Spectrum of anxiety and depression reported in reproductive-aged women diagnosed with gynaecological disorders at a tertiary healthcare facility in Ghana

Background: Patients with gynaecological disorders often suffer from psychological disorders including anxiety and depression. Although depression and anxiety have been studied in Ghana, data regarding the prevalence of these disorders in patients with gynaecological disorders is non-existent. The aim of the study was to investigate the prevalence of anxiety and depression in reproductive-aged women diagnosed with gynaecological disorders.Methods: Cross-sectional observational study was conducted at the Gynaecology Clinic of Korle-Bu Teaching Hospital, a tertiary health facility in Accra, Ghana. Patients of reproductive age seeking gynaecological care at the facility from December 2018 to January 2019 were assessed for anxiety and depression using the Generalized anxiety disorder (GAD) questionnaire and the Beck depression inventory (BDI) respectively. Sociodemographic and clinical information was gathered as well.Results: Of the 120 patients interviewed (mean age 34.33±0.66), 36.7% were depressed while 51.6% were reported anxiety disorders. Patients aged 35-45 years had the highest prevalence of anxiety (24.58%) and depression (29.18%). Again, prevalence rates were highest among respondents with senior high school as the highest educational qualification, (anxiety (22.15%); depression (24.20%). Patients suffering from pelvic floor disorder recorded the highest prevalence of anxiety (11.40%) and depression (13.77%). There was a significant association between depression and gynaecological disorders [χ2(25) =53.915, p=0.001, CI=95%], but there was not enough evidence of an association between anxiety and gynaecological disorders [χ2(15) =22.791, p=0.089, CI=95%].Conclusions: Anxiety and depression are prevalent amongst women in their reproductive age diagnosed presenting with gynaecological disorders and there is a significant association between gynaecological disorders and the prevalence of depression

A review of pharmacological effects of xylopic acid

Xylopic acid (15β-acetyloxy-kaur-16-en-19-oic acid) is a kaurene diterpene that can be obtained from various Xylopia spp. Xylopic acid has demonstrated several pharmacological activities in vitro and in vivo. The compound has shown promising effect as a potent analgesic, anti-inflammatory and anti-allergic agent. Xylopic acid is a CNS depressant and was able to ameliorate anxiety-like symptoms in mice in addition to its neuroprotective effects. Deleterious effects of xylopic acid on the reproductive system of mice have been well documented but extensive toxicity study detailing effect of the acid upon chronic exposure needs to be determined. Due to the heavy consumption of X. aethiopica fruits, it is recommended that the pharmacokinetics of xylopic acid be determined to ascertain the possible food-drug interaction that may occur when conventional drugs are taken together with foods containing xylopic acid

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)