Safety, feasibility, tolerability, and clinical effects of repeated psilocybin dosing combined with non-directive support in the treatment of obsessive-compulsive disorder: protocol for a randomized, waitlist-controlled trial with blinded ratings

BackgroundTo date, few randomized controlled trials of psilocybin with non-directive support exist for obsessive-compulsive disorder (OCD). Results and participant feedback from an interim analysis of an ongoing single-dose trial (NCT03356483) converged on the possibility of administering a higher fixed dose and/or more doses of psilocybin in future trials for presumably greater benefits.ObjectivesThis trial aims to evaluate the safety, feasibility, tolerability, and clinical effects of two doses of psilocybin paired with non-directive support in the treatment of OCD. This trial also seeks to examine whether two doses of psilocybin lead to greater OCD symptom reduction than a single dose, and to elucidate psychological mechanisms underlying the effects of psilocybin on OCD.DesignA randomized (1:1), waitlist-controlled design with blinded ratings will be used to examine the effects of two doses of oral psilocybin paired with non-directive support vs. waitlist control on OCD symptoms. An adaptive dose selection strategy will be implemented (i.e., first dose: 25 mg; second dose: 25 or 30 mg).Methods and analysisThis single-site trial will enroll 30 adult participants with treatment-refractory OCD. Aside from safety, feasibility, and tolerability metrics, primary outcomes include OCD symptoms assessed on the Yale-Brown Obsessive-Compulsive Scale – Second Edition (Y-BOCS-II). A blinded independent rater will assess primary outcomes at baseline and the primary endpoint at the end of the second dosing week. Participants will be followed up to 12 months post-second dosing. Participants randomized to waitlist will be rescreened after 7 weeks post-randomization, and begin their delayed treatment phase thereafter if still eligible.EthicsWritten informed consent will be obtained from participants. The institutional review board has approved this trial (protocol v. 1.7; HIC #2000032623).DiscussionThis study seeks to advance our ability to treat refractory OCD, and catalyze future research seeking to optimize the process of psilocybin treatment for OCD through understanding relevant psychological mechanisms.Clinical trial registration: ClinicalTrials.gov, identifier NCT05370911

Detection of True Hybrids in Pearl Millet Cross Combinations by Employing SSR Molecular Markers

Pearl millet (Pennisetum glaucum L. R. Br.), a crucial staple food and significant cereal crop, is gaining prominence due to its versatile applications as feed, food, and fodder. Heterosis of this crop has been extensively harnessed to increase productivity. Hybrid variants exhibit superior grain and stover yields compared to open-pollinated varieties.Top of Form The primary aim of this investigation was to evaluate and confirm authentic hybrids within three resultant F1 progenies. The assessment of parents and F1 hybrids was carried out during the Kharif 2021for the purpose of accurate discrimination and rapid verification of true hybrids by employing 20 SSR molecular markers. The experimental materials consisted of three distinct cytoplasmic male sterile (CMS) lines including ICMA 843-22, ICMA 04999, and ICMA 02333, used as female parents along with three fertility restorers, viz., ICMR 01004, ICMR 20233, and ICMR 20342, were utilized as male parents. The analysis of SSR profiles was based on distinctive banding patterns, resulting in unique profiles for the hybrids. The amplified fragment sizes ranged between 90 to 300 base pairs (bp), effectively enabling the differentiation of authentic hybrids. Within the specific crosses, the percentage of polymorphism was determined 75% for the cross ICMA 843-22 × ICMR 01004, 80% for the cross combination  ICMA 04999 × ICMR 20233, and 75% for the cross ICMA 02333 × ICMR 20342. The true hybrids were calculated using hybrid purity percentage formula using heterozygous banding patten among total plants evaluated. Among a total of 100 F1 plants, 85, 86, and 88 plants were accurately identified as true hybrids in the respective crosses i.e., ICMA 843-22 × ICMR 01004, ICMA 04999 × ICMR 20233, and ICMA 02333 × ICMR 20342. The identified markers hold significant potential for applications such as hybridity test, genetic purity assessments, diverse germplasm identification, and DNA fingerprinting endeavors in future

Jobs Justice Climate: Redevelopment Proposals for North Dekalb Mall and The Gallery at South Dekalb

This studio report presents socio-economic, energy, water, and transportation benchmarks of existing conditions and compares them to the projected performance of urban design proposals developed by four teams of MSUD, M.Arch, and MS.HPB students. It was presented to Dekalb County Commissioners Terry, Johnson, and Rader in April, 2021.Hypothetical redevelopment and reinhabitation urban design proposals are presented for both shopping malls to help the local DeKalb County Commissioners and their constituents envision and discuss options of what change might look like guided by Green New Deal goals

Establishment of CORONET: COVID-19 Risk in Oncology Evaluation Tool to Identify Cancer Patients at Low Versus High Risk of Severe Complications of COVID-19 Infection Upon Presentation to Hospital

Background: Patients with cancer are at increased risk of severe COVID-19, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 in cancer patients predicting severe disease and build a decision-support online tool; COVID-19 Risk in Oncology Evaluation Tool (CORONET).Methods: Patients with active cancer (stage I-IV) and laboratory confirmed COVID-19 presenting to hospitals worldwide were included. Discharge (within 24hrs), admission (≥24hrs inpatient), oxygen requirement (O2) and death were combined in a 0-3 point severity scale. Association of features with outcome were investigated using Lasso regression and Random Forest (RF) combined with SHapley Additive exPlanations (SHAP). RF was further validated in 4 cohorts, split by geography. The CORONET model was then examined in the entire cohort to build an online CORONET decision-support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions.Findings: The dataset comprised 920 patients; median age 70 (range 5-99), 56% males, 44% females, 81% solid vs. 19% haematological cancers. In derivation, RF demonstrated superior performance over Lasso with lower mean squared error (0.801 vs. 0.807) and was selected for development. During validation, RF achieved mean AUROC 0.77, 0.80 and 0.75 for prediction of admission, O 2 and death, respectively. Using the entire cohort, CORONET cut-offs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died. SHAP explanations revealed National-Early-Warning-Score-2, C-reactive protein and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation.Interpretation: CORONET, a decision-support tool validated in healthcare systems worldwide can aid admission decisions and predict COVID-19 severity in patients with cancer.Funding Information: R. Lee and T. Robinson and J. Weaver are supported by the National Institute for Health Research as Clinical Lecturers. T. Bhogal is supported by the National Institute for Health Research as an academic clinical fellow. U. Khan is an MRC Clinical Training Fellow based at the University of Liverpool supported by the North West England Medical Research Council Fellowship Scheme in Clinical Pharmacology and Therapeutics, which is funded by the Medical Research Council (Award Ref. MR/N025989/1). The Liverpool Experimental Cancer Medicine Centre for providing infrastructure support (Grant Reference: C18616/A25153) and The Clatterbridge Cancer charity (North West Cancer Research). C. Dive is funded by CRUK Core funding to Manchester Institute (C5757/A27412) and is supported by the CRUK Manchester Centre Award (C5759/A25254), and by the NIHR Manchester Biomedical Research Centre. C. Zhou is funded by the CRUK Manchester Centre Award (C5759/A25254), J. Stevenson and P. Fitzpatrick are funded by the CRUK Accelerator Award (29374). This research was funded in part, by the Wellcome Trust [205228/Z/16/Z]. LT is also supported by the National Institute for Health Research Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections (NIHR200907) at University of Liverpool in partnership with Public Health England (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford. LT is based at University of Liverpool. MS is supported by a grant from the Ministry of Science and Higher Education of the Russian Federation for the state support for the creation and development of World Class Research Centers "Digital biodesign and personalized healthcare” N.075-15-2020-926.Declaration of Interests: R Lee research funding (institution) BMS and speaker fees Astrazeneca. A. Croitoru Consulting or Advisory Role: Lilly, Merck, Roche, Bayer, Novartis, Ipsen, Research Funding me and my hospital: Gilead Sciences, Pfizer, Canfite, NanoCarrier, Bristol-Myers Squibb, Merck, Amgen, Servier, Five Prime Therapeutics, Travel Accommodations: Pfizer, Genekor, and oz, Merck, Pfizer, Servier, Roche. O. Michielin reports personal fees from Bristol-Myers Squibb, personal fees from MSD, personal fees from Novartis, personal fees from Roche, personal fees from Amgen, personal fees from NeraCare GmbH, outside the submitted work. E. Romano institutional research grants from Amgen, Astra Zeneca, Bristol-Myers Squibb. G. Pentheroudakis advisory board for Amgen, Astra Zeneca, Bristol-Myers Squibb, Lilly, Merck, MSD, Roche, Abbvie, institutional research grants from Amgen, Astra Zeneca, Boehringer Ingelheim, Bristol Myers Squibb, Debbio, Enorasis, Genekor, Ipsen, Janssen, Lilly, Merck, MSD, Pfizer, Roche, Sanofi, Servier. Solange Peters reports consultation/advisory role: AbbVie, Amgen, AstraZeneca, Bayer, Beigene, Biocartis, Bio Invent, Blueprint Medicines, Boehringer Ingelheim, Bristol-Myers Squibb, Clovis, Daiichi Sankyo, Debiopharm, Eli Lilly, Elsevier, F. Hoffmann-La Roche/Genentech, Foundation Medicine, Illumina, Incyte, IQVIA, Janssen, Medscape, Merck Sharp and Dohme, Merck Serono, Merrimack, Mirati, Novartis, Pharma Mar, Phosplatin Therapeutics, Pfizer, Regeneron, Sanofi, Seattle Genetics, Takeda, Vaccibody, talk in a company’s organized public event: AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, e-cancer, Eli Lilly, F. Hoffmann-La Roche/Genentech, Illumina, Medscape, Merck Sharp and Dohme, Novartis, PER, Pfizer, Prime, RTP, Sanofi, Takeda, receipt of grants/research supports: (Sub)investigator in trials (institutional financial support for clinical trials) sponsored by Amgen, AstraZeneca, Biodesix, Boehringer Ingelheim, Bristol-Myers Squibb, Clovis, F. Hoffmann-La Roche/Genentech, GSK, Illumina, Lilly, Merck Sharp and Dohme, Merck Serono, Mirati, Novartis, and Pfizer, Phosplatin Therapeutics. M Rowe honoraria from Astellas Pharma, speaker fees MSD and Servier. C. Wilson consultancy and speaker fees Pfizer, Amgen, Novartis, A Armstrong conference fee Merck, spouse shares in Astrazeneca. T Robinson financial support to attend educational workshops from Amgen and Daiichi-Sankyo. C Dive, outside of this scope of work, has received research funding from AstraZeneca, Astex Pharmaceuticals, Bioven, Amgen, Carrick Therapeutics, Merck AG, Taiho Oncology, Clearbridge Biomedics, Angle PLC, Menarini Diagnostics, GSK, Bayer, Boehringer Ingelheim, Roche, BMS, Novartis, Celgene, Thermofisher. C Dive is on advisory boards for, and has received consultancy fees/honoraria from, AstraZeneca, Biocartis and Merck KGaA.No other authors have nothing to declare. Ethics Approval Statement: Approval (reference 20/WA/0269) was granted from the UK Research Ethics Committee for the study. Information regarding governance/regulatory approvals for each international cohort are available in the Supp. Methods

An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves

SIMPLE SUMMARY: There have been huge improvements in both vaccination and the management of COVID-19 in patients with cancer. In addition, different variants may be associated with different presentations. Therefore, we examined whether indicators of the severity of COVID-19 in patients with cancer who present to hospital varied during different waves of the pandemic and we showed that these indicators remained predictive. We validated that the COVID-19 Risk in Oncology Evaluation Tool (CORONET), which predicts the severity of COVID-19 in cancer patients presenting to hospital, performed well in all waves. In addition, we examined patient outcomes and the factors that influence them and found that there was increased vaccination uptake and steroid use for patients requiring oxygen in later waves, which may be associated with improvements in outcome. ABSTRACT: Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of >0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants

Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital.

PurposePatients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET).MethodsPatients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O2) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort.ResultsThe model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801 v 0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation.ConclusionCORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer