The expansion of machine learning applications across dif- ferent domains has given rise to a growing interest in tap- ping into the vast reserves of data that are being generated by edge devices. To preserve data privacy, federated learn- ing was developed as a collaboratively decentralized privacy- preserving technology to overcome the challenges of data silos and data sensibility. This technology faces certain lim- itations due to the limited network connectivity of mobile devices and malicious attackers. In addition, data samples across all devices are typically not independent and iden- tically distributed (non-IID), which presents additional chal- lenges to achieving convergence in fewer communication rounds. In this paper, we have simulated attacks, namely Byzantine, label flipping, and noisy data attacks, besides non-IID data. We proposed Robust federated learning against poisoning attacks (RFCaps) to increase safety and accelerate conver- gence. RFCaps incorporates a prediction-based clustering and a gradient quality evaluation method to prevent attack- ers from the aggregation phase by applying multiple filters and also accelerate convergence by using the highest quality gradients. In comparison to MKRUM, COMED, TMean, and FedAvg algorithms, RFCap has high robustness in the pres- ence of attackers and has achieved a higher accuracy of up to 80% on both MNIST and Fashion-MNIST datasets.

Introduction COVID-19 is known to induce plasma cell differentiation by elevating interleukin-6 level. Proliferation of dyscrasic plasma cells lead to production of monoclonal immunoglobulins. Cases with monoclonal gammopathy in COVID-19 have been reported in various countries. In this study, we present nine cases of Korean patients diagnosed with COVID-19 infection, and their consecutive electrophoresis results of monoclonal gammopathies. Material & Methods A total of 34 serial serum samples from 9 patients diagnosed with COVID-19 infection were analyzed for the protein electrophoresis, immunofixation, immunoglobulin quantification, SARS-CoV-2 antibody titers and interleukin-6 level assessments. Cases were classified into four groups based on the disease severity. Results Six cases revealed the presence of monoclonal paraprotein in serum electrophoresis results. Both two expired cases showed monoclonal bands, with one case of notable correlation with the increase of virus activity. Types of immunoglobulins were variable among six cases. Most of the cases showed high interleukin-6 levels and COVID-19 antibody titers, but their fluctuations did not correlate with monoclonal paraprotein patterns. Conclusions In conclusion, monoclonal paraproteins were observed with relatively higher frequency in the severe cases rather than mild cases. Longitudinal monitoring on monoclonal paraproteins associated with COVID-19 infection were proved to be transient in mild cases.

Introduction: OSA in children is an under-recognized and under-diagnosed disease and is an independent risk factor for poor asthma control and an increased number of exacerbations. The association of asthma and OSA is well documented in adults and to a lesser extent in the pediatric population. The association of AHI and lung function in children in a high-altitude city is not described in the literature. Methods: This cross-sectional observational study involved children with asthma (n=64) between 3 and 12 years of age referred to our center in Bogotá (2640m) for polysomnogram between 2012 and 2017. Clinical history, spirometry, oscillometry, and polysomnogram were carried out. OSA was defined from the polysomnographic point of view, as the presence of an obstructive Apnea hypopnea index ≥ 1/hour Results: A total of 64 children with asthma were included. 59.34% were boys and the median age was 7 years. 59.4% of the participants had moderate or severe asthma and 59.4% had uncontrolled asthma. A significant correlation was found between BMI and OAHI. Concerning pulmonary function, a significant negative correlation was found between pre-bronchodilator FEV1 and OAHI as well as a significant correlation between FEV1/FVC and CAHI. For oscillometry, R5 showed a significant negative correlation with AHI. Conclusions: We found in children with asthma living in a high-altitude city a significant and negative correlation between FEV 1 and R5 with AHI as well as a significant and negative correlation between FEV 1/FVC ratio and CAHI. These findings suggest a correlation between distal airway diameter and sleep apnea severity.

We present the first videos depicting the consumption of bats by rusty-spotted genets (Genetta maculata) inside a cave in the Republic of Congo. Following the implementation of a camera-trap monitoring protocol of interactions between cave bats and wildlife, we identified important genet activity in one of the caves in our study. Between 2022 and 2023, we recorded four events of bat or rodent consumption (including two with certainty on bats), one hunting attempt on bats and three feeding behaviors on insects. We detail the various behaviors and discuss the potential implications of genets consuming bats, rodents and insects, and in particular scavenging on dead bats. Finally, we address the potential implications of zoonotic pathogen transmission from bats to humans via genets through the bushmeat trade.

Background: Orthopedic related conditions are the leading contributor to disability worldwide, and corticosteroid injections are a staple in the management of musculoskeletal injuries, such as osteoarthritis, bursitis, or tendinopathy. Importantly, corticosteroid injections are not without risk, and certain populations, specifically, diabetic patients, are currently not eligible for injection, for fear of their glycemic effect, and thus unable to reap these life changing benefits. However, existing data on glycemic effects of corticosteroid injection are mixed, and there is no true systematic review to date summarizing the available data. Objective: Systematically synthesize risks of corticosteroid injection in diabetic patients. Purpose: This manuscript presents the intended methods for a systematic review and meta-analysis on the glycemic effects of corticosteroid injection. Methods: We will follow the Institute of Medicine, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidance. We will conduct a systematic search of PubMed (and Ovid MEDLINE), Cochrane Library Databases, Scopus, CINAHL, and clinical trial registries. Included in the review will be randomized trials, cohort studies, case-control studies, and case series, that evaluate the association between measures of blood glucose and steroid injection. Abstract screening and full-text screening will be independently dually reviewed. We will use the Cochrane revised Risk of Bias tool to assess risk of bias and GRADE to assess the overall quality of evidence. Results: We will present results for all studies and perform subgroup analyses, when possible, to evaluate impact of injection type, location, frequency and dose.

In this paper, BSA surface-imprinted magnetic Fe-Ni bimetallic oxide shell nanorods (m-FeNi@MIPs@PCBMA) were prepared with the assistance of poly(3-[[2-(methacryloyloxy)ethyl]dimethylammonium] propionate (PCBMA) in connection with the surface-imprinting technique. The Fe-Ni bimetallic oxide shell layer nanorods (m-FeNi) with magnetic responsiveness simplified the separation and recovery process of adsorbed materials. The controlled introduction of PCBMA facilitated the reduction of protein non-specific adsorption. At the optimal encapsulation ratio of 1:0.75 (Wm-FeNi@MIPs: WCBMA), m-FeNi@MIPs@PCBMA could adsorb 122.98 ± 5 mg/g of BSA within 80 min, and the value of the imprinting factor (IF) was also increased from 1.68 (m-FeNi@MIPs) to 3.95. In the mixed protein adsorption and real sample separation experiments, m-FeNi@MIPs@PCBMA could selectively separate BSA. Meanwhile, after seven adsorption-desorption experiments, the loss of BSA adsorption by the imprinted nanorods was only 15.9%, which had good reusability. Therefore, m-FeNi@MIPs@PCBMA has a broader application prospect in the field of protein separation and purification.

Real-time polymerase chain reaction (RT-PCR) remains the most prevalent molecular detection technology for sewage analysis but is plagued with numerous disadvantages, such as time consumption, stringent equipment requirements, and susceptibility to false negatives. In this study, we construct an automated robot-driven photoelectrochemical biosensing platform that utilizes the CRISPR/Cas12a system, to achieve fast, ultrasensitive, high specificity detection of biological loads in sewage. The Shennong-1 robot integrates several functional modules, involving sewage sampling and pretreatment to streamline the sewage monitoring. A screen-printed electrode is employed with a vertical graphene-based working electrode and enhanced with surface-deposited Au nanoparticles (NPs). CdTe/ZnS quantum dots (QDs) are further fabricated through the double-stranded DNA anchored on Au nanoparticles. Using cDNA template of Omicron BA.5 spike gene as a model, the photoelectrochemical biosensor demonstrates excellent analytical performance, with a lower detection limit of 2.93×102 zM and an outstanding selectivity at the level of single-base mutation recognition. Furthermore, the rapid, accurate detection of BA.5 in sewage demonstrates the feasibility of the photoelectrochemical platform for sewage monitoring. In conclusion, this platform allows early detection and tracking of infectious disease outbreaks, providing timely data support for public health institutions to take appropriate prevention and control measures.

The present investigation aims to evaluate the algal diversity and water quality status of four different freshwater resources. The study areas were two paddy fields in Thalakkulathur and two natural ponds in Kayanna, Kozhikode district, Kerala, for six months, from August 2022 to January 2023. The samples were collected according to standard procedures at various sites in the study area. During the collection, physical and chemical parameters were analyzed using the collected water samples separately in PET bottles. The observation and documentation of algae were done using the LABOMED Binocular Microscope Lx 400 and photographed using a Sony Cybershot Camera. Different algal species like Nostoc linckia, Oscillatoria annae, Chrococcus indicus, Anabaena sphaerica, Gloeocapsa rupestris of Cyanophyceae; Spirogyra baileyi, Chlorella vulgaris, Oedogonium cardiacum, various species like Cosmarium nitidulum, Staurastrum sp., Botryococcus braunii, Eudorina elegans, Selenastrum gracile, of Chlorophyceae; Rhodomonnas salina, Phacus manginii from Euglenophyceae, various species of Navicula, Gomphonema, Cymbella, Amphora pediculus, Hantzschia amphioxus, Fragillaria capucina from Bacillariophyceae, and Mishococcus confervicola from Xanthophyceae were found. Both terrestrial and aquatic plants present in the study area were also noted. The Shannon-Wiener index, Menhinick index, Nygaard’s index, Boyd’s index, and Palmer pollution index were evaluated using Hemocytometer readings.

In view of the coexistence of lithium and boron in the carbonate-type brine, a novel process for the synchronous recovery of lithium and boron from brine using two tandem fixed-bed adsorbers, is developed, where the first column is packed with PVB-HTO ion-sieves for lithium recovery, and the second column is packed with N-methylglucamine resins for boron recovery. After the recovery of lithium and boron from brine, two packed columns are desorbed independently through acidic solution washing to obtain Li-rich solution and B-rich solution, respectively. The carbonate-type brine mainly contains cations of Na+, K+, Li+ and anions of Cl-, 〖“SO” 〗_4^(2-) , 〖“CO” 〗_3^(2-) and 〖“B(OH)” 〗_4^- , where carbonate and borate in brine have the pH-buffering action to accept or neutralize the released H+ ions enhancing the Li+/H+ ion-exchange. The feasibility and effectiveness of the proposed process is validated experimentally for the synchronous recovery of lithium and boron from brine.

A ultra-high voltage (UHV) shunt reactor is accompanied by continuous strong vibration during operation, and its internal partial discharge is often associated with mechanical vibration of its electrodes. To explore the mechanism of the effect of electrode vibration on the partial discharge inside the reactor, a joint vibration-partial discharge test platform was established based on a partial discharge model of the oil-paper insulated needle board electrode, a joint vibration-partial discharge test platform was established in this paper. The influences of the amplitude and frequency of vibration on the partial discharge characteristics of the needle board in oil under power frequency voltage were studied, and the mechanism of influence of vibration on the partial discharge was explored. The results show that with the increases in the amplitude and frequency of vibration, the discharge process is intensified, the discharge repetition rate and the maximum discharge quantity increase, and the negative half-cycle discharge pattern exhibits “zero crossing”. The discharge intensity of the pin-plate electrode is affected by velocity of the oil flow velocity and field intensity; the field intensity has a more significant effect on the partial discharge. When partial discharge occurs without vibration, the characteristic gases produced by the needle-plate discharge are mainly H2 and C2H2, and contain a small amount of CH4 and C2H4. With the increases in the amplitude and frequency of vibration, the content of each characteristic gas increases, and vibration changes the spatial distribution of dissolved gas in the oil by driving the motion of the insulating oil around the electrode.

Glioblastoma (GBM) is the most common and aggressive brain tumor with poor outcome. Immune checkpoint inhibitors (ICIs) have been tested in GBM and, despite disappointing results, the identification of a small subgroup of responders underlies the need to improve our understanding of the tumor microenvironment (TME) immunity. The aim of this study was to determine whether the expression of selected immune checkpoints on tissue-resident memory T cells (Trm) may predict patient outcome. We conducted a single cohort observational study. Tumor samples were collected from 45 patients with histologically confirmed, IDH wild type, GBM (WHO grade IV) and processed to obtain single cell suspensions. Using multiparametric flow cytometry and uni/multivariate analyses, patients were assessed for the correlation of Trm with overall survival (OS) and progression-free survival (PFS). High and low frequency of Trm expressing PD1 and TIM3 was found to be linked to clinical outcome. In fact, low frequency of Trm expressing PD1 or TIM3 or both markers defined subgroups as independent positive prognostic factors for patient survival. On multivariate analysis, low CD8+CD103+PD1+TIM3+ Trm and KPS ≥70 were confirmed to be the most predictive independent factors associated with longer OS (HR [95%CI]: 0.14 [0.04 - 0.52] p˂0.001, 0.39 [0.16 - 0.96] p=0.04, respectively). The CD8+CD103+ Trm subgroups also resulted age-linked predictors for survival in GBM.

Tumor-infiltrating lymphocytes (TILs) are to be subject to clinical applications by cultured TIL infusion in vivo for adoptive cell therapy (ACT) and by ex vivo TIL analysis for determining immune characteristics to kill autologous tumor cells so that TIL has been administrated tumor’s patients to immune-cell therapy and analyze patients’ immune characteristics for tumor diseases. To study the TIL features, we have established quantitative network modeling by TIL’s TCR signaling pathway, IL2 pathway, and TGF-β pathway for personalized immunotherapy for more than fifteen years. However, machine-learning analysis still has some challenges under the traditional quantitative pathway for network configurations to apply for patient treatment. For example, multiple protein complexes competing for downstream DNA binding-site or protein-protein complex will generate different effects. To address this question, we report here a temporal-spatial quantification network, termed a spatial-timely quantification network, to address the spatial-timely competition of complex proteins binding to downstream proteins or DNA in network analysis. After studying spatial-timely quantitative network modeling by TGF-β pathway activity in spatial-timely order, we discover that multiple protein complexes using spatial-timely quantitative networks are much better than traditional quantitative networks. Once the new system modeling is established, we can further analyze all pathways, such as the TCR signaling pathway and IL2 pathway from TIL, for different immunotherapy.

Background: Ubiquitin and ubiquitin-like (UUL) modifications play pleiotropic functions and are subject to fine regulatory mechanisms frequently altered in cancer. However, the comprehensive impact of UUL modification on breast cancer remains unclear. Methods: Transcriptomic and clinical data of breast cancer were downloaded from TCGA and GEO databases. Molecular subtyping of breast cancer was conducted using the NMF and CIBERSORT algorithms. Prognostic genes were identified via univariate, lasso and multivariate Cox regression analyses. Clinical pathological features, immune cell infiltration, immune therapeutic response and chemotherapy drug sensitivity were compared between groups using the Wilcoxon test. Survival analysis was performed using the Kaplan-Meier method and log-rank test. Results: In breast cancer, 63 UUL modification-related genes were differentially expressed, with 29 up-regulated and 34 down-regulated genes. These genes were used to generate two UUL modification patterns that exhibited significant differences in prognostic features and immune cell infiltration. The UUL modification patterns were associated with 2038 differentially expressed genes that were significantly enriched in nuclear division, chromosome segregation, neuroactive ligand-receptor interaction, cell cycle, and other biological processes. Of these genes, 425 were associated with breast cancer prognosis, which enabled the classification of breast cancer into two clusters with significantly distinct prognoses. We developed a prognostic model, UULscore, which comprised nine genes and showed a significant correlation with partial immune cell infiltration. Furthermore, UULscore demonstrated potential predictive value in breast cancer overall survival prediction, immune therapeutic response, and chemotherapy drug sensitivity. UULscore, stage, radiotherapy, and chemotherapy were identified as independent prognostic factors for breast cancer. Based on these factors, a nomogram model was constructed, which demonstrated exceptional prognostic predictive performance. Conclusion: In conclusion, we identified two UUL modification-derived molecular subtypes in breast cancer, and have successfully constructed a risk scoring model that holds potential value in prognosis, immune infiltration, immune therapeutic response, and chemotherapy sensitivity.

Recent magnetoencephalography (MEG) studies have reported that functional connectivity (FC) and power spectra can be used as neural fingerprints in differentiating individuals. Such studies have mainly used correlations between measurement sessions to distinguish individuals from each other. However, it has remained unclear whether such correlations might reflect a more generalisable principle of individually distinctive brain patterns. Here, we evaluated a machine-learning based approach, termed latent-noise Bayesian reduced rank regression (BRRR) as a means of modelling individual differences in the resting-state MEG data of the Human Connectome Project (HCP), using FC and power spectra as neural features. First, we verified that BRRR could model and reproduce the differences between metrics that correlation-based fingerprinting yields. We trained BRRR models to distinguish individuals based on data from one measurements and used the models to identify subsequent measurement sessions of those same individuals. The best performing BRRR models, using only 20 spatiospectral components, were able to identify subjects across measurement sessions with over 90% accuracy, approaching the highest correlation-based accuracies. Using cross-validation, we then determined whether that BRRR could generalize to unseen subjects, successfully classifying the measurement sessions of novel individuals with over 80% accuracy. The results demonstrate that individual neurofunctional differences can be reliably extracted from MEG data with a low-dimensional predictive model.

The advent of social media has catalyzed a paradigm shift in the way information is disseminated and consumed, giving rise to novel phenomena such as viral trends and information diffusion. This review article provides an in-depth scholarly examination of network science as applied to social media analysis, focusing on the mathematical formulations, algorithmic techniques, and interdisciplinary methodolo-gies that underpin the field. By exploring graph theory, community detection, scale-free networks, centrality measures, machine learning applications, and cultural influences, this study offers a comprehensive and nuanced understanding of network structures and dynamics. As a top-tier contribution to the field of computer science, this review serves as a nexus for the interdisciplinary study of network science, providing valuable insights and directions for future research in the analysis of information diffusion and viral trends within social media platforms.

A document by Charles Kusniec. Click on the document to view its contents.

Considering three viral transmission routes— fomites, droplets, and aerosols— two routes have been the focus of debate about the relative role of droplets and aerosols in SARS-CoV-2 infection. We seek to quantify infection risk in an enclosed space via short-range and long-range airborne transmission to inform public health decision making. Data from five published studies were analyzed to predict relative exposure at distances of 1 m and farther, mediated by droplet size divided into two bins: ≧ 8 μm (medium and large droplets that we call “droplets”) and < 8 μm (small droplets that we call “aerosols”). The results at 1 m from an infectious individual were treated as a boundary condition to model infection risk at shorter and longer distance. At all distances, infection risk was treated as the sum of exposure to aerosols and droplets. It was assumed that number of virions is proportional to particle volume. The largest infection risk occurred close to the infectious individual, and out to approximately 1m, droplets and aerosols both contributed. Farther away, the largest risk was due to aerosols. For one model, droplet exposure disappeared at 1.8 m. Policy concerning physical distancing for meaningful infection reduction relies on exposure as a function of distance, yet within this construct particle size determines respiratory deposition. This two-fold distance effect can be used to evaluate measures such as plexiglass barriers, masking, and ventilation.

Meta-ecosystems are the largest and probably most complex structures investigated in ecol-ogy. Because of their complexity they are often separated into their respective ecosystems and then studied in isolation. This is often done without analysing whether an understanding of the individual ecosystems can lead to a proper understanding of the meta-ecosystem, like-ly because we lack clear guidelines when such a separation is sufficient. We here propose four conceptual models for which a separation appropriately approximates the full dynamics. For each of these models we provide empirical evidence from riparian meta-ecosystems to showcase that the terrestrial or aquatic ecosystem should likely not be studied in isolation. Finally, we provide a new theoretical framework to assess how strongly two ecosystems are linked and discuss how this framework might be applied in future empirical research.

Increasing human pressures threaten fish diversity, with potentially severe but unknown consequences to the functioning of riverine food webs. Using a 17-years dataset from multi-trophic fish communities, we investigated the long-term effects of human pressure (represented by human footprint) on the species richness and energy flux across fish food webs, a measure of ecosystem functioning. Combining metabolic scaling theory and ecological network principles, we calculate the annual energy flux through varying trophic compartments (i.e., top-carnivore, mesocarnivore, detritivore, and omnivore). Species richness across all trophic compartments was positively associated with energy flux. However, species richness decreased over time, alongside with the energy flux at the whole-network level, which reduced by 75%. Human pressure negatively affected both species richness and energy flux, and the negative impacts of human pressure have intensified over time. These results illustrate how human pressure can reduce diversity and erode the energy flux through food webs, with long-term negative implications for the functioning of natural ecosystems

Objective To describe the maternal and neonatal outcomes of pregnant women with fasting blood glucose <4.7 mmol/L for whom the diagnosis and treatment pathways differed due to COVID-19. Design, setting and population. An Australian population-based data linkage study involving 3891 women. Methods: Pregnant women with fasting blood glucose <4.7 mmol/L between 24-32 weeks of gestation were categorized into three groups: women diagnosed with gestational diabetes by postprandial hyperglycaemia (PPGDM; n= 226); normal glucose tolerance group (NGT; n= 3125) and women not tested for postprandial hyperglycaemia, mostly during COVID-19 (LFBG; n= 540). The maternal and neonatal outcomes between groups were compared using generalized linear models. Main outcome measures: Risk of Large for gestational age baby. Results: Mean difference in birth weight (-115 grams, 95%CI -175.5— -32.6) and z-scores (-0.11, 95%CI -0.26— 0.02) were lower in the PPGDM group compared to the LFBG group. However, there were no differences between PPGDM and LFBG groups in the risk of large for gestational age (LGA) infants (RR: 1.09, 95%CI 0.66— 1.78). The maternal and neonatal outcomes in the LFBG group were comparable to the NGT group. Conclusion: The risk of LGA was similar across all groups regardless of post prandial glycemia and its treatment. Overall, women with a fasting blood glucose <4.7 mmol/L can be considered as a low-risk group for adverse perinatal outcomes.

A document by Annika Sinha. Click on the document to view its contents.

Purpose The global older population is growing rapidly, and the rise in polypharmacy has increased potentially inappropriate medication (PIM) encounters. PIMs pose health risks, but detecting them automatically in large medical databases is complex. This review aims to uncover PIM prevalence in people aged over 65 using health databases and emphasize the risk of underestimating PIM prevalence due to underutilization of detection tools. Methods This study conducted a broad search on the Medline database to identify articles about the prevalence of PIMs in older adults using various databases. Articles published between January 2010 and June 2023 were included, and specific criteria were applied for study selection. Two literature reviews conducted before our study period were integrated in order to obtain a perspective from the 1990s to the present day. The selected papers were analysed for variables including database type, screening method, adaptations, and PIM prevalence. The study categorized databases and screening methods for clarity, examined adaptations, and assessed concordance among different screening methods. Results This study encompassed 48 manuscripts, covering 58 sample evaluations. The mean prevalence of PIMs within the general population aged over 65 was 29.3% (95%CI: 18.7%- 37.5%). Significant heterogeneity emerged in both the utilized databases and the detection methods. Adaptation of original methods was observed in 86.2% (50/58) of cases. Half of the methods used for assessing PIMs belonged to the “List of drugs” category. About a third of the studies employed less than half of the original criteria after adaptation. Only three studies used over 75% of the original criteria and more than 50 criteria. Conclusion This extensive review highlights PIM prevalence among the older adults, emphasizing method intricacies and the potential for underestimation due to data limitations and algorithm adjustments. The findings call for enhanced methodologies, transparent algorithms, and a deeper understanding of intricate rules’ impact on public health implications

Background: Valsartan is commonly used for cardiac conditions. In 2018, the Food and Drug Administration recalled generic valsartan due to the detection of impurities. Our objective was to determine if heart failure patients receiving valsartan at the recall date had a greater likelihood of unfavorable outcomes than patients using comparable antihypertensives. Methods: We conducted a cohort study of Optum’s de-identified Clinformatics® Datamart (July 2017-January 2019). Heart failure patients with commercial or Medicare Advantage insurance who received valsartan were compared to persons who received non-recalled angiotensin receptor blockers (ARBs) and angiotensin converting enzyme-inhibitors (ACE-Is) for 1 year prior and including the recall date. Outcomes included a composite for all-cause hospitalization, emergency department (ED), and urgent care (UC) use and a measure of cardiac events which included hospitalizations for acute myocardial infarction and hospitalizations/ED/UC visits for stroke/transient ischemic attack, heart failure or hypertension at 6-months post-recall. Cox proportional hazard models with propensity score weighting compared the risk of outcomes between groups. Results: Of the 87,130 patients, 15% were valsartan users and 85% were users of non-recalled ARBs/ACE-Is. Valsartan use was not associated with an increased risk of all-cause hospitalization/ED/UC use six-months post-recall (HR 1.00; 95%CI 0.96–1.03), compared with individuals taking non-recalled ARBs/ACE-Is. Similarly, cardiac events 6-months post-recall did not differ between individuals on valsartan and non-recalled ARBs/ACE-Is (HR 1.04; 95%CI 0.97–1.12). Conclusions: The valsartan recall did not affect short-term outcomes of heart failure patients. However, the recall potentially disrupted the medication regimens of patients, possibly straining the healthcare system.

Antibody mimetics is a novel antibody engineering approach after the development of polyclonal, monoclonal antibodies, and genetically engineered antibody fragments. Inspired by the structure and function of natural antibodies, antibody mimetics offer many advantages over conventional antibodies and can be constructed by protein-directed evolution, peptide design and synthesis, or fusion of complementarity-determining regions through intervening framework regions. A series of parent protein/peptide structures and technical roadmaps have been established to induce better recognising properties, superior affinity, stability, penetrability, and cost-effectiveness of the designed mimetics. This article aims to summarise the evolution of antibody mimetics engineering, illustrate the highlights and hotpots in this research field using scientometric analysis, and give an anticipatory analysis on this increasing research topic.