Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. Two doses of an inactivated SARS-CoV-2 vaccine (CoronaVac) have been shown to be insufficient to protect against variants of concern (VOCs), while viral vector vaccines remain protective against the infection. Herein, we conducted a preliminary study to evaluate the safety and immunity in an adult population who received the conventional 2 dosage-regimen of inactivated SARS-CoV-2 vaccine; with an additional intradermal ChAdOx1 nCoV-19 reciprocal dosage (1:5). An Intramuscular ChAdOx1 nCoV-19 booster was also included as a control. Immediate and delayed local reactions were frequently observed in the fractional intradermal boost, but systemic side effects were significantly decreased compared to the conventional intramuscular boost. The anti-RBD-IgG levels, the neutralising function against delta variants, and T cell responses were significantly increased after boosting via both routes. Interestingly, the shorter interval elicited higher immunogenicity compared to the extended interval. Taken together, a reciprocal dosage of intradermal ChAdOx1 nCoV-19 booster reduces systemic adverse reactions and enhances non inferiority humoral and cellular immune responses compared to a full dose of intramuscular boosting. These findings provide for an effective vaccine management during the shortages of vaccine supply.

Objectives To determine the relative effectiveness of medications for preventing hypertensive disorders in high-risk pregnant women and to provide a ranking of medications using network meta-analysis. Methods All randomized controlled trials comparing the most commonly used medications to prevent hypertensive disorders in high-risk pregnant women that are nulliparity and pregnant women having family history of preeclampsia, history of pregnancy-induced hypertension in previous pregnancy, obstetric risks, or underlying medical diseases. We received the search results from the Cochrane Pregnancy and Childbirth’s Specialised Register of Controlled Trials, searched on 31st July 2020. At least two review authors independently selected the included studies and extracted the data and the methodological quality. The comparative risk ratios (RR) and 95% confidence intervals (CI) were analyzed using pairwise and network meta-analyses, and treatment rankings were estimated by the surface under the cumulative ranking curve for preventing preeclampsia (PE), gestational hypertension (GHT), and superimposed preeclampsia (SPE). Safety of the medications is also important for decision-making along with effectiveness which will be reported in a separate review. Results This network meta-analysis included 83 randomized studies, involving 93,864 women across global regions. Three medications, either alone or in combination, probably prevented PE in high-risk pregnant women when compared with a placebo or no treatment from network analysis: antiplatelet agents with calcium (RR 0.19, 95% CI 0.04 to 0.86; 1 study; low-quality evidence), calcium (RR 0.61, 95% CI 0.47 to 0.80; 13 studies; moderate-quality evidence), antiplatelet agents (RR 0.69, 95% CI 0.57 to 0.82; 31 studies; moderate-quality evidence), and antioxidants (RR 0.77, 95% CI 0.63 to 0.93; 25 studies; moderate-quality evidence). Calcium probably prevented PE (RR 0.63, 95% CI 0.46 to 0.86; 11 studies; moderate-quality evidence) and GHT (RR 0.89, 95% CI 0.84 to 0.95; 8 studies; high-quality evidence) in nulliparous/primigravida women. Few included studies for the outcome of superimposed preeclampsia were found. Conclusion Antiplatelet agents, calcium, and their combinations were most effective medications for preventing hypertensive disorders in high-risk pregnant women when compared with a placebo or no treatment. Any high-risk characteristics for women are important in deciding the best medications. The qualities of evidence were mostly rated to be moderate. Systematic review registration PROSPERO CRD42018096276

Allergic reactions to medication range from mild to severe or even life-threatening. Proper documentation of patient allergy information is critical for safe prescription, avoiding drug interactions, and reducing healthcare costs. Allergy information is regularly obtained during the medical interview, but is often poorly documented in electronic health records (EHRs). While many EHRs allow for structured adverse drug reaction (ADR) reporting, a free-text entry is still common. The resulting information is neither interoperable nor easily reusable for other applications, such as clinical decision support systems and prescription alerts. Current approaches require pharmacists to review and code ADRs documented by healthcare professionals. Recently, the effectiveness of machine algorithms in natural language processing (NLP) has been widely demonstrated. Our study aims to develop and evaluate different NLP algorithms that can encode unstructured ADRs stored in EHRs into institutional symptom terms. Our dataset consists of 79,712 pharmacist-reviewed drug allergy records. We evaluated three NLP techniques: Naive Bayes—Support Vector Machine (NB-SVM), Universal Language Model Fine-tuning (ULMFiT), and Bidirectional Encoder Representations from Transformers (BERT). We tested different general-domain pre-trained BERT models, including mBERT, XLM-RoBERTa, and WanchanBERTa, as well as our domain-specific AllergyRoBERTa, which was pre-trained from scratch on our corpus. Overall, BERT models had the highest performance. NB-SVM outperformed ULMFiT and BERT for several symptom terms that are not frequently coded. The ensemble model achieved an exact match ratio of 95.33%, a F1 score of 98.88%, and a mean average precision of 97.07% for the 36 most frequently coded symptom terms. The model was then further developed into a symptom term suggestion system and achieved a Krippendorff’s alpha agreement coefficient of 0.7081 in prospective testing with pharmacists. Some degree of automation could both accelerate the availability of allergy information and reduce the efforts for human coding.

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Objective: To investigate the demographic characteristics, seasonal variations, effects associated with air pollution, and geographic morbidity of asthma in Songkhla.Material and Methods: This research conducted a time series analysis of secondary data from 1 January, 2013 to 31 December, 2017. The distributed lag non-linear model was employed to analyze associations between air pollutants and daily asthma outpatient visits, and the Bayesian hierarchical modelling was used to map asthma morbidity spatiotemporally.Results: A total of 250,127 asthma diagnoses corresponding to 36,761 patients were found in the medical records. Most asthma outpatients were female (61.1%); males (1-5 years) constituted the majority of individuals during the first peak, while females (40-50 years) predominated the second peak. The trend analysis revealed a seasonal variation in the number of asthma outpatient visits; the highest rates were during the June-December period. The regression coefficient analysis revealed carbon monoxide (CO), nitrogen dioxide (NO2 ), relative humidity, and visibility to have the most significant positive effect on asthma, while the cos (wind direction) had the highest negative effect/impact. Significant associations were found between outpatient gender and age and CO, NO2 , sulfur dioxide, ozone, and particulate matter less than 10 micron. The Hat Yai and Central districts of Songkhla province were identified as morbidity hotspots.Conclusion: The number of asthma-related outpatient visits increased during the rainy season. Asthma affected primarily young boys and middle-aged women in this province, and they constitute the most sensitive group to air pollutants such as CO and NO2 and meteorological conditions like relative humidity and visibility. The highest morbidity rates were found in urbanized habitats.