METHODOLOGY: A systematic review and meta-analysis of nutritional water productivity (NWP) and nutrient contribution (NC) of selected cereal-legume intercrop systems was conducted through literature searches in Scopus, Web of Science and ScienceDirect databases. After the assessment, only nine articles written in English that were field experiments comprising grain cereal and legume intercrop systems were retained. Using the R statistical software (version 3.6.0), paired t-tests were used to determine if differences existed between the intercrop system and the corresponding cereal monocrop for yield (Y), water productivity (WP), NC, and NWP.
RESULTS: The intercropped cereal or legume yield was 10 to 35% lower than that for the corresponding monocrop system. In most instances, intercropping cereals with legumes improved NY, NWP, and NC due to their added nutrients. Substantial improvements were observed for calcium (Ca), where NY, NWP, and NC improved by 658, 82, and 256%, respectively.
DISCUSSION: Results showed that cereal-legume intercrop systems could improve nutrient yield in water-limited environments. Promoting cereal- legume intercrops that feature nutrient-dense legume component crops could contribute toward addressing the SDGs of Zero Hunger (SDG 3), Good Health and Well-3 (SDG 2) and Responsible consumption and production (SDG 12).
INTRODUCTION: Artificial intelligence (AI) is a relatively new technology that has widespread use in dentistry. The AI technologies have primarily been used in dentistry to diagnose dental diseases, plan treatment, make clinical decisions, and predict the prognosis. AI models like convolutional neural networks (CNN) and artificial neural networks (ANN) have been used in endodontics to study root canal system anatomy, determine working length measurements, detect periapical lesions and root fractures, predict the success of retreatment procedures, and predict the viability of dental pulp stem cells. Methodology. The literature was searched in electronic databases such as Google Scholar, Medline, PubMed, Embase, Web of Science, and Scopus, published over the last four decades (January 1980 to September 15, 2021) by using keywords such as artificial intelligence, machine learning, deep learning, application, endodontics, and dentistry.
RESULTS: The preliminary search yielded 2560 articles relevant enough to the paper's purpose. A total of 88 articles met the eligibility criteria. The majority of research on AI application in endodontics has concentrated on tracing apical foramen, verifying the working length, projection of periapical pathologies, root morphologies, and retreatment predictions and discovering the vertical root fractures.
CONCLUSION: In endodontics, AI displayed accuracy in terms of diagnostic and prognostic evaluations. The use of AI can help enhance the treatment plan, which in turn can lead to an increase in the success rate of endodontic treatment outcomes. The AI is used extensively in endodontics and could help in clinical applications, such as detecting root fractures, periapical pathologies, determining working length, tracing apical foramen, the morphology of root, and disease prediction.
STUDY DESIGN: This was an open-label, randomized clinical trial conducted at 14 public hospitals across Malaysia from February to June 2021 among 500 symptomatic, RT-PCR confirmed COVID-19 patients, aged ≥50 years with ≥1 co-morbidity, and hospitalized within first 7 days of illness. Patients were randomized on 1:1 ratio to favipiravir plus standard care or standard care alone. Favipiravir was administered at 1800mg twice-daily on day 1 followed by 800mg twice-daily until day 5. The primary endpoint was rate of clinical progression from non-hypoxia to hypoxia. Secondary outcomes included rates of mechanical ventilation, intensive care unit (ICU) admission, and in-hospital mortality.
RESULTS: Among 500 patients were randomized (mean age, 62.5 [SD 8.0] years; 258 women [51.6%]; and 251 [50.2%] had COVID-19 pneumonia), 487 (97.4%) patients completed the trial. Clinical progression to hypoxia occurred in 46 (18.4%) patients on favipiravir plus standard care and 37 (14.8%) on standard care alone (OR 1.30; 95%CI, 0.81-2.09; P=.28). All three pre-specified secondary end points were similar between both groups. Mechanical ventilation occurred in 6 (2.4%) vs 5 (2.0%) (OR 1.20; 95%CI, 0.36-4.23; P=.76), ICU admission in 13 (5.2%) vs 12 (4.8%) (OR 1.09; 95%CI, 0.48-2.47; P=.84), and in-hospital mortality in 5 (2.0%) vs 0 (OR 12.54; 95%CI, 0.76- 207.84; P=.08).
CONCLUSIONS: Among COVID-19 patients at high risk of disease progression, early treatment with oral favipiravir did not prevent their disease progression from non-hypoxia to hypoxia.
METHODS: Nine healthy normotensive subjects participated in this randomized placebo-controlled two-way crossover study examining the effects of 5 days' pretreatment of nafcillin 500 mg or placebo four times daily on the pharmacokinetics of an oral dose of nifedipine 10 mg. Plasma nifedipine concentrations were measured by gas chromatography-mass spectro.
RESULTS: The area under the plasma nifedipine concentration-time curve (AUC0-alpha) in nafcillin-pretreated subjects (80.9 +/- 32.9 micro g l-1 h-1) was significantly decreased compared with subjects who received only nifedipine (216.4 +/- 93.2 micro g l-1 h-1) (P < 0.001). Total plasma clearance of nifedipine (CL/F) was significantly increased with nafcillin pretreatment (138.5 +/- 42.0 l h-1 vs 56.5 +/- 32.0 l h-1) (P < 0.002).
CONCLUSIONS: The results show that nafcillin pretreatment markedly increased the clearance of nifedipine and suggest that nafcillin is a potent inducer of CYP enzyme.