A novel approach for credit card fraud transaction detection using deep reinforcement learning scheme.

Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training.

Investigating the causal relationship between human blood/urine metabolites and periodontal disease using two-sample Mendelian randomization.

Background and Aims: The aim is to investigate the cause-and-effect connection between metabolites found in blood/urine and the likelihood of developing periodontal disease (PD) through the utilization of a two-sample Mendelian randomization (MR) method. Methods: Using an inverse variance weighted (IVW) method and two additional two-sample MR models, we examined the relationship between blood/urine metabolites and PD by analyzing data from a comprehensive metabolome-based genome-wide association study and the Genome-Wide Association Studies (GWAS) of PD. To assess the consistency and dependability of the findings, diversity, cross-effects, and sensitivity analyses were conducted. Results: Out of the 35 metabolites found in blood and urine, a total of eight metabolites (C-reactive protein, Potassium in urine, Urea, Cystatin C, Non-albumin protein, Creatinine, estimated Glomerular Filtration Rate, and Phosphate) displayed a possible causal connection with the risk of dental caries/PD using the inverse variance weighted (IVW) method (p < 0.05). This includes five metabolites in the blood and three in the urine. No metabolites were statistically significant in IVW MR models (p < 3.68 × 10- 4). Even after conducting sensitivity analysis with the leave-one-out method and removing the confounding instrumental variables, the impact of these factors on dental caries/PD remained significant. Conclusion: Based on the available evidence, it is not possible to establish a significant causal link between the 35 blood metabolites and the likelihood of developing dental caries and PD.

Frequency tripled semiconductor disk laser with over 0.5 W ultraviolet output power.

Semiconductor disk lasers can produce high output power and good beam quality simultaneously. The high intracavity circulating power of about hundreds of watts, along with the flexibility of tailorable emitting wavelengths, make it an attractive light source for obtaining ultraviolet (UV) radiation from near-infrared lasers through nonlinear frequency conversion. This work reports a frequency tripled 327 nm semiconductor disk laser with record output power and wavelength tuning range by using a type-I phase-matched LiB3O5 (LBO) crystal and a type-I phase-matched ß-BaB2O4 (BBO) crystal as the frequency-doubling and -tripling crystals respectively. Thanks to the obviously larger nonlinear coefficient of the type-I phase-matched BBO compared to the commonly used type-II phase-matched LBO, as well as the small spot size specifically designed at the crystal location, the maximum output power of UV lasers reaches 538 mW, corresponding to an optical-to-optical conversion efficiency from pump to UV laser of about 1.14%. A wavelength tuning range of about 8.6 nm and good power stability with a standard deviation of about 0.94 are also achieved.

[Clinical study of modified suspension reduction method combined with percutaneous vertebroplasty in the treatment of thoracolumbar osteoporotic compression fracture].

OBJECTIVE: To investigate the clinical effect of modified suspension reduction method combined with percutaneous vertebroplasty in the treatment of osteoporotic thoracolumbar compression fractures. METHODS: From February 2020 to October 2021, 92 patients with thoracolumbar osteoporotic compression fracture were treated by percutaneous vertebroplasty. According to different treatment methods, they were divided into the observation group and the control group. The observation group was treated with modified suspension reduction and then percutaneous vertebroplasty, while the control group was treated with percutaneous vertebroplasty alone. The observation group (47 cases), including 20 males and 27 females, the age ranged from 59 to 76 years old with an average of (69.74±4.50) years old, fractured vertebral bodies:T10(2 cases), T11(7 cases), T12(19 cases), L1(14 cases), L2(5 cases);the control group(45 cases), including 21 males and 24 females, the age ranged from 61 to 78 years old with an average of (71.02±3.58) years old, fractured vertebral bodies:T10(3 cases), T11(8 cases), T12(17 cases), L1(12 cases), L2(5 cases);The leakage of bone cement were observed, the visual analogue scale (VAS), Oswestry lumbar dysfunction index (ODI), anterior vertebrae height (AVH), Cobb angle of kyphosis and the amount of bone cement injected before and after operation were recorded and compared between the two groups. RESULTS: All patients were followed up, ranged from 6 to10 with an average of (8.45±1.73) months. Two patients ocurred bone cement leakage in observation group and 3 patients in control group. AVH of observation group increased (P<0.05) and Cobb angle of injured vertebrae decreased (P<0.05). Cobb angle of injured vertebrae and AVH of the control group were not significantly changed (P>0.05). Cobb angle of injured vertebrae of the observation group was lower than that of control group (P<0.05) and AVH was higher than that of the control group (P<0.05). In the observation group, VAS before operation and 1 week, 3 and 6 months after operation respectively were(7.32±1.05) scores, (3.56±1.18) scores, (1.83±0.67) scores, (1.27±0.34) scores, and ODI were(40.12±14.69) scores, (23.76±10.19) scores, (20.15±6.39) scores, (13.45±3.46) scores. In the control group, VAS before operation and 1 week, 3 and 6 months after operation respectively were(7.11±5.26) scores, (3.82±0.68) scores, (1.94±0.88) scores, (1.36±0.52) scores, and ODI were(41.38±10.23) scores, (25.13±14.22) scores , (20.61±5.82) scores, (14.55±5.27) scores . The scores of VAS and ODI after operation were lower than those before operation (P<0.05), but there was no significant difference between the two groups (P<0.05). CONCLUSION: Modified suspension reduction method combined with PVP surgery for osteoporotic thoracolumbar compression fractures has achieved good clinical results, which can effectively relieve lumbar back pain, restore vertebral height, correct kyphosis, improve lumbar function and patients' quality of life.

Randomness-Enhanced Expressivity of Quantum Neural Networks.

As a hybrid of artificial intelligence and quantum computing, quantum neural networks (QNNs) have gained significant attention as a promising application on near-term, noisy intermediate-scale quantum devices. Conventional QNNs are described by parametrized quantum circuits, which perform unitary operations and measurements on quantum states. In this Letter, we propose a novel approach to enhance the expressivity of QNNs by incorporating randomness into quantum circuits. Specifically, we introduce a random layer, which contains single-qubit gates sampled from a trainable ensemble pooling. The prediction of QNN is then represented by an ensemble average over a classical function of measurement outcomes. We prove that our approach can accurately approximate arbitrary target operators using Uhlmann's theorem for majorization, which enables observable learning. Our proposal is demonstrated with extensive numerical experiments, including observable learning, Rényi entropy measurement, and image recognition. We find the expressivity of QNNs is enhanced by introducing randomness for multiple learning tasks, which could have broad application in quantum machine learning.

Self-regulated underwater phototaxis of a photoresponsive hydrogel-based phototactic vehicle.

Incorporating a negative feedback loop in a synthetic material to enable complex self-regulative behaviours akin to living organisms remains a design challenge. Here we show that a hydrogel-based vehicle can follow the directions of photonic illumination with directional regulation inside a constraint-free, fluidic space. By manipulating the customized photothermal nanoparticles and the microscale pores in the polymeric matrix, we achieved strong chemomechanical deformation of the soft material. The vehicle swiftly assumes an optimal pose and creates directional flow around itself, which it follows to achieve robust full-space phototaxis. In addition, this phototaxis enables a series of complex underwater locomotions. We demonstrate that this versatility is generated by the synergy of photothermofluidic interactions resulting in closed-loop self-control and fast reconfigurability. The untethered, electronics-free, ambient-powered hydrogel vehicle manoeuvres through obstacles agilely, following illumination cues of moderate intensities, similar to that of natural sunlight.

A health status estimation method based on interpretable neural network observer for HVs.

Estimating the health status is a crucial step in learning about the health of hypersonic vehicles beforehand. The estimation results can be used to detect abnormal states and provide data reference for fault diagnosis. However, certain conventional neural network-based estimate techniques rely heavily on data and have limited model interpretability, which challenges the accuracy of the estimation results. This research aims to address the problems of data dependency and model interpretability in estimation models. In this study, a block interpretable neural network model with constraints on the trajectory and attitude equations is established. On the basis of the interpretable neural network model, two health status estimation methods are proposed: one that is unsupervised and the other that is supervised. Additionally, in the supervised health status estimate approach, an FC-LN-Mish structure is created to fit the relationship between the fault residual and the fault state parameters. The results indicate that the proposed estimation methods can fit the system mechanism relationship more accurately, improve the model interpretability, reduce data dependency, and ensure high estimation efficiency and precision. The FC-LN-Mish structure can reduce the missed detection rate and false detection rate to some extent, and perform better than other models under the low fault deviation degree. In conclusion, the interpretable neural network model-based observers accurately observe the health status parameters of rudders and RCS, reduce data dependence and data processing costs, and have better performance under high uncertainty interference. It provides effective method for online health estimation.

Association of Sleep Duration with Tooth Loss and Periodontitis: Insights from the National Health and Nutrition Examination Surveys (2005-2020).

OBJECTIVE: Sleep disorders such as insomnia, apnea, and restless leg syndrome can negatively affect a person's overall health and may cause hypertension, heart failure, and coronary heart disease. Likewise, periodontitis, a gum disease, can lead to both physical and psychological health issues, exerting a considerable effect on one's overall well-being-periodontitis stands as a primary cause of tooth loss. Nevertheless, there has been insufficient research on the correlation between the amount of sleep individuals get and the occurrence of periodontitis/tooth loss among Americans. Therefore, this study aimed to assess the influence of sleep length on periodontitis in the American population. METHODS: Periodontitis severity was classified (none, mild, moderate, and severe) using American Periodontal Association criteria. Sleep duration was assessed by self-reported data and categorized into three groups (deficient, adequate, and excessive). Tooth loss was assessed by the oral examination. To establish a connection between the duration of sleep and periodontitis/tooth loss, a weighted multivariable logistic regression analysis was employed. A GAM analysis and smooth curve fitting assessment were conducted to identify non-linear relationships. Subgroup, interaction, and mediation analyses were also performed. RESULTS: The prevalence of tooth loss was significantly high, affecting 96.4% of the individuals, whereas 46.6% of the study sample experienced moderate to severe periodontitis. The average age of participants was 52.7 years. After adjusting for potential confounding factors, the analysis of weighted multivariable logistic regression revealed a significant association between sleep insufficiency and moderate/severe periodontitis (OR 1.15, 95% CI 1.01-1.30, P = 0.0298), as well as tooth loss (OR 1.16, 95% CI 1.01-1.33, P = 0.0371). Additionally, the research showed a correlation between the length of sleep and periodontitis that followed a U-shaped pattern. In addition, the analysis of mediation revealed that high blood pressure explained 7.0% (95% CI 4.0% to 12.9%; P < 0.0001) of the link between the amount of sleep and the likelihood of losing teeth. CONCLUSION: Sleep duration was independently correlated with moderate/severe periodontitis/tooth loss and had a non-linear relationship.

Association between muscle quality index and periodontal disease among American adults aged ≥ 30 years: a cross-sectional study and mediation analysis.

OBJECTIVE: The muscle quality index (MQI) is a measurement of muscle quality that is directly related to overall health. There has been little study on the relationship between the muscle quality index and periodontitis in American people beyond 30 years. Therefore, this study aimed to explore the link between periodontitis and Muscle quality index (MQI) in older Americans. METHODS: Three thousand two hundred fifty-eight individuals (aged 30 to 59) who participated in the National Health and Nutrition Examination Survey (NHANES) 2011-2014 were considered eligible for the cross-sectional investigation. A hand dynamometer was used to determine the handgrip strength (HGS). Dual-energy X-ray absorptiometry was employed to calculate ASM (DXA). MQIArm was calculated by dividing the dominant hand's HGS by the dominant arm's ASM (in kg/kg). MQIApp was calculated by dividing the dominant hand's HGS by the ASM (in kg/kg). MQItotal was calculated by dividing the sum of the dominant and non-dominant hands by the ASM (in kg/kg). To investigate the link between muscle quality index and periodontal disease, the weighted multivariable logistic regression models were used. Using generalized additive models, it was determined if a nonlinear connection existed. Then, we developed a two-piece linear regression model and calculated the inflection point using a recursive approach. A mediation study was performed to determine how much of the impact of MQItotal on periodontitis was mediated by potential variables. RESULTS: Three thousand two hundred fifty-eight participants from the United States were enrolled. The OR (95% CI) for the relationship between MQItotal and periodontitis in the regression model with fully adjusted variables was 0.69 (0.53-0.91), for the connection between MQIArm and periodontitis was 0.90 (0.84-0.97), and for the association between MQIApp and periodontitis was 0.49 (0.30-0.80). MQItotal and periodontitis were shown to have a J-shaped relationship with a change point of 3.64. Before the change point, the OR (95% CI) was 0.69 (0.58, 0.82). In the analysis of drinking and married status, the interaction was statistically significant. Analysis of mediation showed that alcohol use was responsible for 0.4% (0.10 to 1.2) of the effect of MQItotal on periodontitis. CONCLUSION: In American adults aged over 30, the Muscle Quality Index (MQI) exhibited an independent negative correlation with moderate to severe periodontitis, demonstrating a J-shaped relationship. Furthermore, alcohol consumption may act as a mediator in the association between MQI and periodontitis.

Silver Nanowire Reinforced Conductive and Injectable Colloidal Gel for Effective Wound Healing via Electrical Stimulation.

The remarkable biocapacity, injectability, and adaptability of colloidal gels have led to their widespread usage in tissue engineering as irregular defect implants. However, multifunctionalities including electroconductivity and antibacterial property are highly required for colloidal gels. In addition, the inherently weak mechanical property of physically crosslinked colloidal gels limits their application. Herein, we present Ag nanowires (Ag NWs)-reinforced colloidal gels composed of biocompatible gelatin nanoparticles and polydopamine-modified Ag NWs through the controlled electrostatic assembly, which are injectable and conductive. One-dimensional Ag NWs can significantly improve the mechanical and electrical properties of the colloidal gel while maintaining its inherent excellent injectability. Owing to the network of Ag NWs, the storage modulus and conductivity of the optimized Ag NW colloidal gel are 7.5 and 13 times higher, respectively, than those of the colloidal gel made up of polydopamine-modified Ag nanoparticles with equivalent Ag concentration. Furthermore, this Ag NW colloidal gel can adapt to sharp wounds on skin, which accelerates the healing of a MRSA-infected wound via electrical stimulation. This article is protected by copyright. All rights reserved.

Pyroptosis-related gene signatures are associated with prognosis and tumor microenvironment infiltration in head and neck cancer.

Background and Aims: Recent studies have highlighted the biological significance of pyroptosis in cancer development. Nevertheless, it is still uncertain if pyroptosis also plays a part in immune modulation and the creation of the tumor microenvironment (TME). Methods: The pyroptosis regulatory genes (PRGs) were comprehensively assessed in 1938 head and neck cancer samples, and systematically correlated these modification patterns with the infiltration characteristics of TME cells. The unsupervised consensus analysis method was used to identify specific pyroptosis clusters. The single-sample gene set enrichment analysis and CIBERSOFT algorithms were used to evaluate the infiltration levels of various immune cell subsets. A principal component analysis algorithm was used to construct the pyrolysis potential index (PPI) to quantify the pyrolysis regulation patterns in head and neck squamous cell carcinoma (HNSC). Results: Pyrophosphate regulatory genes (PRGs) are often upregulated in tumors due to mutations. PRGs relate to various clinical outcomes and pathways. Molecular subtyping identified pyroptosis patterns, which align with three tumor immunophenotypes: immune-inflamed, immune-excluded, and immune-desert. The PPI measures pyrolysis roles, showing higher PPI in tumor samples linked to subtypes and clinical characteristics. Lower PPI correlates with longer survival, increased immune activity, more tumor mutations, high PD-L1 expression, and mutations in significant genes like PIK3CA. Such patients also experience enhanced immune responses in immunotherapy trials. Conclusion: We conducted a comprehensive examination of pyroptosis in HNSC and developed a PPI indicator that shows a strong correlation with the variety and intricacy of the TME.

Hsa-miR-26a-5p improves OSCC sensitivity to ferroptosis by inhibiting SLC7A11.

OBJECTIVE: SLC7A11 plays a crucial role in ferroptosis and is upregulated in oral squamous cell carcinoma (OSCC) samples. This study mainly aimed to elucidate the association of SLC7A11 with ferroptosis in OSCC and analyze its upstream regulatory mechanism. DESIGN: The expression of SLC7A11 in OSCC and paracancerous tissues was detected. After administration of different concentrations of erastin to OSCC cells, cell viability was examined by MTT, and changes in GSH, MDA and Fe2+ concentrations were determined. Then, mitochondrial changes were examined by transmission electron microscopy. Bioinformatics analysis was performed to predict the upstream regulatory miRNA of SLC7A11, and the interaction between miR-26a and SLC7A11 was confirmed by a dual luciferase reporter gene. The effect of miR-26a mimics on ferroptosis resistance was also examined. RESULTS: SLC7A11 expression was upregulated in both OSCC patients and cells, with high SLC7A11 expression levels in SCC-9 cells with an IC50 = 69.75 µM for erastin and low SLC7A11 expression levels in SCC-4 cells with an IC50 = 8.463 µM for erastin. SCC-9 exhibited a higher level of ferroptosis resistance than SCC-4. miR-26a-5p expression was downregulated in both OSCC patients and cells. A dual luciferase reporter assay confirmed that miR-26a-5p targets binding to the SLC7A11 3'UTR. Transfection of the miR-26a mimic significantly inhibited the viability of OSCC cells and promoted erastin-induced cellular ferroptosis. Transfection of miR-26a inhibitor gave the opposite result. Overexpression of SLC7A11 significantly reversed miR-26a mimic ferroptosis induction. CONCLUSION: miR-26a-5p can exert OSCC inhibitory effects by regulating SLC7A11 and promote ferroptosis in OSCC cells by inhibiting SLC7A11.

Relationship between periodontitis and COVID-19: A bidirectional two-sample Mendelian randomization study.

Background and Aims: Coronavirus disease (COVID-19) is a major danger to world health and has been linked to periodontitis in a number of epidemiological observational studies. However, it is unclear whether COVID-19 causes periodontitis. COVID-19's causal influence on periodontitis was determined using bidirectional Mendelian randomization (MR). Methods: Large-scale COVID-19 and periodontitis genome wide association study data were analyzed. Inverse variance weighting, MR-Egger, weighted median, and MR-PRESSO were used to estimate causal effects. Sensitivity studies were conducted using the Cochran's Q test, the MR-Egger intercept test, the MR-PRESSO, and the leave-one-out (LOO) analysis. Further investigation of potential mediating factors was performed using risk factor analysis. Results: The MR presented no causal relationship between periodontitis and hospitalization for COVID-19 (odds ratio [OR] = 0.97, 95% confidence interval [CI] 0.78-1.20; p = 0.76), vulnerability to COVID-19 (OR = 1.04, 95% CI 0.88-1.21; p = 0.65), COVID-19 disease severity (OR = 1.01, 95% CI 0.92-1.11; p = 0.81). Meanwhile, a noncausal effect of genetic hospitalization for COVID-19, illness severity, and vulnerability to periodontitis was detected. Other MR methods yielded identical results to inverse variance weighting. According to sensitivity analysis, horizontal pleiotropy is unlikely to affect causal estimation. Conclusion: Periodontitis had no link to the risk of COVID-19 hospitalization, susceptibility, or severity. However, the substance in COVID-19 that is responsible for this effect must be studied further.

Neuron-Inspired Nanofluidic Biosensors for Highly Sensitive and Selective Imidacloprid Detection.

Pesticides have caused concerns about food safety due to their residual effects in vegetables and fruits. Imidacloprid, as the frequently used neonicotinoid pesticide, could harm cardiovascular and respiratory function and cause reproductive toxicity in humans. Therefore, reliable methods for portable, selective, and rapid detection are desirable to develop. Herein, we report a neuron-inspired nanofluidic biosensor based on a tyrosine-modified artificial nanochannel for sensitively detecting imidacloprid. The functional tyrosine is modified on the outer surface of porous anodic aluminum oxide to rapidly capture imidacloprid through π-π interactions and hydrogen bonds. The integrated nanofluidic biosensor has a wide concentration range from 10-8 to 10-4 g/mL with an ultralow detection limit of 6.28 × 10-9 g/mL, which outperforms the state-of-the-art sensors. This work provides a new perspective on detecting imidacloprid residues as well as other hazardous pesticide residues in environmental and food samples.

Highly Enhanced Mechanical, Thermal, and Crystallization Performance of PLA/PBS Composite by Glass Fiber Coupling Agent Modification.

To improve the toughness and heat resistance of polylactic acid (PLA), polybutylene succinate (PBS) was sufficiently blended with PLA as the base matrix, and the glass fiber (GF) that was modified with 3-aminopropyltriethoxysilane (KF-GF) was added as the reinforcement. The results demonstrated a noteworthy boost in both mechanical and heat resistance properties when employing KH-GF, in comparison to pristine GF. When the content of KH-GF reached 20%, the tensile, flexural, and IZOD impact strength of the composites were 65.53 MPa, 83.43 MPa, and 7.45 kJ/m2, respectively, which were improved by 123%, 107%, and 189% compared to the base matrix, respectively. This enhancement was primarily attributed to the stronger interfacial adhesion between KH-GF and the PLA/PBS matrix. Furthermore, the Vicat softening temperature of the composites reached 128.7 °C, which was a result of increased crystallinity. In summary, the incorporation of KH-GF into PLA/PBS composites resulted in notable enhancements in their mechanical properties, crystallinity, and thermal characteristics. The high performance KH-GF-reinforced PLA/PBS composite showed a broad application potential in the field of biodegradable packaging, biodegradable textiles, and biodegradable plastic bags.

Skeleton engineering of rigid covalent organic frameworks to alter the number of binding sites for improved radionuclide extraction.

Crystalline porous covalent frameworks (COFs) have been considered as a platform for uranium extraction from seawater and nuclear waste. However, the role of rigid skeleton and atomically precise structures of COFs is often ignored in the design of defined binding configuration. Here, a COF with an optimized relative position of two bidentate ligands realizes full potential in uranium extraction. Compared with the para-chelating groups, the optimized ortho-chelating groups with oriented adjacent phenolic hydroxyl groups on the rigid skeleton endow an additional uranyl binding site, thereby increasing the total number of binding sites up to 150%. Experimental and theoretical results indicate that the uranyl capture is greatly improved via the energetically favored multi-site configuration and the adsorption capacity reaches up to 640 mg g-1, which exceeds that of most reported COF-based adsorbents with chemical coordination mechanism in uranium aqueous solution. This ligand engineering strategy can efficiently advance the fundamental understanding of designing the sorbent systems for extraction and remediation technology.

Quantum Similarity Testing with Convolutional Neural Networks.

The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so far remained open for continuous variable quantum systems. In this Letter, we develop a machine learning algorithm for comparing unknown continuous variable states using limited and noisy data. The algorithm works on non-Gaussian quantum states for which similarity testing could not be achieved with previous techniques. Our approach is based on a convolutional neural network that assesses the similarity of quantum states based on a lower-dimensional state representation built from measurement data. The network can be trained off-line with classically simulated data from a fiducial set of states sharing structural similarities with the states to be tested, with experimental data generated by measurements on the fiducial states, or with a combination of simulated and experimental data. We test the performance of the model on noisy cat states and states generated by arbitrary selective number-dependent phase gates. Our network can also be applied to the problem of comparing continuous variable states across different experimental platforms, with different sets of achievable measurements, and to the problem of experimentally testing whether two states are equivalent up to Gaussian unitary transformations.

Mendelian Randomisation Study on Association of Gut Microbiota and Periodontitis.

OBJECTIVE: Several studies have demonstrated the possible association between gut microbiota and periodontitis. The mechanism by which gut microbiota contribute to periodontitis remains unknown. METHODS: A 2-sample Mendelian randomisation (MR) study was conducted using publicly available Genome-Wide Association Studies (GWAS) data of European ancestry. The relationships between gut microbiota and tooth loss and periodontitis were assessed using summary-level data. Moreover, inverse variance weighted (IVW), MR-Egger, weighted median, and simple Mendelian were used. The results were further validated using sensitivity analyses. RESULTS: A total of 211 gut microbiota were studied, including 9 phyla, 16 classes, 20 orders, 35 families, and 131 genera. The IVW method identified 16 bacterial genera related to the risk of periodontitis and tooth loss. Lactobacillaceae was associated with an increased risk of periodontitis (odds ratio [OR], 1.40, 95% confidence interval [CI], 1.03-1.91, P<.001) and tooth loss (OR, 1.12; 95% CIs, 1.02-1.24, P = .002), whereas Lachnospiraceae UCG008 was linked to a lower risk of tooth loss (P = .041). There was no heterogeneity and horizontal pleiotropy in the sensitivity analysis. CONCLUSIONS: Several microorganisms were identified to be linked to the risk of periodontitis. Furthermore, the findings improved our understanding of gut microbiota and periodontitis pathology.

Integrating GWAS and proteome data to identify novel drug targets for MU.

Mouth ulcers have been associated with numerous loci in genome wide association studies (GWAS). Nonetheless, it remains unclear what mechanisms are involved in the pathogenesis of mouth ulcers at these loci, as well as what the most effective ulcer drugs are. Thus, we aimed to screen hub genes responsible for mouth ulcer pathogenesis. We conducted an imputed/in-silico proteome-wide association study to discover candidate genes that impact the development of mouth ulcers and affect the expression and concentration of associated proteins in the bloodstream. The integrative analysis revealed that 35 genes play a significant role in the development of mouth ulcers, both in terms of their protein and transcriptional levels. Following this analysis, the researchers identified 6 key genes, namely BTN3A3, IL12B, BPI, FAM213A, PLXNB2, and IL22RA2, which were related to the onset of mouth ulcers. By combining with multidimensional data, six genes were found to correlate with mouth ulcer pathogenesis, which can be useful for further biological and therapeutic research.

The causal links between gut microbiota and COVID-19: A Mendelian randomization study.

Several studies have shown a possible correlation between gut microbiota and COVID-19. However, the cause-and-effect relationship between the two has not been investigated. We conducted a two-sample Mendelian randomization study (MR) study using publicly available GWAS data. Inverse variance weighted (IVW) analysis was the main MR analysis technique and was supplemented with other sensitivity analyses. Forty-two bacterial genera were associated with COVID-19 susceptibility, hospitalization, and severity in the IVW method. Among these gut microbiota, five gut microbiota (genus unknowngenus [id.1000005472], family unknownfamily [id.1000005471], genus Tyzzerella3, order MollicutesRF9.id.11579, and phylum Actinobacteria) were significantly associated with COVID-19 hospitalization and severity. Three gut microbiota (class Negativicutes, order Selenomonadales, and class Actinobacteria) were significantly associated with COVID-19 hospitalization and susceptibility, while two microbiota (class Negativicutes and order Selenomonadales) were significantly associated with COVID-19 hospitalization and severity, and susceptibility. Sensitivity analysis did not detect any heterogeneity and horizontal pleiotropy. Our findings demonstrated that several microorganisms were causally linked to COVID-19, and improved our understanding of the relationship between gut microbiota and COVID-19 pathology.