Prosthetic factors influencing the prevalence of peri-implant diseases and marginal bone loss in static computer-assisted implant sites: A cross-sectional study.

Background: Current evidence indicates a possible link between the design of the implant-abutment-prosthesis complex and the development of peri-implant diseases. This cross-sectional study aimed to investigate the association between implant and prosthetic factors and the prevalence of peri-implant diseases and peri-implant marginal bone loss in patients treated with static computer-assisted implant surgery (sCAIS).

Methods: This cross-sectional study included 115 patients with 417 dental implants, all placed using a standardized sCAIS protocol and with more than 1 year of loading.

A U3 snoRNA is required for the regulation of chromatin dynamics and antiviral response in Drosophila melanogaster.

Small nucleolar RNAs (snoRNAs) are prevailing components of the chromatin-associated transcriptome. Here we show that specific snoRNAs are required for the activation of immune response genes and for survival during viral infections in Drosophila melanogaster. We have studied snoRNA:U3:9B, a chromatin-associated snoRNA that binds to a large number of protein coding genes, including immune response genes.

Transfer Learning for Automated Two-class Classification of Pulmonary Tuberculosis in Chest X-Ray Images.

Aim: Early and precise diagnosis is essential for effectively treating and managing pulmonary tuberculosis. The purpose of this research is to leverage artificial intelligence (AI), specifically convolutional neural networks (CNNs), to expedite the diagnosis of tuberculosis (TB) using chest X-ray (CXR) images.

Background: Mycobacterium tuberculosis, an aerobic bacterium, is the causative agent of TB.

Accuracy and Time Efficiency of Artificial Intelligence-Driven Tooth Segmentation on CBCT Images: A Validation Study Using Two Implant Planning Software Programs.

Objectives: To assess the accuracy and time efficiency of manual versus artificial intelligence (AI)-driven tooth segmentation on cone-beam computed tomography (CBCT) images, using AI tools integrated within implant planning software, and to evaluate the impact of artifacts, dental arch, tooth type, and region.

Materials And Methods: Fourteen patients who underwent CBCT scans were randomly selected for this study. Using the acquired datasets, 67 extracted teeth were segmented using one manual and two AI-driven tools.

Relationship of Loneliness with Stress and Adjustment in Working and Homemaker Women.

Background: The rising female labour force participation in India brings social isolation and stress into focus. The unique social challenges of maintaining contacts while juggling multiple roles highlight the significance of this research for understanding the psychological impacts of social isolation and stress in women. The study further emphasis the necessity of providing specific intervention strategies that help alleviate stress and improve techniques of adjustment; if successful, this indeed mitigates loneliness in women.

Precision of complete-arch digital implant scans using photogrammetry and intra-oral scanning. An in vivo cross-over study: Complete-Arch Implant Scans: Photogrammetry vs IOS.

Objective: To evaluate the precision of complete-arch digital implant scans using intraoral scanning (IOS) and photogrammetry (PG).

Methods: Nineteen completely edentulous arches were included. Each arches contained at least four implants.

Comparative Analysis of Full Outline of Unresponsive Score and Glasgow Coma Scale Score for Outcomes Prediction in Children with Impaired Consciousness.

This study aimed to compare two distinct consciousness evaluation scores (the pediatric Glasgow coma scale [GCS] scale and the full outline of unresponsive [FOUR] score) to predict outcomes for children admitted to the pediatric intensive care unit with impaired consciousness.Children aged between 2 and 18 years who presented with impaired consciousness were included in this longitudinal study. The lead investigator evaluated the pediatric GCS score and the FOUR score.

Novel glycovariant biomarkers of CA125 and CA15-3 and their diagnostic performance across histotypes of ovarian cancer: A multi-cohort study in Sweden and Finland.

Objective: To evaluate diagnostic accuracy of novel nanoparticle immunoassays across different histotypes of tubo-ovarian carcinoma (TOC) at diagnosis.

Method: This multicenter observational study consisted of consecutive patients (n = 1,312) having surgery due to suspected ovarian pathology. Serum were analyzed with Sialyl-Thomsen-nouveau (STn) antibody and Macrophage-Galactose-Lectin (MGL) for the detection of cancer antigen 125 (CA125) and 15-3 (CA15-3) glycoforms using CA125 enzyme immunoassay (EIA), CA15-3 and HE4 as references.

Prevalence of Peri-Implant Diseases in Computer-Guided Implant Sites: A Cross-Sectional Study.

Objective: The present cross-sectional study aimed to determine the prevalence and the risk indicators associated with peri-implant diseases (PIDs) in patients who were subjected to computer-guided implant surgery.

Methods: Patients that were treated and screened during regular maintenance visits at one university center were enrolled in this cross-sectional study. Implants were diagnosed into the categories of peri-implant health, peri-implant mucositis, or peri-implantitis according to the 2017 World Workshop established case definitions.

A Deep Neural Network Framework for the Detection of Bacterial Diseases from Chest X-Ray Scans.

Aims: This research aims to develop an advanced deep-learning framework for detecting respiratory diseases, including COVID-19, pneumonia, and tuberculosis (TB), using chest X-ray scans.

Methods: A Deep Neural Network (DNN)-based system was developed to analyze medical images and extract key features from chest X-rays. The system leverages various DNN learning algorithms to study X-ray scan color, curve, and edge-based features.

A Framework for Two-class Classification of Pulmonary Tuberculosis using Artificial Intelligence.

Aim: The study investigates the creation and assessment of Machine Learning (ML) models using different classifiers such as SVM (Support Vector Machine), logistic regression, decision tree, k-nearest neighbour (kNN), and Artificial Neural Network (ANN) for the automated identification of tuberculosis (TB) from chest X-ray(CXR) images.

Background: As a persistent worldwide health concern, TB requires early detection for effective treatment and control of the infection. The differential diagnosis of TB is a challenge, even for experienced radiologists.

Perioperative dental injury awareness among anesthesiologists.

Background And Aims: Perioperative dental injury is a known yet preventable complication. We assessed the anesthesiologists' awareness of the predisposing factors, preventive measures, and subsequent management of these injuries, should they occur.

Material And Methods: A prospective cross-sectional survey was conducted on 385 anesthesiologists practicing in Delhi-NCR to assess their awareness on prevention and management of perioperative dental injuries.

Designing Patient-Centered Interventions for Emergency Care: Participatory Design Study.

Background: Emergency departments (EDs) are high-pressure environments where clinicians diagnose patients under significant constraints, including limited medical histories, severe time pressures, and frequent interruptions. Current ED care practices often inadequately support meaningful patient participation. Most interventions prioritize clinical workflow and health care provider communication, inadvertently overlooking patients' needs.

Combinatorial mapping of E3 ubiquitin ligases to their target substrates.

E3 ubiquitin ligases (E3s) confer specificity of protein degradation through ubiquitination of substrate proteins. Yet, the vast majority of the >600 human E3s have no known substrates. To identify proteolytic E3-substrate pairs at scale, we developed combinatorial mapping of E3 targets (COMET), a framework for testing the role of many E3s in degrading many candidate substrates within a single experiment.

MUC1 and glycan probing of CA19-9 captured biomarkers from cyst fluids and serum provides enhanced recognition of ovarian cancer.

Glycosylation changes of circulating proteins carrying the CA19-9 antigen may offer new targets for detection methods to be explored for the diagnosis of epithelial ovarian cancer (EOC). Search for assay designs for targets initially captured by a CA19-9 antigen reactive antibody from human body fluids by probing with fluorescent nanoparticles coated with lectins or antibodies to known EOC associated proteins. CA19-9 antigens were immobilized from ascites fluids, ovarian cyst fluids or serum samples using monoclonal antibody C192 followed by probing of carrier proteins using anti-MUC16, anti-MUC1 and, anti STn antibodies and seven lectins, all separately coated on nanoparticles.

Computational Model to Predict Potential Therapeutic Targets Employing Generative Adversarial Networks for Analysis of Proteins Involved in Mycobacterium fortuitum Biofilm Formation.

A planktonic population of bacteria can form a biofilm by adhesion and colonization. Proteins known as "adhesins" can bind to certain environmental structures, such as sugars, which will cause the bacteria to attach to the substrate. Quorum sensing is used to establish the population is dense enough to form a biofilm.

Role of cyanotoxins in the development and promotion of cancer.

Cyanotoxins are primarily produced by different species of cyanobacteria, also known as blue-green algae, and have appeared to be environmental poisons that have various toxic effects on animal health, including humans. Cyanotoxins have been linked to the development and promotion of multiple cancers in recent studies. Important cyanotoxins, such as microcystins, nodularins, and cylindrospermopsin, have been found to play significant roles in developing and promoting various cancers.

Putative evolution of 124B02 plasmid pMF1 from a chromosomal segment in another species.

Myxobacteria or order Myxococcales (old nomenclature) or phylum Myxococcota (new terminology) are fascinating organisms well known for their diverse peculiar physiological, taxonomic, and genomic properties. Researchers have long sought to identify plasmids within these organisms, yet thus far, only two organisms from different families have been found to harbor a plasmid. This study delves into the putative evolution of one of these plasmids, i.

Optimizing Ni-Cr patterned boron-doped diamond band electrodes: Doping effects on electrochemical efficiency and posaconazole sensing performance.

There is growing interest in developing diamond electrodes with defined geometries such as, for example, micrometer-sized electrode arrays to acquire signals for electroanalysis. For electroanalytical sensing applications, it is essential to achieve precise conductive patterns on the insulating surface. This work provides a novel approach to boron-doped diamond patterning using nichrome masking for selective seeding on an oxidized silicon substrate.

Smart Seizure Detection System: Machine Learning Based Model in Healthcare IoT.

Background: Epilepsy, the tendency to have recurrent seizures, can have various causes, including brain tumors, genetics, stroke, brain injury, infections, and developmental disorders. Epileptic seizures are usually transient events. They normally leave no trace after the postictal recovery period has passed.

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.

Background: Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising thrombosis, atherosclerosis, cerebral venous thrombosis, or embolic arterial blood clot.

Post-partum cerebral venous sinus thrombosis: A case report.

Introduction: Cerebral venous sinus thrombosis (CVST) is a rare and life-threatening condition that may be encountered during pregnancy and puerperium. The diagnosis of CVST is a challenge because of its varied presentation.

Case Report: A 28-year-old woman presented with headache, projectile vomiting, and generalized tonic-clonic seizures 10 days after delivery by cesarean section.

A Study of the Long-Term Electrochemical Stability of Thin-Film Titanium-Platinum Microelectrodes and Their Comparison to Classic, Wire-Based Platinum Microelectrodes in Selected Inorganic Electrolytes.

This paper discusses the electrochemical properties of thin-film, planar, titanium-platinum (Ti-Pt) microelectrodes fabricated using glass or silicon substrates and compares their performance to the classic platinum (Pt) microelectrodes embedded in glass. To analyze the possible differences coming both from the size of the tested electrodes as well as from the substrate, short- and long-term electrochemical tests were performed on selected water electrolytes (KCl, HCl, KOH). To study the electrochemical response of the electrodes, the cyclic voltammetry (CV) measurements were carried out at different scanning rates (from 5 to 200 mV/s).