Point cloud-based retrieval for place recognition is essential in robotic applications like autonomous driving or simultaneous localization and mapping. However, this remains challenging in complex real-world scenes. Existing methods are sensitive to noisy, low-density point clouds and require extensive storage and computation, posing limitations for hardware-limited scenarios. To overcome these challenges, we propose LWR-Net, a lightweight place recognition network for efficient and robust point cloud retrieval in noisy, low-density conditions. Our approach incorporates a fast dilated sampling and grouping module with a residual MLP structure to learn geometric features from local neighborhoods. We also introduce a lightweight attentional weighting module to enhance global feature representation. By utilizing the Generalized Mean pooling structure, we aggregated the global descriptor for point cloud retrieval. We validated LWR-Net’s efficiency and robustness on the Oxford robotcar dataset and three in-house datasets. The results demonstrate that our method efficiently and accurately retrieves matching scenes while being more robust to variations in point density and noise intensity. LWR-Net achieves state-of-the-art accuracy and robustness with a lightweight model size of 0.4M parameters. These efficiency, robustness, and lightweight advantages make our network highly suitable for robotic applications relying on point cloud-based place recognition. Accurate localization plays a crucial role in the operation of autonomous vehicles and robots. It not only enables confident navigation and obstacle avoidance but also facilitates efficient task execution. Localization serves as the foundation for enhanced mobility and automation. One specific aspect of localization is place recognition, which involves searching a database of geo-tagged scene data to find the descriptor most similar to the query scene. This becomes particularly important when localizing in environments where reliable GPS signals are unavailable  or when The advancement of hardware has dramatically contributed to the development of deep learning in 3D vision ,8, leadiWhile these methods have demonstrated promising results, they have limitations when applied to complex real-world scenes. Most existing methods are trained and evaluated using point clouds with low noise and sufficient point density, leaving the performance relatively unexplored in highly noisy and low-density conditions. Moreover, many of these methods heavily rely on sophisticated local geometric extractors, leading to memory access overhead and large parameter sizes , therebyTo address the challenges above, we propose a lightweight retrieval network called LWR-Net, explicitly designed for efficient point cloud-based place recognition in noisy and low-density conditions. LWR-Net follows a simple two-stage architecture but delivers highly competitive performance. In the feature extraction stage, we introduce a fast dilated sampling and grouping module to collect features from local regions efficiently. These local features are then hierarchically aggregated using a feed-forward residual multi-layer perceptron (MLP) structure. To enhance robustness and generalization, we incorporate a lightweight attentional weighting module during the encoding process, which improves the features from a global perspective. In the feature fusion stage, we utilize Generalized Mean (GeM) pooling  to aggreWe proposed a simple yet effective architecture called LWR-Net for accurately retrieving scene point clouds with a low parameter size. LWR-Net follows a two-stage pipeline architecture, as shown in In the local feature extraction stage, the network needs to explore local geometric information directly from the large-scale point cloud. The feature extractor used in this stage has two major requirements: (1) it can accurately sense the local information in the scene to assist in aggregating more distinctive global features; (2) it must be lightweight to keep the memory and computation efficiency, which means no sophisticated or heavy operations should be used in the feature extractor. To meet the demand, we designed the feature encoding layer consisting of three parts: fast dilated sampling and grouping, residual-based encoding, and attentional weighting.Sampling and grouping are essential to aggregate local neighborhood features for point-based learning structures. Usually, the first step is to sample a subset of points For the grouping process, we chose the K-nearest neighborhood (KNN) algorithm to group tructure  to reducO(logN)) , which fSince the random sampling algorithm is sensitive to uneven point density distributions, areas with high point density tend to have more sampled points, adversely affecting the perception of the scene. To alleviate this issue, we incorporated a strategy called dilated sampling. This strategy first expands the number of sampling point We designed a simple feed-forward residual MLP module to learn the local neighborhood representation. This module takes Features encoded by residual blocks only contain regional geometric information; we embedded an attentional weighting module before Meanwhile, we adopted another By dividing the channel axis into Since the commonly used NetVLAD is inefficient and memory-intensive, we aggregated the global descriptor using the GeM pooling function . GeM pooThe loss function for our network aims at minimizing the feature distance between structurally similar point clouds while maximizing the feature distance between structurally dissimilar point clouds. We adopted triplet margin loss  to trainposed in , which d−3 for 60 epochs, which decayed by 0.1 at epochs 30 and 50. All experiments were conducted on an Intel i7-9700k CPU using a single NVIDIA RTX2070s graphic card.We applied a very shallow network by hierarchically using two feature encoding layers to gather local features of the input point cloud; the number of sampling points of each layer was 1024 and 256, and the number of neighborhood points was 32 and 16. The dilated ratio Following previous works in place recognition, we used the modified Oxford robotcar dataset  and threWe compared the proposed network with a series of advanced methods in the experiments, including PointNetVLAD , PCAN 1, LPD-NetFor the performance evaluation, we assumed that the query point cloud is successfully recognized if at least one of the top-K retrieved database clouds is within 25 m from the ground truth position of the query. Therefore, we followed the same evaluation metrics used in , which iThe evaluation results on the standard Oxford robotcar dataset are illustrated in Firstly, we investigated the influence of point density. We randomly down-sampled the test set with different point numbers to simulate the density changes. We chose Minkloc++, MinkLoc3D, PPT-Net, SOE-Net, EPC-Net, MinkLoc-v2, and SVT-Net to compare in this experiment, and we assume the method fails if the recall indices are less than 30%. Based on the trained model, we evaluated the retrieval accuracy under different point density settings; We developed a simulation strategy to add random outlier noise to the scene point cloud, which includes the following steps: (1) Generate a random noise location within the scene range. (2) Find the nearest neighbor of the generated noise location and calculate the distance. If the distance is less than the set threshold (5 cm in the experiment), we assume this location is too close to the original surfaces in the point cloud (making it more like a measurement noise), and we re-generate a new location according to the previous step; otherwise, we add it into the scene point cloud as an outlier noise point. (3) Loop executes steps 1 and 2 until the number of outlier noise points exceeds the set threshold. We compared LWR-Net to PPT-Net, MinkLoc3d, MinkLoc++, SOE-Net, EPC-Net, MinkLoc-v2, and SVT-Net in this experiment, and we still set 30% as the threshold to determine whether the method failed. Furthermore, we tested the robustness of LWR-Net to the measurement noise. We randomly jittered the points in the test point clouds by noise sampled from In order to verify the generalization ability of each method, we performed a cross-dataset experiment using three in-house datasets. We used the trained model on the Oxford robotcar dataset and directly evaluated the performance on three in-house datasets. We further tested the robustness of our method on three in-house datasets; note that we never trained our approach on the in-house datasets. We conducted experiments to verify the effectiveness of our model design. We eliminate different network components and generate multiple ablated versions of the LWR-Net, including the following: (1) LWR-Core: the attentional weighting module was eliminated and only preserved the core structure of the network. (2) LWR-WithoutRes: we used the ordinary MLP block with the same channel dimension to replace the residual MLP encoding module in the network. (3) LWR-WithoutResLocal/ResGlobal: since we used two different residual blocks  the residual connection of the encoding module can effectively improve the performance of the network; (2) removing either The experiments show that compared to the state-of-the-art place recognition methods, LWR-Net can provide accurate retrieval results and is more robust under various point densities and noise intensity. Moreover, our network has a stronger generalization ability, making it maintain reliable accuracy and robustness under environmental changes. Most importantly, LWR-Net achieves the above performance with only a small number of parameters. The model size of LWR-Net is reduced by more than 50% compared with existing methods, making it more efficient in processing large-scale scene data. In this work, we proposed a lightweight network named LWR-Net for point cloud-based place recognition. We designed LWR-Net based on random sampling and residual MLPs and further introduced an attentional weighting module to promote performance. Experimental results have shown that our network outperforms related works in simplicity and efficiency and is also more robust under low-density and noisy situations. Moreover, LWR-Net performs more generalized under environmental changes. Most importantly, LWR-Net achieves the above performance with a small model size (0.4 million parameters), proving that the sophisticated feature extractor may not be crucial for the place recognition network.Although our network has improved the efficiency and robustness of point cloud retrieval tasks, it still has some limitations. The accuracy and robustness of LWR-Net under low-density environments can be further promoted. LWR-Net proves the effectiveness of random sampling and residual MLPs in designing the place recognition network; future work can further design a more powerful structure under this frame. Since our network is lightweight and robust, another possibility for future work would be to incorporate LWR-Net into SLAM, which can demonstrate its value for robotic applications.


The present meta-analysis was conducted to compare the safety and efficacy of angiontensin receptor neprilysin inhibitor (ARNI) with angiotensin receptor blockers (ARBs) and angiotensin-converting-enzyme inhibitors (ACEi) in patients with heart failure with reduced ejection fraction (HFrEF). This meta-analysis was conducted and reported in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement. Two authors carried out a scientific literature search on online databases, including EMBASE, PubMed, and the Cochrane Library. The following keywords or corresponding Medical Subject Headings (MeSH) were used for the search of relevant articles: “heart failure with reduced ejection fraction,” “angiotensin receptor-neprilysin inhibitor,” “Angiotensin receptor blockers,” and “clinical outcomes.” Outcomes assessed in the present meta-analysis included changes in ejection fraction (EF) from baseline in percentage. Other outcomes assessed in the present meta-analysis included all-cause mortality, cardiovascular death, and hospitalization due to heart failure. Adverse events assessed in the present meta-analysis included hypokalemia, acute kidney injury, and hypotension. Total 10 studies were included. This meta-analysis showed that treatment with ARNI was associated with a significantly lower risk of all-cause mortality and cardiovascular death compared to control groups. There was no significant difference between the two groups in terms of change of EF from baseline or hospitalization related to heart failure. However, the risk of hypotension was significantly higher in patients receiving ARNI. The study findings support the use of ARNI as first-line therapy for heart failure with reduced ejection fraction. Further studies are required to determine the optimal use of ARNI in heart failure management and to investigate the mechanisms underlying the increased risk of hypotension. Heart failure is a clinical condition resulting from various cardiac diseases. In recent years, the incidence of heart failure has been consistently increasing and is predicted to continue due to the ageing population with more cardiovascular risk factors -2. HeartAfter the publication of the Prospective comparison of angiotensin receptor-neprilysin inhibitor (ARNI) with angiotensin-converting enzyme inhibitor (ACEI) to determine the Impact on Global Mortality and morbidity in Heart Failure (PARADIGM-HF) trial , sacubitSince the PARADIGM-HF trial, several new trials and retrospective studies on the efficacy and safety of ARNI trials have been conducted, including the PARALLEL-HF trial  and the MethodologyThis meta-analysis was conducted and reported in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement.Literature SearchTwo authors carried out a scientific literature search on online databases, including EMBASE, PubMed, and the Cochrane Library. The following keywords or corresponding Medical Subject Headings (MeSH) were used for the search of relevant articles: “heart failure with reduced ejection fraction,” “angiotensin receptor-neprilysin inhibitor,” “Angiotensin receptor blockers,” and “clinical outcomes.” We also manually searched the reference lists of the included studies and reviews. The search was limited to articles published in English, and the time limit was from inception to February 1st, 2023.Literature Screening and Data ExtractionTwo researchers independently screened the literature and extracted data. First, title and abstract screening were conducted after removing duplicates. Full texts of all eligible studies were retrieved and screened for eligibility criteria using pre-specified inclusion and exclusion criteria. Disagreements between the two researchers were resolved through discussion. Data were extracted using pre-designed data extraction forms created using Microsoft Excel. Data extracted included the author's name, year of publication, study design, study groups, sample size, follow-up duration, and participants’ characteristics.Eligibility Criteria and Measurement of OutcomesWe included randomized control trials (RCTs) and retrospective studies comparing ARNI with ARB or ACEi in patients with HFrEF. The inclusion criteria were as follows: (a) RCTs or retrospective studies, (b) patients with HFrEF with left ventricular EF of <45%. We excluded studies that did not report outcomes in the present meta-analysis. We excluded case reports, case series, reviews, and editorials. We also excluded studies that lacked a comparison group.Outcomes assessed in the present meta-analysis included changes in ejection fraction (EF) from baseline in percentage. Other outcomes assessed in the present meta-analysis included all-cause mortality, cardiovascular death, and hospitalization due to heart failure. Adverse events assessed in the present meta-analysis included hypokalemia, acute kidney injury, and hypotension .Risk of Bias AssessmentRisk of bias assessment of each included study was assessed by two authors independently using the Cochrane Risk of Bias Assessment tool. Any disagreements between two authors were resolved by consensus and discussion. Seven domains were assessed and each domain was rated as high risk, low risk or unclear risk of bias as per the judgment criteria. For retrospective studies, Newcastle-Ottawa Quality Assessment was used. Statistical AnalysisWe used Review Manager 5.4.1 software  for data analysis. The heterogeneity among the study results was assessed using I-square statistics. An I-square value of less than or equal to 50% indicated low heterogeneity among the study results, and a fixed-effect model was used for data analysis. In cases of heterogeneity of more than 50%, a random-effect model was used. Categorical outcomes were expressed as risk ratio (RR) and 95% confidence interval (CI), while continuous variables were presented as mean difference (MD) with their 95% CI. In the present meta-analysis, a p-value of 0.05 was used as a cut-off. Subgroup analysis was also performed on the basis of study design. ResultsThe process of study selection is presented in Figure Change of Ejection Fraction (EF) from Baseline (%)Five studies that compared the change of EF from baseline between patients who received ARNI and patients in the control group reported no significant differences between the groups in the change of EF from baseline. The mean difference was 0.62, with a 95% confidence interval of -1.73 to 2.96 and a p-value of 0.61. However, high heterogeneity was reported among the study results, with an I-square of 93% as shown in Figure All-Cause Mortality and Cardiovascular MortalityA total of five studies were included in the analysis of the all-cause mortality outcome, comparing ARNI versus control. A fixed effect model was used to estimate the effect size. The results showed that 794 out of 5,667 patients who received ARNI experienced an event, compared to 1,038 out of 6314 patients in the control group. The relative risk (RR) of all-cause mortality was 0.83 , indicating that patients who received ARNI had a significantly lower risk of all-cause mortality compared to those in the control group as shown in Figure Treatment with an ARNI was associated with significantly lower risk of cardiovascular death . Low heterogeneity was reported among the study results (I-square: 24%) as shown in Figure Hospitalization Related to Heart FailureSix studies assessed the impact of ARNI on hospitalization. The meta-analysis reported no significant difference between patients in ARNI group and patients in control group in relation to risk of hospitalization  as shown in Figure Safety AnalysisTable DiscussionThe present meta-analysis compared ARNI with ARB and ACEI, including more recently conducted clinical trials and retrospective studies, and found the superiority of ARNI in terms of all-cause mortality and cardiac death outcomes. However, treatment with ARNI was also associated with an increased risk of hypotension. No significant differences were reported between the two study groups in terms of change in EF. The study conducted by Nielsen et al. performed a pairwise meta-analysis, which included 39 studies , and alsOne study showed that optimal execution of ARNI therapy was empirically estimated to prevent more than 28,000 deaths each year . ARNI coThe present meta-analysis reported an increased risk of hypotension in patients receiving ARNI treatment, possibly due to the diuretic, natriuretic and vasodilatory effects of natriuretic peptides . The incIt is proposed that any eligible patient with HFrEF should consider switching to ARNI. Additionally, it is crucial to improve education among physicians, emphasizing not just the initiation of therapy but also the need to increase the dosage to reach the target level. With the proven benefits of ARNI for HFrEF, insurance approval for ARNI is increasingly being granted . HoweverThe present meta-analysis has certain limitations. Firstly, patient-level data were not available in a large number of studies. So, we were not able to analyze the effect of different patients’ characteristics on the impact of ARNI on outcomes in patients with HFrEF. Studies also differed by definition of certain endpoints, including acute kidney injury. Moreover, differences were there in the choice of the control group for studies included in the current meta-analysis. The majority of the studies included used enalapril (ACEI).In conclusion, this meta-analysis showed that treatment with ARNI was associated with a significantly lower risk of all-cause mortality and cardiovascular death compared to control groups. There was no significant difference between the two groups in terms of change of EF from baseline or hospitalization related to heart failure. However, the risk of hypotension was significantly higher in patients receiving ARNI. The study findings support the use of ARNI as first-line therapy for heart failure with reduced ejection fraction. Further studies are required to determine the optimal use of ARNI in heart failure management and to investigate the mechanisms underlying the increased risk of hypotension.


The health-related quality of life (HRQoL) among long-term Adolescent and Young Adult Cancer Survivors (AYACS) and an age- and sex-matched normative population was examined. Although the HRQoL of AYACS was worse compared to the normative population before and during the COVID-19 pandemic, the scores of AYACS improved over time in contrast to the normative population. Presumably, AYACS are used to adjusting their lives to stressful life events. Furthermore, the lockdown may have been beneficial for AYACS who face difficulties fully participating in society due to the impact of cancer. AYACS who encounter HRQoL issues could benefit from support interventions to empower them and build resilience.The online version contains supplementary material available at 10.1186/s41687-023-00629-0. The coronavirus disease 2019 (COVID-19) pandemic has had a great impact on the capacity of patients and healthcare systems. At the peaks of the pandemic, many appointments and treatments were cancelled or postponed. There is emerging research on the psychological impact of COVID-19 on the general population, but especially on vulnerable populations such as cancer patients .Adolescent and Young Adult cancer survivors (AYACS), in this manuscript defined as adolescents and young adults who had been diagnosed with primary cancer between ages 18–39 years, might be particularly vulnerable to the effects of the pandemic, because of their unique developmental, educational, social and emotional needs . AYACS aCancer patients in general have a higher risk of developing complications from COVID-19, which can be partly ascribed to factors such as older age, higher smoking rates, comorbidities, frequent healthcare exposures and the effects of cancer therapies . During Previously conducted studies on the impact of COVID-19 on AYACS focus on adolescents and young adults who had active treatment or were within 5 years after diagnosis. To the best of our knowledge, there are no existing studies that investigate the impact of the COVID-19 pandemic on the HRQoL among long-term (5 or more years) AYACS. Moreover, other studies do not have a longitudinal research design and do not include data of an age- and sex-matched normative population. Insight into the impact of the COVID-19 pandemic on HRQoL of long-term AYACS is warranted. Understanding the unique challenges that this population may experience currently as a result of the COVID-19 pandemic is necessary in order to support and treat AYACS optimally. Therefore, within a subsample of the SURVAYA study , we inveThe cross-sectional population-based SURVAYA study was conducted among long-term survivors (5–20 years) of cancer on AYA-age (18–39 years) . They reData was collected between May 2019 and June 2021 and the data collection process is described in more detail elsewhere . PatientThe normative population was derived from a reference cohort of the general Dutch population (CentER panel), which is representative for the Dutch-speaking population in the Netherlands . MembersSelf-reported sociodemographic data included sex, marital status (with or without a partner) and educational level  education and higher . Linkage with the Netherlands Cancer Registry (NCR) provided information on age at questionnaire completion, time since diagnosis, type of cancer, tumor stage and primary treatment modality . HRQoL was assessed with the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) . Linear All statistical analyses were performed using SAS . Differences between socio-demographic and clinical characteristics between AYACS and the normative population were assessed with a chi-square test  or independent sample t-test (continue variables). The HRQoL mean changes of AYACS and normative population over time were assessed with paired sample t-tests. Analyses of covariance (ANCOVA) were carried out to compare HRQoL between AYACS and the normative population, adjusted for educational level (high-medium/low) and partner status (yes/no). Due to the multiple statistical tests conducted in this study and to avoid type 1 errors, all differences with a P-value < 0.01 were indicated as statistically significant. A graphical representation of the intraindividual longitudinal changes over time is displayed for HRQoL scales that showed medium and small clinically relevant differences.In the SURVAYA study, 4010 AYACS participated, of whom 1089 participated before the COVID-19 pandemic and were eligible to participate in the additional COVID-19 questionnaire study  and insomnia  between AYACS and the normative population.AYACS reported better HRQoL scores during the COVID-19 pandemic than before its start. The improvements in cognitive functioning  and fatigue  were of medium clinical relevance. Differences in social functioning  and pain  were considered of small clinical relevance. The HRQoL scores of the normative population remained stable over time. Figure This study aimed to investigate the longitudinal impact of the COVID-19 pandemic on the HRQoL of long-term AYACS and to compare their HRQoL with the general population before and during the pandemic. AYACS experienced worse HRQoL scores, before and during the COVID-19 pandemic compared to the normative population. However, in contrast to the normative population, the HRQoL of AYACS improved during the pandemic with the largest differences in social functioning, cognitive functioning, fatigue and pain.Our results showed that long-term AYACS experienced better HRQoL scores during the pandemic than before it. To the best of our knowledge, no studies exist regarding the impact of COVID-19 on HRQoL in long-term AYACS. There are several studies with a cross-sectional study design among adolescents and young adults in active treatment or shortly after diagnosis that investigate the impact of the COVID-19 pandemic on psychological well-being. High levels of psychological distress during the COVID-19 pandemic were reported , 14, 21.In the long-term, AYACS reported worse HRQoL outcomes in all HRQoL domains overall compared to the general population. This is in line with results from a review by Quinn et al. and other recent international studies , 22, 23.Considering that the HRQoL reported by long-term AYACS was still lower than that of the normative population, there are fruitful avenues for future research and interventions in AYA care are needed. Psychological and physical support is important for those at risk of poor HRQoL scores, even 5–20 years after diagnosis. To assess whether there are differences in HRQoL between subgroups of AYACS, additional analyses of HRQoL outcomes in a larger sample of AYACS would enable greater insight into the characteristics of those AYACs at greater risk of poor HRQoL outcomes. Analysing subgroups of AYACS stratified by age, education level, time since diagnosis and cancer type can inform health care services by informing the development and planning of interventions that take the needs of AYACS into consideration. Psychological and physical support interventions must address empowerment and building resilience to help AYACS live with the short- and long-term effects of cancer.The interpretation of the findings of this study must consider some limitations. Among the SURVAYA respondents, participants were asked to complete the COVID-19 specific questionnaire study. Selection bias was reported within the SURVAYA study, since study participation was significantly lower among specific subgroups, such as males and survivors with a lower socio-economic status . Non-resCompared to an age- and sex matched normative population, AYACS experience worse HRQoL scores, both before and during the COVID-19 pandemic. However, the HRQoL of AYACS improved during the pandemic in contrast to the matched normative population. The lockdown of the society could have been beneficial for AYACS who experience short-and long-term effects of cancer which can hinder full participation in society just like their peers. Moreover, AYACS may be more adept at coping with abnormal life-threatening situations than the general population.Additional file 1. Fig. S1: Flowchart of the data collection process of the SURVAYA-COVID-19 study.


In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilized whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalized models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management. Due to ageing populations and the current lack of effective treatment, AD constitutes a major socioeconomic burden in industrialised countries 3. AD is characterised by progressing brain pathology and declining cognitive function, in addition to metabolic network failures resulting in changes in the metabolome 4.Alzheimer’s disease (AD) is the world’s leading neurodegenerative disease and the most common cause of dementia 6. The gut microbiome affects and is affected by downstream organs, such as the liver, kidney, and brain 7, and is a major source of neuroprotective  and neurotoxic  metabolites 8. Recently, microbial dysbiosis in AD has been reported 10. Moreover, changes in gut microbial metabolites, such as primary and secondary bile acids (BAs), have been reported to correlate with mild cognitive impairment and AD dementia 12.The human gut microbiome plays an important role in human health and well-being, performing a wide range of essential functions 13. COBRA generally relies on manually refined genome-scale metabolic reconstructions that are built bottom-up from the target organisms’ genome sequences 14. These genome-scale reconstructions can be converted into condition-specific metabolic models through the application of constraints, such as dietary information, metagenomics, and metabolomics, thereby enabling the computation of the biologically feasible solution space of these metabolic models through widely used methods, such as flux balance analysis 15. To meet the scale of human gut microbiomes, a resource of genome-scale reconstructions of 773 human gut microbes, called AGORA 16, was generated and subsequently expanded in size and scope (AGORA2), now accounting for 7,302 human microbes 17. Using AGORA or AGORA2, personalised microbiome models can be constructed using microbial relative abundances derived from metagenomics data through tools, such as the Microbiome modelling toolbox 19. Using personalised microbiome modelling, it is possible to provide insights and identify changes in the metabolic capabilities of gut microbiomes in disease states, including in inflammatory bowel disease 19 and colorectal cancer. Such predicted metabolic capabilities could be validated against faecal metabolome data 20. Additionally, sex-specific whole-body models (WBMs) of human metabolism have been published 21, which represent human metabolism in an organ-resolved and anatomically accurate manner. These WBMs can be contextualised with physiological parameters, dietary input, and personalised microbiome models 21. For the first time, these models can also be used to investigate the interaction between the host and microbiota metabolisms 22and host-virus co-metabolism 23. These simulation-derived novel hypotheses can be subsequently validated.Creating mechanistic computational models capable of integrating multi-omics data is essential for understanding the connections between shifts in the microbiome, changes in the metabolome, alterations in host metabolism, and the onset of diseases. The constraint-based reconstruction and analysis (COBRA) approach provides a powerful tool to better understand metabolic changes and the biochemical pathways affected by the disease In this study, we aimed to characterise metabolic changes associated with cognitive decline and AD as well as to elucidate the role of the human gut microbiota in these changes. We first generated 1D-NMR urine metabolome data from individuals on the clinical AD spectrum and from cognitively healthy age-matched individuals from the Germany-based DELCODE study cohort. We identified a statistically significant decrease in formate and fumarate concentrations in people with AD dementia. To dissect potential microbial contributions, we then used WGS data generated from an independent cohort consisting of 24 participants with AD dementia and 24 cognitively unimpaired age-sex-BMI-matched controls to construct personalised host-microbiome models for each individual. We found that the formate microbial secretion was lowered in AD host-microbiome models compared to healthy control host-microbiome models. Furthermore, we found that amino acids and sugar host-microbiome co-metabolism were involved in the predicted urine formate production. The implications of these two host pathways in AD were further supported by known associations of the underlying genes with AD pathology. Taken together, our analyses point towards alterations in the microbiome’s formate secretion capacity between AD cases and controls and host-microbiome interactions on its production. These findings have potential clinical relevance for understanding the interplay between the host and the microbiome in AD.24  and the Wisconsin Alzheimer’s Disease Research Center cohort (WADRC) 26. As expected, there was a higher carriage of the APOE4 allele among people with AD dementia  30 in Qiita 31. The median amount of reads the samples contained was 2,561,443 (interquartile range (IQR) = 2,344,948) reads.  reads. . No stat17, and discarded all reads belonging to species that could not be mapped onto AGORA2. We generated pan-species microbial metabolic reconstructions consisting of the union of all metabolites and reactions present in any corresponding AGORA2 strains of the same species  samples compared to the healthy controls (Table S04). Due to the compositional nature of the relative abundance data, we performed a log ratio analysis of the increased species against the decreased ones using their gOTU counts. This analysis confirmed the alteration found in the relative abundances  21 and added the personalised microbiome models to the large-intestinal lumen of either the female or male WBM. Formate, which we found altered in the urine of people with AD in the DELCODE study, was part of the microbiome models and the WBMs. This metabolic overlap enabled our investigation of its direct effect on host-microbiome co-metabolism. We computed the maximum urine secretion flux and the maximum microbiome production flux for formate for all the microbiome-associated WBMs and the two sex-specific germ-free WBMs. The simulation setup of the WBMs was identical except for sex-specific host metabolism and the microbiome models. Hence, we included sex as a covariate in the subsequent statistical analyses. No statistically significant differences could be observed between healthy and AD individuals (Table S05).Formate is a fermentation production of anaerobic bacteria in the gut and the gut microbiome metabolism can account for up to 50% of the overall formate production in the host To evaluate the presence and contribution of host-microbial co-metabolism in formate urinary production, we investigated the predicted differences between the formate urine fluxes of the microbiome-personalised WBMs and non-microbiome-personalised WBMs, i.e., germ-free WBMs. The difference was higher than the amount of formate secreted by the microbiomes meaning that other microbially derived metabolites must be involved in the host-microbiome co-metabolism of formate -B. To de32. We considered 47 reactions that have been suggested to be implicated in host formate metabolism and three transport reactions into or from the peroxisome and the endoplasmic reticulum to complete the pathways  reaction deletions reduced the urine formate secretion flux by more than 10%  33 a network-based integrative resource for AD, we found that 28 of the 47 (59.5%) reactions had genes associated with AD/cognitive decline phenotypes  and medications are also likely to contribute to changes in host-microbiome co-metabolism, urine metabolome, and AD pathology.While the COBRA modelling approach is a very valuable approach for investigating host-microbiome co-metabolism involvement in AD, certain limitations should be noted. For instance, we used a relatively small cohort of subjects and controls. Hence, our results need to be validated in larger independent cohorts. It has to be noted that discarded microbes not accounted for by the AGORA2 resource could lead to loss of metabolic capacities in the correspondent microbiome models. Additionally, the models were built on microbial relative abundance data generated from the reads count data, and results were subject to the intrinsic compositional structure of the models. Genome-scale metabolic reconstructions are also continuously updated as new experimental data and biochemical knowledge become available In conclusion, in this study, we combined omics data with COBRA modelling on the level of the microbiome and the whole human supra-organism and highlighted the role of microbiome-host interplay on formate-producing pathways. In particular, the microbiome’s role in linking aminoacidic and glucose metabolism with formate, a possible early marker for AD, could be of clinical importance, potentially contributing to the AD phenotype. The underlying mechanism suggested by our model, that both gut microbes and host genetics contribute to an altered formate metabolism in AD, needs to be assessed with more targeted validation studies. Our study delivers proof of the concept of personalised whole-body modelling in the context of a complex human disease. As such, the paradigm has demonstrated promise in uncovering host-microbiome co-metabolism involving biomarkers found in metabolomic studies validating or suggesting pathology hypotheses.This study did not generate new unique reagents.24. Note that the DELCODE study did not include individuals with a current major depressive episode, major psychiatric disorders, neurological diseases other than AD, or unstable medical conditions 24. For our study, we also excluded individuals who had no urine samples available. The resulting sub-cohort consisted of 49 healthy controls, 45 cases with subjective cognitive decline (n = 45), 49 cases with mild cognitive impairment, and 34 cases with AD dementia  workgroup guidelines 57 were used to define the core clinical criteria for MCI and dementia AD-related. The control subjects showed no objective cognitive impairment in cognitive tests, had no history of neurological or psychiatric disease, and did not report a self-perceived cognitive decline. Informed consent was provided by all participants or their representatives. The study protocol was approved by the local institutional review boards and ethics committees of the participating centres and the study was conducted in accord with the Helsinki Declaration of 1975.We used an interim data-freeze from the DELCODE study conducted by the Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) dementia . We defi58. The urine samples from 185 individuals were measured at the University Medicine Greifswald on a Bruker AVANCE-II 600 NMR spectrometer operated by TOPSPIN 3.2 software . The spectrometer was equipped with a 5-mm z-gradient probe and an automated tuning and matching (ATMA) unit . Specimens were automatically delivered to the spectrometer via SampleJet  into standard 5 mm NMR tubes. The acquisition temperature was set to 300°K. A standard one-dimensional 1H-NMR pulse sequence with suppression of the water peak (NOESYPREAST) was used 59. Urine samples of four individuals could not be processed at all due to too little biomaterial available for 1H-NMR measurements. After processing the raw spectra, 50 metabolites were quantified using the Bruker Suite B.I.Quant-UR b™ for targeted analyses. To account for dilution, two different normalisation approaches were performed: 1) creatinine normalisation and 2) PQN normalisation. Different normalisation approaches have been shown to have different advantages as well as disadvantages such that it is recommended to use multiple approaches and to compare results across normalisation techniques 60. For quality check purposes, creatinine was measured via an enzymatic standard kit to compare 1H-NMR-derived creatinine measurements with standard creatinine measurements. Four further observations were excluded for strong differences between enzymatic and 1H-NMR creatinine measurements. The remaining observations showed a correlation > .95 between the two types of creatinine measurements. In the end, n = 177 spectra were included in statistical analyses.The urine samples were handled and prepared as in Metadata and urine samples for 177 individuals belonging to four study groups were available for analysis . As descDescriptive statistics were derived for 49 metabolites after creatinine normalisation. Of those 49 metabolites, only 16 metabolites had non-zero urinary concentration measurements in more than 50% of the samples. Importantly, a zero measurement in the context of NMR measurement does not necessarily mean that the metabolite was below the limit of detection. The signal of the correspondent metabolite may also have been clouded by other metabolites or may have been shifted due to pH differences in ion concentrations, hence, we treated zero measurements as missing values. We screened all metabolites fulfilling the 50% criteria via multivariable linear regressions including age, sex, and BMI as covariates. Log-transformed, creatinine-normalised metabolite concentrations were used as the dependent variables, and the study group variables  were the predictors of interest. We performed the global test using the Wald-test method to investigate 1. whether the study group variable contributed overall to the statistical model and 2. whether there were specific differences between the AD group and the healthy controls. For correction of multiple testing, the false discovery rate was utilised.APOE genotype, diagnosis, and cognitive test results were collected 62. Our study used a subset of the cohort comprising 48 individuals . Covariates were compared against the clinical diagnosis using Fisher’s exact test for APOE4 and sex, and Welch’s t-test for age and BMI.Our study utilised data from the Wisconsin Alzheimer’s Disease Research Center (WADRC) and the Wisconsin Registry for Alzheimer's Prevention (WRAP) studies. Participants with AD-Dementia were recruited from the WADRC, while cognitively unimpaired participants were recruited from the WADRC and the WRAP studies. Data on age, sex, BMI, 31 using the default workflow for metagenomics data. In short, the raw files were loaded in multiple preparations to represent the multiple runs and processed with default parameters; then the raw sequencing data were demultiplexed and trimmed at 150 bases. Adapter removal was carried out using fastp 63 and human reads were filtered using minimap2 64. Genomic OTUs (gOTUs) were generated using the Woltka Toolkit 29 by aligning reads to the Web of Life (Release 1) reference genome database 30 using bowtie2 65.The faecal samples were sequenced and then processed through Qiita For descriptive statistics, metric variables were expressed in means and standard deviations, while categorical variables were described by proportions. All p-values were reported as two-tailed. The statistical analyses were performed with STATA 16.1/MP .17. We normalised the considered reads so that the sum of all reads was one, thereby obtaining the relative abundances of each microbe in the sample. The abundances were then mapped to 1,742 AGORA2 pan-species reconstructions, the union of reactions and metabolites of each strain-specific reconstruction of one species. Then, sample-specific microbiome models were derived using the mgPipe module of the Microbiome Modelling Toolbox 19. Briefly, for each individual, all the pan-species reconstructions found in the corresponding sample were assembled into one microbiome reconstruction 18, which was converted into a microbiome model by personalising the community biomass reaction based on the relative species abundance data in the sample. Further constraints were then applied to couple each microbial reaction to its corresponding biomass reaction. These ‘coupling constraints’ ensure that flux through a pan-genome model within the microbiome model was only non-zero if the corresponding biomass reaction carried a non-zero flux. The coupling factor was arbitrarily chosen to be 400 67. We tested for differences in measures of metabolic content in the microbiome models between cases and controls using linear regressions correcting for the presence of the APOE4 allele.First, we assigned taxonomy to gOTUs using the Web of Life database, and we summed together the gOTUs with matching taxonomical information. We considered all the reads assigned to the species accounted for by our database of genome-scale metabolic microbial reconstructions (AGORA2) APOE genotype, while we excluded all the microbes present in less than 50% of the models to avoid statistical artefacts. Alteration in relative species abundances was checked using log-ratio analysis, after calculating the sum of the gOTUs for the increased species and decreased species, the ratio was calculated and a t-test was run between groups.To investigate differences in the AGORA2-covered microbial abundances between cases and controls, we fitted fractional regressions using the relative microbial abundances as the response variable and the health status as an independent one correcting for categorical 68, supplemented with bile acids (cholic acid and chenodeoxycholic acid), was applied to further constrain each microbiome model and to convert them into condition-specific models (Table S11). The diet constraints were defined to be in mmol/person/day. We integrated the uptake fluxes values defined by the diet with all the microbiome models using the Microbiome Modelling Toolbox 18 implemented in the COBRA toolbox 69, and we ensured that all the pan-species models could grow under the defined diet.An average European Diet 70 to obtain the corresponding maximum net secretion capacity for all the model’s secretion reactions, FVA solves maximisation problems with the interested reactions secretion fluxes as objective subject to different constraints to specify the feasible region: predetermined upper and lower bound of reactions fluxes stated in the model, diet fluxes, and mass balance in form of . For each metabolite secretion capacity, zero values meant that the model could not secrete the corresponding metabolite, while a positive value corresponded to the microbiome model’s capacity of secreting the metabolite, under the given simulation constraints. All simulations were performed in MATLAB  version R2018b using IBM CPLEX  as a linear programming solver and the COBRA Toolbox v3 69 and the Microbiome Modelling Toolbox 19.Each microbiome model under the given condition-specific constraints was interrogated using flux variability analysis (FVA) 21. We computed the maximum urine secretion fluxes of metabolites, which were significant in the previous analyses and could be produced by the microbiome-associated WBM under an average European diet 68. The male and female WBMs, which correspond to a reference man and woman 21, respectively, were not further personalised as the sub-cohort was age and BMI matched between healthy and AD individuals. The maximum urine secretion rate for each microbiome-associated WBM was calculated by choosing the corresponding urine metabolite exchange reaction  as an objective function and maximising for this reaction, using the PSCM toolbox 21. Maximum urine secretion rates for the investigated metabolites were also calculated for non-personalised, germ-free, male and female WBMs. The maximum microbial secretion fluxes were calculated by choosing the corresponding microbial metabolite exchange reaction between the microbial lumen [luM] and large intestinal lumen [luLI] and solving the models minimising the reaction.We added the individual microbiome models to the large intestinal lumen of either female or male organ-resolved, whole-body models of human metabolism (WBMs), as appropriate, using the PSCM toolbox APOE genotype, and sex being the WBM models sex-specific.To investigate differences in reaction fluxes between cases and controls, we fitted linear regression using the fluxes as the response variable, the health status as an independent variable correcting for categorical To investigate the involvement of host-microbiota co-metabolism in formate production, we considered the difference in secretion of formate between the personalised and the germ-free models and we compared these differences with the community microbial secretion of the same metabolite to evaluate the amount of urine secretion directly correlated to the microbial secretion and the amount correlated to host-microbiota co-metabolism.We confirmed the capacity of the whole-body models to produce formate from other microbial metabolites by adding individually to the diet metabolites that can be secreted by the microbiome. All metabolites were added to the diet setting their potential maximum intake to 300 mmol/d. These implemented diets were tested for maximum urine formate production on the germ-free models to evaluate changes in the previous baseline value.25 to be involved in the formate metabolism including the ones we found responsible for the host-microbiota co-metabolism. We retrieved all the genes associated with these reactions from the Virtual Metabolic Human database (VMH) 68, which houses the generic human metabolic reconstruction, Recon3D 51, and then we investigated for associations between genes and health status using the AD Atlas 33 a network-based integrative resource for AD. The AD Atlas integrates multi-omics data from large-scale population-based and AD case-control studies, enabling users to annotate genes, metabolites, or phenotypes of interest in an AD-related context. Here, the AD atlas was queried using the genes identified to be involved in formate metabolism, retrieving information on brain-region-specific differential gene expression and differential protein abundance in AD, as well as genetic associations to AD-related phenotypes inferred from large-scale genome-wide association studies (GWAS). A gene-wise significance threshold was applied to filter GWAS results, as implemented by the AD Atlas 33.We considered all the reactions known from the literature


The reported number of cases and deaths from common infectious diseases can change during major public health crises. We explored whether the coronavirus disease 2019 (COVID-19) had an impact on tuberculosis (TB) incidence and mortality in China based on routinely reported TB data.We used TB data used from the monthly national notifiable infectious disease reports in China from January 2015 to January 2023. Based on an interrupted time series (ITS) design, we applied Poisson and negative binomial regression models to assess the changes of reported TB incidence and mortality before and during the COVID-19 pandemic.We found a significant and immediate decrease in the levels of both reported TB incidence (relative risk (RR) = 0.887; 95% confidence interval (CI) = 0.810-0.973) and mortality  at the start of COVID-19 outbreak. During the pandemic, the slope of reported incidence decreased significantly , while the slope of reported mortality increased sharply  owing to an abrupt rise in reported mortality after January 2022.Both TB incidence and mortality decreased immediately at the start of the COVID-19 pandemic. Over a longer period, the COVID-19 pandemic had contributed to a sustained and more significant decrease in reported incidence, and a delayed but sharp increase in reported mortality. The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to the rapid spread of the coronavirus disease 2019 (COVID-19) and the ensuing COVID-19 pandemic. In response, many countries took emergency countermeasures by placing restrictions on public gatherings, mandating, medical isolation, dedicating healthcare resources to prevent transmission, and implementing pharmacological interventions [The consequences of deferring TB diagnosis and treatment could lead to a transitory, but false reduction in reported incidence (given that patients who were not detected promptly might contribute to a subsequent rise in incidence) and an increase in mortality, which might adversely affect the struggle to end TB -7. For eConsidering the impact of the pandemic, TB-related changes were likely to exist in China as well. Previous studies have reported a marked and short-term decrease in reported TB incidence in the country due to COVID-19 ,9, yet iFollowing the severe acute respiratory syndrome (SARS) outbreak in 2003, China has strengthened and adopted the China Infectious Disease Reporting Information System, a direct, country-wide network reporting system. As of the date of publication, forty notifiable infectious diseases were reported compulsorily and TB was listed as a Class B infectious disease.We obtained data on monthly TB case notifications and reported deaths from January 2015 to January 2023 from the monthly national notifiable infectious disease reports announced by the China Bureau of Disease Prevention and Control  = (Number of cases (deaths) per month/Average population for the year) × 100 000/100 000We implemented an interrupted time series (ITS) design to assess the COVID-19 pandemic’s impact on reported TB incidence and mortality in China. The ITS design is an evaluation approach with strong internal validity, widely used to assess the effect of population interventions or exposures, especially those related to health outcomes at the population level . It estiThe Ethics Committee, School of Public Health, Lanzhou University (IRB20103001) approved the study. The data obtained from the China Notifiable Infectious Disease Reports did not involve any individual/identifiable information and did not require informed patient consent.We defined the intervention in this study’s ITS design as the COVID-19 pandemic in China. Thus, we considered two periods in the study: the pre-COVID-19 period from January 2015 to December 2019 and the COVID-19 pandemic period from January 2020 to January 2023. Our outcomes of interest were the monthly reported TB incidence and mortality.We first fitted a linear regression model using historical TB data from 2015 to 2019 to predict the expected incidence and mortality of TB without the impact of the COVID-19 pandemic between January 2020 to January 2023 and compare them with the reported rates, and then generated the counterfactual plots to demonstrate the difference between expected and reported rates. The adopted linear regression model could capture both seasonal variation and year-to-year trends in incidence and mortality, as detailed by Karlinsky and Kobak .We proposed the level and slope changes model to analyse the transitory and long-term impacts of the COVID-19 pandemic on reported TB incidence and mortality in China simultaneously, as detailed in prior studies ,12. GiveOverdispersion can result in a decrease in the standard error of estimates and an increase in the false positive rate of a parametric test . TherefoP < 0.05. All models in the ITS analysis were compared by means of Akaike information criterion (AIC) values.We adopted negative binomial regression models adjusted to the autocorrelation and seasonality to conduct sensitivity analyses and assess the robustness of the results. We used relative risk (RR) and its 95% confidence interval (CI) in the Poisson and negative binomial regression models as the measures of effect size, with statistical significance set at We constructed the linear regression model in Python, version 3.10  and performed other analyses in Stata 14.0  , with an average monthly incidence of 6.6276 per 100 000 person-months and 10 231 reported deaths with an average monthly mortality of 0.0122 per 100 000 person-months  and a slope RR of 0.995   and seasonality in data on TB case notifications (Table S3 in the P < 0.001) and a slope RR of 1.035  .We also found overdispersion  and seasonality in the reported TB mortality data . Previous studies ,9 had suMycobacterium tuberculosis. With the dissemination of this knowledge on disinfection, people were more attentive of hand hygiene and disinfection of their living environments, which helped reduce the risk of exposure to Mycobacterium tuberculosis and in turn led to decreased reported TB incidence over the long-term period. The beneficial prevention against COVID-19 had promoted a sustained decrease in reported TB incidence. However, as of January 2023, medical services in China were still affected by the COVID-19 pandemic, and it remained unclear whether the decrease in reported incidence was due to beneficial prevention or low reporting. To determine this, further studies are needed for the period after the COVID-19 pandemic in China.Reported TB incidence in China rapidly decreased from 5.0884 to 4.7964 per 100 000 persons in the first month of the pandemic (January 2020), without a significant recovery in reported incidence afterwards. Simultaneously, the long-term trend had decreased more sharply compared with the previous five years. The pandemic led to the concerning trends of stringent lockdown, missed or at least delayed diagnosis among newly-infected TB patients, and reduced proportions of the population seeking timely medical care, which might have caused the avoidance of ongoing disease reporting and consequent decrease in reported TB incidence . HoweverPrevious studies from other countries found that the reported TB mortality increased following the COVID-19 outbreak ,18. The However, we observed that, despite the initial decrease, reported mortality increased sharply after January 2022, which resulted in its long-term upward trend directly during the pandemic. The high reported mortality after January 2022 likely resulted from the disruption of TB treatment and care efforts caused by the COVID-19 pandemic. A report from England estimated that for every 30 COVID-19 deaths, there was at least one patient being admitted to hospital for non-COVID-19 reasons, but dying of the disruption of healthcare quality caused by the pandemic . The WHOOne of the strengths of this study was its ITS quasi-experimental design which could provide a robust analysis of intervention effect at a population level. Even without a control group, it had a strong internal validity because it could control confounding effects caused by methodological issues, such as seasonality and autocorrelation in time-series data, which could not be addressed by simply comparing the means before and after intervention . As a poHowever, our study has some limitations. First, the TB data used in the study only included confirmed and reported cases and deaths by hospitals or centres of disease prevention and control, without the latent TB infection (LTBI) patients, so we did not analyse changes in LTBI. Second, there were inevitable under-reporting or under-diagnosis proportions in the routine reporting system, and there was also a decrease in healthcare seeking-behaviour during the COVID-19 pandemic, so the reported TB incidence and mortality might be of a lower level than in reality. Finally, the design was inevitably limited in causal inference due to the lack of a control group . We did Due to the severe impact of COVID-19 on the healthcare system and healthcare-seeking behaviour among people in China, we observed a notable decrease in both reported TB incidence and mortality when COVID-19 first emerged. There further appeared to be a positive impact on reported incidence over the long-term, owing to its persistent and larger downward trend; in contrast, reported mortality increased sharply after two years of low prevalence. Further monitoring and reporting of TB incidence and mortality after the COVID-19 pandemic in China is necessary to determine the full impact of COVID-19 on TB.


Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2. (SARS-CoV-2). It spreads mainly through saliva droplets or nasal discharge. Dentists are among the professionals with the greatest risk of contracting and transmitting COVID-19. We compared the efficacy of surgical masks versus N95 respirators in preventing COVID-19 infection in dental settings. PubMed, Scopus, Web of Science, and Cochrane Library databases were searched. Search terms corresponded to a predefined PICOS  question. The risk of bias was evaluated using AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews-2), ROBIS (Risk of Bias in Systematic Reviews), and Health Evidence tools. A total of 191 articles were screened, and nine of them were further evaluated for eligibility, of which five articles (fulfilled the selection criteria) and were included in this study. Two studies concluded that surgical masks could provide equivalent protection to N95 respirators. Another study found that N95 respirators were superior to surgical masks. The fourth study found that better protection can be achieved when using surgical masks by the aerosol source than when the recipient uses an N95 respirator, while the last study concluded that surgical masks or N95 respirators alone do not provide full protection. Thus, according to this systematic review, N95 respirators provide better protection against COVID-19 infection compared to surgical masks. Coronavirus disease 2019 (COVID-19) is an infectious disease discovered in Wuhan, Hubei Province, China in late December 2019 . DentistThe airway is considered the main transmission route of COVID-19, accordingly, respiratory protective equipment (RPE) is an important tool for minimizing the transmission of COVID-19 infection . RPE incThis systematic review was guided by the research question: Does the surgical mask provide similar protection from COVID-19 infection as the N95 mask? This systematic review aimed to compare the efficacy of surgical masks versus N95 respirators as a part of PPE in the era of the pandemic COVID-19 infection in dental settings.MethodsWe have adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines for reporting this review .Search Terms and Eligibility CriteriaThe systematic review included studies that met the predefined PICOs . Inclusion criteria were applied as such: population , intervention  comparison (N95 respirators), outcome (prevention of COVID-19/coronavirus/severe acute respiratory syndrome coronavirus 2. (SARS-CoV-2) infection/transmission), study design . We excluded studies evaluating other respiratory illnesses like influenza, flu, and common cold, studies not comparing surgical masks and N95 respirators, articles not written in English OR not available in the English language, as well as abstracts, surveys, grey literature, reviews , editorials, and comments.Electronic Data Search and Study SelectionTwo reviewers independently performed an electronic data search with a single search string developed using the predefined PICOS. Four electronic databases, PubMed, Scopus, Web of Science, and Cochrane Library, were searched for studies published from January 2020 to December 2021. The references were managed with Reference Manager 2.63.0 . The selection of articles was carried out in three stages: title and abstract screening in stage one, full-text review in stage two, and extraction and evaluation of the data in stage three. Two investigators independently performed each stage and a third reviewer was referred in case of any disagreement. Abstracts that did not contain the required information were included in the full-text analysis to avoid the exclusion of potentially relevant studies. The selected studies were read thoroughly. The PRISMA flowchart of the process is given in Figure Risk of Bias and Quality Assessment of Individual StudiesThe risk of bias and quality assessment of the eligible studies was performed independently by two investigators. A third reviewer was consulted in case of any disagreement. The risk of bias of the included systematic reviews and meta-analysis was performed using three tools: AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews), ROBIS (Risk of Bias in Systematic Reviews), and Health Evidence ,13.ResultsFive studies fulfilled the selection criteria and were analyzed in detail. These five articles included one laboratory study, two systematic reviews, one network meta-analysis, and one systematic review and meta-analysis of randomized trials. Two studies concluded that surgical masks could provide equivalent protection to N95 respirators ,15. Yin Using the AMSTAR-2 tool, all four systematic reviews/meta-analyses in the five assessed articles were classified as critically low-quality reviews. In addition, the four systematic reviews were classified as having a high risk of bias using the ROBIS tool. However, using the Health Evidence tool, the four systematic reviews were assessed; three had strong ratings and one had a moderate rating. A summary of included systematic reviews' risk of bias is shown in Tables DiscussionSurgical Facemasks Versus N95 RespiratorsThe use of RPE is a powerful protective tool for HCWs . HoweverAirborne particulate filtering efficacy: Coronavirus infection is transmitted by aerosols and droplets. Accordingly, it is important to evaluate their particle properties and aerodynamic behavior . FrequenWearing time and mask-fit: The protection and prevention efficiency of respiratory protective equipment (RPE) is affected by the wearing time . CompareShielding Efficiency of Protective Eyewear and Face ShieldsThe viral load on the exterior surfaces of masks and respirators is reduced when a face shield is used ,17. ThisAvailability of PPEThe aim of PPE is to control the spread of infection within dental settings. It has always been emphasized that the correct choice of PPE (that provides protection as well as proper fit and comfort) will facilitate compliance with infection control guidelines by dental care providers . HoweverSystematic Reviews Risk of Bias Assessment ToolsA recent rise has been observed in the number of published systematic reviews and meta-analyses ,33,34. TAMSTAR-2: AMSTAR stands for “A MeaSurement Tool to Assess systematic Reviews” . In 2007ROBIS: Another tool for assessing the risk of bias in systematic reviews is ROBIS ("Risk Of Bias In Systematic reviews") . It consHealth Evidence Tool: It is a quality assessment tool designed to appraise systematic reviews and meta-analyses to determine the effectiveness of interventions. This tool includes a total of 10 critical appraisal questions. A total of 10 points indicates the quality rating of the review. A point for each question, 1 point taken if answered "Yes" and 0 if answered "No". To our knowledge, there are no studies assessing the Health Evidence tool. The Health Evidence tool is less sensitive when compared to AMSTAR-2 and ROBIS. Although the Health Evidence tool was easy to use, some questions were not specific. Accordingly, more questions and answer options would improve its sensitivity. The Health Evidence tool can be used as a complementary tool or by those who are not experienced in risk of bias assessment.So far there is no gold standard for evaluating the quality of systematic reviews . AMSTAR-Limitations of the reviewThere are certain limitations in the current systematic review. First, articles in languages other than English were excluded. This might have led to excluding many relevant articles, keeping in mind that the COVID-19 outbreak started in China and had severe impacts on non-English-speaking countries such as Italy. Therefore, the current findings may have not presented the whole picture. Second, the studies included had a high risk of bias and their methodologies had certain flaws, which in turn might have affected the validity of the results they reached. Third, in the current systematic review, we did not consider the vaccination status, which can have a profound effect on the extent of prevention and as such eliminate the differences between the surgical mask and the N95. Lastly, in the current systematic review, we did not look into the grey literature, which could have added more information to the current findings.There is a lack of evidence-based data for a comparative conclusion about the efficacy and superiority of surgical masks vs N95 respirators. Surgical masks and N95 respirators are essential parts of PPE in the post-COVID-19 era. RPE plays a vital role in protecting dentists and other HCWs from respiratory infectious diseases. As good as the protection N95 respirators offer, they can cause several drawbacks. Future studies are needed to compare the efficiency of the different types of N95. According to this systematic review, N95 respirators provided better protection against COVID-19 infection compared to surgical masks. Based on our assessment, AMSTAR-2 is the preferred risk-of-bias assessment tool as it combines both sensitivity and simplicity.


Insight into the clinical potential of convalescent plasma in patients with coronavirus disease (COVID-19) is important given the severe clinical courses in unvaccinated and seronegative individuals. The aim of the study was to investigate whether there is a survival benefit of convalescent plasma therapy in COVID-19 patients. The authors independently assessed randomized controlled trials (RCTs) identified by the search strategy for inclusion, extracted data, and assessed risk of bias. The binary primary outcome was all-cause mortality. Risk ratio (RR) of the convalescent plasma treatment (vs. best standard care) and its associated standard error (effect size) were calculated. A random-effects model was employed to statistically pool the effect sizes of the selected studies. We included 19 RCTs with 17,021 patients. The random-effects model resulted in an estimated pooled RR of 0.94 , showing no statistical evidence of the benefit of convalescent plasma therapy on all-cause mortality. Convalescent plasma therapy was not found to be effective in reducing all-cause mortality in COVID-19 patients. Further studies are needed to determine in which patients convalescent plasma therapy may lead to a reduction in mortality. The clinical manifestations of the disease have covered a broad spectrum ranging from no symptoms to mild, severe, life-threatening or fatal disease. A meta-analysis including forty-five nonrandomized, retrospective observational studies up to the March 15, 2020 assessed intensive care unit (ICU) admission rates as high as 10.9% among patients with COVID-19 2, however ICU admission rate and all-cause mortality rate in patients infected with newer virus variants can show different results, which are currently not well defined. There is evidence that patients with hypertension and diabetes type II are at higher risk of developing lethal complications3.According to the World Health Organization (WHO), more than 640 million cumulative cases of coronavirus disease (COVID-19) and 6.61 million COVID-related cumulative deaths have been registered worldwide since the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 20197. Convalescent plasma therapy is a form of passive immunization that takes plasma obtained from a patient who recently recovered from a disease and transfuses it into a patient who is currently ill from the same disease8. The sick person benefits from the antibodies produced by the recovered person. This modality is considered effective in the treatment of infectious diseases that are caused by viruses such as flaviviruses, influenza viruses A, Ebola virus, and respiratory betacoronaviruses9. Nevertheless, there is insufficient evidence regarding the clinical efficacy of convalescent plasma therapy in the treatment of COVID-19.There are several modalities for the treatment of COVID-19-related deterioration of the health condition. These have had varying degrees of success, with the use of convalescent plasma producing contradictory results in recently published RCTs12. For the high percentage of the world’s population not actively immunized through vaccination, passive immunization with measures such as convalescent plasma therapy remains a potentially appropriate treatment option for patients who develop a severe clinical course.The exact mechanism of action of convalescent plasma is not yet well defined; however, it is supposed that viral neutralization, antibody-induced cellular cytotoxicity, complement activation, and phagocytosis can play a major role in enhancing recoveryThis meta-analysis aimed to investigate the clinical efficacy of convalescent plasma in severely or critically ill COVID-19 patients in order to find out whether patients who receive convalescent plasma in addition to the best standard medical care (or placebo) have a lower all-cause mortality rate, and whether a shorter time from symptom onset to plasma transfusion leads to any clinical benefit. It is especially important to gain insight into the clinical potential of convalescent plasma therapy in the treatment of COVID-19 patients, because there is poor evidence regarding the efficacy of the current vaccines in treating the SARS-CoV-2 Omicron variant (B.1.1.529), or further variants that could appear in the future. Clinicians need to know whether plasma therapy could be an appropriate alternative (or rescue therapy) if vaccines fail to induce an immune response, or if patients are unvaccinated.20 were selected and included in our meta-analysis. After a renewed bibliographic search on January 29, 2022, using the same search strategy we identified four more RCTs24 which fulfilled the eligibility criteria, and as a result of an updated scan on June 24, 2023, we included three more studies in the analysis27. The total number of subjects included in the selected 19 RCTs was 17,021. Of those, 8738 participants received convalescent plasma in addition to the best standard care and 8283 subjects received the best standard care alone or with placebo, thus included in the control group  with an I2 statistic of 10.2% [95% CI 0.0–46.0%], indicating only low between-study heterogeneity. The prediction interval based on the random effects model ranges from RR = 0.70 to RR = 1.25, suggesting no clear preference for a particular treatment. Note that given the large sample sizes compared to other studies, the two RCTs by Horby18 and Estcourt25 contribute the most to the overall pooled effect, with a collective weight of 53.1%.We started by examining the results of the random-effects model shown in the Forest Plot Fig. . The ranSupplement 29. The studies by Bar 202121 and O’Donnell 20215 feature the largest contribution to the heterogeneity, whereas the large trial by the RECOVERY Collaborative Group18 displays the largest influence on the pooled result. The impact of each study on the pooled effect size is further examined within a leave-one-out analysis, presented in Fig. 18 study results in the largest change in the estimated pooled effect size with respect to the estimate based on all available studies with an estimate of RR = 0.90 (95% CI 0.75–1.08). However, as above, the results of no statistical evidence of the benefit of convalescent plasma therapy on all-cause mortality in COVID-19 patients is robustly supported by leave-one-out analysis.Figure We conclude by examining the small-study effects in the context of publication bias, shown with a Funnel Plot in Fig. 7) to 30 days . Time to transfusion was not associated with the studies’ effect size (p = 0.50).The median time to transfusion was available in 14 of the 19 studies considered, and varied between 3 days . It is suggested that in a high-risk population, passive immunization may play an important role due to the co-existing immunodeficiency and potentially long-lasting seronegativity). Interestingly, Libster et al.7 reported a lower risk of disease progression to severe respiratory failure in older adults when plasma was transfused early in the clinical course. Similarly, Simonovich et al.16 observed a worse clinical outcome in patients younger than 65 years compared with the enrolled elderly.As a main result we observed no statistically significant difference in all-cause mortality between the group of patients who received convalescent plasma treatment with the best standard medical care, and the group of patients who received the best standard medical care alone or with placebo. Some of the included RCTs7 and 1023 days after symptom onset, except Li et al. (30 days)6. Some authors20 suggest that the early administration of convalescent plasma leads to significant clinical improvement  and reduces the probability of disease progression. Based on the results of the included RCTs we suspect that convalescent plasma may be more effective in reducing disease progression when administered early in the clinical course, when patients have not achieved a sufficient titer of own neutralizing antibodies.According to our results, there is no clinical benefit in the context of reduction in mortality rate in patients who receive convalescent plasma therapy earlier in the clinical course, at the beginning of the symptoms. Most of the studies included in our meta-analysis reported a median time to plasma transfusion between 331 published a meta-analysis reporting a positive impact of convalescent plasma therapy on the reduction of all-cause mortality in COVID-19 patients, in contrast with our results. However, in addition to RCTs, Bansal et al. also included retrospective observational studies, case series and case reports. As a result of a subgroup analysis performed for ten RCTs from the same meta-analysis, there was no statistically significant reduction in all-cause mortality rate in the convalescent plasma group compared to the control group31, as well as in our study, which included RCTs only. Nevertheless, there are several case reports and case series which describe a significant improvement in the clinical status of the examined patients after plasma therapy33. According to these results, we presume that convalescent plasma therapy will not lead to a statistically significant clinical benefit in all severely ill COVID-19 patients, but there may be a particular group of patients who can benefit from plasma transfusion. Therefore, this meta-analysis does not support the routine use of convalescent plasma in all COVID-19-infected patients, but suggests that highly vulnerable patients with coexisting immunodeficiency due to advanced age, cancer treatment, immune disorders of different etiologies (suspected seronegative patients) may benefit from convalescent plasma therapy. Moreover, convalescent plasma may be beneficial in cases of high-risk exposure, as post-exposure prophylaxis, especially in the mentioned vulnerable patient populations.Interestingly, Bansal et al.16, where a single dose of 500 ml was administered). However, we assume that the titer of neutralizing antibody in each convalescent plasma unit can be considered a determining factor for the efficacy of convalescent plasma therapy. We hypothesize that the antibody content of plasma may be a more important factor than plasma volume; however, information on antibody titer is not available in all included studies. In addition, the timing of convalescent plasma administration seems to play an important role, and some authors7 suggest that early intervention  or 7 days  after symptom onset) is critical for clinical efficacy.Our study has some limitations which could affect the results. First, we did not analyze the information regarding the administered plasma volume and the IgG antibody titer in the transfused plasma. It is possible that in some of the RCTs included in our meta-analysis the antibody titer was not high enough to lead to any clinical change. In the included RCTs, the single dose of convalescent plasma ranged from 200 to 350 ml and was administered once or twice on the same day or on consecutive days  are exposed to and respond to the local viral ecology, so antibody production also changes depending on the underlying viral infection. The strength of our study is that we included high-quality peer-reviewed RCTs, all of which had control group, and therefore we provide comprehensive information about the efficacy of convalescent plasma therapy in reducing mortality rate in a general population infected with COVID-19.Second, in our meta-analysis we did not include any information regarding other medications administered to the patients in the analyzed RCTs. Thus, which medications and procedures formed the “best standard medical care” in each particular RCT was not well defined, and could lead to inaccuracy in the results. Third, there is no information on the specific variants responsible for each infection in the included studies. We can only guess which variant might be the cause of the underlying clinical condition based on the date (months) of patient inclusion. However, the study by O’Donnel et al.34.This systematic review and meta-analysis was prospectively registered on August 26, 2021 with the international Prospective Register of Systematic Reviews (PROSPERO registration number: CRD42021243629). Results are reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statementconvalescent plasma, plasma therapy, COVID-19, SARS-CoV-2, and mortality. After the bibliographic search was performed, all the potentially eligible titles and abstracts were screened, and full-length articles potentially meeting the inclusion criteria were evaluated. A manual search of the references of the included studies was also performed in order to supplement the electronic search. Renewed bibliographic search was conducted on January 29, 2022 and June 24, 2023 using the same databases and the same mesh terms.The literature search was conducted by two authors  independently. The systematic bibliographic research was conducted on the September 15, 2021 by searching for full-length articles focusing on the efficacy of convalescent plasma in reducing all-cause mortality in patients with COVID-19. Major medical databases screened were PubMed, Cochrane Library, Medline and Google Scholar. The search strategy consisted of the combination of the following mesh terms: The eligibility criteria for the meta-analysis were the following: (a) randomized controlled trials (RCTs) involving hospitalized patients with COVID-19; (b) studies analyzing the use of convalescent plasma as a treatment method in patients with COVID-19 in comparison the best medical treatment or placebo (as a control group); (c) studies with information about all-cause mortality rate; (d) full-text articles and (e) English-language literature. The reviewed studies included in our analysis focused on adult patients (˃ 18 years of age). Case reports, case series, observational studies, and studies investigating pregnant women were excluded.28. This method assessed a fixed set of potential sources of bias. Areas assessed included aspects of study design, conduct, and reporting. An assessment of the risk of bias resulting from the randomization process, deviations from planned measures, missing outcome data, outcome measurement, and selection of the reported outcome, as well as the overall risk of bias, could be rated as “low” or “high” risk of bias or indicate “some concern”. Overall risk of bias was rated as the least favorable score across all five domains. Any discrepancies were resolved by discussion between the authors.Titles and abstracts were independently screened by two reviewers . Any relevant full-text articles were further analyzed for eligibility using the pre-defined inclusion criteria. The same investigators independently performed data collection and all authors performed the analysis and interpreted the data together. Two reviewers  independently evaluated the risk of bias in each study using the revised Cochrane risk-of-bias tool for randomized trialsOnce the studies were determined to meet the inclusion criteria, two reviewers  independently reviewed and extracted the data related to trial design, total number of participants, age and gender of patients, disease severity, all-cause mortality rate and comorbidities of the patients for each eligible study. For the patients in the intervention arm, information was collected regarding the timing of convalescent plasma treatment from symptom onset, and amount of administered plasma. Data were collected and tabulated using Microsoft Excel Version 2019 . The extracted data were reviewed for accuracy by all authors.The binary primary outcome was all-cause mortality and the risk ratio (RR) of the convalescent plasma treatment group versus best standard medical care (control group) and its associated standard error was chosen as effect size.35 procedure. A Knapp-Hartung adjustment36 was used to calculate the confidence interval around the pooled effect. Sensitivity studies investigating the effect of the variance estimator replaced the PM-procedure with the Restricted Maximum Likelihood and Maximum Likelihood estimation37, the Empirical Bayes estimation38, DerSimonian-Laird estimation39 and the Sidik-Jonkman40 procedure as well as omitting the Knapp-Hartung adjustment. To pool the effect sizes of the selected studies, the Mantel–Haenszel method without continuity correction was used to calculate the weights of studies with binary outcome data42. The between-study heterogeneity was quantified with the I2 statistic and its associated 95% confidence intervals43 and prediction intervals based on the t-distribution are shown44.A random-effects model was employed to statistically pool the effect sizes of the selected studies: the variance of distribution of true effect sizes (τ^2) and its associated 95% confidence interval were estimated by the Paule-Mandel (PM)29 was used to examine the contribution of each study to the between-study heterogeneity. We further examined the influence of each study within a leave-one-out framework and presented pooled effect sizes and I2 values when each study is systematically removed from the entire set of selected studies.We examined the influence of each selected study on the pooled effect sizes with two methods: a diagnostic Baujat plot45 for asymmetry and formally examined possible asymmetry with Peters’ regression test46, in which the effect size is log-transformed first and subsequently regressed on the inverse of the sample size.To investigate the small-study effects in the context of possible publication bias, we visually inspected contour-enhanced funnel plotsFor exploratory analysis, we used a mixed-effect model accounting both for sampling error and between-study heterogeneity to assess the linear relationship between effect size and the study-level predictor “median time to plasma transfusion” (in days) in a meta-regression framework. Model fit was examined by the percentage of between-study heterogeneity attributable to the predictor. Statistical significance of the predictor and its associated p-value were assessed with a t-distributed test statistic using the Knapp-Hartung adjustment.47, in particular with the packages meta48, metafor49 and dmetar50.A p-value < 0.05 was considered statistically significant. All computations were performed with R version 4.0.5Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.