Measured proton electromagnetic structure deviates from theoretical predictions.

The visible world is founded on the proton, the only composite building block of matter that is stable in nature. Consequently, understanding the formation of matter relies on explaining the dynamics and the properties of the proton's bound state. A fundamental property of the proton involves the response of the system to an external electromagnetic field. It is characterized by the electromagnetic polarizabilities1 that describe how easily the charge and magnetization distributions inside the system are distorted by the electromagnetic field. Moreover, the generalized polarizabilities2 map out the resulting deformation of the densities in a proton subject to an electromagnetic field. They disclose essential information about the underlying system dynamics and provide a key for decoding the proton structure in terms of the theory of the strong interaction that binds its elementary quark and gluon constituents. Of particular interest is a puzzle in the electric generalized polarizability of the proton that remains unresolved for two decades2. Here we report measurements of the proton's electromagnetic generalized polarizabilities at low four-momentum transfer squared. We show evidence of an anomaly to the behaviour of the proton's electric generalized polarizability that contradicts the predictions of nuclear theory and derive its signature in the spatial distribution of the induced polarization in the proton. The reported measurements suggest the presence of a new, not-yet-understood dynamical mechanism in the proton and present notable challenges to the nuclear theory.

Horizontal transfer of miR-383 sensitise cells to cisplatin by targeting VEGFA-Akt signalling loop.

BACKGROUND: Cellular resistance to cisplatin has been one of the major obstacles in the success of combination therapy for many types of cancers. Emerging evidences suggest that exosomes released by drug resistant tumour cells play significant role in conferring resistance to drug sensitive cells by means of horizontal transfer of genetic materials such as miRNAs. Though exosomal miRNAs have been reported to confer drug resistance, the exact underlying mechanisms are still unclear. METHODS AND RESULTS: In the present study, mature miRNAs secreted differentially by cisplatin resistant and cisplatin sensitive HepG2 cells were profiled and the effect of most significantly lowered miRNA in conferring cisplatin resistance when horizontally transferred, was analysed. we report miR-383 to be present at the lowest levels among the differentially abundant miRNAs expressed in exosomes secreted by cisplatin resistant cells compared to that that of cisplatin sensitive cells. We therefore, checked the effect of ectopic expression of miR-383 in altering cisplatin sensitivity of Hela cells. Drug sensitivity assay and apoptotic assays revealed that miR-383 could sensitise cells to cisplatin by targeting VEGF and its downstream Akt mediated pathway. CONCLUSION: Results presented here provide evidence for the important role of miR-383 in regulating cisplatin sensitivity by modulating VEGF signalling loop upon horizontal transfer across different cell types.

Phytosynthesis of zinc oxide nanoparticles for enhanced antioxidant, antibacterial, and photocatalytic properties: A greener approach to environmental sustainability.

Multifunctional nanoparticles (NPs) production from phytochemicals is a sustainable process and an eco-friendly method, and this technique has a variety of uses. To accomplish this, we developed zinc oxide nanoparticles (ZnONPs) using the medicinal plant Tinospora cordifolia (TC). Instruments such as UV-Vis, XRD, FTIR, FE-SEM with EDX, and high-resolution TEM were applied to characterize the biosynthesized TC-ZnONPs. According to the UV-vis spectra, the synthesized TC-ZnONPs absorb at a wavelength centered at 374 nm, which corresponds to a 3.2 eV band gap. HRTEM was used to observe the morphology of the particle surface and the actual size of the nanostructures. TC-ZnONPs mostly exhibit the shapes of rectangles and triangles with a median size of 21 nm. The XRD data of the synthesized ZnONPs exhibited a number of peaks in the 2θ range, implying their crystalline nature. TC-ZnONPs proved remarkable free radical scavenging capacity on DPPH (2,2-Diphenyl-1-picrylhydrazyl), ABTS (2,2-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid), and NO (Nitric Oxide). TC-ZnONPs exhibited dynamic anti-bacterial activity through the formation of inhibition zones against Pseudomonas aeruginosa (18 ± 1.5 mm), Escherichia coli (18 ± 1.0 mm), Bacillus cereus (19 ± 0.5 mm), and Staphylococcus aureus (13 ± 1.1 mm). Additionally, when exposed to sunlight, TC-ZnONPs show excellent photocatalytic ability towards the degradation of methylene blue (MB) dye. These findings suggest that TC-ZnONPs are potential antioxidant, antibacterial, and photocatalytic agents.

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor.

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model's performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model's efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.

Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer.

Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper utilized four public databases where a similar amount of 986 mammograms each for three classes (normal, benign, malignant) are taken for evaluation. Herein, three deep CNN models such as VGG-11, Inception v3, and ResNet50 are employed as base classifiers. The research adopts an ensemble method where the proposed approach makes use of the modified Gompertz function for building a fuzzy ranking of the base classification models and their decision scores are integrated in an adaptive manner for constructing the final prediction of results. The classification results of the proposed fuzzy ensemble approach outperform transfer learning models and other ensemble approaches such as weighted average and Sugeno integral techniques. The proposed ResNet50 ensemble network using the modified Gompertz function-based fuzzy ranking approach provides a superior classification accuracy of 98.986%.

Detection of Marchiafava Bignami disease using distinct deep learning techniques in medical diagnostics.

PURPOSE: To detect the Marchiafava Bignami Disease (MBD) using a distinct deep learning technique. BACKGROUND: Advanced deep learning methods are becoming more crucial in contemporary medical diagnostics, particularly for detecting intricate and uncommon neurological illnesses such as MBD. This rare neurodegenerative disorder, sometimes associated with persistent alcoholism, is characterized by the loss of myelin or tissue death in the corpus callosum. It poses significant diagnostic difficulties owing to its infrequency and the subtle signs it exhibits in its first stages, both clinically and on radiological scans. METHODS: The novel method of Variational Autoencoders (VAEs) in conjunction with attention mechanisms is used to identify MBD peculiar diseases accurately. VAEs are well-known for their proficiency in unsupervised learning and anomaly detection. They excel at analyzing extensive brain imaging datasets to uncover subtle patterns and abnormalities that traditional diagnostic approaches may overlook, especially those related to specific diseases. The use of attention mechanisms enhances this technique, enabling the model to concentrate on the most crucial elements of the imaging data, similar to the discerning observation of a skilled radiologist. Thus, we utilized the VAE with attention mechanisms in this study to detect MBD. Such a combination enables the prompt identification of MBD and assists in formulating more customized and efficient treatment strategies. RESULTS: A significant breakthrough in this field is the creation of a VAE equipped with attention mechanisms, which has shown outstanding performance by achieving accuracy rates of over 90% in accurately differentiating MBD from other neurodegenerative disorders. CONCLUSION: This model, which underwent training using a diverse range of MRI images, has shown a notable level of sensitivity and specificity, significantly minimizing the frequency of false positive results and strengthening the confidence and dependability of these sophisticated automated diagnostic tools.

Refining neural network algorithms for accurate brain tumor classification in MRI imagery.

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model's effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model's decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools.

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework.

Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers.

Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering.

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2 ).

Molecular characterization and antifungal activity of lipopeptides produced from Bacillus subtilis against plant fungal pathogen Alternaria alternata.

Over 380 host plant species have been known to develop leaf spots as a result of the fungus Alternaria alternata. It is an aspiring pathogen that affects a variety of hosts and causes rots, blights, and leaf spots on different plant sections. In this investigation, the lipopeptides from the B. subtilis strains T3, T4, T5, and T6 were evaluated for their antifungal activities. In the genomic DNA, iturin, surfactin, and fengycin genes were found recovered from B. subtilis bacterium by PCR amplification. From different B. subtilis strains, antifungal Lipopeptides were extracted, identified by HPLC, and quantified with values for T3 (24 g/ml), T4 (32 g/ml), T5 (28 g/ml), and T6 (18 g/ml). To test the antifungal activity, the isolated lipopeptides from the B. subtilis T3, T4, T5, and T6 strains were applied to Alternaria alternata at a concentration of 10 g/ml. Lipopeptides were found to suppress Alternaria alternata at rates of T3 (75.14%), T4 (75.93%), T5 (80.40%), and T6 (85.88%). The T6 strain outperformed the other three by having the highest antifungal activity against Alternaria alternata (85.88%).

Comprehensive analysis of physiological and metabolomic responses to drought reveals specific modulation of acquired tolerance mechanisms in rice.

Mild stresses induce "acquired tolerance traits" (ATTs) that provide tolerance when stress becomes severe. Here, we identified the genetic variability in ATTs among a panel of rice germplasm accessions and demonstrated their relevance in protecting growth and productivity under water-limited conditions. Diverse approaches, including physiological screens, association mapping and metabolomics, were adopted and revealed 43 significant marker-trait associations. Nontargeted metabolomic profiling of contrasting genotypes revealed 26 "tolerance-related-induced" primary and secondary metabolites in the tolerant genotypes (AC-39000 and AC-39020) compared to the susceptible one (BPT-5204) under water-limited condition. Metabolites that help maintain cellular functions, especially Calvin cycle processes, significantly accumulated more in tolerant genotypes, which resulted in superior photosynthetic capacity and hence water use efficiency. Upregulation of the glutathione cycle intermediates explains the ROS homeostasis among the tolerant genotypes, maintaining spikelet fertility, and grain yield under stress. Bioinformatic dissection of a major effect quantitative trait locus on chromosome 8 revealed genes controlling metabolic pathways leading to the production of osmolites and antioxidants, such as GABA and raffinose. The study also led to the identification of specific trait donor genotypes that can be effectively used in translational crop improvement activities.

Coronavirus Receptors as Immune Modulators.

The Coronaviridae family includes the seven known human coronaviruses (CoV) that cause mild to moderate respiratory infections (HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1) as well as severe illness and death (MERS-CoV, SARS-CoV, SARS-CoV-2). Severe infections induce hyperinflammatory responses that are often intensified by host adaptive immune pathways to profoundly advance disease severity. Proinflammatory responses are triggered by CoV entry mediated by host cell surface receptors. Interestingly, five of the seven strains use three cell surface metallopeptidases (CD13, CD26, and ACE2) as receptors, whereas the others employ O-acetylated-sialic acid (a key feature of metallopeptidases) for entry. Why CoV evolved to use peptidases as their receptors is unknown, but the peptidase activities of the receptors are dispensable, suggesting the virus uses/benefits from other functions of these molecules. Indeed, these receptors participate in the immune modulatory pathways that contribute to the pathological hyperinflammatory response. This review will focus on the role of CoV receptors in modulating immune responses.

Integrin ß4 induced epithelial-to-mesenchymal transition involves miR-383 mediated regulation of GATA6 levels.

BACKGROUND: The process of transdifferentiating epithelial cells to mesenchymal-like cells (EMT) involves cells gradually taking on an invasive and migratory phenotype. Many cell adhesion molecules are crucial for the management of EMT, integrin ß4 (ITGB4) being one among them. Although signaling downstream of ITGB4 has been reported to cause changes in the expression of several miRNAs, little is known about the role of such miRNAs in the process of EMT. METHODS AND RESULTS: The cytoplasmic domain of ITGB4 (ITGB4CD) was ectopically expressed in HeLa cells to induce ITGB4 signaling, and expression analysis of mesenchymal markers indicated the induction of EMT. ß-catenin and AKT signaling pathways were found to be activated downstream of ITGB4 signaling, as evidenced by the TOPFlash assay and the levels of phosphorylated AKT, respectively. Based on in silico and qRT-PCR analysis, miR-383 was selected for functional validation studies. miR-383 and Sponge were ectopically expressed in HeLa, thereafter, western blot and qRT-PCR analysis revealed that miR-383 regulates GATA binding protein 6 (GATA6) post-transcriptionally. The ectopic expression of shRNA targeting GATA6 caused the reversal of EMT and ß catenin activation downstream of ITGB4 signaling. Cell migration assays revealed significantly high cell migration upon ectopic expression ITGB4CD, which was reversed upon ectopic co-expression of miR-383 or GATA6 shRNA. Besides, ITGB4CD promoted EMT in in ovo xenograft model, which was reversed by ectopic expression of miR-383 or GATA6 shRNA. CONCLUSION: The induction of EMT downstream of ITGB4 involves a signaling axis encompassing AKT/miR-383/GATA6/ß-catenin.

Efficacy of therapeutic plasma exchange in pediatric cases of acute liver failure as an extracorporeal liver support system.

BACKGROUND: Acute liver failure in the pediatric population is often accompanied by deranged metabolism, severe encephalopathy and coagulopathy. A liver transplant is the most viable option for the management of such patients. Therapeutic plasma exchange (TPE) is helpful in improving the liver biochemistry profile, thereby, increasing their likelihood of undergoing a liver transplant METHOD: The study was conducted over a period of 3 years (January 2018 to December 2021). Indications mainly consisted of ALF with hepatic encephalopathy, worsening liver parameters in spite of medical management, and candidacy for undergoing a liver transplant. Plasma exchange was performed daily or alternatively until the patient recovered, succumbed, or was stable enough to undergo a transplant. Biochemical parameters serum bilirubin, ALT, AST serum ammonia serum urea, serum creatinine were recorded before and after TPE sessions. RESULTS: The study group comprised 14 patients of which a total of 28 TPE was performed. There were a total of 5 cases of cryptogenic ALF, 4 of Wilson disease, 2 cases each of infection-related ALF and autoimmune hepatitis, and a single case of drug-induced hepatitis. A total of 5 out of 14 patients underwent a liver transplant and amongst the 9 who did not undergo a transplant, 4 patients expired due to septic shock syndrome; the remaining 5 were discharged in a stable condition following TPE sessions. The disease-free survival was 78.9% and the transplant-free survival was 35.71%. CONCLUSION: TPE plays a crucial role in improving the biochemistry profile of the liver in children with liver failure.

Exploration of a library of piperonylic acid-derived hydrazones possessing variable aryl functionalities as potent dual cholinesterase and monoamine oxidase inhibitors.

A library of piperonylic acid-derived hydrazones possessing variable aryl moiety was synthesized and investigated for their multifunctional properties against cholinesterases (ChEs) and monoamine oxidases (MAOs). The in vitro enzymatic assay results revealed that the tested hydrazones have exhibited excellent cholinesterase inhibition profile. Compound 4i, (E)-N'-(2,3-dichlorobenzylidene)benzo[d][1,3]dioxole-5-carbohydrazide showed promising dual inhibitory profile against AChE (0.048 ± 0.007 µM), BChE (0.89 ± 0.018 µM), and MAO-B (0.95 ± 0.12 µM) enzymes. SAR exploration revealed that the truncation of the linker connecting both the aryl binding sites of the semicarbazone scaffold, by one atom, has relatively suppressed the AChE inhibitory potential. Kinetic studies disclosed that the compound 4i reversibly inhibited AChE enzyme in a competitive manner (Ki = 8.0 ± 0.076 nM), while it displayed a non-competitive and reversible inhibition profile against MAO-B (Ki = 9.6 ± 0.021 µM). Moreover, molecular docking studies of synthesized compounds against ChEs and MAOs provided the crucial molecular features that enable their close association and interaction with the target enzymes. All atomistic simulation studies confirmed the stable association of compound 4i within the active sites of AChE and MAO-B. In addition, theoretical ADMET prediction studies demonstrated the acceptable pharmacokinetic profile of the dual inhibitors. In summary, the attempted lead simplification study afforded a potent dual ChE-MAO-B inhibitor compound that merits further investigation.

Prognostic and Clinical Implications of UNC13C expression in Hepatocellular Carcinoma Patients.

Aberrant expression of UNC13C (Unc-13 Homolog C) has been observed during the progression of oral squamous cell carcinoma. However, the expression pattern and clinical relevance of UNC13C in Hepatocellular carcinoma (HCC) remain to be elucidated. The purpose of this study is to examine UNC13C expression in HCC and explore its role in clinicopathological factor or prognosis in HCC. Two hundred and sixty-five patients diagnosed with HCC were included in the present study. The expression of UNC13C in HCC tissues was analyzed by immunohistochemistry analysis. The relationship between UNC13C protein and clinicopathological characteristics in HCC was investigated. Moreover, the high expression of UNC13C was significantly correlated with T stage, AJCC stage and overall survival rates. Cox regression analysis identified UNC13C as an independent prognostic indicator for HCC patients. UNC13C might be a prognostic biomarker and therapeutic target in HCC. Further studies with larger sample sets are needed to understand the clinical implications of UNC13C in hepatocellular carcinoma.

Exploring alternative strategies for Staphylococcus aureus nasal decolonization: insights from preclinical studies.

Nasal decolonization of Staphylococcus aureus with the antibiotic mupirocin is a common clinical practice before complex surgical procedures, to prevent hospital acquired infections. However, widespread use of mupirocin has led to the development of resistant S. aureus strains and there is a limited scope for developing new antibiotics for S. aureus nasal decolonization. It is therefore necessary to develop alternative and nonantibiotic nasal decolonization methods. In this review, we broadly discussed the effectiveness of different nonantibiotic antimicrobial agents that are currently not in clinical practice, but are experimentally proved to be efficacious in promoting S. aureus nasal decolonization. These include lytic bacteriophages, bacteriolytic enzymes, tea tree oil, apple vinegar, and antimicrobial peptides. We have also discussed the possibility of using photodynamic therapy for S. aureus nasal decolonization. This article highlights the importance of further large scale clinical studies for selecting the most suitable and alternative nasal decolonizing agent.

Nanomaterials in anticancer applications and their mechanism of action - A review.

The current challenges in cancer treatment using conventional therapies have made the emergence of nanotechnology with more advancements. The exponential growth of nanoscience has drawn to develop nanomaterials (NMs) with therapeutic activities. NMs have enormous potential in cancer treatment by altering the drug toxicity profile. Nanoparticles (NPs) with enhanced surface characteristics can diffuse more easily inside tumor cells, thus delivering an optimal concentration of drugs at tumor site while reducing the toxicity. Cancer cells can be targeted with greater affinity by utilizing NMs with tumor specific constituents. Furthermore, it bypasses the bottlenecks of indiscriminate biodistribution of the antitumor agent and high administration dosage. Here, we focus on the recent advances on the use of various nanomaterials for cancer treatment, including targeting cancer cell surfaces, tumor microenvironment (TME), organelles, and their mechanism of action. The paradigm shift in cancer management is achieved through the implementation of anticancer drug delivery using nano routes.

High prevalence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) antibodies among unvaccinated children of Chandigarh, Northwest India, in a household-based paediatric serosurvey post-second wave of pandemic (June to July 2021).

OBJECTIVE: Current national severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination policy covers children aged >12 years. Unvaccinated, uninfected children remain susceptible to SARS-CoV-2 and play a role in community transmission, as paediatric infection is mostly mild or asymptomatic. To estimate the proportion of susceptible children in a community for public health measures, there is a need to assess the extent of natural infection. STUDY DESIGN: We performed a cross-sectional household serosurvey of SARS-CoV-2 antibodies in unvaccinated children aged between 6 and 18 years after the second COVID-19 wave. METHODS: Anti-SARS-CoV-2 immunoglobin G (IgG) testing in serum was done using chemiluminescence immunoassay. We used a logistic regression model to investigate predicted factors of seropositivity. RESULTS: We observed a high prevalence (weighted average: 68.3%) of anti-SARS-CoV-2 IgG in 2700 enrolled children. Logistic regression for predictors of IgG seropositivity showed lower odds in households with completely vaccinated adults (adjusted odds ratio [OR]: 0.43, 95% confidence interval [CI]: 0.26-0.71, P = 0.0011) compared with households with unvaccinated adults. Other factors for low seropositivity included frontline workers as family members (adjusted OR: 0.69, 95% CI: 0.52-0.91, P = 0.0091) and non-crowded households (adjusted OR: 0.74, 95% CI: 0.61-0.89, P = 0.0019). CONCLUSION: A high SARS-CoV-2 IgG prevalence in unvaccinated children was indicative of previous exposure to potentially infected contacts. This implies in-person academic activities for children can be continued during future community transmission. Comparatively lower seropositivity in children of completely vaccinated households or frontline workers suggests decreased transmission due to vaccination-induced immunity of family members. Vaccination will still be required in these children to maintain protective IgG levels, particularly in low seroprevalence groups.

Early Implant Bone Loss in the Preprosthetic Phase: A Retrospective Study.

Initiation of the inflammatory response begins with the surgical placement of an implant that stimulates bone remodeling. The occurrence of crestal bone loss during submerged healing affects the prognosis of an implant. Hence, this study was conducted to estimate the early implant bone loss during the preprosthetic phase on bone level implants placed equicrestally. This retrospective observational study included evaluation of crestal bone loss around 271 two-piece implants placed in 149 patients from the archived postsurgical (P1) and preprosthetic (P2) digital orthopantomographic records using MicroDicom software. The outcome was categorized based on (1) sex (male or female), (2) time of implant placement (immediate [I] vs conventional [D]), (3) duration of healing period before loading (conventional [T1] vs delayed [T2]), (4) region of implant placement (maxilla [M1] vs mandible [M2]), and (5) site of implant placement (anterior [A] vs posterior [P]). To find the significant difference between the bivariate samples in the independent groups, an unpaired sample t test was used. The average marginal bone loss during the healing phase was 0.56 ± 0.573 mm in the mesial region and 0.44 ± 0.549 mm in the distal region of the implant, with a statistically significant difference (P < .01). There was no statistically significant difference in crestal bone level with the (1) sex of the patient (male or female), (2) type of implant placement (I or D), (3) time of implant loading (T1 or T2), (4) region of implant placement (M1 or M2), or (5) site of implant in the arch (A or P) (P > .05). An average of 0.50 mm crestal bone loss occurred in the peri-implant region during the preprosthetic phase. We found that the delayed placement of an implant and a delay in the healing period would further increase the early implant bone loss. The difference in the healing period did not alter the outcome of the study.