Leveraging Mann-Whitney U test on large-scale genetic variation data for analysing malaria genetic markers

Cross Talk between MicroRNAs and Dengue Virus

Dengue fever (DF) is an endemic infectious tropical disease and is rapidly becoming a global problem. Dengue fever is caused by one of the four dengue virus (DENV) serotypes and is spread by the female Aedes mosquito. Clinical manifestations of DF may range from asymptomatic to life-threatening severe illness with conditions of hemorrhagic fever and shock. Early and precise diagnosis is vital to avoid mortality from DF. A different approach is required to combat DF because of the challenges with the vaccines currently available, which are nonspecific; each is capable of causing cross-reaction and disease-enhancing antibody responses against the residual serotypes. MicroRNAs (miRNAs) are known to be implicated in DENV infection and are postulated to be involved in most of the host responses. Thus, they might be a suitable target for new strategies against the disease. The involvement of miRNAs in cellular activities and pathways during viral infections has been explored under numerous conditions. Interestingly, miRNAs have also been shown to be involved in viral replication. In this review, we summarize the role of known miRNAs, specifically the role of miRNA Let-7c (miR-Let-7c), miR-133a, miR-30e, and miR-146a, in the regulation of DENV replication and their possible effects on the initial immune reaction.

Use of in vivo Raman spectroscopy and cryoablation for diagnosis and treatment of bladder cancer

Transurethral resection of bladder tumor (TURBT) is the first-line treatment option for non-muscle invasive bladder cancer (NMIBC), but residual tumor often remains after TURBT, thereby leading to cancer recurrence. Here, we introduce combined use of in vivo Raman spectroscopy and in vivo cryoablation as a new approach to detect and remove residual bladder tumor during TURBT. Bladder cancer (BCa) patients treated with TURBT at our urological department between Dec 2019 and Jan 2021 were collected. First, Raman signals were collected from 74 BCa patients to build reference spectra of normal bladder tissue and of bladder cancers of different pathological types. Then, another 53 BCa patients were randomly categorized into two groups, 26 patients accepted traditional TURBT, 27 patients accepted TURBT followed by Raman scanning and cryoablation if Raman detected existence of residual tumor. The recurrence rates of the two groups until Oct 2022 were compared. Raman was capable of discriminating normal bladder tissue and BCa with a sensitivity and specificity of 90.5% and 80.8 %; and discriminating invasive (T1, T2) and noninvasive (Ta) BCa with a sensitivity and specificity of 83.3 % and 87.3 %. During follow-up, 2 in 27 patients had cancer recurrence in Raman-Cryoablation group, while 8 in 26 patients had cancer recurrence in traditional TURBT group. Combined use of Raman and cryoablation significantly reduced cancer recurrence (p = 0.0394). Raman and cryoablation can serve as an adjuvant therapy to TURBT to improve therapeutic effects and reduce recurrence rate.

A comparative analysis of converters of tabular data into image for the classification of Arboviruses using Convolutional Neural Networks

Tabular data is commonly used in business and literature and can be analyzed using tree-based Machine Learning (ML) algorithms to extract meaningful information. Deep Learning (DL) excels in data such as image, sound, and text, but it is less frequently utilized with tabular data. However, it is possible to use tools to convert tabular data into images for use with Convolutional Neural Networks (CNNs) which are powerful DL models for image classification. The goal of this work is to compare the performance of converters for tabular data into images, select the best one, optimize a CNN using random search, and compare it with an optimized ML algorithm, the XGBoost. Results show that even a basic CNN, with only 1 convolutional layer, can reach comparable metrics to the XGBoost, which was trained on the original tabular data and optimized with grid search and feature selection. However, further optimization of the CNN with random search did not significantly improve its performance.

A Non-Coding Fc Gamma Receptor Cis-Regulatory Variant within the 1q23 Gene Cluster Is Associated with Plasmodium falciparum Infection in Children Residing in Burkina Faso

Antibodies play a crucial role in activating protective immunity against malaria by interacting with Fc-gamma receptors (FcγRs). Genetic variations in genes encoding FcγRs can affect immune cell responses to the parasite. In this study, our aim was to investigate whether non-coding variants that regulate FcγR expression could influence the prevalence of Plasmodium falciparum infection. Through bioinformatics approaches, we selected expression quantitative trait loci (eQTL) for FCGR2A, FCGR2B, FCGR2C, FCGR3A, and FCGR3B genes encoding FcγRs (FCGR), in whole blood. We prioritized two regulatory variants, rs2099684 and rs1771575, located in open genomic regions. These variants were identified using RegVar, ImmuNexUT, and transcription factor annotations specific to immune cells. In addition to these, we genotyped the coding variants FCGR2A/rs1801274 and FCGR2B/rs1050501 in 234 individuals from a malaria-endemic area in Burkina Faso. We conducted age and family-based analyses to evaluate associations with the prevalence of malarial infection in both children and adults. The analysis revealed that the regulatory rs1771575-CC genotype was predicted to influence FCGR2B/FCGR2C/FCGR3A transcripts in immune cells and was the sole variant associated with a higher prevalence of malarial infection in children. In conclusion, this study identifies the rs1771575 cis-regulatory variant affecting several FcγRs in myeloid and neutrophil cells and associates it with the inter-individual capacity of children living in Burkina Faso to control malarial infection.

A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites

The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis.

What is a capable guardian to older fraud victims? Comparison of younger and older victims' characteristics of online fraud utilizing routine activity theory

Objective

The paper compares victim group characteristics: we test routine activities theory to compare the differences in online fraud vulnerabilities of victims aged 18-54 and victims of 55 and above.

Methods/sample

A representative sample of US citizens 18 and above was collected in October 2020. Victims under 55 encompassed 35.3% (n = 915), victims 55 and above 12.9% (n = 334) of the total sample (n = 2,589). We utilized non-parametric statistical methods for testing whether older and younger victims' characteristics can be derived from the same independent variables.

Results

Computer time, computer familiarity, and technical guardians determine online victimization in older individuals, similarly to younger age groups. However, older victims differ in characteristics from younger victims. Seniors were less likely to apply technical guardians such as camera cover, identity theft monitoring, and credit card freeze, even after experiencing online scams. Being a single parent was a protective factor for older individuals, but having a full-time job made older individuals more prone to experience online fraud victimization compared to being retired. In addition, older victims were less likely to report scams than younger ones.

Conclusion/implications

Although this research found significant differences between older and younger fraud victims' characteristics, target suitability and capable guardianship must be further investigated and conceptualized when applying routine activities theory for online fraud against older people.

Ancient DNA suggests anaemia and low bone mineral density as the cause for porotic hyperostosis in ancient individuals

Porotic hyperostosis (PH) is a disease that had high prevalence during the Neolithic. Several hypotheses have been suggested to explain the origin of the disease, such as an iron deficiency diet, low B12 intake, malaria caused by Plasmodium spp., low haemoglobin levels or low vitamin D levels. None of these hypotheses have been tested genetically. Here, I calculated different genetic scores to test each hypothesis. Additionally, I calculated a genetic score of bone mineral density as it is a phenotype that seems to be selected in ancient Europeans. I apply these genetic scores on 80 ancient samples, 33 with diagnosed PH. The results seem to suggest anaemia and low bone mineral density as the main cause for this disease. Additionally, Neolithic individuals show the lowest genetic risk score for bone mineral density of all other periods tested here, which may explain the highest prevalence of the porotic hyperostosis during this age.

A Preclinical Investigation on the Role of IgG Antibodies against Coagulant Components in Multiple Sclerosis

The coagulation-inflammation interplay has recently been identified as a critical risk factor in the early onset of multiple sclerosis (MS), and antibodies against coagulation components have been recognized as contributing factors to thrombotic and inflammatory signaling pathways in diseases with overlapping symptoms to MS, paving the way for further research into their effects on MS pathology. The current study aimed to enlighten the role of IgG antibodies against coagulation components by performing a preclinical study, analyzing the astrocytic activation by purified IgG antibodies derived from 15 MS patients, and assessing their possible pro-inflammatory effects using a bead-based multiplexed immunoassay system. The results were compared with those obtained following astrocyte treatment with samples from 14 age- and gender-matched healthy donors, negative for IgG antibody presence. Serum samples collected from 167 MS patients and 40 age- and gender-matched controls were also analyzed for pro- and anti-inflammatory factors. According to our results, astrocytic activation in response to IgG treatment caused an upregulation of various pro-inflammatory factors, including cytokines, chemokines, and interleukins. Conversely, in serum samples from patients and controls, the pro-inflammatory factors did not differ significantly; medication may lower the levels in patients. Our findings suggest that antibodies may function as effectors in neuroinflammation and serve as targets for new treatments that eventually benefit novel therapeutic approaches.

Impact of Different Transportation Modes on the Transmission of COVID-19: Correlation and Strategies from a Case Study in Wuhan, China

Transportation is the main carrier of population movement, so it is significant to clarify how different transportation modes influence epidemic transmission. This paper verified the relationship between different levels of facilities and epidemic transmission by use of the K-means clustering method and the Mann-Whitney U test. Next, quantile regression and negative binomial regression were adopted to evaluate the relationship between transportation modes and transmission patterns. Finally, this paper proposed a control efficiency indicator to assess the differentiated strategies. The results indicated that the epidemic appeared 2-3 days earlier in cities with strong hubs, and the diagnoses were nearly fourfold than in other cities. In addition, air and road transportation were strongly associated with transmission speed, while railway and road transportation were more correlated with severity. A prevention strategy that considered transportation facility levels resulted in a reduction of the diagnoses of about 6%, for the same cost. The results of different strategies may provide valuable insights for cities to develop more efficient control measures and an orderly restoration of public transportation during the steady phase of the epidemic.