Blastocystis is a prevalent enteric protozoan that infects a variety of vertebrates. Infection with Blastocystis in humans has been associated with abdominal pain, diarrhea, constipation, fatigue, skin rash, and other symptoms. Researchers using different methods and examining different patient groups have reported asymptomatic infection, acute symptomatic infection, and chronic symptomatic infection. The variation in accounts has lead to disagreements concerning the role of Blastocystis in human disease, and the importance of treating it. A better understanding of the number of species of Blastocystis that can infect humans, along with realization of the limitations of the existing clinical laboratory diagnostic techniques may account for much of the disagreement. The possibility that disagreement was caused by the emergence of particular pathogenic variants of Blastocystis is discussed, along with the potential role of Blastocystis infection in irritable bowel syndrome (IBS). Findings are discussed concerning the role of protease-activated receptor-2 in enteric disease which may account for the presence of abdominal pain and diffuse symptoms in Blastocystis infection, even in the absence of fever and endoscopic findings. The availability of better diagnostic techniques and treatments for Blastocystis infection may be of value in understanding chronic gastrointestinal illness of unknown etiology.

Background: Dengue has become one of the major vector-borne diseases, which has been an important public health concern. We aimed to estimate the disease burden of dengue in major endemic regions from 1990 to 2019, and explore the impact pattern of the socioeconomic factors on the burden of dengue based on the global burden of diseases, injuries, and risk factors study 2019 (GBD 2019). Methods: Using the analytical strategies and data from the GBD 2019, we described the incidence and disability-adjusted life years (DALYs) of dengue in major endemic regions from 1990 to 2019. Furthermore, we estimated the correlation between dengue burden and socioeconomic factors, and then established an autoregressive integrated moving average (ARIMA) model to predict the epidemic trends of dengue in endemic regions. All estimates were proposed as numbers and age-standardized rates (ASR) per 100,000 population, with uncertainty intervals (UIs). The ASRs of dengue incidence were compared geographically and five regions were stratified by a sociodemographic index (SDI). Results: A significant rise was observed on a global scale between 1990 and 2019, with the overall age-standardized rate (ASR) increasing from 557.15 (95% UI 243.32–1212.53) per 100,000 in 1990 to 740.4 (95% UI 478.2–1323.1) per 100,000 in 2019. In 2019, the Oceania region had the highest age-standardized incidence rates per 100,000 population (3173.48 (95% UI 762.33–6161.18)), followed by the South Asia region (1740.79 (95% UI 660.93–4287.12)), and then the Southeast Asia region (1153.57 (95% UI 1049.49–1281.59)). In Oceania, South Asia, and Southeast Asia, increase trends were found in the burden of dengue fever measured by ASRs of DALY which were consistent with ASRs of dengue incidence at the national level. Most of the countries with the heaviest burden of dengue fever occurred in areas with low and medium SDI regions. However, the burden in high-middle and high-SDI countries is relatively low, especially the Solomon Islands and Tonga in Oceania, the Maldives in South Asia and Indonesia in Southeast Asia. The age distribution results of the incidence rate and disease burden of dengue fever of major endemic regions showed that the higher risk and disease burden are mainly concentrated in people under 14 or over 70 years old. The prediction by ARIMA showed that the risk of dengue fever in South and Southeast Asia is on the rise, and further prevention and control is warranted. Conclusions: In view of the rapid population growth and urbanization in many dengue-endemic countries, our research results are of great significance for presenting the future trend in dengue fever. It is recommended to policy makers that specific attention needs to be paid to the negative impact of urbanization on dengue incidence and allocate more resources to the low-SDI areas and people under 14 or over 70 years old to reduce the burden of dengue fever.

Endosymbiotic bacteria inhabit a variety of arthropods including ticks and may have multiple effects on the host’s survival, reproduction or pathogen acquisition and transmission. Rhipicephalus haemaphysaloides is one of the most widely distributed tick species in China. The symbiotic bacteria composition and their impacts to R. haemaphysaloides ticks have not been studied. The present study investigated the composition of microbial community in R. haemaphysaloides ticks and then assessed the effects of endosymbionts on the host’s fecundity by antibiotic treatment experiments. The microbial population of female and male R. haemaphysaloides ticks was analyzed using Illumina Miseq sequencing of 16S rRNA gene. Thirty engorged female ticks were then randomly divided into five groups and injected with ampicillin, ciprofloxacin, kanamycin, tetracycline, or phosphate-buffered solution (PBS), respectively. Effects of antibiotic treatments on maternal oviposition, egg hatching and density of endosymbionts were evaluated. Illumina Miseq sequencing showed that Coxiella and Rickettsia were the predominant bacterial genera inhabiting R. haemaphysaloides ticks. Antibiotic treatment experiments found that kanamycin reduced the density of Coxiella-like endosymbiont (Coxiella-LE hereafter) in eggs, ciprofloxacin reduced the density of Rickettsia-like endosymbiont (Rickettsia-LE), and tetracycline had effect on both endosymbionts, while ampicillin affected neither. Meanwhile hatching rates of eggs were observed to decrease greatly in the kanamycin or tetracycline-treated group but maintained in the ampicillin or ciprofloxacin-treated group. Furthermore, the reduced hatching rates were found to be associated with density of Coxiella-LE in eggs. The findings indicate that Coxiella-LE is essential for the reproduction of R. haemaphysaloides ticks, and that kanamycin can be used to study the role of Coxiella-LE on ticks.

Many tick species have great morphological similarity and are thus grouped into species complexes. Molecular methods are therefore useful in the classification and identification of ticks. However, little is known about the genetic diversity of hard ticks in China, especially at the subspecies level. Tengchong is one of the epidemic foci of tick-borne diseases in China, but the tick species inhabiting the local area are still unknown. Eighteen villages in Tengchong County, China, were selected for sampling carried out from September to October 2014. Infesting hard ticks were removed from the body surface of domestic animals and questing ticks were collected from grazing fields. After morphological identification, molecular characteristics of each tick species were analyzed based on both 16S rRNA and cytochrome c oxidase subunit 1 (cox1) gene fragments. Six tick species were identified based on morphology: Rhipicephalus microplus, R. haemaphysaloides, Ixodes ovatus, Haemaphysalis longicornis, H. shimoga and H. kitaokai. Phylogenetic analysis using the cox1 gene revealed that R. microplus ticks from the present study belong to clade C. For tick samples of both R. haemaphysaloides and I. ovatus, three phylogenetic groups were recognized, and the intergroup genetic distances exceeded the usual tick species boundaries. Haemaphysalis longicornis ticks were clustered into two separate clades based on the cox1 gene. For ticks from both H. shimoga and H. kitaokai, two phylogenetic groups were recognized based on the phylogenetic analysis of the 16S rRNA gene, and the intergroup genetic distances also exceeded the known boundaries for closely related tick species. According to molecular analyses, new species or subspecies closely related to R. haemaphysaloides, I. ovatus, H. shimoga and H. kitaokai probably exist in the China-Myanmar border Tengchong County, or these ticks form species complexes with highly divergent mitochondrial lineages. Morphological comparisons are warranted to further confirm the taxonomic status of these tick species.

Background: Gut microbes can contribute to their hosts in food digestion, nutrient absorption, and inhibiting the growth of pathogens. However, only limited studies have focused on the gut microbiota of freshwater snails. Pomacea canaliculata is considered one of the worst invasive alien species in the world. Elucidating the diversity and composition of the microbiota in the gut of P. canaliculata snails may be helpful for better understanding the widespread invasion of this snail species. In this study, the buccal masses, stomachs, and intestines were isolated from seven P. canaliculata snails. The diversity and composition of the microbiota in the three gut sections were then investigated based on high-throughput Illumina sequencing targeting the V3-V4 regions of the 16S rRNA gene. Results: The diversity of the microbiota was highest in the intestine but lowest in the buccal mass. A total of 29 phyla and 111 genera of bacteria were identified in all of the samples. In general, Ochrobactrum, a genus of putative cellulose-degrading bacteria, was the most abundant (overall relative abundance: 13.6%), followed by Sediminibacterium (9.7%), Desulfovibrio (7.8%), an unclassified genus in the family Aeromonadaceae (5.4%), and Cloacibacterium (5.4%). The composition of the microbiota was diverse among the different gut sections. Ochrobactrum (relative abundance: 23.15% ± 7.92%) and Sediminibacterium (16.95 ± 5.70%) were most abundant in the stomach, an unclassified genus in the family Porphyromonadaceae (14.28 ± 7.29%) and Leptotrichia (8.70 ± 4.46%) were highest in the buccal mass, and two genera in the families Aeromonadaceae (7.55 ± 4.53%) and Mollicutes (13.47 ± 13.03%) were highest in the intestine. Conclusions: The diversity and composition of the microbiome vary among different gut sections of P. canaliculata snails. Putative cellulose-degrading bacteria are enriched in the gut of P. canaliculata.

Objective: This study aimed to improve dengue fever predictions in Singapore using a machine learning model that incorporates meteorological data, addressing the current methodological limitations by examining the intricate relationships between weather changes and dengue transmission. Method: Using weekly dengue case and meteorological data from 2012 to 2022, the data was preprocessed and analyzed using various machine learning algorithms, including General Linear Model (GLM), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared ($R^2$) were employed. Results: From 2012 to 2022, there was a total of 164,333 cases of dengue fever. Singapore witnessed a fluctuating number of dengue cases, peaking notably in 2020 and revealing a strong seasonality between March and July. An analysis of meteorological data points highlighted connections between certain climate variables and dengue fever outbreaks. The correlation analyses suggested significant associations between dengue cases and specific weather factors such as solar radiation, solar energy, and UV index. For disease predictions, the XGBoost model showed the best performance with an MAE = 89.12, RMSE = 156.07, and $R^2$ = 0.83, identifying time as the primary factor, while 19 key predictors showed non-linear associations with dengue transmission. This underscores the significant role of environmental conditions, including cloud cover and rainfall, in dengue propagation. Conclusion: In the last decade, meteorological factors have significantly influenced dengue transmission in Singapore. This research, using the XGBoost model, highlights the key predictors like time and cloud cover in understanding dengue’s complex dynamics. By employing advanced algorithms, our study offers insights into dengue predictive models and the importance of careful model selection. These results can inform public health strategies, aiming to improve dengue control in Singapore and comparable regions.