The potential use of mitochondrial ribosomal genes (12S and 16S) in DNA barcoding and phylogenetic analysis of trematodes

Background: Genetic markers like the nuclear ribosomal RNA (rRNA) genes, internal transcribed spacer regions, mitochondrial protein-coding genes, and genomes have been utilized for molecular identification of parasitic trematodes. However, challenges such as the design of broadly applicable primers for the vast number of species within Digenea and the genetic markers’ ability to provide sufficient species-level resolution limited their utility. This study presented novel and broadly applicable primers using the mitochondrial 12S and 16S rRNA genes for Digenea and aimed to show their suitability as alternative genetic markers for molecular identification of orders Plagiorchiida, Echinostomida, and Strigeida. Results: Our results revealed that the mitochondrial 12S and 16S rRNA genes are suitable for trematode molecular identification, with sufficient resolution to discriminate closely related species and achieve accurate species identification through phylogenetic placements. Moreover, the robustness of our newly designed primers to amplify medically important parasitic trematodes encompassing three orders was demonstrated through successful amplification. The convenience and applicability of the newly designed primers and adequate genetic variation of the mitochondrial rRNA genes can be useful as complementary markers for trematode molecular-based studies. Conclusions: We demonstrated that the mitochondrial rRNA genes could be alternative genetic markers robust for trematode molecular identification and potentially helpful for DNA barcoding where our primers can be widely applied across the major Digenea orders. Furthermore, the potential of the mitochondrial rRNA genes for molecular systematics can be explored, enhancing their appeal for trematode molecular-based studies. The novelty of utilizing the mitochondrial rRNA genes and the designed primers in this study can potentially open avenues for species identification, discovery, and systematics in the future

Sensitive and accurate DNA metabarcoding of parasitic helminth mock communities using the mitochondrial rRNA genes

Next-generation sequencing technologies have accelerated the pace of helminth DNA metabarcoding research, enabling species detection in bulk community samples. However, finding suitable genetic markers with robust species-level resolution and primers targeting a broad species range among parasitic helminths are some of the challenges faced. This study aimed to demonstrate the potential use of the mitochondrial 12S and 16S rRNA genes for parasitic helminth (nematodes, trematodes, cestodes) DNA metabarcoding. To demonstrate the robustness of the 12S and 16S rRNA genes for DNA metabarcoding, we determined the proportion of species successfully recovered using mock helminth communities without environment matrix and mock helminth communities artificially spiked with environmental matrices. The environmental matrices are human fecal material, garden soil, tissue, and pond water. Our results revealed the robustness of the mitochondrial rRNA genes, through the high sensitivity of the 12S rRNA gene, and the effectiveness of the 12S and 16S primers targeting platyhelminths. With the mitochondrial rRNA genes, a broad range of parasitc helminths were successfully detected to the species level. The potential of the mitochondrial rRNA genes for helminth DNA metabarcoding was demonstrated, providing a valuable gateway for future helminth DNA metabarcoding applications like helminth detection and biodiversity studies

Model evaluation of target product profiles of an infant vaccine against respiratory syncytial virus (RSV) in a developed country setting

Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract disease in children worldwide and is a significant cause of hospital admissions in young children in England. No RSV vaccine has been licensed but a number are under development. In this work, we present two structurally distinct mathematical models, parameterized using RSV data from the UK, which have been used to explore the effect of introducing an RSV paediatric vaccine to the National programme. We have explored different vaccine properties, and dosing regimens combined with a range of implementation strategies for RSV control. The results suggest that vaccine properties that confer indirect protection have the greatest effect in reducing the burden of disease in children under 5 years. The findings are reinforced by the concurrence of predictions from the two models with very different epidemiological structure. The approach described has general application in evaluating vaccine target product profiles

Classification of elderly pain severity from automated video clip facial action unit analysis: a study from a Thai data repository

Data from 255 Thais with chronic pain were collected at Chiang Mai Medical School Hospital. After the patients self-rated their level of pain, a smartphone camera was used to capture faces for 10 s at a one-meter distance. For those unable to self-rate, a video recording was taken immediately after the move that causes the pain. The trained assistant rated each video clip for the pain assessment in advanced dementia (PAINAD). The pain was classified into three levels: mild, moderate, and severe. OpenFace© was used to convert the video clips into 18 facial action units (FAUs). Five classification models were used, including logistic regression, multilayer perception, naïve Bayes, decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Out of the models that only used FAU described in the literature (FAU 4, 6, 7, 9, 10, 25, 26, 27, and 45), multilayer perception is the most accurate, at 50%. The SVM model using FAU 1, 2, 4, 7, 9, 10, 12, 20, 25, and 45, and gender had the best accuracy of 58% among the machine learning selection features. Our open-source experiment for automatically analyzing video clips for FAUs is not robust for classifying pain in the elderly. The consensus method to transform facial recognition algorithm values comparable to the human ratings, and international good practice for reciprocal sharing of data may improve the accuracy and feasibility of the machine learning's facial pain rater

Assessing the early stage of eHealth adoption: a case study from a community hospital in Thailand

In this paper, the authors implement and determine the success the eHealth adoption for queue management when it was first deployed for a community hospital setting in Thailand. The electronic queue system was first implemented to improve conventional operations; then extensive evaluations were conducted to measure the effectiveness for each stakeholder. The healthcare staff shared a common perception that the new system could reduce their workload and increase the efficacy of queue fairness. The overall patient satisfaction and actual waiting time patients spent at the nurse interview station improved significantly. The majority of the patients agreed that the notification for attention from the computerized system is more effective. The community healthcare has strong potential to adopt the eHealth system. Being more automated enabled a reduced burden of administration jobs and significantly reduced waiting times for patients. Patients responded that they had greater satisfaction after the introduction of the electronic queue system

Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach

Background: Malaria continues to pose a significant health threat. Rapid identification of malaria infections and the deployment of active surveillance tools are crucial for achieving malaria elimination in regions where malaria is endemic, such as certain areas of Thailand. In this study, an anomaly detection system is introduced as an early warning mechanism for potential malaria outbreaks in countries like Thailand. Methods: Unsupervised clustering-based, and time series-based anomaly detection algorithms are developed and compared to identify abnormal malaria activity in Thailand. Additionally, a user interface tailored for anomaly detection is designed, enabling the Thai malaria surveillance team to utilize these algorithms and visualize regions exhibiting unusual malaria patterns. Results: Nine distinct anomaly detection algorithms we developed. Their efficacy in pinpointing verified outbreaks was assessed using malaria case data from Thailand spanning 2012 to 2022. The historical average threshold-based anomaly detection method triggered three times fewer alerts, while correctly identifying the same number of verified outbreaks when compared to the current method used in Thailand. A limitation of this analysis is the small number of verified outbreaks; further consultation with the Division of Vector Borne Disease could help identify more verified outbreaks. The developed dashboard, designed specifically for anomaly detection, allows disease surveillance professionals to easily identify and visualize unusual malaria activity at a provincial level across Thailand. Conclusion: An enhanced early warning system is proposed to bolster malaria elimination efforts for countries with a similar malaria profile to Thailand. The developed anomaly detection algorithms, after thorough comparison, have been optimized for integration with the current malaria surveillance infrastructure. An anomaly detection dashboard for Thailand is built and supports early detection of abnormal malaria activity. In summary, the proposed early warning system enhances the identification process for provinces at risk of outbreaks and offers easy integration with Thailand’s established malaria surveillance framework

An artesunate pharmacometric model to explain therapeutic responses in falciparum malaria.

Background: The artemisinins are potent and widely used antimalarial drugs that are eliminated rapidly. A simple concentration–effect pharmacometric model does not explain why dosing more frequently than once daily fails to augment parasite clearance and improve therapeutic responses in vivo. Artemisinins can induce a temporary non-replicative or ‘dormant’ drug refractory state in Plasmodium falciparum malaria parasites which may explain recrudescences observed in clinical trials despite full drug susceptibility, but whether it explains the dosing–response relationship is uncertain. Objectives: To propose a revised model of antimalarial pharmacodynamics that incorporates reversible asexual parasite injury and temporary drug refractoriness in order to explain the failure of frequent dosing to augment therapeutic efficacy in falciparum malaria. Methods: The model was fitted using a Bayesian Markov Chain Monte Carlo approach with the parasite clearance data from 39 patients with uncomplicated falciparum malaria treated with artesunate from western Cambodia and 40 patients from northwestern Thailand reported previously. Results: The revised model captured the dynamics of parasite clearance data. Its predictions are consistent with observed therapeutic responses. Conclusions: A within-host pharmacometric model is proposed in which it is hypothesized that some malaria parasites enter a temporary drug refractory state after exposure to artemisinin antimalarials, which is followed by delayed parasite death or reactivation. The model fitted the observed sequential parasite density data from patients with acute P. falciparum malaria, and it supported reduced ring stage activity in artemisinin-resistant infections

How to model the impact of vaccines for policymaking when the characteristics are uncertain: a case study in Thailand prior to the vaccine rollout during the COVID-19 pandemic

Thailand faced a dilemma of which groups to prioritise with a limited first tranche of COVID-19 vaccinations in early 2021, at a time when there was low incidence and low mortality in the country. A mathematical modelling analysis was performed to compare the potential short-term impact of allocating the available doses to either the high severity group (over 65-year-olds) or the high transmission group (aged 20-39). At the time of the analysis, there was uncertainty about the precise characteristics of the vaccines available, in terms of their potential impact on transmission and reductions to the severity of infection. As such, a range of vaccine characteristic scenarios, with differing levels of severity and transmission reductions were explored. Using the evidence available at the time regarding severity reduction of infection due to the vaccines, the model suggested that vaccinating high severity group should be the priority if reductions in deaths is the priority. Vaccinating this group was found to have a direct impact on reducing the number of deaths, while the incidence and hospitalisations remained unchanged. However, the model found that vaccinating the high transmission group with a vaccine with sufficiently high protection against infection (more than 70%) could provide enough herd effects to delay the expected epidemic peak, resulting in both case and death reductions in both target groups. The model explored a 12-month time horizon. These analyses helped to inform the vaccination strategy in Thailand throughout 2021 and can inform future modelling studies for policymaking when the characteristics of vaccines are uncertain

Assessing the cost-effectiveness of COVID-19 vaccines in a low incidence and low mortality setting: the case of Thailand at start of the pandemic

Objective This study aimed to assess the cost-effectiveness of COVID-19 vaccines, preferred COVID-19 vaccine profiles, and the preferred vaccination strategies in Thailand. Methods An age-structured transmission dynamic model was developed based on key local data to evaluate economic consequences, including cost and health outcome in terms of life-years (LYs) saved. We considered COVID-19 vaccines with different profiles and different vaccination strategies such as vaccinating elderly age groups (over 65s) or high-incidence groups, i.e. adults between 20 and 39 years old who have contributed to more than 60% of total COVID-19 cases in the country thus far. Analyses employed a societal perspective in a 1-year time horizon using a cost-effectiveness threshold of 160,000 THB per LY saved. Deterministic and probabilistic sensitivity analyses were performed to identify and characterize uncertainty in the model. Results COVID-19 vaccines that block infection combined with social distancing were cost-saving regardless of the target population compared to social distancing alone (with no vaccination). For vaccines that block infection, the preferred (cost-effective) strategy was to vaccinate the high incidence group. Meanwhile, COVID-19 vaccines that reduces severity (including hospitalization and mortality) were cost-effective when the elderly were vaccinated, while vaccinating the high-incidence group was not cost-effective with this vaccine type. Regardless of vaccine type, higher vaccination coverage, higher efficacy, and longer protection duration were always preferred. More so, vaccination with social distancing measures was always preferred to strategies without social distancing. Quarantine-related costs were a major cost component affecting the cost-effectiveness of COVID-19 vaccines. Conclusion COVID-19 vaccines are good value for money even in a relatively low-incidence and low-mortality setting such as Thailand, if the appropriate groups are vaccinated. The preferred vaccination strategies depend on the type of vaccine efficacy. Social distancing measures should accompany a vaccination strategy

The last man standing is the most resistant: eliminating artemisinin-resistant malaria in Cambodia.

BACKGROUND: Artemisinin combination therapy (ACT) is now the recommended first-line treatment for falciparum malaria throughout the world. Initiatives to eliminate malaria are critically dependent on its efficacy. There is recent worrying evidence that artemisinin resistance has arisen on the Thai-Cambodian border. Urgent containment interventions are planned and about to be executed. Mathematical modeling approaches to intervention design are now integrated into the field of malaria epidemiology and control. The use of such an approach to investigate the likely effectiveness of different containment measures with the ultimate aim of eliminating artemisinin-resistant malaria is described. METHODS: A population dynamic mathematical modeling framework was developed to explore the relative effectiveness of a variety of containment interventions in eliminating artemisinin-resistant malaria in western Cambodia. RESULTS: The most effective intervention to eliminate artemisinin-resistant malaria was a switch of treatment from artemisinin monotherapy to ACT (mean time to elimination 3.42 years (95% CI 3.32-3.60 years). However, with this approach it is predicted that elimination of artemisinin-resistant malaria using ACT can be achieved only by elimination of all malaria. This is because the various forms of ACT are more effective against infections with artemisinin-sensitive parasites, leaving the more resistant infections as an increasing proportion of the dwindling parasite population. CONCLUSION: Containment of artemisinin-resistant malaria can be achieved by elimination of malaria from western Cambodia using ACT. The "last man standing" is the most resistant and thus this strategy must be sustained until elimination is truly achieved