Assessing spatial distribution of COVID-19 prevalence in Brazil using decentralised sewage monitoring

Brazil has become one of the epicentres of the COVID-19 pandemic, with cases heavily concentrated in large cities. Testing data is extremely limited and unreliable, which restricts health authorities' ability to deal with the pandemic. Given the stark demographic, social and economic heterogeneities within Brazilian cities, it is important to identify hotspots so that the limited resources available can have the greatest impact. This study shows that decentralised monitoring of SARS-CoV-2 RNA in sewage can be used to assess the distribution of COVID-19 prevalence in the city. The methodology developed in this study allowed the identification of hotspots by comprehensively monitoring sewers distributed through Belo Horizonte, Brazil's third largest city. Our results show that the most vulnerable neighbourhoods in the city were the hardest hit by the pandemic, indicating that, for many Brazilians, the situation is much worse than reported by official figures.

Emerging strategies to target RAS signaling in human cancer therapy

RAS mutations (HRAS, NRAS, and KRAS) are among the most common oncogenes, and around 19% of patients with cancer harbor RAS mutations. Cells harboring RAS mutations tend to undergo malignant transformation and exhibit malignant phenotypes. The mutational status of RAS correlates with the clinicopathological features of patients, such as mucinous type and poor differentiation, as well as response to anti-EGFR therapies in certain types of human cancers. Although RAS protein had been considered as a potential target for tumors with RAS mutations, it was once referred to as a undruggable target due to the consecutive failure in the discovery of RAS protein inhibitors. However, recent studies on the structure, signaling, and function of RAS have shed light on the development of RAS-targeting drugs, especially with the approval of Lumakras (sotorasib, AMG510) in treatment of KRASG12C-mutant NSCLC patients. Therefore, here we fully review RAS mutations in human cancer and especially focus on emerging strategies that have been recently developed for RAS-targeting therapy.

Coral larval recruitment in north-western Australia predicted by regional and local conditions

Understanding ecological processes that shape contemporary and future communities facilitates knowledge-based environmental management. In marine ecosystems, one of the most important processes is the supply of new recruits into a population. Here, we investigated spatiotemporal variability in coral recruitment at 15 reefs throughout the Dampier Archipelago, north-western Australia between 2015 and 2017 and identified the best environmental predictors for coral recruitment patterns over this period. Large differences in recruitment were observed among years with the average density of recruits increasing by 375% from 0.017 recruits cm^-2 in 2015 to 0.059 recruits cm^-2 in 2017. Despite differences in recruitment among years, the rank order of coral recruit density among reefs remained similar among years, suggesting that spatial variation in recruitment within the Dampier Archipelago is partly deterministic and predictable. The density of coral recruits was best explained by percent cover of live corals at both local (within 5 m) and meso-scales (within 15 km), water turbidity and an oceanographic model that predicted larval dispersal. The highest density of coral recruits (~0.13 recruits cm^-2 or 37 recruits per tile) occurred on reefs within sub-regions (15 km) with greater than 35% coral cover, low to moderate turbidity (KD490 < 0.2) and moderate to high modelled predictions of larval dispersal. Our results demonstrate that broad-scale larval dispersal models, when combined with local metrics of percent hard coral cover and water turbidity, can reliably predict the relative abundance of coral recruits over large geographical areas and thus can identify hotspots of recruit abundance and potential recovery following environmental disturbances; information that is essential for effective management of coral reefs.

Enhanced characterization of the thyA system for mutational analysis in Escherichia coli: Defining mutationally "hot" regions of the gene

We have extensively characterized base substitution mutations in the 795 base pair (bp) long E. coli thyA gene to define as many of the base substitution mutational sites that inactivate the gene as possible. The resulting catalog of mutational sites constitutes a system with up to 5 times as many sites for monitoring each of the six base substitution mutations as the widely used rpoB/Rif^r system. We have defined 75 sites for the G:C -> A:T transition, 68 sites for the G:C -> T:A transversion, 53 sites for the G:C -> C:G transversion, 49 sites for the A:T -> G:C transition, 39 sites for the A:T -> T:A transversion, and 59 sites for the A:T -> C:G transversion. The system is thus comprised of 343 base substitution mutations at 232 different base pairs, all of which can be sequenced with a single primer pair. This allows for the examination of mutational spectra using a more detailed probe of known mutations, while still allowing one to compare the number of repeated occurrences at specific sites. We have examined several mutagens and mutators with this system, and show its utility by looking at the spectrum of cisplatin, that has a single hotspot, underscoring the value of having as large an array of sites as possible at which one can monitor repeat occurrences. To test for regions of the gene that might be hotspots for a number of mutagens, or "hot" (mutaphilic) regions, we have looked at the ratio of mutations per set of an equal number of mutational sites throughout the gene. The resulting graphs suggest that there are "hot" regions at intervals, and this may reflect aspects of secondary structures, of the higher order structure of the chromosome, or perhaps the nucleoid structure of the chromosome plus histone-like protein complexes.

Spatial-temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast

BACKGROUND

The over-distributed pattern of malaria transmission has led to attempts to define malaria "hotspots" that could be targeted for purposes of malaria control in Africa. However, few studies have investigated the use of routine health facility data in the more stable, endemic areas of Africa as a low-cost strategy to identify hotspots. Here the objective was to explore the spatial and temporal dynamics of fever positive rapid diagnostic test (RDT) malaria cases routinely collected along the Kenyan Coast.

METHODS

Data on fever positive RDT cases between March 2018 and February 2019 were obtained from patients presenting to six out-patients health-facilities in a rural area of Kilifi County on the Kenyan Coast. To quantify spatial clustering, homestead level geocoded addresses were used as well as aggregated homesteads level data at enumeration zone. Data were sub-divided into quarterly intervals. Kulldorff's spatial scan statistics using Bernoulli probability model was used to detect hotspots of fever positive RDTs across all ages, where cases were febrile individuals with a positive test and controls were individuals with a negative test.

RESULTS

Across 12 months of surveillance, there were nine significant clusters that were identified using the spatial scan statistics among RDT positive fevers. These clusters included 52% of all fever positive RDT cases detected in 29% of the geocoded homesteads in the study area. When the resolution of the data was aggregated at enumeration zone (village) level the hotspots identified were located in the same areas. Only two of the nine hotspots were temporally stable accounting for 2.7% of the homesteads and included 10.8% of all fever positive RDT cases detected.

CONCLUSION

Taking together the temporal instability of spatial hotspots and the relatively modest fraction of the malaria cases that they account for; it would seem inadvisable to re-design the sub-county control strategies around targeting hotspots.

A bibliometric analysis of global research output on network meta-analysis

Background

Network meta-analysis (NMA) has been widely used in the field of medicine and health, but the research topics and development trends are still unclear. This study aimed to identify the cooperation of countries and institutes and explore the hot topics and future prospects in the field of NMA.

Methods

Data of publications were downloaded from the Web of Science Core Collection. We used CiteSpace V, HistCite 2.1, and Excel 2016 to analyze literature information, including years, journals, countries, institutes, authors, keywords, and co-cited references.

Results

NMA research developed gradually before 2010 and rapidly in the following years. 2846 NMA studies were published in 771 journals in six languages. The PLoS One (110, 3.9%) was the most productive journal, and N Engl J Med (5904 co-citations) was the most co-cited journal. The most productive country was the United States (889, 31%) and the most productive institute was the University of Bristol (113, 4.0%). The active collaborations were observed between developed countries and between productive institutes. Of the top 10 authors, four were from the UK, and among the top 10 co-cited authors, six were from the UK. Randomized evidence, oral anti-diabetic drugs, coronary artery bypass, certolizumab pegol, non-valvular atrial fibrillation, and second-line antihyperglycemic therapy were the hot topics in this field.

Conclusions

NMA studies have significantly increased over the past decade, especially from 2015 to 2017. Compared with developing countries, developed countries have contributed more to these publications and have closer cooperation, indicating that cooperation between developed and developing countries should be further strengthened. The treatment of diabetes, cardiovascular diseases, and immune rheumatism are the main hot topics.

Risk assessment and hotspots identification of heavy metals in rice: A case study in Longyan of Fujian province, China

The potential threats of heavy metals in rice have attracted increasing attention worldwide. In this study, we assessed the pollution status and health risk of rice collected from Longyan in Fujian, China. Meanwhile, we explored the spatial pattern and hotspots of those metals. The results showed that the average concentrations of Cd, Hg, As, Pb, Cr, Ni, Cu, and Zn in rice were 0.064, 0.002, 0.464, 0.072, 0.138, 0.106, 10.819, and 23.788 mg kg^-1, respectively. Among them, As and Cu remarkably accumulated with the exceeding ratio of 50.30% and 55.12%, respectively. Furthermore, the values of the target hazard quotient in rice ranked as As > Cu > Zn > Cd > Pd > Ni > Hg > Cr, which As and Cu was greater than 1.0. And the carcinogenic risk values were in the order of As > Cd > Ni > Cr, which all exceeded the tolerance level (1 × 10^-4). Risk assessment indicated that both children and adults were posed the non-carcinogenic and carcinogenic risk from rice intake, and As had the largest contribution rate for them. Comparison found that the spatial patterns of heavy metals distribution were consistent with the hotspots. The hotspots for As and Zn located in the western part (Changting and Wuping), Cd and Cu in the eastern part (Xinluo and Yongding), Cr and Ni were simultaneously found in the northeast (Zhangping), while Hg and Pb were mainly located in the central region (Shanghang). Overall, combining the pollution status, risk assessment, and hotspot distribution in rice, the western region (Changting and Wuping) were identified as priority areas for remediation.

Worldwide research tendency and hotspots on hip fracture: a 20-year bibliometric analysis

We analyzed the overall knowledge structure, development trends, and research hotspots of 7684 publications related to hip fracture through the bibliometric method. Our results indicate this area has received more and more attention from researchers. Prevention of complications will be the focus of future studies.

INTRODUCTION

Hip fracture is an international public health problem, with high morbidity, mortality, and associated health care costs. Research on hip fracture has been developed rapidly in recent years, but no bibliometric studies have been performed. We aimed to identify the publication changes in scientific output relating to hip fracture over the past two decades.

METHODS

The scientific output relating to hip fracture from 2000 to 2019 was identified and selected from the Web of Science Core Collection. Excel 2019 was used to summarize the quantitative indicators including publication number, citations, H-index, journal's impact factors, and journal citation reports. VOS viewer and CiteSpace software tools were used for co-authorship, citation, co-citation, and co-occurrence analyses between countries, institutions, journals, authors, references, and keywords. Data were analyzed on November 13, 2020.

RESULTS

A total of 7684 publications were extracted. The USA was the leading contributor in this field with the largest publications (1876, 24.41%), the most citations (75,423 times), and the highest H-index (124). The number of publications in the Western European region is 1.82 times higher than that of North America region, and 3.59 times that of Eastern Asia region. The most productive institutions on hip fracture were University of Maryland (160). Injury (506) had the highest number of publications, while Osteoporosis International (20,483 times) was the most co-cited journal. Magaziner J and Parker MJ were the key researchers. The keywords were stratified into five clusters: cluster 1 ("operative approaches study"), cluster 2 ("rehabilitation study"), cluster 3 ("osteoporosis study"), cluster 4 ("outcomes and complications study"), and cluster 5 ("epidemiology study"). For hotspots, "tranexamic acid" showed a relatively latest average appearing years of 2017.52, followed by "30-day-mortality," "readmission," and "length of stay."

CONCLUSIONS

There will be an increasing number of publications on the hip fracture research based on the current global trends, and the USA stays ahead in this field. In terms of region, Western Europe had the greater impact than North America. It is recommended to pay attention to the promising hotspots, such as tranexamic acid, 30-day-mortality, readmission, and length of stay.

Understanding plastic degradation and microplastic formation in the environment: A review

Plastic waste are introduced into the environment inevitably and their exposure in the environment causes deterioration in mechanical and physicochemical properties and leads to the formation of plastic fragments, which are considered as microplastics when their size is < 5 mm. In recent years, microplastic pollution has been reported in all kinds of environments worldwide and is considered a potential threat to the health of ecosystems and humans. However, knowledge on the environmental degradation of plastics and the formation of microplastics is still limited. In this review, potential hotspots for the accumulation of plastic waste were identified, major mechanisms and characterization methods of plastic degradation were summarized, and studies on the environmental degradation of plastics were evaluated. Future research works should further identify the key environmental parameters and properties of plastics affecting the degradation in order to predict the fate of plastics in different environments and facilitate the development of technologies for reducing plastic pollution. Formation and degradation of microplastics, including nanoplastics, should receive more research attention to assess their fate and ecological risks in the environment more comprehensively.

Neutrophil transendothelial migration hotspots - mechanisms and implications

During inflammation, leukocytes circulating in the blood stream exit the vasculature in a process called leukocyte transendothelial migration (TEM). The current paradigm of this process comprises several well-established steps, including rolling, adhesion, crawling, diapedesis and sub-endothelial crawling. Nowadays, the role of the endothelium in transmigration is increasingly appreciated. It has been established that leukocyte exit sites on the endothelium and in the pericyte layer are in fact not random but instead may be specifically recognized by migrating leukocytes. Here, we review the concept of transmigration hotspots, specific sites in the endothelial and pericyte layer where most transmigration events take place. Chemokine cues, adhesion molecules and membrane protrusions as well as physical factors, such as endothelial junction stability, substrate stiffness, the presence of pericytes and basement membrane composition, may all contribute to local hotspot formation to facilitate leukocytes exiting the vasculature. In this Review, we discuss the biological relevance of such hotspots and put forward multiple mechanisms and factors that determine a functional TEM hotspot.

Wildlife roadkill in the Tsavo Ecosystem, Kenya: identifying hotspots, potential drivers, and affected species

Roadkill is one of the highest causes of wildlife mortality and is of global conservation concern. Most roadkill studies have focused on wildlife in developed countries such as the United States of America and temperate biomes, but there are limited data for the impacts of roads on wildlife in the African tropics, where road infrastructure development is projected to grow rapidly in natural environments and conservation areas. The Tsavo Conservation Area is an important biodiversity hotspot in eastern Kenya and is bisected by a major highway and railways that connect the port of Mombasa to the interior. Along this infrastructure corridor, roadkill was recorded for 164 days over an 11-year period (2007–2018). In total, 1,436 roadkill were recorded from 13,008 km driven of a 164.42 km Nairobi-Mombasa road representing 0.11 collisions per kilometer. The majority of roadkill were small to medium sized mammals (<15kg) (53%; n = 756), whereas birds comprised 32% (n = 460), reptiles 10% (n = 143), with the remaining 5% (n = 77) being large mammals (>15kg). Of the 460 birds recorded, 264 were identifiable represented by 62 species. All large mammals comprising 10 species were identified, including the African elephant, Loxodonta africana and the endangered African wild dog, Lycaon pictus. Thirteen species of small mammal were also identified dominated by Kirk's dik-dik (Madoqua kirkii). Reptiles were represented by 11 species which were identified to the species level. Roadkill hotspots were identified using a kernel density method. The spatial distribution of roadkill was associated with adjacent shrub vegetation and proximity to permanent and seasonal rivers, and differences in seasonality and habitats were observed. Roadkill was lowest on road sections that traversed settled areas as opposed to roads adjacent to the protected areas. The results demonstrate that roadkill for two of the taxonomic groups - mammals and birds - appear high with numerous species detected in the Tsavo Conservation Area. These results can be used to focus efforts to reduce wildlife mortality by guiding future mitigation efforts.

Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study

Background

Leprosy is known to be unevenly distributed between and within countries. High risk areas or ‘hotspots’ are potential targets for preventive interventions, but the underlying epidemiologic mechanisms that enable hotspots to emerge, are not yet fully understood. In this study, we identified and characterized leprosy hotspots in Bangladesh, a country with one of the highest leprosy endemicity levels globally.

Methods

We used data from four high-endemic districts in northwest Bangladesh including 20 623 registered cases between January 2000 and April 2019 (among ~ 7 million population). Incidences per union (smallest administrative unit) were calculated using geospatial population density estimates. A geospatial Poisson model was used to detect incidence hotspots over three (overlapping) 10-year timeframes: 2000–2009, 2005–2014 and 2010–2019. Ordinal regression models were used to assess whether patient characteristics were significantly different for cases outside hotspots, as compared to cases within weak (i.e., relative risk (RR) of one to two), medium (i.e., RR of two to three), and strong (i.e., RR higher than three) hotspots.

Results

New case detection rates dropped from 44/100 000 in 2000 to 10/100 000 in 2019. Statistically significant hotspots were identified during all timeframes and were often located at areas with high population densities. The RR for leprosy was up to 12 times higher for inhabitants of hotspots than for people living outside hotspots. Within strong hotspots (1930 cases among less than 1% of the population), significantly more child cases (i.e., below 15 years of age) were detected, indicating recent transmission. Cases in hotspots were not significantly more likely to be detected actively.

Conclusions

Leprosy showed a heterogeneous distribution with clear hotspots in northwest Bangladesh throughout a 20-year period of decreasing incidence. Findings confirm that leprosy hotspots represent areas of higher transmission activity and are not solely the result of active case finding strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-021-00817-4.

Spatial clustering of fourteen tick species across districts of Zimbabwe

Background

Ticks transmit several diseases that result in high morbidity and mortality in livestock. Tick-borne diseases are an economic burden that negatively affect livestock production, cost countries billions of dollars through vaccine procurement and other disease management efforts. Thus, understanding the spatial distribution of tick hotspots is critical for identifying potential areas of high tick-borne disease transmission and setting up priority areas for targeted tick disease management. In this study, optimised hotspot analysis was applied to detect hotspots and coldspots of 14 common tick species in Zimbabwe. Data on the spatial distribution of tick species were obtained from the Epidemiology Unit of the Division of Veterinary Field Services of Zimbabwe.

Results

A total of 55,133 ticks were collected with Rhipicephalus decoloratus being the most common species (28.7%), followed by Amblyomma hebraeum (20.6%), and Rhipicephalus sanguineus sensu lato (0.06%) being the least common species. Results also showed that tick hotspots are species-specific with particular tick species occupying defined localities in the country. For instance, Amblyomma variegatum, Rhipicephalus appendiculatus, Rhipicephalus decoloratus, Rhipicephalus compostus, Rhipicephalus microplus, Rhipicephalus pravus, and Rhipicephalus simus were concentrated in the north and north eastern districts of the country. In contrast, Amblyomma hebraeum, Hyalomma rufipes, Hyalomma trancatum and Rhipicephalus evertsi evertsi were prevalent in the southern districts of Zimbabwe.

Conclusion

The occurrence of broadly similar hotspots of several tick species in different districts suggests presence of spatial overlaps in the niche of the tick species. As ticks are vectors of several tick-borne diseases, there is high likelihood of multiple disease transmission in the same geographic region. This study is the first in Zimbabwe to demonstrate unique spatial patterns in the distribution of several tick species across the country. The results of this study provide an important opportunity for the development of spatially-targeted tick-borne disease management strategies.

Proximity to international airports and early transmission of COVID-19 in the United States-An epidemiological assessment of the geographic distribution of 490,000 cases

Background

Identifying hotspots in a pandemic is essential for early containment. In the context of the rapid global dissemination of the Covid-19 pandemic, describing viral infection rates in relation to international air travel early during the pandemic can help inform future public health policy. The objective of this study is to determine whether proximity to an international airport predicted higher infection rates during the early phase of the Covid-19 pandemic in the United States (US).

Methods

In this cross-sectional study, the authors examined the incidence of Covid-19 in areas near US international airports in the first weeks after detection of Covid-19 in all 50 states, using publicly available county-level incidence of Covid-19 data. They performed a multiple regression to determine the relative effects of population density and air traffic in the Counties Containing Airports (CCA) and the number of Covid-19 cases, and determined the odds of Covid-19 in CCA compared to the rest of the state.

Results

Multiple regression analysis revealed that air traffic was significantly correlated with Covid-19 cases during the initial phase of pandemic while population density was not significantly correlated. Three weeks into the pandemic, the pooled odds of Covid-19 cases in CCA was 2.66 (95% CI [2.64, 2.68], p < 0.0001).

Conclusions

The counties in the US containing international airports represented initial hotspots for Covid-19 transmission. Early public health containment efforts focused on these areas may help mitigate disease transmission during future similar novel respiratory virus epidemics.

A 10-year bibliometric analysis of osteosarcoma and cure from 2010 to 2019

BACKGROUND

In recent decades, the 5-year survival rate of osteosarcoma remains poor, despite the variety of operations, and exploration of drug therapy has become the key to improvement. This study investigates the contribution of different aspects in osteosarcoma and cure, and predicts research hotspots to benefit future clinical outcomes.

METHODS

The Web of Science and PubMed databases were queried to collect all relevant publications related to osteosarcoma and cure from 2009 to 2019. These data were imported into CiteSpace and the Online Analysis Platform of Literature Metrology for bibliometric analysis. Bi-clustering was performed on Bibliographic Item co-occurrence Matrix Builder (BICOMB) and gCLUTO to identify hotspots. Additionally, completed clinical trials on osteosarcoma with results past phase II were collated.

RESULTS

A total of 2258 publications were identified in osteosarcoma and cure from 2009 to 2019. China has the largest number of publications (38.49%), followed by the United States (23.03%) with the greatest impact (centrality = 0.44). The centrality of most institutions is < 0.1, and Central South University and Texas MD Anderson Cancer Center possess the highest average citation rates of 3.25 and 2.87. BMC cancer has the highest average citation rate of 3.26 in 772 journals. Four authors (Picci P, Gorlick R, Bielack SS and Bacci G) made the best contributions. We also identified eight hotspots and collected 41 clinical trials related to drug research on osteosarcoma.

CONCLUSIONS

The urgent need exists to strengthen global academic exchanges. Overcoming multidrug resistance in osteosarcoma is the focus of past, present and future investigations. Transformation of the metastasis pattern, microenvironment genetics mechanism, alternative methods of systemic chemotherapy and exploration of traditional Chinese medicine is expected to contribute to a new upsurge of research.

Uncertainty in critical source area predictions from watershed-scale hydrologic models

Watershed-scale hydrologic models are frequently used to inform conservation and restoration efforts by identifying critical source areas (CSAs; alternatively 'hotspots'), defined as areas that export relatively greater quantities of nutrients and sediment. The CSAs can then be prioritized or 'targeted' for conservation and restoration to ensure efficient use of limited resources. However, CSA simulations from watershed-scale hydrologic models may be uncertain and it is critical that the extent and implications of this uncertainty be conveyed to stakeholders and decision makers. We used an ensemble of four independently developed Soil and Water Assessment Tool (SWAT) models and a SPAtially Referenced Regression On Watershed attributes (SPARROW) model to simulate CSA locations for flow, phosphorus, nitrogen, and sediment within the ~17,000-km² Maumee River watershed at the HUC-12 scale. We then assessed uncertainty in CSA simulations determined as the variation in CSA locations across the models. Our application of an ensemble of models - differing with respect to inputs, structure, and parameterization - facilitated an improved accounting of CSA prediction uncertainty. We found that the models agreed on the location of a subset of CSAs, and that these locations may be targeted with relative confidence. However, models more often disagreed on CSA locations. On average, only 16%-46% of HUC-12 subwatersheds simulated as a CSA by one model were also simulated as a CSA by a different model. Our work shows that simulated CSA locations are highly uncertain and may vary substantially across models. Hence, while models may be useful in informing conservation and restoration planning, their application to identify CSA locations would benefit from comprehensive uncertainty analyses to avoid inefficient use of limited resources.

Predicting potential residues associated with lung cancer using deep neural network

Lung cancer is a prominent type of cancer, which leads to high mortality rate worldwide. The major lung cancers lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) occur mainly due to somatic driver mutations in proteins and screening of such mutations is often cost and time intensive. Hence, in the present study, we systematically analyzed the preferred residues, residues pairs and motifs of 4172 disease prone sites in 195 proteins and compared with 4137 neutral sites. We observed that the motifs LG, QF and TST are preferred in disease prone sites whereas GK, KA and ISL are predominant in neutral sites. In addition, Gly, Asp, Glu, Gln and Trp are preferred in disease prone sites whereas, Ile, Val, Lys, Asn and Phe are preferred in neutral sites. Further, utilizing deep neural networks, we have developed a method for predicting disease prone sites with amino acid sequence based features such as physicochemical properties, conservation scores, secondary structure and di and tri-peptide motifs. The model is able to predict the disease prone sites at an accuracy of 81 % with sensitivity, specificity and AUC of 82 %, 78 % and 0.91, respectively, on 10-fold cross-validation. When the model was tested with a set of 417 disease-causing and 413 neutral sites, we obtained an accuracy and AUC of 80 % and 0.89, respectively. We suggest that our method can serve as an effective method to identify the disease causing and neutral sites in lung cancer. We have developed a web server CanProSite for identifying the disease prone sites and it is freely available at-https://web.iitm.ac.in/bioinfo2/CanProSite/.

Increase in HIV-1-transmitted drug resistance among ART-naïve youths at the China-Myanmar border during 2009 ~ 2017

Background

HIV-transmitted drug resistance (TDR) is found in antiretroviral therapy (ART)-naïve populations infected with HIV-1 with TDR mutations and is important for guiding future first- and second-line ART regimens. We investigated TDR and its effect on CD4 count in ART-naïve youths from the China-Myanmar border near the Golden Triangle to better understand TDR and effectively guide ART.

Methods

From 2009 to 2017, 10,832 HIV-1 infected individuals were newly reported along the Dehong border of China, 573 ART-naïve youths (16 ~ 25 y) were enrolled. CD4 counts were obtained from whole blood samples. HIV pol gene sequences were amplified from RNA extracted from plasma. The Stanford REGA program and jpHMM recombination prediction tool were used to determine genotypes. TDR mutations (TDRMs) were analyzed using the Stanford Calibrated Population Resistance tool.

Results

The most common infection route was heterosexuals (70.51%), followed by people who inject drugs (PWID, 19.20%) and men who have sex with men (MSM) (8.90%). The distribution of HIV genotypes mainly included the unique recombinant form (URF) (44.08%), 38.68% were CRFs, 13.24% were subtype C and 4.04% were subtype B. The prevalence of TDR increased significantly from 2009 to 2017 (3.48 to 9.48%) in ART-naïve youths (4.00 to 13.16% in Burmese subjects, 3.33 to 5.93% in Chinese subjects), and the resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs), nucleoside and nucleotide reverse transcriptase inhibitors (NRTIs), and protease inhibitors (PIs) were 3.49, 2.62, and 0.52%, respectively. Most (94.40%, n = 34) of HIV-1-infected patients with TDRM had mutation that conferred resistance to a single drug class. The most common mutations Y181I/C and K103N, were found in 7 and 9 youths, respectively. The mean CD4 count was significantly lower among individuals with TDRMs (373/mm³ vs. 496/mm³, p = 0.013).

Conclusions

The increase in the prevalence of HIV-1 TDR increase and a low CD4 count of patients with TDRMs in the China-Myanmar border suggests the need for considering drug resistance before initiating ART in HIV recombination hotspots.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-05794-5.

Landscape of meiotic crossovers in Hericium erinaceus

Meiotic crossover shows marked interspecific and intraspecific variation, and knowledge about the molecular mechanism of crossover variation remains limited. Herein, we described the genome-wide scanning of crossover in one mushroom-forming fungus Hericium erinaceus. Utilizing the whole-genome single-nucleotide polymorphism (SNP) data-sets of a 127 F₁ haploid progeny, we localized a total of 1316 crossover events and found that they were more likely to occur in the genic than intergenic regions. More than 30 % of the crossovers were concentrated in 59 crossover hotspots that were preferentially located close to chromosome ends. We then examined the genomic features around crossover hotspots. Results showed that the crossover hotspots were associated with increased gene density and guanine-cytosine (GC) content. An 8-bp GC-rich motif (GCGTCAGC) was found to be significantly enriched in these hotspots. The presence of mating-type loci affected the crossover at local scale rather than the overall crossover number. In order to dissect the genetic mechanisms shaping crossover variation, we then conducted quantitative trait locus (QTL) mapping for the total crossovers (TCO) and the crossover events that solely occurred within hotspots (HCO). Genome-wide QTL scanning identified four TCO-QTLs and two HCO-QTLs, which all located within or next to the crossover-hotspots. Crossover variations were shaped by multiple small-effect loci, with individual QTL contributing 6.9 %-11.7 % of variation. A few recombination pathway genes, including Spo11, Msh5, and Smc5 were found to be co-localized with the mapped crossover QTLs. Taken together, findings of this study offer insights into the crossover distribution and genetic factors conferring crossover variation in H. erinaceus, and advance our understandings for meiotic recombination in mushroom-forming fungi.

Visualization and bibliometric analysis of cAMP signaling system research trends and hotspots in cancer

Cyclic adenosine monophosphate (cAMP) is an essential second messenger that widely distributed among prokaryotic and eukaryotic organisms. cAMP can regulate various biological processes, including cell proliferation, differentiation, apoptosis and immune functions. Any dysregulation or alteration of cAMP signaling may cause cell metabolic disorder, immune dysfunction and lead to disease or cancer. This study aimed to conduct a scientometric analysis of cAMP signaling system in cancer field, and explored the research trend, hotspots and frontiers from the past decade. Relevant literatures published from 2009 to 2019 were collected in the Web of Science Core Collection database. EndNote X9 was used to remove duplicate articles, and irrelevant articles were manually filtered. Bibliometric analyses were completed by CiteSpace V. A total of 4306 articles were included in this study. The number of related literatures published each year is gradually increasing. Most of them belong to “Biochemistry & Molecular Biology”, “Oncology”, “Cell Biology”, “Pharmacology & Pharmacy” and “Endocrinology & Metabolism” areas. In the past decade, USA, China, and Japan contributed the most to the research of cAMP signaling system in cancer. The frontiers and hotspots of cAMP signaling pathway system related to cancer fields mainly focused on cancer cell apoptosis, metastasis, and multiple tumors occurrence in patients with Carney complex. Intervention of the cAMP metabolic pathway may be a potential and promising therapeutic strategy for controlling clinical cancer and tumor diseases.

Hotspots and correlates of soil-transmitted helminth infections in a Venezuelan rural community: Which are the "wormy" houses?

OBJECTIVES

To determine spatial clustering and risk factors for occurrence and intensity of infection for soil-transmitted helminthiasis (STH), namely Ascaris lumbricoides, Trichuris trichiura, Strongyloides stercoralis and hookworms in a Venezuelan rural community.

METHODS

MIF-fixed faecal samples were individually collected for STH testing. The Getis-Ord statistic was used to determine significant STH clustering within 25/50/100 m radiuses around houses. Individual- and house-level factors associated with STH occurrence and intensity of infection were determined using generalized estimating equations.

RESULTS

Significant clusters of "wormy" houses for one or multiple parasites were found at distances of 25-50 m around 13 houses. Risk factors differed between occurrence and intensity of infection. Overcrowding in the house increased occurrence of S. stercoralis, T. trichiura and hookworm infections, while poor housing conditions increased A. lumbricoides infection risk. Overcrowding, poor faecal disposal system, economic dependency and lack of basic services differentially influenced the STHs. The "wormy" houses were mainly those built with waste materials, under economic dependency and lacking indoor water supply.

CONCLUSIONS

STH distribution in a community is clustered, with significant hotspots of STH occurrence and intensity of infection and different associated risk factors. Targeting the "wormy" houses is expected to affect STH morbidity more efficiently.

Forest malaria in Cambodia: the occupational and spatial clustering of Plasmodium vivax and Plasmodium falciparum infection risk in a cross-sectional survey in Mondulkiri province, Cambodia

Background

After a marked reduction in malaria burden in Cambodia over the last decades, case numbers increased again in 2017–2018. In light of the national goal of malaria elimination by 2025, remaining pockets of high risk need to be well defined and strategies well-tailored to identify and target the persisting burden cost-effectively. This study presents species-specific prevalence estimates and risk stratification for a remote area in Cambodia.

Methods

A cross-sectional survey was conducted in 17 villages in the high-incidence province Mondulkiri in the dry season (December 2017 to April 2018). 4200 randomly selected participants (2–80 years old) were tested for Plasmodium infection by PCR. Risk of infection was associated with questionnaire-derived covariates and spatially stratified based on household GPS coordinates.

Results

The prevalence of PCR-detectable Plasmodium infection was 8.3% (349/4200) and was more than twice as high for Plasmodium vivax (6.4%, 268) than for Plasmodium falciparum (3.0%, 125, p < 0.001). 97.8% (262/268) of P. vivax and 92.8% (116/125, p < 0.05) of P. falciparum infections were neither accompanied by symptoms at the time of the interview nor detected by microscopy or RDT. Recent travels to forest sites (aOR 2.17, p < 0.01) and forest work (aOR 2.88, p < 0.001) were particularly strong risk factors and risk profiles for both species were similar. Large village-level differences in prevalence of Plasmodium infection were observed, ranging from 0.6% outside the forest to 40.4% inside. Residing in villages at the forest fringe or inside the forest compared to outside was associated with risk of infection (aOR 2.14 and 12.47, p < 0.001). Villages inside the forest formed spatial hotspots of infection despite adjustment for the other risk factors.

Conclusions

Persisting pockets of high malaria risk were detected in forested areas and in sub-populations engaging in forest-related activities. High levels of asymptomatic infections suggest the need of better case detection plans and the predominance of P. vivax the implementation of radical cure. In villages inside the forest, within-village exposure was indicated in addition to risk due to forest activities. Village-level stratification of targeted interventions based on forest proximity could render the elimination efforts more cost-effective and successful.

Computational methods for detecting cancer hotspots

Cancer mutations that are recurrently observed among patients are known as hotspots. Hotspots are highly relevant because they are, presumably, likely functional. Known hotspots in BRAF, PIK3CA, TP53, KRAS, IDH1 support this idea. However, hundreds of hotspots have never been validated experimentally. The detection of hotspots nevertheless is challenging because background mutations obscure their statistical and computational identification. Although several algorithms have been applied to identify hotspots, they have not been reviewed before. Thus, in this mini-review, we summarize more than 40 computational methods applied to detect cancer hotspots in coding and non-coding DNA. We first organize the methods in cluster-based, 3D, position-specific, and miscellaneous to provide a general overview. Then, we describe their embed procedures, implementations, variations, and differences. Finally, we discuss some advantages, provide some ideas for future developments, and mention opportunities such as application to viral integrations, translocations, and epigenetics.

Research trends and hotspots of high tibial osteotomy in two decades (from 2001 to 2020): a bibliometric analysis

BACKGROUND

The purpose of this study is to comprehensively analyze the global application trend of high tibial osteotomy (HTO) and identify promising research hotspots of HTO based on bibliometrics and visual analysis.

METHODS

Publications (articles and reviews) related to HTO from 2001 to 2020 were retrieved from the Web of Science Core Collection database (WOSCC). The country, institution, year, author, journal, average citations per item, H index, title, abstract, keywords of publication, and the top 10 cited articles were extracted and analyzed in detail. The VOSviewer software was used to analyze theco-occurrence of keywords to predict the hotspots of HTO.

RESULTS

A total of 1883 articles were included. In the past 20 years, the number of HTO articles has shown an increasing trend in general. The top 3 countries (the USA, Germany, and South Korea) accounted for 49.547% of all articles published. The USA has the largest number of publications. The University of Western Ontario is the largest contributor. The Knee Surgery Sports Traumatology Arthroscopy is the most influential journal. Professors Saito T and Imhoff AB are the leading scholars who made great achievements in the HTO field. The research direction can be divided into the following 5 clusters: "prognosis and outcome", "HTO combined with cartilage restoration techniques", "animal experimental research", "study on bone union and plate fixation at osteotomy", and "surgical technique research".

CONCLUSION

In terms of the trend of previous years, an increasing number of literatures related to HTO will be published in the future. The USA is a world leader in the field of HTO. South Korea presented great potential in this area. HTO combined with cartilage restoration techniques, postoperative prognosis and outcome, and surgical technique research may be the future hotspots in the field of HTO research.

Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: Integration of GIS, spatial statistics and machine learning algorithms

•

Lower respiratory infections (LRI) are the cause of a significant number of hospitalizations in the US.

•

No previous nationwide study examined geographic variations of LRI mortality rates and their association with underlying factors.

•

There was a shift in the location of LRI hotspots from west coast to southeast over time.

•

Decision tree classifiers could predict LRI mortality hotspots with high accuracies.

•

Higher spring temperature and increased precipitation during winter were among the most substantial predictors of presence or absence of LRI hotspots.

Objective

Although lower respiratory infections (LRI) are among the leading causes of mortality in the US, their association with underlying factors and geographic variation have not been adequately examined.

Methods

In this study, explanatory variables (n = 46) including climatic, topographic, socio-economic, and demographic factors were compiled at the county level across the continentalUS.Machine learning algorithms - logistic regression (LR), random forest (RF), gradient boosting decision trees (GBDT), k-nearest neighbors (KNN), and support vector machine (SVM) - were employed to predict the presence/absence of hotspots (P < 0.05) for elevated age-adjusted LRI mortality rates in a geographic information system framework.

Results

Overall, there was a historical shift in hotspots away from the western US into the southeastern parts of the country and they were highly localized in a few counties. The two decision tree methods (RF and GBDT) outperformed the other algorithms (accuracies: 0.92; F1-scores: 0.85 and 0.84; area under the precision-recall curve: 0.84 and 0.83, respectively). Moreover, the results of the RF and GBDT indicated that higher spring minimum temperature, increased winter precipitation, and higher annual median household income were among the most substantial factors in predicting the hotspots.

Conclusions

This study helps raise awareness of public health decision-makers to develop and target LRI prevention programs.