the Columbia Suicide Severity Rating Scale (C-SSRS) screener at 6 months. Secondary outcomes include C-SSRS at 12 months, and loneliness, return to crisis care for suicidality, and utilization of outpatient mental health services at 6 and 12 months. DISCUSSION: Directly comparing ISC and CC will determine which follow-up intervention is most effective for suicide prevention in adolescents and adults.

Insular biogeography of the montane butterfly faunas in the Great Basin: comparison with birds and mammals.

Butterfly species lists were assembled for 18 Great Basin mountain ranges for which distributional data on mammals and birds have been analysed previously by other workers. The ranges represent remnant islands of the boreal habitat that once was continuous across the Great Basin but is now restricted to higher elevations as a result of climatic change at the close of the Pleistocene. The effects of biogeographic factors (area, distance, elevation) and habitat diversity on butterfly species number were examined. The Great Basin boreal butterfly faunas were found to be depauperate overall relative that of the principal mainland source, the Rocky Mountains, and were found to have fewer species than predicted by the mainland species-area data. However, only a weak area effect, and no distance effect, was detected by bivariate and multivariate analysis. Furthermore, the habitat diversity score found to explain virtually all the variation in bird species number in the same ranges in previous studies is only marginally significantly correlated with butterflies. When the butterflies are subdivided according to their vagility, the relative differences in the species-area correlation and slope (z-value) between the vagility categories were consistent with those found previously for mammals and birds, and, as predicted by theory, less vagile taxa exhibit higher species-area correlations and z-values. Overall, differences in the insular biogeography of buttterflies and vertebrates seem to reflect fundamental ecological differences between the taxa.