Assessing corn recovery from early season nutrient stress under different soil moisture regimes.

Corn (Zea mays) biomass accumulation and nutrient uptake by the six-leaf collar (V6) growth stage are low, and therefore, synchronizing nutrient supply with crop demand could potentially minimize nutrient loss and improve nutrient use efficiency. Knowledge of corn's response to nutrient stress in the early growth stages could inform such nutrient management. Field studies were conducted to assess corn recovery from when no fertilizer application is made until the V6 growth stage, and thereafter, applying fertilizer rates as those in non-stressed conditions. The early season nutrient stress and non-stress conditions received the same amount of nutrients. As the availability of nutrients for plant uptake is largely dependent on soil moisture, corn recovery from the early season nutrient stress was assessed under different soil moisture regimes induced via irrigation scheduling at 50% and 80% field capacity under overhead and subsurface drip irrigation (SSDI) systems. Peanut (Arachis hypogaea) was the previous crop under all conditions, and the fields were under cereal rye (Secale cereale) cover crop prior to planting corn. At the V6 growth stage, the nutrient concentrations of the early season-stressed crops, except for copper, were above the minimum threshold of sufficiency ranges reported for corn. However, the crops showed poor growth, with biomass accumulation being reduced by over 50% compared to non-stressed crops. Also, the uptake of all nutrients was significantly lower under the early season nutrient stress conditions. The recovery of corn from the early season nutrient stress was low. Compared to non-stress conditions, the early season nutrient stress caused 1.58 Mg ha-1 to 3.4 Mg ha-1 yield reduction. The percent yield reduction under the SSDI system was 37.6-38.2% and that under the overhead irrigation system was 11.7-13%. The high yield reduction from the early season nutrient stress under the SSDI system was because of water stress conditions in the topsoil soil layer. The findings of the study suggest ample nutrient supply in the early season growth stage is critical for corn production, and thus, further studies are recommended to determine the optimum nutrient supply for corn at the initial growth stages.

The Safety and Comparative Effectiveness of Non-Psychoactive Cannabinoid Formulations for the Improvement of Sleep: A Double-Blinded, Randomized Controlled Trial.

BACKGROUND: Clinical evidence on the use of cannabidiol (CBD) for sleep remains limited. Even fewer studies have tested the comparative effectiveness of cannabinoid formulations found within CBD products used for sleep or how they compare to other complementary therapies such as melatonin. METHODS: Participants (N = 1,793 adults experiencing symptoms of sleep disturbance) were randomly assigned to receive a 4-week supply of 1 of 6 products (all capsules) containing either 15 mg CBD or 5 mg melatonin, alone or in combination with minor cannabinoids. Sleep disturbance was assessed over a period of 5 weeks (baseline week and 4 weeks of product use) using Patient-Reported Outcomes Measurement Information System (PROMIS™) Sleep Disturbance SF 8A, administered via weekly online surveys. A linear mixed-effects regression model was used to assess the differences in the change in sleep disturbance through time between each active product arm and CBD isolate. RESULTS: All formulations exhibited a favorable safety profile (12% of participants reported a side effect and none were severe) and led to significant improvements in sleep disturbance (p < 0.001 in within-group comparisons). Most participants (56% to 75%) across all formulations experienced a clinically important improvement in their sleep quality. There were no significant differences in effect, however, between 15 mg CBD isolate and formulations containing 15 mg CBD and 15 mg cannabinol (CBN), alone or in combination with 5 mg cannabichromene (CBC). There were also no significant differences in effect between 15 mg CBD isolate and formulations containing 5 mg melatonin, alone or in combination with 15 mg CBD and 15 mg CBN. CONCLUSIONS: Our findings suggest that chronic use of a low dose of CBD is safe and could improve sleep quality, though these effects do not exceed that of 5 mg melatonin. Moreover, the addition of low doses of CBN and CBC may not improve the effect of formulations containing CBD or melatonin isolate.

A Randomized, Double-Blind, Placebo-Controlled Decentralized Trial to Assess Sleep, Health Outcomes, and Overall Well-Being in Healthy Adults Reporting Disturbed Sleep, Taking a Melatonin-Free Supplement.

Inadequate sleep is a global health concern. Sleep is multidimensional and complex; new multi-ingredient agents are needed. This study assessed the comparative effects of two multi-ingredient supplements on sleep relative to placebo. Adults (N = 620) seeking better sleep were randomly assigned to receive one of three study products. Sleep A (contained lower (0.35 mg THC and higher levels of botanicals (75 mg each hops oil and valerian oil), Sleep B (contained higher THC (0.85 mg) and lower botanicals (20 mg each hops oil and valerian oil) or placebo) for 4 weeks. Sleep disturbance was assessed at baseline and weekly using NIH's Patient-Reported Outcomes Measurement Information System (PROMIS™) Sleep Disturbance SF 8A survey. Anxiety, stress, pain, and well-being were assessed using validated measures at baseline and weekly. A linear mixed-effects regression model was used to assess the change in health outcome score between active product groups and the placebo. There was a significant difference in sleep disturbance, anxiety, stress, and well-being between Sleep A and placebo. There was no significant difference in any health parameter between Sleep B and placebo. Side effects were mild or moderate. There were no significant differences in the frequency of side effects between the study groups. A botanical blend containing a low concentration of THC improved sleep disturbance, anxiety, stress, and well-being in healthy individuals that reported better sleep as a primary health concern.

Development of a model to predict antidepressant treatment response for depression among Veterans.

BACKGROUND: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans.

BACKGROUND: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample. RESULTS: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics. LIMITATIONS: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results. CONCLUSIONS: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

Development of a model to predict psychotherapy response for depression among Veterans.

BACKGROUND: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Are Veterans Getting Their Preferred Depression Treatment? A National Observational Study in the Veterans Health Administration.

BACKGROUND: Physician responsiveness to patient preferences for depression treatment may improve treatment adherence and clinical outcomes. OBJECTIVE: To examine associations of patient treatment preferences with types of depression treatment received and treatment adherence among Veterans initiating depression treatment. DESIGN: Patient self-report surveys at treatment initiation linked to medical records. SETTING: Veterans Health Administration (VA) clinics nationally, 2018-2020. PARTICIPANTS: A total of 2582 patients (76.7% male, mean age 48.7 years, 62.3% Non-Hispanic White) MAIN MEASURES: Patient self-reported preferences for medication and psychotherapy on 0-10 self-anchoring visual analog scales (0="completely unwilling"; 10="completely willing"). Treatment receipt and adherence (refilling medications; attending 3+ psychotherapy sessions) over 3 months. Logistic regression models controlled for socio-demographics and geographic variables. KEY RESULTS: More patients reported strong preferences (10/10) for psychotherapy than medication (51.2% versus 36.7%, McNemar χ21=175.3, p<0.001). A total of 32.1% of patients who preferred (7-10/10) medication and 21.8% who preferred psychotherapy did not receive these treatments. Patients who strongly preferred medication were substantially more likely to receive medication than those who had strong negative preferences (odds ratios [OR]=17.5; 95% confidence interval [CI]=12.5-24.5). Compared with patients who had strong negative psychotherapy preferences, those with strong psychotherapy preferences were about twice as likely to receive psychotherapy (OR=1.9; 95% CI=1.0-3.5). Patients who strongly preferred psychotherapy were more likely to adhere to psychotherapy than those with strong negative preferences (OR=3.3; 95% CI=1.4-7.4). Treatment preferences were not associated with medication or combined treatment adherence. Patients in primary care settings had lower odds of receiving (but not adhering to) psychotherapy than patients in specialty mental health settings. Depression severity was not associated with treatment receipt or adherence. CONCLUSIONS: Mismatches between treatment preferences and treatment type received were common and associated with worse treatment adherence for psychotherapy. Future research could examine ways to decrease mismatch between patient preferences and treatments received and potential effects on patient outcomes.

Comorbid mental disorders, depression symptom severity, and role impairment among Veterans initiating depression treatment through the Veterans Health Administration.

BACKGROUND: Psychiatric comorbidities may complicate depression treatment by being associated with increased role impairments. However, depression symptom severity might account for these associations. Understanding the independent associations of depression severity and comorbidity with impairments could help in treatment planning. This is especially true for depressed Veterans, who have high psychiatric comorbidity rates. METHODS: 2,610 Veterans beginning major depression treatment at the Veterans Health Administration (VHA) were administered a baseline self-report survey that screened for diverse psychiatric comorbidities and assessed depression severity and role impairments. Logistic and generalized linear regression models estimated univariable and multivariable associations of depression severity and comorbidities with impairments. Population attributable risk proportions (PARPs) estimated the relative importance of depression severity and comorbidities in accounting for role impairments. RESULTS: Nearly all patients (97.8%) screened positive for at least one comorbidity and half (49.8%) for 4+ comorbidities. The most common positive screens were for generalized anxiety disorder (80.2%), posttraumatic stress disorder (77.9%), and panic/phobia (77.4%). Depression severity and comorbidities were significantly and additively associated with impairments in multivariable models. Associations were attenuated much less for depression severity than for comorbidities in multivariable versus univariable models. PARPs indicated that 15-60% of role impairments were attributable to depression severity and 5-32% to comorbidities. LIMITATIONS: The screening scales could have over-estimated comorbidity prevalence. The cross-sectional observational design cannot determine either temporal or causal priorities. CONCLUSIONS: Although positive screens for psychiatric comorbidity are pervasive among depressed VHA patients, depression severity accounts for most of the associations of these comorbidities with role impairments.

Treatment Differences in Primary and Specialty Settings in Veterans with Major Depression.

INTRODUCTION: The Veterans Health Administration (VHA) supports the nation's largest primary care-mental health integration (PC-MHI) collaborative care model to increase treatment of mild to moderate common mental disorders in primary care (PC) and refer more severe-complex cases to specialty mental health (SMH) settings. It is unclear how this treatment assignment works in practice. METHODS: Patients (n = 2610) who sought incident episode VHA treatment for depression completed a baseline self-report questionnaire about depression severity-complexity. Administrative data were used to determine settings and types of treatment during the next 30 days. RESULTS: Thirty-four percent (34.2%) of depressed patients received treatment in PC settings, 65.8% in SMH settings. PC patients had less severe and fewer comorbid depressive episodes. Patients with lowest severity and/or complexity were most likely to receive PC antidepressant medication treatment; those with highest severity and/or complexity were most likely to receive combined treatment in SMH settings. Assignment of patients across settings and types of treatment was stronger than found in previous civilian studies but less pronounced than expected (cross-validated AUC = 0.50-0.68). DISCUSSION: By expanding access to evidence-based treatments, VHA's PC-MHI increases consistency of treatment assignment. Reasons for assignment being less pronounced than expected and implications for treatment response will require continued study.

Association of Medicaid Expansion With Enrollee Employment and Student Status in Michigan.

Importance: Medicaid community engagement requirements (work, school, job searching, or community service) are being implemented by several states for the first time, but the association of Medicaid coverage with enrollees' employment and school attendance is unclear. Objective: To assess longitudinal changes in enrollees' employment or student status after Michigan's Medicaid expansion. Design, Setting, and Participants: This survey study included 4090 nonelderly, adult Healthy Michigan Plan enrollees from March 1, 2017, to January 31, 2018. Main Outcomes and Measures: Self-reported employment or student status. Proportionate sampling was stratified by income and geographic region. Mixed-effects regression models with time indicators were used to assess longitudinal changes in the proportion of enrollees who were employed or students. Results: The response rate for the initial survey was 53.7% and for the follow-up survey was 83.4%. Of the 3104 respondents to the 2017 follow-up survey (mean [SD] age in 2017, 42.2 [13.0] years; 1867 [53.0%] female), 54.3% were employed or students in 2016, and this number increased to 60.0% in 2017 (percentage point change, 5.7; P < .001). Non-Hispanic black enrollees had significantly larger gains in employment or student status compared with non-Hispanic white enrollees (percentage point change, 10.7 vs 3.5; P = .02). Changes in employment or student status were not associated with improved health status. Conclusions and Relevance: Employment or student status increased from 2016 to 2017 among Michigan Medicaid expansion enrollees. These findings provide information about whether Medicaid coverage or community engagement requirements are best to promote the desired outcomes of employment and student status.

Association of Expanded Medicaid Coverage With Health and Job-Related Outcomes Among Enrollees With Behavioral Health Disorders.

OBJECTIVES: The study objective was to assess the impact of Medicaid expansion on health and employment outcomes among enrollees with and without a behavioral health disorder (either a mental or substance use disorder). METHODS: Between January and October 2016, the authors conducted a telephone survey of 4,090 enrollees in the Michigan Medicaid expansion program and identified 2,040 respondents (48.3%) with potential behavioral health diagnoses using claims-based diagnoses. RESULTS: Enrollees with behavioral health diagnoses were less likely than enrollees without behavioral health diagnoses to be employed but significantly more likely to report improvements in health and ability to do a better job at work. In adjusted analyses, both enrollees with behavioral health diagnoses and those without behavioral health diagnoses who reported improved health were more likely than enrollees without improved health to report that Medicaid expansion coverage helped them do a better job at work and made them better able to look for a job. Among enrollees with improved health, those with a behavioral health diagnosis were as likely as those without a behavioral health diagnosis to report improved ability to work and improved job seeking after Medicaid expansion. CONCLUSIONS: Coverage interruptions for enrollees with behavioral health diagnoses should be minimized to maintain favorable health and employment outcomes.

Primary Care, Health Promotion, and Disease Prevention with Michigan Medicaid Expansion.

BACKGROUND: Medicaid expansion in Michigan, known as the Healthy Michigan Plan (HMP), emphasizes primary care and preventive services. OBJECTIVE: Evaluate the impact of enrollment in HMP on access to and receipt of care, particularly primary care and preventive services. DESIGN: Telephone survey conducted during January-November 2016 with stratified random sampling by income and geographic region (response rate = 53.7%). Logistic regression analyses accounted for sampling and nonresponse adjustment. PARTICIPANTS: 4090 HMP enrollees aged 19-64 with ≥ 12 months of HMP coverage MAIN MEASURES: Surveys assessed demographic factors, health, access to and use of health care before and after HMP enrollment, health behaviors, receipt of counseling for health risks, and knowledge of preventive services' copayments. Utilization of preventive services was assessed using Medicaid claims. KEY RESULTS: In the 12 months prior to HMP enrollment, 33.0% of enrollees reported not getting health care they needed. Three quarters (73.8%) of enrollees reported having a regular source of care (RSOC) before enrollment; 65.1% of those reported a doctor's office/clinic, while 16.2% reported the emergency room. After HMP enrollment, 92.2% of enrollees reported having a RSOC; 91.7% had a doctor's office/clinic and 1.7% the emergency room. One fifth (20.6%) of enrollees reported that, before HMP enrollment, it had been over 5 years since their last primary care visit. Enrollees who reported a visit with their primary care provider after HMP enrollment (79.3%) were significantly more likely than those who did not report a visit to receive counseling about health behaviors, improved access to cancer screening, new diagnoses of chronic conditions, and nearly all preventive services. Enrollee knowledge that some services have no copayments was also associated with greater utilization of most preventive services. CONCLUSIONS: After enrolling in Michigan's Medicaid expansion program, beneficiaries reported less forgone care and improved access to primary care and preventive services.

Estrogen receptor expression in the prostate of rats treated with dietary genistein.

Steroid hormones and their receptors play critical roles in the growth, development, and maintenance of the male reproductive tract. Genistein, a naturally occurring isoflavonoid primarily found in soybeans, interacts with estrogen receptors alpha and beta (ER alpha and beta), with preferential affinity for ER beta. This is one mechanism whereby genistein may affect growth and development and potentially alter susceptibility to carcinogenesis. Previous studies have indicated effects of soy and/or genistein in the male rodent reproductive tract under certain exposure conditions. The current study was undertaken to determine if modulation of the expression of ER alpha and ER beta by dietary genistein may contribute to those effects. Rats in a two-generation study were fed 0, 5, 100, or 500 ppm genistein prior to mating and through pregnancy and lactation. At weaning, male pups were selected in each of the F(1) and F(2) generations and half of the pups continued on the same diet as their dams (G/G, continuous exposure) while their litter mates were placed on control chow (G/C, gestational and lactational exposure) until sacrifice on PND 140. Male reproductive organ weights, serum levels of testosterone and dihydrotestosterone (DHT), and ER alpha and ER beta protein levels in the ventral and dorsolateral prostate were the endpoints measured. Prostate sections were also evaluated microscopically. Statistically significant elevations in testosterone and DHT were observed in PND 140 animals from the F(1) generation, but they were not accompanied by organ weight changes. Body weight in the continuously dosed 500 ppm F(1) PND 140 animals was depressed relative to control, but organ weights in animals of either generation showed few treatment-related effects. While estrogen receptor levels were quite variable, levels of ER beta in the dorsolateral prostate were significantly depressed in all dose groups in the G/C exposure and the high dose group of the G/G exposure in F(1) rats, but not in F(2) rats. Given the growing body of knowledge on the significance of ER beta in the prostate, the evidence for apparent down regulation of this receptor by genistein may have implications for reproductive toxicity and carcinogenesis that warrant further investigation.