Pulmonary hypertension (PH) is a serious condition linked to immune-system dysfunction. Myositis-specific/associated antibodies (MSA/MAA) play a role in idiopathic inflammatory myopathy (IIM) and interstitial lung disease (ILD), but their significance in PH remains unclear. We believe the presence of these antibodies, may be underestimated. This study analyzed adult PH patients without pre-existing IIM for MSA/MAA prevalence using a line-blot assay. We compared PH patients with and without ILD signs to a cohort clinically suspected of IIM/ILD (n=558). Our PH cohort (n=121) showed a significantly higher prevalence of overall weak positive MSA/MAAs and positive overlap syndrome-associated MAAs than the suspected IIM/ILD group (p

Due to the health crisis caused by SARS-CoV-2, the creation of a new vaccine platform based on mRNA was implemented. Globally, around 13.32 billion COVID-19 vaccine doses of diverse platforms have been given, and up to this date, 69.7% of the total population received at least one injection of a COVID-19 vaccine. Although these vaccines prevent hospitalization and severe forms of the disease, increasing evidence has shown they do not produce sterilizing immunity, allowing people to suffer frequent re-infections. Recent research has also raised concerns that mRNA vaccines could induce immune tolerance, which, added to that caused by the virus itself, could complicate the clinical course of a COVID-19 infection. Furthermore, recent investigations have found high IgG4 levels in people who were administered two or more injections of mRNA vaccines. It has been suggested that an increase in IgG4 levels could have a protecting role by preventing immune over-activation, similar to that occurring during successful allergen-specific immunotherapy by inhibiting IgE-induced effects. Altogether, evidence suggests that the reported increase in the IgG4 levels detected after repeated vaccination with the mRNA vaccines is not a protective mechanism; rather, it may be a part of the immune tolerance mechanism to the spike protein that could promote unopposed SARS-CoV2 infection and replication by suppressing natural antiviral responses. IgG4-induced suppression of the immune system due to repeated vaccination can also cause autoimmune diseases, promotes cancer growth, and autoimmune myocarditis in susceptible individuals.

Encephalitis is a condition with a variety of etiologies, clinical presentations and degrees of severity. The causes of these disorders include both neuroinfections and autoimmune diseases in which host antibodies are pathologically directed against self antigens. In autoimmune encephalitis, autoantibodies are expressed in the central nervous system. The incidence of this disease is approximately 4% of all reported cases of encephalitis. Autoimmune encephalitis can be induced by antibodies against neuronal surface antigens such as N-methyl-D-aspartate-activated glutamate receptors (NMDAR), ⍺-amino-3-hydroxy-5-methyl-4-isoxazole propionate receptors (AMPAR) or gangliosides GQ1b, DPPX, CASPR2, LGI1, as well as by antibodies against neuronal intracellular antigens. The paper presents a number of both mental and neurological symptoms of autoimmune encephalitis. Moreover, the coexistence of psychoses, neoplastic diseases and the methods of diagnosing autoimmune encephalitis are discussed. Attention was also drawn to the fact that early diagnosis, as well as early initiation of targeted treatment, increases the chance of a successful course of the therapeutic process.

The term "code smell" refers to an indication of a problem with the quality of source code. Numerous studies have been conducted to identify problematic features in source code. Initially, the focus was on utilizing metric-based and heuristic-based approaches. In recent years, however, there has been a shift towards using machine learning and deep learning (DL) techniques for smell detection. Nevertheless, the current algorithms are still considered to be in the early stages of development. Recognizing the challenges associated with identifying smells using DL methods, both academics and software developers have made efforts to address these obstacles. This work involves constructing and evaluating new DL models for code smell detection. Two models are built upon stacked autoencoders, employing a hybrid architecture that combines bidirectional long short-term memory and convolutional neural network components.

Monoclonal antibody (mAb) therapy has revolutionized the treatment of various diseases, including cancer and autoimmune disorders. However, the emergence of SARS-CoV-2 variants, as well as other pathogenic variants, poses challenges in maintaining the therapeutic efficacy of mAbs. In this mini review article, we aim to explore the strategies to overcome variants resistance in mAb therapy against viral infections. Firstly, we discuss the mechanisms through which variants can evade the neutralizing effects of mAbs. Understanding these mechanisms is crucial in developing targeted approaches to combat resistance. Next, we delve into the strategies being pursued to address variants resistance. These include developing new mAbs or antibody cocktails that target multiple epitopes, engineering mAbs with improved binding affinities or enhanced neutralizing capabilities, and exploring alternative therapeutic modalities such as bispecific antibodies or antibody-drug conjugates. Furthermore, we highlight the role of computational modeling and artificial intelligence in predicting variant escape mutations and aiding in the design of more effective mAbs. Additionally, we examine the importance of continuous surveillance and monitoring of emerging variants to inform treatment strategies. This article emphasizes the urgent need for proactive approaches to tackle variants resistance in mAb therapy. By combining innovative design strategies, computational modeling, and vigilant surveillance, we can maintain the therapeutic effectiveness of mAbs and stay ahead in the battle against evolving pathogens and viral infections.

IgA vasculitis is a hypersensitivity vasculitis, which is usually self-limiting. Renal involvement is the most damaging long-term complication of IgA vasculitis, happening in 20% - 100% of cases. Some factors have been reported to be associated with renal involvement in IgA vasculitis; however, no biomarker has been proved as a risk factor for renal involvement and its severity yet. We followed 48 patients with a confirmed diagnosis of IgA vasculitis for six months. We checked these patients for renal involvement by microscopic urine examination. We checked aPL antibodies in all patients on admission and 12 weeks later. Urinalysis showed renal involvement in 14 of 48 patients with IgA vasculitis (29.16%). Antiphospholipid antibodies were positive in 9 patients with IgA vasculitis and renal involvement (9 out of 14, 64.28%), while they were positive in only six patients with IgA vasculitis without renal involvement (6 out of 34, 17.64%), showing a moderate correlation between positive aLP and renal involvement in patients with IgA vasculitis, with a kappa index of 0.457. Serum aPL antibodies, as a tool to predict renal involvement in IgA vasculitis, show a sensitivity of 64.3%, a specificity of 82.4%, PPV of 60.0%, and NPV of 84.8%, demonstrating that a positive serum aPL antibody can be used to positively predict the renal involvement, while a negative result is not strong enough to rule out future renal involvement.

Several vaccines against COVID -19 have been developed and licenced to enhance the immune response against SARS-CoV-2. Similarly, infection with SARS-CoV-2 before vaccination has been shown to provide significant protection against severe infection and hospitalisation. The aim of this study was to investigate the effect of three doses of Sinopharm vaccine and SARS-CoV-2 infection on the specific immune response in 103 volunteers, measuring neutralizing antibodies, anti-S1 IgG, anti-RBD IgM, anti-N IgM, anti-N IgG antibodies and interferon γ. Our results showed that the presence of cardiovascular diseases increased the level of anti-N-IgG antibodies, while endocrinological diseases decreased the level of neutralizing antibodies and anti-N-IgG antibodies, suggesting that these diseases alter the effect of vaccine immunity. In addition, there was a significant decrease in anti-S1 IgG levels 6 months and in anti-N IgG levels 18 months post-infection, while neutralizing antibody and interferon γ levels were constant at 3, 6 and 18 months post-infection. Therefore, our results confirm the importance of hybrid immunity as the strongest and most durable compared to exclusively natural or vaccine-induced immunity. Significant positive correlations were found between humoral and cellular immunity markers: neutralizing antibodies, anti-S1 IgG, anti-N IgG and interferon γ, indicating a unique coordinated response specific to COVID-19.

This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS). These data sets include BI (bodily injury), PD (property damage) and COLL (collision) coverage, each documenting policy characteristics and claims across a four year period. The datasets are explored, including summaries of all variables, then the methods for modeling are set forth. Models are tuned and the tuning results are displayed, after which we train the final models and seek to explain select predictions. All of the code will be made available on GitHub. Data was provided by a private insurance carrier to the CAS after anonymizing the data set. This data is available to actuarial researchers for well-defined research projects that have universal benefit to the insurance industry and the public. Our hope is that the methods demonstrated here can be a good foundation for future ratemaking models to be developed and tested more efficiently.

Botnet Attacks are one of the many types of ways to attack users in their day to day browsing, often of different trends and techniques to take your information without your control. Most firewalls can amply protect you against these trends and techniques, but scarily enough, recent articles and research have shown that they are starting to bypass said firewalls through layers of attacks, confusing your best line of defense. In this Report, we will be looking into exactly what are Botnets and the different subsets, what we can do to combat Botnet Attacks, where do they come from, their origin, the analysis of their kit and what we can put together from information collected. Through this we will be able to reach a conclusion of what we can use to possibly mitigate the effect they have on the internet and stop them from enacting their purpose.

Systemic autoimmune diseases (SAIDs) such as systemic lupus erythematosus (SLE), systemic sclerosis (SSc) and rheumatoid arthritis (RA) are fully related to the unregulated innate and adaptive immune systems involved in their pathogenesis. They have similar pathogenic characteristics including the interferon signature, loss of tolerance to self-nuclear antigens, and enhanced tissue damage like necrosis and fibrosis. Glucocorticoids and immunosuppressants, which have limited specificity and are prone to tolerance, are used as the first-line therapy. A plethora of novel immunotherapies have been developed including monoclonal and bispecific antibodies, and other biological agents to target cellular and soluble factors involved in disease pathogenesis such as B cells, co-stimulatory molecules, cytokines or their receptors, and signalling molecules. Many of these have shown encouraging results in clinical trials. CAR-T cell therapy is considered the most promising technique for curing autoimmune diseases, with recent successes in the treatment of SLE and SSc.

Background: Antiphospholipid antibodies (aPL) are a laboratory criterion for the classification of antiphospholipid syndrome (APS), and are known to cause clinical symptoms: vascular thrombosis or obstetric complications. It is suggested that aPL may be associated with thromboembolism in severe COVID-19 cases. Aim: to combine clinical data with aPL findings at 4 time points (admission, worsening, discharge, 3-month follow-up) in patients hospitalized with COVID-19 pneumonia. Methods: current and past history of thrombosis and obstetric complications; aPL determined at 4 time points: anticardiolipin (aCL), anti-β2-glycoprotein I (anti- β2GPI) and antiphosphatidylserine/prothrombin (aPS/PT) of the IgM, IgG or IgA isotypes. Results: 111 patients with COVID-19 pneumonia were enrolled. During hospitalization, 7 patients died, 3 of them due to pulmonary artery thromboembolism (none was aPL positive). Only one of the five who developed pulmonary artery thrombosis was aPL positive. Of 9/101 patients with a history of thrombosis, 5 had arterial thrombosis and none was aPL positive at admission and follow-up; 4 had venous thrombosis and one was aPL positive at all time points (newly diagnosed APS). Of these 9/101 patients, 55.6% were transiently aPL positive at discharge only, compared to 26.1% without history of thrombosis (p=0.05). Conclusions: aPL were not associated with fatal outcomes and vascular thrombosis; aPL were transiently positive in more than half of patients with a history of thrombosis.

In the pre era of synthetic antibodies, pharmaceutical companies depend on finding novel drugs from medicinal plants and other traditional resources; while in present, technological advances in biology, computer and robotics give the researchers the ability to rewrite and edit DNA in order to synthesize very large sets of drug candidates; these novel and improved candidates serves the basis for creating another library of drug candidates and so on until we find the right biomolecule for the disease of interest. all these technologies combined together to synthesize therapeutic antibodies for many types of cancer, autoimmune diseases, and infectious diseases, that can address diseases much more readily to very rapidly get therapeutics into patients so that we can potentially have an impact on disease. The antibodies mechanism is recognize and bind to disease cells and pinpoint the immune system to attack those cells effectively. Now a days, they dependent on computational approach to guide and accelerate the process of antibodies engineering by combination of selection system and use of high-throughput data acquisition and analysis to build and construct populations of next generation antibodies that are thermo-stable, non-immunogenic as possible, and to be administered to many humans as possible. In this review, I will discuss the latest in silico methods for antibodies engineering.

Auto-labeling is one of the main challenges in 3D vehicle detection. Auto-labeled datasets can be used to identify objects in LiDAR data, which is a challenging task due to the large size of the dataset. In this work, we propose a novel methodology to generate new 3D based auto-labeling datasets with a different point of view setup than the one used in the most recognized datasets (KITTI, WAYMO, etc.). The performance of the methodology has been further demonstrated with the development of our own dataset with the auto-generated labels and tested under boundary conditions on a bridge in a fixed position. The proposed methodology is based on the YOLO model trained with the KITTI dataset. From a camera-LiDAR sensory fusion, it is intended to auto-label new datasets while maintaining the consistency of the Ground Truth. The main contribution of this work is a novel methodology to auto-label autonomous driving datasets using YOLO as the main labelling system. The performance of this approach is measured retraining the contrast models of the KITTI benchmark.

We describe a multi-resolution approach for audio classification and illustrate its application to the open data set for environmental sound classification. The proposed approach utilizes a multi-resolution based ensemble consisting of targeted feature extraction of approximation (coarse scale) and detail (fine scale) portions of the signal under the action of multiple transforms. This is paired with an automatic machine learning engine for algorithm and parameter selection and the LSTM algorithm, capable of mapping several sequences of features to a predicted class membership probability distribution. Initial results show an improvement in multi-class classification accuracy.

Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, the localization system based on received signal strength (RSS) is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm, named joint denoising auto-encoder (JDAE), which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on UJIIndoorLoc dataset and Tampere dataset, experimental results show that the proposed model increases the positioning accuracy dramatically comparing with other existing methods.

With the fast development of the mobile Internet, the online platforms of social networks have rapidly been developing for the purpose of making friends, sharing information, etc. In these online platforms, users being related to each other forms social networks. Literature reviews have shown that social networks have community structure. Through the studies of community structure, the characteristics and functions of networks structure and the dynamical evolution mechanism of networks can be used for predicting user behaviours and controlling information dissemination. Therefore, this study proposes a deep community detection method which includes (1) matrix reconstruction method, (2) spatial feature extraction method and (3) community detection method. The original adjacency matrix in social network is reconstructed based on the opinion leader and nearer neighbors for obtaining spatial proximity matrix. The spatial eigenvector of reconstructed adjacency matrix can be extracted by an auto-encoder based on convolution neural network for the improvement of modularity. In experiments, four open datasets of practical social networks were selected to evaluate the proposed method, and the experimental results show that the proposed deep community detection method obtained higher modularity than other methods. Therefore, the proposed deep community detection method can effectively detect high quality communities in social networks.

This study presents an instant preview and analysis system implementation of intelligent stereo cameras (ISCs). A parameter optimization prototype adopted the instant preview and analysis system of the ISCs has been achieved the automatic alignment function, and obtained optimal stereo films with the automatic alignment function by adjusting gap and angle between dual cameras. The instant preview and analysis system of the ISCs with parameter optimization can enhance the quality of stereo films effectively and reduce filmed errors and save retouched cost and time in harsh filmed environment.

The vast majority of antibodies generated against a virus will be non-neutralising1. However, this does not denote an absence of protective capacity. Yet, within the field there is typically a large focus on antibodies capable of directly blocking infection (neutralising antibodies, nAbs) of either specific viral strains or multiple viral strains (broadly-neutralising antibodies, bnAbs). More recently, a focus on non-neutralising antibodies (nnAbs), or neutralisation-independent effects of nAbs, has emerged. These can have additive effects on protection or in some cases, be a major correlate of protection. As their name suggests, nnAbs do not directly neutralise infection but instead, through their Fc domains, may mediate interaction with other immune effectors to induce clearance of viral particles or virally infected cells. nnAbs may also interrupt viral replication within infected cells. Developing technologies of antibody modification and functionalisation may lead to innovative biologics that harness the activities of nnAbs for antiviral prophylaxis and therapeutics. In this review, we discuss specific examples of nnAb actions in viral infections where they have known importance. We also discuss the potential detrimental effects of such responses. Finally, we explore new technologies for nnAb functionalisation to increase efficacy or introduce favourable characteristics for their therapeutic applications.

Background and Objectives: Thyroid disease has been associated with autoimmune disorders. As systemic lupus erythematosus (SLE) is a systemic autoimmune disease with diverse manifestations spanning across all organ systems, the relationship of SLE with thyroid disorders needs investigation. In particular, the relationship of SLE with autoimmune thyroid disease has attracted the interest of the research community. The aim was to evaluate the relationship of SLE with autoimmune thyroid disease. Materials and Methods: A cohort of 45 consecutive patients, mean age 47,97 years (range 21-79 years) and 45 age and sex-matched controls were prospectively studied over a period of 12 months for the presence of thyroid disease and the prevalence of antithyroid antibodies. Results: Four patients (8.9%) were found to suffer from primary hypothyroidism, 5 (11.11%) from subclinical hypothyroidism and 1 (2.22%) from hyperthyroidism, whereas 1 (2.22%) of the controls had primary hypothyroidism and 1 (2.22%) had hyperthyroidism. Five patients (11.11%) had a thyroid hormone profile compatible with the presence of euthyroid sick syndrome. Thyroid peroxidase (TPOab) and thyroglobulin (Tgab) antibodies were detected in 20/45 and 15/45 of the SLE population and in 7/45 and 5/45 of the controls, respectively (p<0.05, chi square test). Conclusions: In conclusion, the incidence of clinical thyroid disease is greater amongst SLE patients than in a control population and in a significant number of these patients antithyroid antibodies are detectable. Thus, a subset of lupus patients appears to be predisposed to the development of thyroid disease and this should be considered when evaluating patients with SLE.

Importance: p53 is an unequivocal tumor suppressor altered in half cancers. The immune system produces systemic p53 autoantibodies (p53 Abs) in many cancer patients. Objective: The focus of this systemic review and meta-analysis is on the prognostic value of p53 Abs expressed in the serum of patients with solid tumors. Data Sources: All the clinical investigations were searched on PubMed, MBase and Cochrane from 1993 reporting the first study until May 2021. Study Selection: Studies were included that met the following criteria: 1) participants with cancer; 2) outcome results expressed in relation to the presence of a p53 antibody; 3) a primary outcome (disease free survival, overall survival or progression free survival) expressed as hazard ratio (HR). The following exclusion criteria were used: 1) insufficient data available to evaluate outcomes; 2) animal studies; 3) studies with less than 10 participants. 1333 potentially relevant articles; studies as duplicates, non-patients studies or reviews were excluded. After viewing the titles and abstracts of the 52 remaining studies, the full texts of 34 studies were retrieved and 12 studies were included in the analysis. Data Extraction and Synthesis: PRISMA guidelines were used for abstracting and assessing data quality and validity by three independent observers. The summary estimates were generated using a fixed-effect model (Mantel–Haenszel method) or a random-effect model (DerSimonian–Laird-method) depending on the absence or presence of heterogeneity (I2). Main Outcome(s) and Measure(s): The primary study outcome was to determine the prognostic value of p53 Abs from a large population size of patients with solid tumors, as determined before data collection. Results: In total 12 clinical studies and of which 2094 patients were included and it was determined that p53-wt Abs expression in the serum significantly correlated with a worse survival of cancer patients (95% CI 1.48 [1.24, 1.77]; p<0.00001). On the contrary, data from literature indicated that there was a potential association between p53-mut Abs antibodies with better survival. Conclusions and Relevance: This is the first meta-analysis proving the diagnostic utility of p53-Abs for cancer patients, predicting a worse outcome. The serum-p53 value (s-p53-value) could be useful for future theranostics.

Neosporosis is an infection caused by the protozoan parasite Neospora caninum and has substantial veterinary hazards. Neosporosis cannot be controlled via vaccination or a chemotherapeutic agent. Thus, accurate diagnosis followed by isolation and culling of infected animals is regarded as the most efficient way of control. In vivo diagnosis often relies on serologic testing of the animals, and milk represents a non-invasive and easy to collect sample matrix. However, ELISAs specifically designed for the antibody detection in milk are sometimes not easily available and it is tempting to use ELISA kits designed for use in serum also for milk samples. Herein, we evaluated a widely used commercial ELISA (ID Screen® Neospora caninum competition Multispecies ELISA (ID. Vet, Grabels, France)), developed for detection of N. caninum antibodies in serum samples, for its performance with milk samples instead. Milk samples from dairy ruminants (cows, buffaloes, sheep, goats; n = 149) were tested in parallel with the serum ELISA and a commercial milk ELISA as a standard test (Neospora caninum Milk Competitive ELISA, ID. Vet, Grabels, France). The detected prevalence rates were 42 (28.2%), 26 (17.4%), and 26 (17.4%) using milk ELISA, serum ELISA, and both ELISAs, respectively. Sensitivity, specificity, positive predictive value, and negative predictive value for the serum ELISA used with milk samples were 61.9%, 100%, 100%, 87%, respectively: Agreement and Kappa value between the two ELISAs were 89.3%, and 0.70, respectively. High values of Pearson correlation coefficient (0.991) and area under ROC curve (0.789) demonstrated a high diagnostic performance of serum ELISA also in milk samples. Also, Bland-Altman plot and Histogram describing the frequency of distribution of ELISA optical densities confirmed the high agreement of both serum and milk ELISAs. The current results revealed a high specificity but moderate sensitivity of the serum ELISA used for milk samples compared with the milk ELISA. However, the excellent positive predictive value of the serum ELISA makes it an alternative option in case of unavailability of milk ELISA. With this study, we provided additional evidence that a widely used serum ELISA test kit may also be used for detection of N. caninum antibodies in milk samples.

The emergence of bispecific antibodies has transformed cancer immunotherapy, highlighting increased clinical efficacy, especially in hematological malignancies. These innovative molecules uniquely target two distinct tumor antigens or separate epitopes simultaneously, demonstrating potent antitumor activity across various cancers. Despite their promise, challenges like rapid drug clearance, off-target effects, and cytokine release syndrome hinder their widespread therapeutic application. Recent engineering advancements in bispecific antibody systems aim to overcome these challenges, broadening therapeutic coverage. This review offers insights into the latest clinical and preclinical progress in bispecific immunotherapy, outlining key challenges faced by the technique, and exploring emerging strategies to address these obstacles.

Here, one hundred patients (50 smokers and 50 non-smokers) clinically diagnosed with COVID-19 were studied. Yet, bioinformatics was used to predict epitopes on Tobacco mosaic virus coat protein (TMV-CP) to produce antibodies towards SARS-CoV-2. Death was three times higher in non-smokers than in smokers. However, biochemical parameters did not separate the groups. Bioinformatics analysis predicted the presence of B-cell epitopes in TMV-CP, suggesting the production of antibodies anti-TMV-CP in smoker patients. Smokers may develop severe forms of COVID-19, but survival was superior in the evaluated group than in non-smokers. Anti-TMV-CP antibodies, potentially present in smokers, might act as a pro-immune agent against SARS-CoV-2 at earlier stages of infection. These data are helpful for future studies assessing COVID-19 in smokers.

Objectives: Monoclonal antibodies (mAbs) have proven to be a valuable tool against COVID-19, mostly among subjects with risk factors for progression to severe illness. Tixagevimab/cilgavimab (TIX/CIL), a combination of two Fc-modified human monoclonal antibodies, has been recently approved to be employed as early treatment. Methods: Two groups of immunocompromised patients exposed to different early treatments (i.e., TIX/CIL vs. other mAbs [casirivimab/imdevimab, bamlanivimab/etesevimab, sotrovimab]) were compared in terms of clinical outcomes (hospitalization and mortality within 14 days from administration) and time to the negativity of nasal swabs. We used either Pearson’s chi-square or Fisher’s exact test for categorical variables, whereas the Wilcoxon rank–sum test was employed for continuous ones. Kaplan–Meier curves were produced to compare the time to nasopharyngeal swab negativity. Results: Early treatment with TIX/CIL was administered to 19 immunocompromised patients, while 89 patients received other mAbs. Most of them were solid organ transplant recipients or suffering from hematologic or solid malignancies. Overall, no significant difference was observed between the two groups in terms of clinical outcomes. In the TIX/CIL group, one patient (1/19, 5.3%), who was admitted to the emergency room within the first 14 days from treatment and was hospitalised due to COVID-19 progression, died. Regarding the time to nasal swab negativity, no significant difference (p=0.088) emerged. Conclusions: Early treatment of SARS-CoV-2 infection with TIX/CIL shows favourable outcomes in a small group of immunocompromised patients, reporting no significant difference when compared to similar patients treated with other mAbs.

Propagation of human cytomegalovirus (CMV) in cultured cells results in genetic adaptations that confer improved growth in vitro and significant attenuation in vivo. Mutations in RL13 arise quickly during cell culture passage, while mutations in the UL128-131A locus emerge later during fibroblast passage and disrupt expression of a glycoprotein complex that is important for entry into epithelial and endothelial cells. As in vivo CMV replicates in the context of host antibodies, we reasoned that antibodies might mitigate the accumulation of adaptive mutations during cell culture passage. To test this, CMV in infant urine was used to infect replicate fibroblast cultures. One lineage was passaged in the absence of CMV-hyperimmuneglobulin (HIG) while the other was passaged with HIG in the culture medium. The former lost epithelial tropism and aquired mutations disrupting RL13 and UL131A expression, whereas the latter retained epithelial tropism and both gene loci remained intact after 22 passages. An epitheliotropic RL13+/ UL131A+ virus was isolated by limiting-dilution in the presence of HIG and expanded to produce a working stock sufficient to conduct cell tropism experiments. Thus, culture in the presence of antibodies may facilitate in vitro experiments using viruses that are genetically more authentic than has been previously possible.

： Anti-MDA5 antibody bearing (anti-MDA5+)-dermatomyositis (DM) or polymyositis (PM) is notorious for causing rapidly progressive interstitial lung disease (RPILD) &amp;/or cancers with high mortality rate. However, anti-MDA5 antibodies (Abs) are also found in other connective tissue diseases and their link with RPILD especially with regards to the mortality rate are unknown. We retrospectively recruited 71 patients bearing anti-MDA5-Abs in serum, stratified them in terms of a presence or absence of RPILD, and evaluated their clinical features, laboratory findings, associated myositis antibodies, concurrent connective tissue disease (CTD) as well as newly developed malignancies. Thirty-nine (55%) patients presented with DM/PM but 32 (45%) didn’t. Twenty-two of the former and 11 of the latter developed RPILD eventually, accounting for a total of 46% of all MDA5 bearing patients. On the other hand, 15 of all 71 (21.1%) patients had cancers. Among 32 patients who didn’t have DM/PM, 27 (38.0% of all 71) had other CTDs, indicating that only 5 (7.0% of 71) patients did not have CTDs. Senility (odds ratio [OR]=1.816, P=0.032), presence of anti-Ro-52 antibody (OR=1.676, P=0.018), elevated C-reactive protein (CRP, OR= 4.354, P&lt;0.001) and carcinoembryonic antigen (CEA, OR=2.625, P=0.005) posed risks for RPILD. High lactose dehydrogenase (LDH, p=0.009), CRP (p=0.001), CEA (p=0.001), ferritin (p ≤ 0.001) and low albumin (p ≤ 0.001) were significantly associated with mortality. Anti-SAE antibodies were negatively correlated with RPILD as analyzed by univariate (OR=0.245, P=0.017) and multivariate (OR= 0.058, P=0.036) regressions, indicating that they may be a protective factor from RPILD (OR= 0.543, P=0.008) or fatality (OR= 0.707, P=0.012), which was also demonstrated in subgroup analyses. In contrast to various risk factors for RPILD or mortality, anti-SAE antibodies might be conversely a protective factor in anti-MDA5+ patients.

Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and L1 loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.

Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is that how to integrate highly sparse genetic genomics data with a mass of minor effects into prediction model for improving prediction power. We find that deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower dimensional continuous data in a non-linear way. This idea may provide benefits in risk prediction based on genome-wide data associated e.g. integrating most of the information in the genotype data. Hence, we developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for risk prediction. Specifically, we first reduced the number of biomarkers via a univariable regression model to a moderate size. Then a trainable auto-encoder was used to extract compact representations from the reduced data. Next, we performed a LASSO problem process over a grid of tuning parameter values to select the optimal combination of extracted features. Finally, we applied such feature combination to two prognostic models, and evaluated predictive effect of the models. The results of simulation studies and real data applying indicated that these highly compressed transformation features could better improve predictive performance and did not easily lead to over-fitting.

Machine Learning has completely transformed health care system, which transmits medical data through IOT sensors. So it is very important to encrypt them to protect patient data. encrypting medical images from a performance perspective consumes time; hence the use of an auto encoder is essential. An auto encoder is used in this work to compress the image as a vector prior to the encryption process. The digital image passes across description function and a decoder to get back the image in the proposed work; various experiments are carried out on hyper parameters to achieve the highest outcome of the classification. The findings demonstrate that the combination of Mean Square Logarithmic Error as the loss function, ADA grad as an optimizer, two layers for the encoder, and another reverse for the decoder, RELU as the activation function generates the best auto encoder results. The combination of Mean square error (lose function), RMS prop (optimizer), three layers for the encoder and another reverse for the decoder, and RELU (activation function) has the best classification result. All the experiments with different hyper parameter has run almost very close to each other even when changing the number of layers. The running time is between 9 and 16 second for each epoch.

In this paper, a capacitor microphone system is presented to improve the conversion of mechanical energy to electrical energy using a nonlinear auto regressive moving average-L2 (NARMA-L2) and model predictive control (MPC) controllers for the analysis of the open loop and closed loop system. The open loop system response shows that the output voltage signal need to be improved. The comparison of the closed loop system with the proposed controllers have been analyzed and a promising result have been obtained using Matlab/Simulink.

Background and Objectives: Age-related macular degeneration (AMD) is one of the major causes of central vision loss in the older age group and its dry form accounts for the majority of cases. Although several causes and mechanisms for the development and progression of AMD have previously been identified, the pathogenesis of this complex disease is still not entirely understood. As the inflammation and involvement of the immune system are strongly suggested to have a central role to promote the degenerative process and to stimulate the onset of complications, we aimed to analyze the prevalence of serum anti-retinal (ARA) and anti-endothelial cell antibodies (AECA) in patients with dry AMD and to determine their relationship with the clinical features of the disease, notably area of geographic atrophy (GA). Materials and Methods: We enrolled 41 patients with dry AMD and 50 sex- and age-matched healthy controls without AMD. The presence of ARA was determined by indirect immunofluorescence using monkey retina as an antigen substrate, while the presence of AECA was determined using cultivated human umbilical vein endothelial cells and primate skeletal muscle. Results: ARA were detected 36 (87.8%) AMD patients (titers range from 1:20 to 1:320) and in 16 (39.0%) (titers range from 1:10 to 1:40) of controls (p=0.0000). Twenty of the 41 patients (48.8%) were positive for AECA, while in a control group AECA were present only in 5 sera (10.0%). The titers of AECA in AMD patients ranged from 1:100 to 1:1000 and in the control group the AECA titers were 1:100 (p=0.0001). There were no significant correlations between the presence of AECA and disease activity. Conclusions: This study provides evidence for the possible involvement of an autoimmune process against retina and retinal vessel antigens in the pathogenesis of geographic atrophy associated with dry AMD.

A common task in the immunodetection of structurally close compounds is to analyze the selectivity of immune recognition: it is required to understand the regularities of immune recognition and to elucidate the basic structural elements which provide it. Triazines are compounds of particular interest for such a research due to their high variability and the necessity of their monitoring to provide safety of agricultural products and foodstuffs. We evaluated the binding of 20 triazines with polyclonal (pAb) and monoclonal (mAb) antibodies obtained using atrazine as the immunogenic hapten. A total of >3000 descriptors was used in QSAR analysis of binding activities (pIC50). Comparison of the two enzyme immunoassay systems showed that the system with pAb is much easier to describe using 2D QSAR methodology, while the system with mAb can be described using the 3D QSAR COMFA. Thus, for the 3D QSAR model of the polyclonal antibodies, the main statistical parameter q² (‘leave-many-out’) is equal 0.498, and for monoclonal antibodies q² is equal 0.566. Obviously, in the case of pAb, we deal with several targets, while in the case of mAb the target is one, and therefore it is easier to describe it using specific fields of molecular interactions distributed in space.

Antibody-drug conjugates (ADCs), a groundbreaking advancement in cancer therapy, synergistically combine the precision of monoclonal antibodies with the potent destruction capabilities of cytotoxic drugs, signifying a new era in the targeted oncological treatments. This comprehensive paper delves into the evolving field of antibody-drug conjugates (ADCs) and their pivotal role in cancer therapy. We have conducted an in-depth exploration of the structural components of ADCs, including target antigens, monoclonal antibodies, chemical linkers, and cytotoxic payloads, each playing a crucial role in determining the efficacy and safety profile of these novel therapies. A detailed market analysis reveals the leading pharmaceutical companies shaping the ADC landscape, reflecting the growing interest and potential in this therapeutic approach. We further dissect the engineering intricacies of ADCs, focusing on their ability to target specific cancer cell antigens while minimizing off-target effects, thus offering a more focused treatment with reduced systemic side effects compared to traditional chemotherapy. This approach holds promise in a heightened therapeutic index and decreased toxicity. The mechanisms and effector functions of ADCs are explored, highlight- ing their multifaceted strategy in combating cancer. We include a comprehensive look at how ADCs operate from initial blood circulation to the induction of apoptosis in cancer cells. We also review several key ADCs currently in use or under investigation, offering insights into their unique mechanisms of action, pharmacokinetics, and clinical implications. The discussion extends to drug creation modalities, focusing on innovative conjugation techniques that enhance ADC homogeneity and therapeutic efficacy. In conclusion, the paper underscores the transformative potential of ADCs in the realm of cancer treatment, marking a shift towards more precise and targeted therapeutic approaches. By offering a thorough analysis of ADC technology, this paper sheds light on the prospects of these groundbreaking therapies in the ongoing fight against cancer.

Immune disorders, characterized by dysregulation at cellular and inflammatory levels, result from a complex interplay of genetics and environmental factors that lead to an abnormal immune response against autoantigens, triggering tissue damage. Recent research highlights reactive antibodies as key players in autoimmune diseases and graft rejection, but the complexity of their determination limits their use in clinic. Hence, we studied the specific binding profile of serological antibodies against a panel of cellular membranes in order to determine whether this antigenic panel could be used to diagnose specific immune disorders in humans. For this purpose, cell membrane microarrays of spleen, liver, and kidney tissues of monkey, rat, and human were developed, and human sera were analyzed, including healthy controls, patients with autoimmune disorders, and kidney transplant patients. A significant increase in antibody reactivity against specific monkey kidney and spleen membranes was observed in the serum of patients with lupus nephritis, while kidney transplant patients showed a significant enhancement against human tissues and Human embryonic kidney 293 cells. These results show the potential importance for clinical and basic research purposes of studying the presence of specific IgG against membrane antigens in the serum of patients as potential biomarkers of immune disorders.

Neuraminidase (NA)-based immunity could reduce the harmful impact of novel antigenic variants of influenza viruses. Detection of neuraminidase-inhibiting (NI) antibodies in parallel with anti-hemagglutinin (HA) antibodies may enhance research of immunogenicity and duration of antibody response to influenza vaccines. To assess anti-NA antibodies after vaccination with seasonal inactivated influenza vaccines, we used the enzyme-linked lectin assay, and anti-HA antibodies were detected in hemagglutination inhibition assay. The dynamics of anti-NA antibody response differed depending on the virus subtype: antibodies to A/H3N2 virus neuraminidase increased later than antibodies to A/H1N1pdm09 subtype neuraminidase, and persisted longer. In contrast to HA antibodies, the fold increase in antibody titers to NA after vaccination poorly depended on the preexisting level. At the same time, NA antibody level after vaccination directly correlated with titers before vaccination. Difference was found in response to NA antigen between split and subunit adjuvanted vaccines and in NA functional activity in the vaccine formulations.

This study was to determine the prevalence of hepatitis C virus (HCV) among febrile patients attending a General Hospital in Emohua LGA, Rivers State, Nigeria. Eighty-nine patients, including 31 males and 58 females, aged 2 to 60 years, were recruited for this study. Blood samples were screened for antibodies to hepatitis C virus (HCV) using a commercially available anti-HCV-Ab enzyme-linked immunosorbent assay (ELISA) based kits following the manufacturer's description. The results showed an overall prevalence of 5.6%. Higher prevalence of HCV was observed among females (6.9%), age groups >41 years (11.1%), singles (7.9%) and patients with primary education (33.3%). Among all the variables evaluated, only education (p = 0.007) was significantly associated with the prevalence of HCV. Age (p = 0.21), sex (p = 0.47) and marital status (p = 0.42) were not associated with the prevalence of HCV among the studied population. This study showed a prevalence rate (5.6%) that is slightly alarmingly well above several other studies done in the past in Nigeria. The seropositivity of HCV among febrile patients remains a great danger to public health. Therefore, HCV screening by ELISA methods in all patients is recommended. Planned prevention, screening and treatment are needed to reduce further transmission.

The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), etiological agent of the novel coronavirus disease 2019 (COVID-19), has spread since December 2019, resulting in massive health and economic crisis worldwide. While efforts to stop the pandemic are crucial, collecting epidemiological data to help manage current and future pandemics will be important. In addition to humans, serological and molecular based studies have demonstrated SARS CoV-2 exposure in several wild, domestic and farmed animals. For examples Shriner and the team showed serologically an exposure of 40% to the white deer living in close proximity to urban centers. Additional reports have also emerged of susceptibility of animal’s species like cats, ferrets, raccoon dogs, cynomolgus macaques, rhesus macaques, white-tailed deer, rabbits, Egyptian fruit bats, and Syrian hamsters to SARS-CoV-2 infection.. It’s worth emphasizing that these reports are based on experimental data mostly derived from Europe, USA, South America and parts of Asia. In limited instances natural infections of SARS-CoV-2 have been reported in pet dogs, cats, tigers, lions, snow leopards, pumas, gorillas at zoos and farmed mink and ferrets. The presence of the virus in animal species and an understanding of whether these are natural or recent human to animal transmissions is important. It’s possible that such transmission could passage the virus or subject the virus to a different immunological pressure thereby helping with the development of viral variants in addition to being a host for future reservoirs of the virus. In Kenya SARS-CoV-2 was first detected on March 12th 2020 from imported human cases of persons who had travelled from the United States. This was followed by detection of imported cases majorly from China, Sweden and United Kingdom. Later infections were confirmed in Nairobi and Mombasa suggesting further cases of disease importations through the major ports of entry. However, no comparable data on animal exposure have hitherto been generated in Kenya. To address this key concern, we focused on three objectives; 1) development of a robust antibody ELISA based on crude SARS-CoV-2 lysate. 2) SARS-CoV-2 serology of domestic animals in Kenya. 3) Corroboration of the crude lysate based seroprevalence data and a commercial ELISA kit based on the Spike receptor binding domain (RBD) antigen. Our sample set included camel sera (both pre- & post outbreak sera), as well as sera from cats and dogs collected at the peak of the pandemic. Our results using the ELISA based on crude SARS-CoV-2 lysate indicated SARS-CoV-2 antibodies in camels (71%, N=145), cats 11% (N=16) and dogs (81%, N=36) with varying titer levels. These findings were comparable to those obtained using the commercial ELISA kit based on the spike RBD antigens. In summary, the data warrants two key conclusions: (i) we have demonstrated that the crude lysate ELISA allows for SARS-CoV-2 antibody detection, and given its potential to offer robust detection could be applied for initial mass screening (ii) although the current study cannot disentangle the relative contributions of antigenic cross-reactivity, pre-pandemic exposure to SARS-CoV-2 or human-animal transmission, it nonetheless demonstrates for the first time the prevalence of SARS-CoV-2 like antibodies in domestic and wild animals in Kenya. Our findings set the scene for further research into the prevalence of SARS-CoV-2 in domestic and wild animals to understand their potential epidemiological implications.

This study focuses on understanding how global auto parts companies manage innovation by analyzing keywords in CEO messages from their reports. The study involved three key phases: (1) extraction of innovation-related keywords emphasized in CEO messages, (2) comparison of innovation-related keyword usage between high-performing (upper group) and low-performing (lower group) companies, and (3) analysis of the characteristics of innovation management via internal meaning and the structural interpretation of keywords. A comparison between the upper and lower groups revealed distinct differences in the emphasized keywords. The mapping of open innovation success factors, based on keywords extracted according to eigenvector centrality, identified Provision of Resources and Governance as central to innovation management in the upper group. These findings serve as a benchmark for establishing the direction, vision, and strategy of innovation management in the lower group.

Tuning tensor program generation involves navigating a vast search space to find optimal program transformations and measurements for a program on target hardware. The complexity of this process is further amplified by the exponential combinations of transformations, especially in heterogeneous environments. This research addresses these challenges by introducing a novel approach that learns the joint neural network and hardware features space, facilitating knowledge transfer to new, unseen target hardware. A comprehensive analysis is conducted on the existing state-of-the-art dataset, TenSet, including a thorough examination of test split strategies and the proposal of methodologies for dataset pruning. Leveraging an attention-inspired technique, we tailor the tuning of tensor programs to embed both neural network and hardware-specific features. Notably, our approach substantially reduces the dataset size by up to 53% compared to the baseline without compromising Pairwise Comparison Accuracy (PCA). Furthermore, our proposed methodology demonstrates competitive or improved mean inference times with only 25%40% of the baseline tuning time across various networks and target hardware. The attention-based tuner can effectively utilize schedules learned from previous hardware program measurements to optimize tensor program tuning on previously unseen hardware, achieving a top-5 accuracy exceeding 90%. This research introduces a significant advancement in auto-tuning tensor program generation, addressing the complexities associated with heterogeneous environments and showcasing promising results regarding efficiency and accuracy.

This study compares the efficacy of the two most commonly used surgical methods for pterygium in the Polish population, conjunctival autograft versus amniotic membrane transplantation, and to evaluate the postopera-tive recurrence rate. We retrospectively analysed the medical records of 65 patients who underwent surgery for primary or recurrent pterygium at an ophthalmology clinic in Bialystok, Poland between 2016 and 2020. Surgical success (no regrowth) occurred in almost half of the amniotic membrane patients (44%) and in most of the conjunctival autograft patients (79%); this was a significant relationship. The odds of successful surgery were 79% lower for subjects with amniotic membranes than for those with conjunc-tival autografts (OR with 95% CI = 0.21 (0.05; 0.94]; p = 0.045). Our study confirms that in Polish Caucasian population the success rate of the pro-cedure using conjunctival autograft versus the use of amniotic membrane, is in favoured for the procedure with conjunctival graft.

The constant news about the corona virus is scary. It is not possible to separate treatment for Cancer due to COVID-19. An effective treatment comparison strategy is needed. We need to have a handy tool to understand cancer progression in this unprecedented scenario. Linking different events of cancer progression is the need of the hour. It is a methodological challenge. We provide the solutions to overcome the issue with interval between two consecutive events in motivating head and neck cancer (HNC) data.

A statistical analysis of semantic memory should reflect the complex, multifactorial structure of the relations among its items. Still, a dominant paradigm in the study of semantic memory has been the idea that the mental representation of concepts is structured along a simple branching tree spanned by superordinate and subordinate categories. We propose a generative model of item representation with correlations that overcomes the limitations of a tree structure. The items are generated through "factors" that represent semantic features or real-world attributes. The correlation between items has its source in the extent to which items share such factors and the strength of such factors: if many factors are balanced, correlations are overall low; whereas if a few factors dominate, they become strong. Our model allows for correlations that are neither trivial nor hierarchical, but may reproduce the general spectrum of correlations present in a data-set of nouns. We provide an estimate of the number of concepts that can be stored and retrieved by a large-scale cortical network, the Potts network, which is perhaps approximately 10⁷ with human cortical parameters. When this storage capacity is exceeded, however, retrieval fails completely only for balanced factors; above a critical degree of imbalance, a phase transition leads to a regime where the network still extracts considerable information about the cued item, even if not recovering its detailed representation: partial categorization seems to emerge spontaneously as a consequence of the dominance of particular factors, rather than being imposed ad hoc. We argue this to be a relevant model of semantic memory resilience in Tulving’s remember/know paradigms.

For automobile insurance, it has long been implied that when a policyholder made at least one claim in the prior year, the subsequent premium is likely to increase. When this happens, the policyholder may seek to switch to another insurance company to possibly avoid paying for a higher premium. In such situations, insurers may be faced with the challenges of policyholder retention by keeping premiums low in the face of competition. In this paper, we seek to find empirical evidence of possible association between policyholder switching after a claim and the associated change in premium. In accomplishing this goal, we employ the method of association rule learning, a data mining technique that has its origins in marketing for analyzing and understanding consumer purchase behavior. We apply this unique technique in two stages. In the first stage, we identify policyholder and vehicle characteristics that affect the size of the claim and resulting change in premium regardless of policy switch. In the second stage, together with policyholder and vehicle characteristics, we identify the association among the size of the claim, the level of premium increase and policy switch. This empirical process is often challenging to insurers because they are unable to observe the new premium for those policyholders who switched. However, we used a 9-year claims data for the entire Singapore automobile insurance market that allowed us to track information before and after the switch. Our results provide evidence of a strong association among the size of the claim, the level of premium increase and policy switch. We attribute this to the possible inefficiency of the insurance market because of the lack of sharing and exchange of claims history among the companies.

Abstract: Presence in a recipient of antibodies directed against donor specific antigens represent a major obstacle in transplanting such patients. Removal of these antibodies represents a challenge for physicians dealing with kidney transplantation. Several strategies, techniques and old and new drugs are to date used to desensitize these patients. Desensitization may occur before transplantation or at the time of transplantation or after transplantation according whether physicians deal with living or deceased donors. Different techniques may be used to reveal the presence of antibodies in the recipients; each technique has different sensitivity and specificity, different advantages and disadvantages. The target of the drugs used to desensitize are B cells, Plasma cells, the antibodies themselves and finally, the complement that is the final actor causing tissue disruption. If B cells are relatively easy to be targeted, more difficult are the plasma cell. Indeed, several new drugs are used in randomized trials also to defeat plasma cells. Antibodies may be removed rather easily, but often their removal is followed by a rebound. Complement is not easy to be defeated and new drugs are to date used to be successful in this action. However is to date possible to desensitize many patients and to obtain successful transplantation.

The binding activity of various trastuzumab biosimilars versus the branded trastuzumab towards the glycosylated extracellular domain of the HER2 target in the presence of pertuzumab was in-vestigated. We employed size exclusion chromatography with tetra-detection methodology to simultaneously determine absolute molecular weight, concentration, molecular size, and intrinsic viscosity. All trastuzumab molecules in solution exhibit analogous behavior in their binary action towards HER2 regardless of the order of addition of trastuzumab/pertuzumab. This analogous behavior of all trastuzumab molecules, including biosimilars, highlights the robustness and con-sistency of their binding activity towards HER2. Furthermore, the addition of HER2 to a mixture of trastuzumab and pertuzumab leads to increased formation of high-order HER2 complexes, up to concentrations of one order of magnitude higher than in the case of sequential addition. The observed increase suggests a potential synergistic effect between these antibodies, which could enhance their therapeutic efficacy in HER2-positive cancers. These findings underscore the im-portance of understanding the complex interplay between therapeutic antibodies and their target antigens, providing valuable insights for the development of more effective treatment strategies.

Cancer, characterized by abnormal cell proliferation driven by genetic mutations, remains a significant health challenge. Tailoring effective therapies depends on factors like tumor location, grade, and stage, alongside patient condition. Leveraging Nano-carrier encapsulated drugs offers a promising avenue, as they accumulate preferentially at tumor sites, enhancing therapeutic efficacy while mitigating side effects. Polymeric Nano-carriers, with their customizable properties, further optimize drug delivery efficiency and specificity. Incorporating active targeting ligands such as antibodies, carbohydrates, peptides, folates, and aptamers-onto polymeric nanoparticle surfaces through techniques like functionalization, adsorption, and conjugation plays a pivotal role in directing therapeutic agents to tumor cells. Consequently, polymeric nanoparticles emerge as the carrier of choice for delivering drugs to various tumor types, including breast, colon, lung, prostate, liver, and spleen tumors.

The COVID-19 pandemic has become the largest public health crisis since the 1918 influenza pandemic. Despite the extensive research conducted on the SARS-CoV-2 virus in humans, little is still known about animal-related transmission and its consequences. Therefore, this study contributed to a better understanding of this issue by focusing on the serological survey of SARS-CoV-2 in samples from the serum bank of the Bovine Virology Laboratory at the Biological Institute of São Paulo, as well as horses and dogs from the Military Police of the State of São Paulo, and tapirs and bats from the states of Mato Grosso do Sul and São Paulo. To achieve this, the possible presence of anti-SARS-CoV-2 antibodies in domestic and wild animal species was evaluated using the ID Screen® "SARS-CoV-2 Double Antigen Multi Species" ELISA test (ID-Vet.®), following the manufacturer's recommendations. The findings of this study demonstrate a higher occurrence of anti-SARS-CoV-2 antibodies in domestic animals compared to wild animals, as well as different antibody profiles among the species analyzed, with horses showing a wide range of seroconversion comparable to humans.

Autoimmune thyroid disease refers to a spectrum of various diseases, with two extremes of clinical presentation, hypo-thyroidism (Hashimoto's thyroiditis), and hyperthyroidism (Graves-Basedow disease). Both conditions are character-ized by presenting a cellular and humoral autoimmune reaction, with an increase in the synthesis and secretion of an-tibodies directed towards various thyroid antigens, together with a phenomenon of thyrocyte necrosis and apoptosis (in Hashimoto's thyroiditis) and a persistent thyrotropin-receptor stimulation (in Graves-Basedow disease). The diagnosis of both entities is based on the clinical, laboratory and imaging findings. In this review, the usefulness of thyroid antibodies in the diagnostic approach to autoimmune thyroid disease is de-scribed.

The antibodies targeting invasive proteins of Plasmodium falciparum (Pf) merozoites is a clever strategy against malaria parasite. The recent finding that Plasmodium merozoites surface enolase is a target for growth neutralizing antibodies has generated interest in identifying its re-ceptor (s) on erythrocytes. In the present study, several biochemical experiments were undertaken involving recombinant Pf enolase and antibody against it to search for its cognate receptor. The binding of rPfeno to erythrocytes was selectively sensitive to trypsin but resistant to neuramini-dase. Failure of yeast enolase binding to human red blood cells under similar conditions indicat-ed high specificity of parasite Pfeno. The mass spectrometric and immunological analyses of pro-teins obtained in pull downs and co-immunoprecipitation samples led to identification of band 3 as an interactor/receptor for Plasmodium enolase. Structural characterization of sequences of band 3 - rPfeno interacting regions revealed C-terminal exocytic regions of band 3 to be binding to Pfeno. Similarly, band 3 binding regions of rPfeno were also identified. Identification of receptor-ligand (band 3: Pfeno) pair paves the way for antimalarial anti-Pf enolase-based vaccine, anti-Pf enolase antibodies as drug and also for developing novel small molecule-based invasion inhibi-tory therapeutics including peptidomimetics that could disrupt the band 3: Pfeno protein-protein interactions.

Oncological patients need the proper doses of medications to facilitate their recovery. The two basic approaches used in dosing Monoclonal Antibodies (mAbs) are fixed-dose combination and variable dosing. In Fixed-Dose Combination Drugs (FDCs), two or more active components are combined in a single formulation at a predetermined dose. Variable dosage, which has long been the industry standard, is the polar opposite of this approach. The body changes over time; the Body Surface Area (BSA) in square meters is often used as a Measure (m2). This study uses a systematic review. Most mAbs used in oncology are predominantly given as cytotoxic anticancer drugs using body-size-based (variable) regimens. Despite the benefits of fixed-dose, variable dosing has become the industry standard, despite being criticized for ineffectiveness. While variable dosing has some advantages, the prevalent view is that continuous dosing has significant advantages based on the balance of probabilities. After assessing each alternative, including its benefits and drawbacks, history of use, and suitability in the current context, fixed dosing emerges as a viable option.

Background: The robustness of sero-surveillence has delineated the high burden of SARS-CoV-2 infection in children; however, these existing data showed wide variation. This study aimed to identify the serostatus of antibodies against SARS-CoV-2 and associated factors among children following the fourth pandemic wave in Vietnam. Methods: A cross-sectional study was conducted at Vietnam National Children’s Hospital (VNCH) between March 13 and April 3, 2022. 4,032 eligible children seeking medical care for any medical condition not related to acute Covid-19 infections was tested for IgG SARS-CoV-2 Antibodies by ADVIA Centaur® SARS-CoV-2 IgG (sCOVG) assay using the residuals of routine blood samples. Results: The median age of enrolled children was 39 (IQR=14-82) months. The overall seropositive prevalence was 59.2%, and the median antibody titer was 4.78 [IQR 2.38-9.57] UI/mL. The risk of seropositivity and the median antibody titer was not related to gender (58.6% versus 60.1%, 4.9 versus 4.6 UI/mL, all p&gt;0.05). Among age groups, the highest seroprevalence was reported in the children aged 13 to &lt;36 months old. Children aged ≤12 months were likely to be seropositive compared to children aged 36 to &lt;60 months (59.2% versus 57.5%, p=0.49) and those aged ≥144 months (59.2% versus 65.5%, p=0.16). Children aged ≥144 months exhibited a significantly higher titer of protective COVID-19 antibodies than other age groups (p &lt;0.001). In multivariate logistic regression, we observed independent factors associated with SARS-CoV-2 seropositivity, including the age 13 to &lt;36 months (OR=1.29, 95%CI=1.06-1.56, p=0.01), 60 to &lt;144 months (OR=79, 95%CI=0.67-0.95, p=0.01), ≥144 months (OR=1.84, 95%CI=1.21-2.8, p=0.005), the presence of infected household members (OR=2.36, 95%CI=2.06–2.70, p&lt;0.001), participants from Hanoi (OR=1.54, 95%CI=1.34-1.77, p&lt;0.001), underlying conditions (OR=0.71, 95%CI=0.60-0.85, p&lt;=0.001), and using corticosteroids or immunosuppressants (OR=0.64, 95%CI=0.48-0.86, p=0.003). Conclusions: This study highlights a high seroprevalence of antibodies against SARS-CoV-2 among children seeking medical care for non-COVID-19-related conditions in a tertiary children’s hospital in Hanoi, Vietnam. In the context of reopening in-person schools and future emerged COVID-19 variants, this point will also be a key message about the necessity of “rush-out” immunization coverage for children, especially those under the age of three years.

Background: Cytokine storm in COVID-19 is heterogenous. There are at least three subtypes: cytokine release syndrome (CRS), macrophage activation syndrome (MAS), and sepsis. Methods: A retrospective study comprising 276 patients with SARS-CoV-2 pneumonia. All patients were tested for ferritin, interleukin-6, D-Dimer, fibrinogen, calcitonin, and C-reactive protein. According to the diagnostic criteria, three groups of patients with different subtypes of cytokine storm syndrome were identified: MAS, CRS or sepsis. In each group, treatment results were assessed depending on whether or not tocilizumab was used. Results: MAS was diagnosed in 9.1% of the patients examined, CRS in 81.8%, and sepsis in 9.1%. Median serum ferritin in patients with MAS was significantly higher (5894 vs. 984 vs. 957 ng/ml, p &lt;0.001) than in those with CRS or sepsis. Hypofibrinogenemia and pancytopenia were also observed in MAS patients. In CRS patients, a higher mortality rate was observed among those who received tocilizumab, 21 vs. 10 patients (p=0.043), RR = 2.1 (95% CI 1.0-4.3). In MAS patients, tocilizumab decreased the mortality, 13 vs. 6 patients (p=0.013), RR = 0.50 (95% CI 0.25-0.99). Сonclusions: Tocilizumab therapy in patients with COVID-19 and CRS was associated with increased mortality, while in MAS patients it contributed to reduced mortality.

Background: Meniere's disease (MD) has been recently linked to gluten assumption. Approximately 75% of MD patients show positive skin test to food and about 50% of the positive responses are specific to the gliadin acid extract fraction. Aim of this study was to investigate the humoral immune responses to wheat antigens and related autoantigens in MD patients. Methods. We assessed the reactivity of sera from 28 patients with definite MD and 100 healthy controls against a repertoire of 51 antigens usually associated with immune reaction to gluten. Results. MD patients showed an increase of anti-wheat IgA, anti-cerebellar peptide IgA and anti-glutamic acid decarboxylase (GAD) 65 IgM compared to healthy controls. In particular, the increase of anti-wheat IgA and GAD 65 IgM has been confirmed in a subgroup of MD patients symptomatically responding to a gluten free diet (GFD). Conclusion. In MD patients, an increase of the antibody production against gluten biomarkers was observed; in particular, anti-wheat IgA seems to be associated to clinical response to GFD.

Ship pilots and maritime safety administration have an urgent need for more accurate and earlier warnings on strong wind gusts. This study firstly investigates the “Oriental Star” cruise ship cap-sizing event in 2015, one of the deadliest shipwreck events in recent years, and explores all related hydro-meteorological components in a global mesoscale model. It is found that, rather than the missing signal in raw surface wind prediction, the cumulus precipitation variable (CP) increases dramatically during the accident occurrence, which significantly corresponds to the sub-grid strong wind gust. The effective lead-time can be extended from 24hr (deterministic model) to 48hr (en-semble model). This finding is then verified in another two recent deadly cruise boat accidents. The introduction of the new variable is bearing the hope of improvement to the current maritime safeguard system, on predicting sub-grid strong wind gusts for small-size cruise boats in offshore and inland rivers. Finally, an automatic-response system is developed to provide economical con-vection prediction via Inmarsat email communication, aiming to explore operational severe con-vective gust early-warning and appropriate numerical mesoscale model application.

Mechanochemical technology is developing rapidly, judging by the scientific information in both basic and applied studies. However, many issues and points of view remain to be discussed. This review presents some new key issues for the optimization of mechanochemical processes in theoretical and practical aspects. Emphasis is placed on powder technology aspects, which are not always discussed compared to functional or microscopic viewpoints. The transfer of chemical species across the interparticle interface between organic and inorganic species during the mechanosynthesis of nanocomposite oxides offers many new possibilities, which are discussed here in detail. Since all material transport is preceded by charge transfer, its driving force has been searched for using terminology beyond the well-established electrochemical ones. The role of organic compounds during the whole process is emphasized, regardless of their survival in the final product. The similarity with the pharmaceutical phenomena is pointed out, although its mentality is very different from that of the synthesis of nanocomposites. Not only for the rational amorphization of the active pharmaceutical ingredient, but also for the stabilization of molecular dispersion states with the participation of excipients are discussed. The effects of liquids, either added or formed by mechanochemical auto-liquefaction, are presented with reference to the comparison between wet and dry grinding. Finally, mechanisms of apparent stabilization of mechanically activated states of products are elucidated to investigate the practical applicability of mechanochemically synthesized products

The spatial distribution of elements can be regarded as a numerical field of concentration values with a continuous spatial coverage. An active area of research is to discover geologically meaningful relationships among elements from their spatial distribution. To solve this problem, we propose an association rule mining method based on clustered events of spatial auto-correlation and applied it to the polymetallic deposits of the Chahanwusu River area, Qinghai Province, China. The elemental data for stream sediments were first clustered into HH (high-high), LL (low-low), HL (high-low), and LH (low-high) groups by using local Moran’s I clustering map (LMIC). Then the Apriori algorithm was used to mine the association rules among different elements in these clusters. More than 86% of the mined rule points are located within 1000 m of faults and near known ore occurrences, and occur in the upper reaches of the stream and catchment areas. In addition, we found that the Indosinian granodiorite is enriched in sulfophile elements, e.g., Zn, Ag and Cd, and the Variscan granite quartz diorite (P1γδο) coexists with Cu and associated elements. Therefore, the proposed algorithm is an effective method for mining co-existence patterns of elements and provides an insight into their enrichment mechanisms.

This paper describes the use of the Chinese Gaofen-1 (GF-1) satellite imagery to automatically extract tertiary Linear Archaeological Traces of Tuntian Irrigation Canals (LATTICs) located in the Miran site. The site is adjacent to the ancient Loulan Kingdom at the eastern margin of the Taklimakan Desert in western China. GF-1 data was processed following atmospheric and geometric correction, and spectral analyses were carried out for multispectral data. The low values produced by SSI indicate that it is difficult to distinguish buried tertiary LATTICs from similar backgrounds using spectral signatures. Thus, based on the textual characteristics of high-resolutionGF-1 panchromatic data, this paper proposes an automatic approach that combines joint morphological bottom and hat transformation with a Canny edge operator. The operator was improved by adding stages of geometric filtering and gradient vector direction analysis. Finally, the detected edges of tertiary LATTICs were extracted using the GIS-based draw tool and converted into shapefiles for archaeological mapping within a GIS environment. The proposed automatic approach was verified with an average accuracy of 95.76% for 754 tertiary LATTICs in the entire Miran site and compared with previous manual interpretation results. The results indicate that GF-1 VHR PAN imagery can successfully uncover the ancient tuntian agricultural landscape. Moreover, the proposed method can be generalized and applied to extract linear archaeological traces such as soil and crop marks in other geographic locations.

This paper presents the design and implementation of a multichannel bio-impedance spectroscopy system on field programmable gate arrays (FPGA). The proposed system is capable of acquiring multiple signals from multiple bio-impedance sensors, process the data on the FPGA and store the final data in the on-board Memory. The system employs the Digital Automatic Balance Bridge (DABB) method to acquire data from biosensors. The DABB measures initial data of a known impedance to extrapolate the value of the impedance for the device under test. This method offers a simpler design because the balancing of the circuit is done digitally in the FPGA rather than using an external circuit. Calculations of the impedance values for the device under test were done in the processor. The final data is sent to an onboard Flash Memory to be stored for later access. The control unit handles the interfacing and the scheduling between these different modules (Processor, Flash Memory) as well as interfacing to multiple Balance Bridge and multiple biosensors. The system has been simulated successfully and has comparable performance to other FPGA based solutions. The system has a robust design that is capable of handling and interfacing input from multiple biosensors. Data processing and storage is also performed with minimal resources on the FPGA.

Background: Accurate estimation of the spread of SARS-CoV-2 is crucial for guiding public health strategies. Underreporting from PCR-based testing, especially in low- and middle-income countries, often obscures the true extent of exposure and immunity to coronavirus disease 2019 (COVID-19). This study aimed to determine the seroprevalence and antibody levels against SARS-CoV-2 among patients visiting hospital-based sentinel sites in Kenya's Rift Valley region following the Omicron wave. Method: We conducted a cross-sectional study in four hospital-based sentinel sites among patients with acute respiratory infection symptoms. Patients aged six months or older visiting the sites between January 2022 and December 2022, presenting with symptoms of acute respiratory infection, were enrolled. Clinical and sociodemographic data were collected using a structured questionnaire. We used a quantitative ELISA kit to detect IgG antibodies against SARS-CoV-2 S protein in serum samples. Results: Among 557 individuals enrolled in the study, the seroprevalence of SARS-CoV-2 antibodies was 97.8% (n=545). Vaccinated individuals demonstrated significantly higher odds of seropositivity compared to unvaccinated counterparts, with an odds ratio of 3.33 (95% CI: (0.4, 27.87)). While seropositivity remained consistent across age groups, older adults exhibited the highest odds. Site-specific differences in enrolment and vaccination rates highlight potential access-related disparities. Conclusion: Our findings indicate widespread prior exposure to SARS-CoV-2 within the study region, underscoring the significant impact of both natural infection and vaccination efforts in driving SARS-CoV-2 seroprevalence within the Rift Valley region. Despite the underestimation of infections and disparities in vaccination access, this study demonstrates the effectiveness of vaccination for conferring SARS-CoV-2 immunity. Targeted interventions are crucial to address accessibility barriers and vaccine hesitancy, ensuring equitable vaccination coverage.

Saliva, as a diagnostic medium, offers a promising alternative to blood by virtue of its non-invasive collection, which enhances patient compliance, especially in paediatric and geriatric populations. In this study, we assessed the utility of saliva as a non-invasive medium for measuring V. cholerae-specific serum antibodies in naturally infected individuals. We tested paired serum and saliva samples obtained from a total of 63 cholera patients enrolled in a cohort study. Vibriocidal antibodies assay (IgM/IgG) as markers for accurate determination was used to determine cholera specific antibody levels. Using receiver operating characteristics (ROC) curve, we found that the best cut-off that maximizes (sensitivity + specificity) is 10 titers. At this saliva titer, the sensitivity is 76.9% (95%CI: 60.9%, 87.7%) and specificity is 80.0% (95%CI: 56.6%, 92.5%). Using Spearman’s correlation coefficient, we also found evidence of a positive correlation between Vibrio Cholerae saliva and serum antibodies (rho=0.66, p&lt;0.001). In conclusion, saliva-based diagnostic cholera tests has high diagnostic accuracy, and would be advantageous, cheaper, and quicker for early diagnosis of severe cholera outcomes.

Free-of-charge HCV screening in some key populations and in 1969–1989 birth cohorts have been funded in Italy as the first step to diagnosing individuals unaware of their infection. The aim of this study is to use available in-hospital routine screening data to understand the inpatients HCV screening in a region of North Italy. A retrospective search of in-hospital patients tested for HCV-antibody (HCV-Ab) from January 2017 to the end of December 2022, was conducted according to age categories (older than 76 years, birth group 1947-1968; birth group1969-1989 and two other categories with patients younger than 33 years) using the TriNetX network. Of 42,805 in-hospital patients tested, 1,297 (3.03%) resulted HCV-Ab positive. The proportion decreased from 2017 to 2022 (from 2.03% to 0.36%) years). The higher prevalence of HCV-Ab was in patients older than 76 years (5.3%) while, the lowest was in the younger birth cohort (2000-2022, 0.16%). Of 1,297 patients found HCV-Ab positive, only 198 (15.3%) were tested for the presence of HCV RNA. Although the birth cohort 1969-1989 had a lower seroprevalence (1.5%), they represented the most affected age group with 44.4% of HCV RNA positivity. The in-hospital HCV screening including birth cohort 1947-1989 could be a valuable option respect to the screening for birth group 1969-1989 in the general population. Since a high proportion of patients were not tested for HCV RNA during hospitalization, reflex testing promptly ascertaining an active infection could facilitate linkage to care to stop the burden of HCV.

A cross-sectional study was conducted to gain insight into the epidemiology of canine ehrlichiosis and rickettsiosis in northern Portugal. Specific IgG antibodies to Ehrlichia canis were analysed using a commercial indirect ELISA test, and antibodies to Rickettsia conorii using a commercial IFAT. A total of 113 dogs from two different shelters were sampled, and seroprevalence values of 0.9% (95% confidence [CI]: 0.2–4.8%) for E. canis and 9.7 (95% CI: 5.5–16.6%) for R. conorii were found. Univariable models investigated risk factors for seropositivity. The odds ratios (OR) of R. conorii seropositivity were higher for female dogs (OR = 2.32; 95% CI: 1.58–3.39) and for dogs belonging to Shelter 2 (OR = 3.0; 95% CI: 2.28–3.95). Seropositive dogs for co-infection (E. canis + R. conorii) were more frequently observed in females (OR = 2.4; CI 95%: 1.66–3.47) and in Shelter 2 (OR = 2.72; 95% CI: 1.97–3.76). These findings show that shelter dogs in northern Portugal are exposed to E. canis and R. conorii, which can affect both canines and humans. It is imperative to adopt a One Health approach to educate the public about the hazards of canine zoonoses and develop legislation and procedures to control their spread and preserve public health.

Aquaporins (AQPs; AQP0-AQP12) are water channels expressed in many and diverse cell types, participating in various functions of cells, tissues and systems, including the central nervous system (CNS). AQP dysfunction and autoimmunity to AQPs are implicated in several diseases. The best-known example of autoimmunity against AQPs is the antibodies to AQP4 which are involved in the pathogenesis of Neuromyelitis Optica spectrum disorder (NMOSD), a demyelinating disease of the CNS. The present review focuses on the discovery and the potential role of antibodies against AQP1 in the CNS, and their potential involvement in CNS demyelination and specifically in NMOSD. We describe (a) the several techniques developed for the detection of the AQP1-antibodies, with emphasis on methods that specifically identify antibodies targeting the extracellular domain of AQP1, i.e., those of potential pathogenic role, and (b) the available evidence supporting the pathogenic relevance of AQP1-antibodies.

Accurate data on susceptibility rates against measles in general population of Greece are scarce. Many studies estimate the vaccination coverage but none has calculated the nationwide immunity rate, involving all age groups, against measles virus. The purpose of our study was to determine the immunity status to measles, especially after the latest outbreak in 2017-2018. In total, 3,972 leftover blood samples were collected from a nationwide laboratory network using a geograph-ically stratified sampling strategy and were tested for the presence of measles specific IgG anti-bodies. The overall crude seroprevalence was calculated 89.6% and the adjusted 89.8% (95% CI: 88.8% - 90.8%). There was no statistically significant difference in seropositivity among sexes (p=0.783). Higher immunity rates and antibody titer were found in older age groups ≥ 41 years old (94.9%, 95% CI: 93.7% - 95.9%, and 730.0 IU/l) in comparison to younger individuals 1-40 years old (83.4%, 95% CI: 81.6% - 85.7%, and 616.5 IU/l). ). Comparing the seroprevalence among Nomenclature of Territorial Units for Statistics (NUTS 2), a statistically significant difference was estimated among them (<0.001). The two regions where higher measles incidence was observed during the 2017-2018 outbreak, Eastern Macedonia and Thrace and Western Greece, were among the four regions with the lower seropositivity (84.6%, 95% CI: 79.9% -89.4% and 85.9%, 95% CI: 81.4% - 90.4%, respectively). Our study showed a measles immunity gap that affects younger age groups and makes a new measles outbreak likely. Enforcement of vaccination campaigns and ad-dressing vaccine hesitancy could bridge it and achieve the required target for herd immunity.

Coronavirus disease 19 (COVID-19) continues to spread worldwide as a severe pandemic. The Omicron BA.2 became the predominant variant and the protagonist of the ongoing surge. As the virus continues to mutate, using of approved drugs or developing new therapeutic or prophylactic therapies against COVID-19 could be more complex. Sotrovimab is a monoclonal antibody (mAb) targeting the conserved epitope on the spike protein receptor; the most recent studies observed that it has substantially decreased in vitro activity against the Omicron BA.2 subvariant, but real-life data are still scarce. We describe the outcome of a case series of outpatients with BA.1 or BA.2 infection treated with sotrovimab. We conducted a retrospective observational study including all non-hospitalized adult patients treated with sotrovimab, for which a Sanger sequencing of SARS-CoV-2 was performed within a regional genomic surveillance program. Eleven (50%) patients with BA.1 infection and eleven (50%) with BA.2 infection were considered. Most patients were immunocompromised. During the follow-up period, no patient died and only one with BA.1 infection was hospitalized for severe COVID-19 pneumonia onset. One month after treatment, 90.9% of patients were completely asymptomatic in each group. We demonstrated that patients carrying the BA.2 variant treated with sotrovimab did not evolve to severe COVID-19, showing a similar outcome to BA.1 infected patients. Further studies are needed to prove that vaccination or the presumably high doses of mAbs used can protect this group of patients at high risk of progression.

The COVID-19 pandemic is the biggest public health threat facing the globe today. Multiple vaccines have been approved, however the emergence of viral variants such as the recent Omicron, raises the possibility of booster doses to achieve adequate protection. In Brazil, the CoronaVac (Sinovac) vaccine was used, however it’s important to assess the immune response to this vaccine over time. This study aimed to monitor the anti-SARS-CoV-2 antibody responses in those immunized with CoronaVac and SARS-CoV-2 infected individuals. Samples were collected between August 2020 and August 2021. Within the vaccinated cohort, some individuals had history of infection by SARS-CoV-2 prior to immunization and others not. We analyzed RBD-specific and neutralizing- antibodies. Anti-RBD antibodies were detected in both cohorts, with a peak between 45-90 days post infection or vaccination, followed by a steady decline over time. In those with previous history of COVID-19, a higher, longer, more persistent response was observed. This trend was mirrored in the neutralization assays, where infection followed by immunization resulted in higher, longer lasting responses which were conditioned on the presence of levels of RBD antibodies right before the vaccination. This supports the necessity of booster doses of CoronaVac in due course to prevent serious disease.

SARS-CoV-2 is a novel coronavirus that is the causative agent of Coronavirus infectious disease 2019 (COVD-19). As of the 17^th April 2020, it has infected 2 114 269 people resulting in 145 144 deaths. The timing, magnitude and longevity of humoral immunity is not yet understood for SARS-CoV-2. Nevertheless, understanding this is urgently required to inform the likely future dynamics of the pandemic, to guide strategies to allow relaxation of social distancing measures and to understand how to deploy limiting vaccine doses when they become available to achieve maximum impact. SARS-CoV-2 is the seventh human coronavirus to be described. Four human coronaviruses circulate seasonally and cause common colds. Two other coronaviruses, SARS and MERS, have crossed from animal sources into humans but have not become endemic. Here we review what is known about the human humoral immune response to epidemic SARS CoV and MERS CoV and to the seasonal, endemic coronaviruses. Then we summarize recent, mostly non-peer reviewed studies into SARS-CoV-2 serology and reinfection in humans and non-human primates and summarize current pressing research needs.

The appearance of the novel betacoronavirus 2019-nCoV represents a major threat to human health, and its diffusion around the world is predicted to have dramatic economic consequences. The knowledge of the 3D structures of 2019-nCoV proteins can facilitate the development of diagnostic and therapeutic molecules. Herein, we apply our energy-based method for the prediction of potential epitopes on viral proteins to design peptide-based molecules that can subsequently be used in diagnostic and therapeutic applications. We discuss these aspects in the paper.The designs have not been tested. Our aim is to share information that can be useful in the development of novel biomolecules with potential interesting activities against 2019-nCoV.

The vehicles number continuously growing lead to increasingly intense and congested traffic and it will additionally demand the braking system, and drivers behave more aggressively and as result is required that the braking system to be durable and efficient. For this is necessary the study the braking system behavior in conditions of intense and moderate traffic to increase the safety of traffic participants, respectively to demonstrate the need for more frequent replacement of some braking system elements. Thus, on a vehicle were performed a series of successive tests, through which the degree of wear of the brake pads and discs was monitored periodically and as a result the efficiency evolution of the braking system. The tests were carried out both in laboratory (on dynamometer) and in traffic to establish the efficiency of the braking system according to some parameters considered essential. The experimental tests showed that the recommendations regarding the frequency of replacement of brake pads and disks are inconsistent with their actual wear. Therefore, the aim of this paper is the establishment of the braking system efficiency of an auto-vehicle, subject to testing depending on the auto-vehicle mass, travel speed, distance driven, and braking time, based on experimental on stand and in-traffic tests, according the road safety regulations.

The question of the relevance of human and natural capital, as well as the potential adverse effect of natural capital on economic growth, has gained increased attention in development economics. The aim of this paper is to theoretically and empirically assess the relevance of several forms of capital on economic growth in small economies that are dependent upon tourism or natural resources. The empirical framework is based on Impulse Response Functions obtained from Vector Autoregressive models in which we focus on the model where economic growth is the dependent variable for ten small economies that are dependent upon either tourism or natural resources. We find that there is evidence of the ‘’natural resource curse’’, especially in the economies that have a strong dependence on resources that are easily substitutable and whose prices constantly fluctuate. We further find that in the majority of observed cases the type of capital these small economies are most dependent on for their economic growth causes negative impulses in the majority of the observed periods. The main policy recommendation should be to assure that even these small economies should strive towards further diversification and avoid dependence on only one segment of their economy.

Currently, SARS-CoV-2 has evolved into various variants, including the numerous highly mutated Omicron sub-lineages, significantly increasing immune evasion ability. The development raises concerns about possibly diminished effectiveness of available vaccines and antibody-based therapeutics. Here, we describe those representative categories of broadly neutralizing antibodies (bnAbs) that retain prominent effectiveness against emerging variants including Omicron sub-lineages. The molecular characteristics, epitope conservation, and resistance mechanisms of these antibodies are further detailed, aiming to offer suggestion or direction for the development of therapeutic antibodies, and facilitate the vaccine design with broad-spectrum potential.

The recent setbacks in the withdrawal and approval delays of antibody treatments of neurodegenerative disorders (NDs) can be attributed to their poor entry across the blood-brain barrier (BBB). While several invasive and noninvasive approaches can be used to make the BBB more permeable, their inconsistency makes them a poor choice; however, if an antibody is chemically modified to attach a transferrin molecule that enhances its permeation across the BBB, this could be the most practical and useful modification that can transform the science of ND treatment. Antibodies can be conjugated with transferrin in vitro, but preferably through mRNA, as we propose to convert current treatments to bring faster treatment options. The mRNA technology yields products at a meager cost, which is a consideration for the affordability of these drugs globally. This paper provides complete details with templates to convert the current medicines and those under development into mRNA delivery systems to ensure clinical success, avoiding the current failures in their efficacy, more specifically the products that have demonstrated efficacy but are being removed from the market.

Background: The COVID-19 pandemic has affected Madagascar, Cameroon, and the Central African Republic (CAR), with each experiencing multiple waves by mid-2022. This study aimed to evaluate immunity against SARS-CoV-2 strains Wuhan (W) and BA.2 (BA.2) among healthcare workers (HCWs) in these countries, focusing on vaccination and natural infection effects. Methods: HCWs' serum samples were analyzed for neutralizing antibodies (nAbs) against W and BA.2 variants, with statistical analyses comparing responses between countries and vaccination statuses. Results: Madagascar showed significantly higher nAb titers against both strains compared to CAR and Cameroon. Vaccination notably increased nAb levels against W by 2.6-fold in CAR and 1.8-fold in Madagascar, and against BA.2 by 1.6-fold in Madagascar and 1.5-fold in CAR. However, in Cameroon, there was no significant difference in nAb levels between vaccinated and unvaccinated groups. Conclusion: The study highlights the complex relationship between natural and vaccine-induced immunity, emphasizing the importance of assessing immunity in regions with varied epidemic experiences and low vaccination rates.

The Bunyavirales order includes at least fourteen families with diverse but related viruses, which are transmitted to vertebrate hosts by arthropod or rodent vectors. These viruses are responsible for an increasing number of outbreaks worldwide and represent a threat to public health. Infection in humans can be asymptomatic, or it may present with a range of conditions from a mild, febrile illness to severe hemorrhagic syndromes and/or neurological complications. There is a need to develop safe and effective vaccines, a process requiring better understanding of the adaptive immune responses involved during infection. This review highlights the most recent findings regarding T cell and antibody responses to the five bunyavirus families with known human pathogens (Peribunyaviridae, Phenuiviridae, Hantaviridae, Nairoviridae, and Arenaviridae). Future studies that define and characterize mechanistic correlates of protection against bunyavirus infections or disease will help inform the development of effective vaccines.

Background: COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a recurrent endemic disease affecting the whole world. Since November 2021, Omicron and its subvariants are dominating. In order to prevent severe courses of disease, vaccines are needed to boost and maintain antibody levels capable of neutralizing Omicron. Recently we produced and characterized a SARS-CoV-2 vaccine based on a recombinant fusion protein consisting of hepatitis B virus (HBV)-derived PreS and two SARS-CoV-2 wild-type RBDs. Objectives: To develop a PreS-RBD vaccine which induces high levels of Omicron-specific neutralizing antibodies. Methods: We designed, produced, characterized and compared strain-specific (wild-type: W-PreS-W; Omicron: O-PreS-O), bivalent (mix of W-PreS-W and O-PreS-O) and chimeric (i.e., W-PreS-O) SARS-CoV-2 protein subunit vaccines. Immunogens were characterized in vitro by protein chemical methods, mass-spectrometry, circular dichroism in combination with thermal denaturation and immunological methods. In addition, BALB/c mice were immunized with aluminum hydroxide-adsorbed proteins and aluminum hydroxide alone (i.e., placebo) to study specific antibody and cytokine responses, safety and Omicron neutralization. Results: Defined and pure immunogens could be produced in large amounts as secreted and folded proteins in mammalian cells. Antibodies induced after vaccination with different doses of strain-specific, bivalent and chimeric PreS-RBD fusion proteins reacted with wild-type and Omicron RBD in a dose-dependent manner and resulted in a mixed Th1/Th2 immune response. Interestingly, RBD-specific IgG levels induced by the different vaccines were comparable but the W-PreS-O-induced virus neutralization titers against Omicron (median VNT50: 5000) were 7- and 2-fold higher than the W-PreS-W- and O-PreS-O-specific ones, respectively and 6-fold higher than those of the bivalent vaccine. Conclusion: Among the tested immunogens, the chimeric PreS-RBD subunit vaccine, W-PreS-O, induced the highest neutralizing antibody titers against Omicron. Thus, W-PreS-O seems to be a highly promising COVID-19 vaccine candidate for further preclinical and clinical evaluation.

Antiphospholipid antibody syndrome (APS) is an autoimmune disorder characterised by thrombosis and the presence of antiphospholipid antibodies (aPL): lupus anticoagulant and/or IgG/IgM anti-β2-glycoprotein I and anticardiolipin antibodies. APS carries significant morbidity for a relatively young patient population from recurrent thrombosis in any vascular bed (arterial, venous or microvascular), often despite current standard of care, which is anticoagulation with vitamin K antagonists (VKA). Platelets have established roles in thrombosis at any site, and platelet hyperreactivity is clearly demonstrated in the pathophysiology of APS. Together with excess thrombin generation, platelet activation and aggregation are the common end result of all the pathophysiological pathways leading to thrombosis in APS. However, antiplatelet therapies play little role in APS, reserved as a possible option of low dose aspirin in addition to VKA in arterial or refractory thrombosis. This review outlines the current evidence and mechanisms for excessive platelet activation in APS, how it plays a central role in APS-related thrombosis, what evidence for antiplatelets is available in clinical outcomes studies and potential future avenues to define how to target platelet hyperreactivity better with minimal impact on haemostasis.

Background Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder characterized by a variety of both signs and symptoms; it mainly affects women of childbearing age, with an estimated prevalence of 24/100,000 people in Europe and North America. SLE is often described as an antibodies-driven disease as its clinical manifestations are usually associated with the presence or the absence of specific antibodies. Objectives To evaluate clinical manifestations in patients with SLE and to assess the relationship with the presence of specific antibodies by using real-world data. Methods A retrospective study was performed; the 2019 EULAR/ACR Classification Criteria for Systemic Lupus Erythematosus were used to classify patients with SLE. Data concerning serological profiles (which included Antinuclear antibodies – ANA, anti dsDNA, anti-Ro/SS-A, anti-La/SS-B, anti-Sm) were gathered along with medical records of clinical manifestations. Complement levels were also tested for possible clinical correlations. χ² or Fisher's exact tests were utilized to establish associations between autoantibodies and symptoms. The odds ratios (OR) and their 95% confidence intervals (CI) were computed. No correction was made for multiple testing; only a p-value 0.01 ≤ was considered significant. Results One-hundred and twenty-seven patients (n=127, mean age 53.43 ±14.02) were enrolled in this study. Anti-dsDNA antibodies were found to be statistically significant for both malar rash and proteinuria; anti-Ro/SSA antibodies showed an association with photosensitivity and pericarditis; furthermore, a strong association was found between anti-Ro antibodies and proteinuria, but only if anti-dsDNA antibodies were present as well. Patients who tested positive for anti-La/SSB antibodies correlated with a threefold increase in the risk of developing pericarditis. Lastly, anti-Smith appeared to be associated with NPSLE as well as an increased risk for both autoimmune haemolytic anemia and thrombocytopenia. Conclusions In our study, many associations confirmed those found in previous studies; however, new relationships between antibodies and clinical manifestations were found thus indicating the need for additional evaluations to assess these correlations further.

Saliva is a promising matrix with several purposes. Our aim is to verify if salivary anti-SARS-CoV-2 antibody determination is suitable for monitoring immune response. One-hundred eighty-seven subjects were enrolled at University-Hospital Padova: 105 females (56.1%) and 82 males (43.9%), 95 (50.8%) children and 92 (49.2%) adults. Subjects self-collected saliva using Salivette; nineteen subjects collected three different samples within the day. A serum sample was obtained for all individuals. The N/S anti-SARS-CoV-2 salivary IgG (sal-IgG) and serum anti-SARS-CoV-2 S-RBD IgG (ser-IgG) were used for determining anti-SARS-CoV-2 antibodies. The mean (min-max) of age was 9.0 (1–18) for children and 42.5 (20–61) for adults. Of 187 samples, 63 were negative for sal-IgG (33.7%), while 7 were negative for ser-IgG (3.7%). Spearman’s correlation was 0.56 (p&lt;0.001). Sal-IgG and ser-IgG levels were correlated with age, but not with gender, comorbidities, prolonged therapy, previous SARS-CoV-2 infection or time from last COVID-19 infection/vaccination. The repeatability ranged from 23.8% (7.4kAU/L) to 4.0% (3.77kAU/L). Linearity of the assay missed in 4/6 samples. No significant intra-subject differences were observed in sal-IgG across samples collected at different time points. Sal-IgG have a good agreement with ser-IgG. Non-invasive saliva collection represents an alternative method for antibody measurement, especially in children.

Lambert-Eaton myasthenic syndrome is a primary autoimmune or paraneoplastic neuromuscular disorder, which causes muscle weakness, areflexia and autonomic disorders through the production of antibodies to voltage-gated calcium channels. Diagnosis may be challenging, yet at the same time extremely important, allowing the detection of a possible underlying malignancy, in cases of paraneoplastic presentation. Some antibodies have good sensitivity and specificity for malignancy and are also discussed. Although most of times being correlated to small cell lung cancer, other types of tumors may be also found in the paraneoplastic syndrome. Misdiagnosis with Myasthenia Gravis may occur and differences between these two disorders are discussed. Therapy choice may vary from each patient depending on drug response and the situation in which the patient is encounter. Overall, the present paper brings a review of Lambert-Eaton, involving the diagnosis, as well as, the pathophysiology, differential diagnosis, tumor investigation and treatment. Also, a brief overview through guidance on therapy during the Corona Virus Disease 2019 pandemic is analyzed.

Background: Myasthenia Gravis (MG) is a rare autoimmune disease presenting with au-to-antibodies that affect the neuromuscular junction. Aside from symptomatic treatment options, novel therapeutics include monoclonal antibodies (mAbs). IMGT®, the international ImMunoGe-neTics information system® (https://www.imgt.org), extends the characterization of therapeutic antibodies with a systematic description of their mechanisms of action (MOA) and makes them available through its database for mAbs and fusion proteins, IMGT/mAb-DB. Methods: Using the available literature data combined with the amino acid sequence analyses from mAbs managed in IMGT/2Dstructure-DB, the IMGT® protein database, biocuration allowed to define in a standardized way descriptions of MOA of mAbs that target molecules towards MG treatment. Results: New therapeutic targets include FcRn and molecules such as CD38, CD40, CD19, MS4A1 and interleukin-6 receptor. A standardized graphical representation of the MOA of selected mAbs was created and integrated within IMGT/mAb-DB. The main mechanisms involved in these mAbs are either blocking or neutralizing. Therapies directed to B cell depletion and plasma cells have a blocking MOA with an immunosuppressant effect along with Fc-effector function (MS4A1, CD38) or FcγRIIb engager effect (CD19). Monoclonal antibodies targeting the complement also have blocking MOA with a complement inhibitor effect and treatments targeting T cells have a blocking MOA with an immunosuppressant effect (CD40) and Fc-effector function (IL6R). On the other hand, FcRn antagonists present a neutralizing MOA with an FcRn inhibitor effect. Conclusion: The MOA of each new mAb needs to be considered in association with the immuno-pathogenesis of each of the subtypes of MG in order to integrate the new mAbs as a viable and safe option in the therapy decision process. In IMGT/mAb-DB, mAbs for MG are characterized by their sequence, domains, chains and their MOA is described.

Aim: The aim of this study was to use a multi target approach to testing with both serological tests and an in-house real-time molecular test to investigate the prevalence of the caprine arthritis-encephalitis virus (CAEV) in goats from three hobbyist farms in the Republic of Tatarstan, Russia. Materials and Methods: We have approached the detection of using a multi target approach testing with both ELISA and an in-house real-time PCR test to investigate the prevalence of CAEV in goats. Animals from three hobbyist farms were used in this study. The animals from two farms (n=13 for F1 and n=8 for F2) had clinical signs of arthritis and mastitis. In the third farm (n=15 for F3), all goats were homebred and had no contact with imported animals. Results: CAEV antibodies (ELISA targets TM env and gag genes) were detected in serum samples from two farms (F1 and F2), indicating a seroprevalence 87.50-92.31%. Specific CAEV antibodies were also detected in milk samples. CAEV proviral DNA was detected in 53.85-62.50%. Results from all tests performed in the third farm (F3) were negative, indicting all tests were 100% specific.Conclusion: Results of this work show that CAEV is circulating and present in small hobbyist goat farms in Russia. Serological and molecular tests could be of importance for CAEV control and eradication programs in Russia for hobbyist goat farms.

Knowing the “point of view” of the immune system is essential to understand the characteristic of a pandemic, such as that generated by the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV)-2, responsible for the Coronavirus Disease (COVID)-19. In this review, we will discuss the general host/pathogen interactions dictating protective immune response or immunopathology, addressing the role of immunity or immunopathology in influencing the clinical infection outcome, and debate the potential immunoprophylactic and immunotherapy strategies required to fight the virus infection.

The progression of the recent COVID-19 pandemic surprised political authorities as well as scientists. The possibility to design powerful strategies for health care and preserving economic and social activities strongly relies on the capacity to monitor correctly the virus spreading and the immune response in the symptomatic and asymptomatic population. The available data relative to the first pandemic months indicate that the test reliability was progressively improved but also that the extremely variable methodologies used in the diagnostic studies generated data that are often not comparable. This condition prevents a simple metadata analysis for the identification of reliable diagnostics guidelines. Nevertheless, there are converging evidences that combinations of complementary approaches may enable more precise identification of virus infection. Furthermore, it appears that the similarities between SARS-CoV2 and the related types SARS-CoV1 and MERS that caused outbreaks in the last 20 years can be exploited to infer some information for which no direct evidence is still available

We review aspects of the antibody response to SARS-CoV-2, the causative agent of the COVID- 19 pandemic. The topics we cover are relevant to immunotherapy with plasma from recovered patients and with monoclonal antibodies against the viral S-protein. The development of vaccines against SARS-CoV-2, an essential public health tool, will also be informed by an understanding of the antibody response in infected patients. Although virus-neutralizing antibodies are likely to protect, antibodies could potentially trigger immunopathogenic events in SARS-CoV-2-infected patients or enhance infection. An awareness of these possibilities may benefit clinicians and the developers of antibody-based therapies and vaccines.

Abstract Optimal targeting of nanoparticles (NP) to dendritic cells (DCs) receptors to deliver cancer-specific antigens is key to an efficient induction of anti-tumor immune responses. Poly (lactic-co-glycolic acid) (PLGA) nanoparticles containing tètanus toxoid and gp100 melanoma-associated antigen, toll-like receptor adjuvants were targeted to the DC-SIGN receptor in DCs by specific humanized antibodies or by ICAM3-Fc fusion proteins mimicking natural ligand. Despite higher binding and uptake efficacy of anti-DC-SIGN antibody-targeted NP vaccines than ICAM3-Fc ligand, no difference were observed in DC activation markers CD80, CD83, CD86 and CCR7 induced. DCs loaded with NP coated with ICAM3-Fc appeared more potent in activating T cells via cross-presentation than antibody-coated NP vaccines. This fact could be very crucial in the design of new cancer vaccines.

With the development of sensor and communication technologies, Internet of Things (IoT) subsystem is gradually becoming a crucial part in vehicles. It can effectively enhance functionalities of vehicles. However, new attack types are also emerged. For example, a driver with the smart key in the pocket can push the start button to start a car when. At the same time, security issues in the push-to-start scenario are alerted, such as smart key forgery. In this paper, we propose a vehicle Passive Entry Passive Start (PEPS) system that uses deep learning algorithms to recognize the driver by the Electrocardiogram (ECG) signals measured by his or her smart watch. ECG signals are used for verification. Smart watches, as a new smart key of PEPS system, can replace traditional smart keys to improve security. Experiment results show that Long Short-Term Memory (LSTM) models have achieved the best accuracy score for identity recognition (91%) when single ECG cycle is used. However, it takes at least 30 minutes for training. The training time of Auto Encoder successfully reduces to 5 minutes. When 15 continuous ECG cycles are used, it can achieve 100% identity accuracy.

In order to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional network (TCN) is proposed. Firstly, construct a high-dimensional feature set in the multi-domain of vibration signals, and use comprehensive evaluation indicators to preliminarily screen performance degradation indexes with good sensitivity and strong trend. Secondly, the kernel principal component analysis (KPCA) method is adopted to eliminate redundant information between multi-domain features, and construct a health index (HI) based on convolutional auto-encoder (CAE) network. Thirdly, a TCN-based performance degradation trend prediction model is constructed, and direct multi-step prediction is used to predict the performance degradation trend of the monitored object. On this basis, the validity of the proposed method is verified using the bearing public data, and it is successfully applied to performance degradation trend prediction of rolling contact fatigue specimen. The results show that the feature set can be reduced from 14 dimensions to 4 dimensions by using KPCA, while 98.33% of the information of the original feature set is retained. Furthermore, the method of constructing HI based on CAE network is effective. The change process of the HI constructed truly reflects the performance degradation process of the rolling contact fatigue specimen. Compared with the two commonly used HI construction methods, auto-encoding (AE) network and gaussian mixture model (GMM), this method has obvious advantages. At the same time, the prediction model based on TCN can accurately predict the performance degradation of the rolling contact fatigue specimen with the root mean square error 0.0146 and the mean absolute error 0.0105, which has better performance and higher prediction accuracy than the prediction model based on the long short-term memory (LSTM) network and the gated recurrent unit (GRU). This method has general significance and may be extended to the performance degradation prediction of other mechanical equipment/parts.

Variations in groundwater levels that are noticed in different parts of the world are typically the primary focus of research carried out in hydrology. The fluctuating levels of groundwater can be ascribed to several different factors, some of which include an increase in the demand for water, inappropriate irrigation practices, improper management of soil, and unregulated extraction from aquifers. It is necessary to effectively manage groundwater resources to have a trustworthy method of measuring and forecasting groundwater levels. The dynamics of groundwater are inherently uneven and difficult to understand. As a result, applying methods driven by data could potentially yield significant gains in hydrology. In this study, two data-driven models were utilized to estimate groundwater levels at a total of 4 monitoring wells located in Colorado state, USA. These models included ARIMA and ANN. The comparative analysis of the algorithms made use of a data set that had one month's worth of information from each of the years 1980 to 2019. The models were put through their paces, and the results of those tests were statistically and visually assessed. To evaluate the relative accuracy and precision of the models, the mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE) were used. When compared to ARIMA, the analysis demonstrates that ANN can produce the most accurate forecasts of groundwater levels in Colorado state USA.

In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparameters, it is worth trying out the automatic machine learning features provided by several cloud-based and cloud-agnostic platforms. This paper explores the possibility of generating automatic machine learning models with low-code experience. We have developed criteria to compare different machine learning platforms for generating automatic machine learning models and presenting their results. Thereafter, lessons learned by developing automatic machine learning models from a sample dataset across four different machine learning platforms were elucidated. We have also interviewed machine learning experts to conceptualize their domain-specific problems that automatic machine learning platforms could address. Results showed that automatic machine learning platforms could provide a fast track for organizations seeking digitalization of their businesses. Automatic machine learning platforms help produce results, especially for time-constrained projects where resources are lacking. The contribution of this paper is in the form of a lab experiment in which we demonstrate how low-code platforms could provide a viable option to many business cases and henceforth provide a lane that is faster than the usual hiring and training of already scarce data scientists and to analytics projects that suffer from overruns.

The purpose of this study is to propose a consecutive manufacturing process system to secure the productivity of excellent STKN540B steel tube in the respect of economy and safety as the supporting material for mega structures such as building, bridge and ship. The components of consecutive manufacturing are press-bending, orbital auto welding and steel tube correction. By using STKN540B a high-strength steel material with low yield point that requires a special manufacturing process unlike other steel materials, an actual tube manufacturing is carried out at each stage in this experimental study. With this, the quality of steel tube and the efficiency of the manufacturing process are analyzed to draw out some points to improve in the future.

Background: This study retrospective evaluates patients with stage IV melanoma treated with Nivolumab and Ipilimumab combination therapy from two regional oncology centers from Romania between the years of 2019 up to the end of 2022. Methods: The data were analyzed in SAS for Windows, V9.4. The survival curves were estimated using the Kaplan-Meier method, and survival distributions were compared with log-rank test. The effects of the main clinical and pathological variables on OS and PFS were investigated with Cox regression. Results: Kaplan-Meier curve of OS in all evaluable patients enrolled in the study resulted in a median OS of 346 days (95% CI: 150-NA) and a median PFS of 211 days (95% CI: 113-430). 45.3% of the patients experienced adverse events during the Nivolumab + Ipilimumab treatment with some of them having multiple organ systems involved. Discussion: The OS values were lower than that reported in approval clinical trials, but the results show a marked improvement when comparing to results obtained by chemotherapy regimens previously used in these scenarios. Conclusion: This study provides real-world insights into the survival data and safety profiles of combination therapy with anti-PD-1 antibodies and anti-CTLA-4 antibodies.

Objective: The study aimed to evaluate the effects of monoclonal antibodies (mAbs) acting on the calcitonin gene-related peptide (CGRP) or its receptor (anti-CGRP/R mAbs) on comorbid symptoms of depression, anxiety, and fatigue in migraine patients resistant to traditional prophylaxis. Methods: The study was an open-label prospective study assessing comorbidities in patients with high frequency (HFEM) and chronic migraine (CM), medication overuse headache (MOH), and resistance to traditional prophylaxis treated with anti-CGRP/R mAbs for 3 months. Results: 77 patients were enrolled with either HFEM (21%) or CM (79%) with or without MOH (56% and 44% respectively). We identified 21 non-responders (27%) and 56 responders (73%), defined on the reduction ≥ 50% of headache frequency. The two groups were highly homogeneous for investigated comorbidities. Disease severity in terms of headache frequency, migraine-related disability, and affective comorbid symptoms were reduced in both groups with different thresholds; allodynia and fatigue were ameliorated only in responders. Conclusion: anti-CGRP/R antibodies improve pain together with affection, fatigue, and sensory sensitization in migraine patients.

Claudins are a family of 27 proteins that have an important role in the formation of tight junc-tions. They also have an important function in ions exchange, cell mobility, and in in the epitheli-al-to-mesenchymal transition, the latter being very important in cancer invasion and metastasis. Therapeutic targeting of claudins has been investigated to improve cancer outcomes. Recent evi-dence shows improved outcomes when combining monoclonal antibodies against Claudin 18.2 with chemotherapy for patients with gastroesophageal junction cancer. Currently Chimeric anti-gen receptor T-cells targeting Claudin 18 are under investigation.

The use of antibiotics as growth promotors or for the prevention of some poultry diseases has face global concern and serious criticism. Their addition to poultry feed show hazardous effects including development of antimicrobial resistance and the potential harmful effect on humans’ health. To eliminate these threats, there is increasing interest to search for natural alternatives. Plant derivatives such as garlic (Allium sativum L.) and its derivatives are nowadays extensively used in poultry production system. Dietary supplementation of broilers and layers with garlic induced improvement of production parameters, carcass quality, and intestinal integrity. Modulation of the immune response against important viral diseases and increasing the immune organs body weight ratio resulting from supplementation of poultry with garlic. Moreover, garlic showed modulation of gut health through the antibacterial and antiparasitic activities. Treatment with garlic also can mitigate the oxidative stress and reduce the free radicals production. Reduction of cholesterol level, but improvement of some liver and blood parameters were also reported following dietary inoculation of garlic. This review was designed to search on the impact of garlic as feed additives on performance, immunity, gut health, antioxidant status, and blood parameters of poultry.

Recently, nanobodies have taken center stage in research on drug discovery and development. Several therapeutic possibilities using nanobodies are presently undergoing clinical trials and waiting for FDA approval. The goal of this study was to emphasize the potential of nanobodies as therapeutic agents by concentrating on the most recent published studies that examined their properties, manufacturing, and possible applications. This article demonstrates that the unique properties of nanobodies in comparison to conventional antibodies, which are based on their small size and offer a number of benefits, make them seem to have a promising future. These advantages include the ability to access complex or hidden target sites that may be elusive to their larger antibody counterparts, and increased resilience against extreme conditions like tempera-ture changes and pH variations. As a result, nanobodies seem to have a bright future as adaptable tools for imaging in cancer and non-malignant diseases, as well as for in vitro and in vivo diagnostic and therapeutic potential for a variety of conditions, including oncology, infectious, metabolic, neurological, and other conditions like ophthalmologic, immune-mediated, and genetic disorders. More research is required to determine their effectiveness and safety in clinical applications. The current analysis offers a thorough overview of the therapeutic uses for nanobody products that are either on the market or undergoing clinical trials.

The landscape of therapeutic measures to treat multiple myeloma has undergone a seismic shift since the dawn of the current century. This has been driven largely by the introduction of new classes of small molecules, such as proteasome blockers (e.g., bortezomib) and immunomodulators (e.g., lenalidomide), as well as by immunotherapeutic agents starting with the anti-CD38 monoclonal antibody daratumumab in 2015. Recently, other immunotherapies have been added to the armamentarium of drugs available to fight this malignancy. These include the bispecifics teclistamab, talquetamab, and elranatamab, and the chimeric antigen receptor (CAR) T-cell products idecabtagene vicleucel (ide-cel) and ciltacabtagene autoleucel (cilta-cel). While the accumulated benefits of these newer agents have resulted in a more than doubling of the disease’s five-year survival rate to nearly 60% and improved quality of life, the disease remains incurable, as patients become refractory to the drugs and experience relapse. This review covers the current scope of anti-myeloma immunotherapeutic agents, both those in clinical use and in development. Included in the discussion are additional monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs), bi- and multi-targeted mAbs, and CAR T-cells and emerging natural killer (NK) cells, including products intended for “off-the-shelf” (allogeneic) applications. Emphasis is placed on the benefits of each along with the challenges that need to be surmounted if MM is to be cured.

Background: BNT162b2 represents the authorized mRNA vaccine against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS–CoV–2) causing Covid–19. Methods: We conducted a prospective, single–center observational study with 273 employees of a medical facility to determine the efficacy and safety of the BNT162b2 third dose in relation to sex and BMI, and explore the possible determinants of neutralizing IgG and 25–hydroxy (25–OH) vitamin D levels. Results: At the median follow–up of 4.7 months, 38 of the participants had Covid–19 following vaccination. The incidence of adverse events (AEs) was significantly higher in females vs. males (p < 0.0108) and subjects with a high BMI of ≥ 30 kg/m2 vs. low BMI (p < 0.0117). The values of IgG were significantly enhanced in males vs. females (p < 0.0004), participants with high BMI vs. low BMI (p < 0.0003), a high number of ≥3 AEs vs. low number of AEs (p < 0.0003), and following infection with Covid–19 vs. without Covid–19 (p < 0.0005). Multivariate analysis (MVA) showed sex, BMI, number of AEs, and Covid-19 to be of predictive significance. On the contrary, 25–OH vitamin D concentrations in individuals with high vs. low BMI were significantly reduced (p < 0.0144) but this was not evident in MVA. Conclusions: This study confirmed the high efficacy of BNT162b2 in real–world scenario and revealed that its safety was affected by sex and BMI. Higher IgG levels suggesting better protection against infection were present in males, participants with high BMI, a high frequency of AEs, and those who overcame Covid-19 following vaccination.

The Receptor Binding Domain (RBD) of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, is the functional region of the viral Spike protein (S), which is involved in cell attachment to target cells. The virus has accumulated progressively mutations in its genome, particularly in the RBD region, many of them associated with immune evasion to the host neutralizing antibodies. Some of the viral lineages derived from this evolution, have been classified as Variant of Interest (VOI) or Concern (VOC). The neutralizing capacity of a F(ab’)2 preparation from sera of horses immunized with viral RBD was evaluated, by lytic plaque reduction assay, against different SARS-CoV-2 variants. A F(ab’)2 preparation of an hyperimmune serum with 15 immunizations with RBD, exhibited a high titer of neutralizing antibodies against the ancestral-like strain (1/18,528). A reduction in the title of the F(ab’)2 preparation was observed against the different variants tested. The highest reduction was observed for the Omicron VOC (4.7 fold), followed by the Mu VOI (2.6), Delta VOC (1.8 fold) and Gamma VOC (1.5). Even if a progressive reduction in the neutralizing antibodies titer against the different variants evaluated was observed, the serum still exhibited a significant neutralizing titer, against the Mu VOI and the Omicron VOC (1/7113 and 1/3918 respectively), the strains evaluated most resistant to neutralization.

Immune complexes (ICs) made of antibody-bound antigens exhibit immunomodulatory activities exploitable in a vaccination strategy to optimize vaccine efficacy. The modulatory effects of ICs are typically attributed to the Fc fragments of the antibody components, which engage Fc receptors, complement and complement receptors on various immune cells. These Fc-mediated functions facilitate the critical interplay between innate and adaptive immune systems to impact the quality and quantity of the elicited adaptive responses. In addition to the Fc contribution, the Fab fragment also plays an immunoregulation role. The antigen-binding domains of the Fab fragment can bind their specific epitopes at high affinity to sterically occlude these antigenic sites from recognition by other antibodies. Moreover, the Fab-mediated binding have been demonstrated to induce allosteric alterations at nearby or distant antigenic sites. In this review article, we survey published studies to illuminate how the immunomodulatory functions of ICs have been investigated or utilized in a vaccination strategy to fight against an array of infectious pathogens, culminating with IC vaccine designs aimed at preventing HIV-1 infection. In particular, we highlight IC vaccine candidates that exploit Fab-mediated steric and allosteric effects to direct antibody responses away or toward the V1V2 domain, the V3 loop, and other antigenic sites on the HIV-1 envelope gp120 glycoprotein. Like other HIV-1 vaccine approaches, the path for IC-based vaccines to reach the clinic faces major hurdles yet to be overcome; however, investigations into this vaccine strategy have provided insights into the multifaceted activities of antibodies beyond their conventional roles in the host defense against HIV-1 and other microbial pathogens.

Almost 50 papers on surface plasmon resonance biosensors, published between 2016 and mid-2018, are reviewed. Papers concerning the determination of large particles such as vesicles, exosomes, cancer cells, living cells, stem cells and microRNA are excluded, as these are covered by a very recent review. The reviewed papers are categorized into five groups, depending on the degree of maturity of the reported solution: ranging from simple marker detection to clinical application of a previously developed biosensor. Instrumental solutions and details of biosensor construction are analyzed, including the chips, receptors and linkers used, as well as calibration strategies. Biosensors with a sandwich structure containing different nanoparticles are considered separately, as are SPR applications for investigating the interactions of biomolecules. An analysis is also made of the markers determined using the biosensors. Concluding, there is shown to be a growing number of SPR applications in the solution of real clinical problems.