Efficient imaging and computer vision detection of two cell shapes in young cotton fibers.

Premise: The shape of young cotton (Gossypium) fibers varies within and between commercial cotton species, as revealed by previous detailed analyses of one cultivar of G. hirsutum and one of G. barbadense. Both narrow and wide fibers exist in G. hirsutum cv. Deltapine 90, which may impact the quality of our most abundant renewable textile material. More efficient cellular phenotyping methods are needed to empower future research efforts. Methods: We developed semi-automated imaging methods for young cotton fibers and a novel machine learning algorithm for the rapid detection of tapered (narrow) or hemisphere (wide) fibers in homogeneous or mixed populations. Results: The new methods were accurate for diverse accessions of G. hirsutum and G. barbadense and at least eight times more efficient than manual methods. Narrow fibers dominated in the three G. barbadense accessions analyzed, whereas the three G. hirsutum accessions showed a mixture of tapered and hemisphere fibers in varying proportions. Discussion: The use or adaptation of these improved methods will facilitate experiments with higher throughput to understand the biological factors controlling the variable shapes of young cotton fibers or other elongating single cells. This research also enables the exploration of links between early cell shape and mature cotton fiber quality in diverse field-grown cotton accessions.

Incretin Hormones: Pathophysiological Risk Factors and Potential Targets for Type 2 Diabetes.

Type 2 diabetes (T2D) is a multifaceted metabolic disorder associated with distinctive pathophysiological disturbances. One of the pathophysiological risk factors observed in T2D is dysregulation of the incretin hormones, glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1). Both hormones stimulate insulin secretion by acting postprandially on pancreatic ß-cell receptors. Oral glucose administration stimulates increased insulin secretion in comparison with isoglycemic intravenous glucose administration, a phenomenon known as the incretin effect. While the evidence for incretin defects in individuals with T2D is growing, the etiology behind this attenuated incretin effect in T2D is not clearly understood. Given their central role in T2D pathophysiology, incretins are promising targets for T2D therapeutics. The present review synthesizes the recent attempts to explain the biological importance of incretin hormones and explore potential pharmacological approaches that target the incretins.

Predicting COVID-19-Related Health Care Resource Utilization Across a Statewide Patient Population: Model Development Study.

BACKGROUND: The COVID-19 pandemic has highlighted the inability of health systems to leverage existing system infrastructure in order to rapidly develop and apply broad analytical tools that could inform state- and national-level policymaking, as well as patient care delivery in hospital settings. The COVID-19 pandemic has also led to highlighted systemic disparities in health outcomes and access to care based on race or ethnicity, gender, income-level, and urban-rural divide. Although the United States seems to be recovering from the COVID-19 pandemic owing to widespread vaccination efforts and increased public awareness, there is an urgent need to address the aforementioned challenges. OBJECTIVE: This study aims to inform the feasibility of leveraging broad, statewide datasets for population health-driven decision-making by developing robust analytical models that predict COVID-19-related health care resource utilization across patients served by Indiana's statewide Health Information Exchange. METHODS: We leveraged comprehensive datasets obtained from the Indiana Network for Patient Care to train decision forest-based models that can predict patient-level need of health care resource utilization. To assess these models for potential biases, we tested model performance against subpopulations stratified by age, race or ethnicity, gender, and residence (urban vs rural). RESULTS: For model development, we identified a cohort of 96,026 patients from across 957 zip codes in Indiana, United States. We trained the decision models that predicted health care resource utilization by using approximately 100 of the most impactful features from a total of 1172 features created. Each model and stratified subpopulation under test reported precision scores >70%, accuracy and area under the receiver operating curve scores >80%, and sensitivity scores approximately >90%. We noted statistically significant variations in model performance across stratified subpopulations identified by age, race or ethnicity, gender, and residence (urban vs rural). CONCLUSIONS: This study presents the possibility of developing decision models capable of predicting patient-level health care resource utilization across a broad, statewide region with considerable predictive performance. However, our models present statistically significant variations in performance across stratified subpopulations of interest. Further efforts are necessary to identify root causes of these biases and to rectify them.