Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers.

Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer's disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors are leveraged in predicting the transition to the MCI stage at a future time point. The activity dataset from the Oregon Center for Aging and Technology (ORCATECH) includes measures representing various domains such as walk, sleep, etc. Each sensor-captured activity measure is constructed as a time series, and a variety of summary statistics is computed. The similarity between one individual's activity time series and that of the remaining individuals is also computed as distance measures. The long short-term memory (LSTM) recurrent neural network is trained with time series statistics and distance measures for the prediction modeling, and performance is evaluated by classification accuracy. The model outcomes are explained using the SHapley Additive exPlanations (SHAP) framework. LSTM model trained using the time series statistics and distance measures outperforms other modeling scenarios, including baseline classifiers, with an overall prediction accuracy of 83.84%. SHAP values reveal that sleep-related features contribute the most to the prediction of the cognitive stage at the future time point, and this aligns with the findings in the literature. Findings from this study not only demonstrate that a practical, less expensive, longitudinal monitoring of older adults' activity routines can benefit immensely in modeling AD progression but also unveil the most contributing features that are medically applicable and meaningful.

Estimation of SPIO Nanoparticles Uptakes by Macrophages Using Transmission Electron Microscopy.

Due to their interesting size-dependent magnetic characteristics and relative biocompatibility, magnetic superparamagnetic iron oxide (SPIO) nanoparticles have been widely exploited as probes for cell and subcellular structure identification, as well as medication and gene delivery. A thorough understanding of the mechanics of the interaction between nanoparticles and macrophages is vital in managing dynamic processes in nanomedicine. In this study, the interaction behavior and uptake of SPIO nanoparticles by M1- and M2-type macrophages were investigated. Mice monocytes were differentiated into M1 and M2 macrophages, and the uptake of SPIO nanoparticles was studied using a TEM microscope. A high resolution image of 1 nm resolution, an image processing technique, was developed to extract the SPIO-NPs from tomographic TEM microscopic images. Lysosomes appear to be the zones of high concentrations of SPIO inside macrophages. Lysosomes were first selected in each image, and then segmentation by the Otsu thresholding method was used to extract the SPIO-NPs. The Otsu threshold method is a global thresholding technique used to automatically differentiate SPIOs from the background. The SPIO-NPs appear in red colors, and the other pixels in the image are considered background. Then, an estimation of the SPIO-NP uptakes by lysosomes is produced. Higher uptake of all-sized nanoparticles was observed in M1- and M2-type macrophages. An accurate estimation of the number of SPIO-NPs was obtained. This result will help in controlling targeted drug delivery and assessing the safety impact of the use of SPIO-NPs in nanomedicine for humans.

Auditory brainstem, middle and late latency responses to short gaps in noise at different presentation rates.

OBJECTIVE: The effects of rate on auditory-evoked potentials (AEP) to short noise gaps (12 ms) recorded at high sampling rates using wide-band filters were investigated. DESIGN: Auditory brainstem (ABR), middle latency (MLR), late latency (LLR) and steady-state (ASSR) responses were simultaneously recorded in adult subjects at four gap rates (0.5, 1, 5 and 40 Hz). Major components (V, Na, Pa, Nb, Pb, N1 and P2) were identified at each rate and analysed for latency/amplitude characteristics. Gap responses at 40 Hz were recovered from Quasi-ASSRs (QASSR) using the CLAD deconvolution method. STUDY SAMPLE: Fourteen right ears of young normal hearing subjects were tested. RESULTS: All major components were present in all subjects at 1 Hz. P1 (P50) appeared as a low-pass filtered component of Pa and Pb waves. At higher rates, N1 and P2 disappeared completely while major ABR-MLR components were identified. Peak latencies were mostly determined by noise onsets slightly delayed by offset responses. CONCLUSIONS: Major AEP components can be recorded to short gaps at 1 Hz using high sampling rates and wide-band filters. At higher rates, only ABR and MLRs can be recorded. Such simultaneous recordings may provide a complete assessment of temporal resolution and processing at different levels of auditory pathways.

Helicobacter pylori: A Contemporary Perspective on Pathogenesis, Diagnosis and Treatment Strategies.

Helicobacter pylori (H. pylori) is a Gram-negative bacterium that colonizes the gastric mucosa and is associated with various gastrointestinal disorders. H. pylori is a pervasive pathogen, infecting nearly 50% of the world's population, and presents a substantial concern due to its link with gastric cancer, ranking as the third most common cause of global cancer-related mortality. This review article provides an updated and comprehensive overview of the current understanding of H. pylori infection, focusing on its pathogenesis, diagnosis, and treatment strategies. The intricate mechanisms underlying its pathogenesis, including the virulence factors and host interactions, are discussed in detail. The diagnostic methods, ranging from the traditional techniques to the advanced molecular approaches, are explored, highlighting their strengths and limitations. The evolving landscape of treatment strategies, including antibiotic regimens and emerging therapeutic approaches, is thoroughly examined. Through a critical synthesis of the recent research findings, this article offers valuable insights into the contemporary knowledge of Helicobacter pylori infection, guiding both clinicians and researchers toward effective management and future directions in combating this global health challenge.

Pesticides: Unintended Impact on the Hidden World of Gut Microbiota.

A vast range of pesticides have been routinely employed for plant protection throughout the last few decades. Pesticides can enter non-target organisms in various ways, posing health hazards. Exposure to different environmental pollutants, including pesticides, can affect the human gut flora. Metabolites generated from the gut microbiota play an essential role in the host's health by regulating metabolic homeostasis. A disruption in this equilibrium can lead to the emergence of numerous illnesses and their etiology. Pesticides have been shown in a few recent studies to harm the host's gut microbiome. As a result, there is an urgent need to investigate the impact of pesticides on gut microbiota-mediated immunity. Metabolic alterations in the host may give a better understanding of pesticide-induced harm. This review highlights the potential consequences of pesticide exposure on gut microbiota composition and function, mainly focusing on how it might alter the production of secondary metabolites with potential downstream implications for host health.

Evaluation of medical equipment maintenance and skilled manpower requirement among private dental centres in Riyadh, Saudi Arabia: A pilot study.

BACKGROUND: Adequate equipment maintenance is necessary to ensure efficient dental care facilities and satisfactory medical equipment performance. OBJECTIVE: This study aimed to assess maintenance practices and manpower adequacy in private dental centres (DCs) across Riyadh, Saudi Arabia. METHODS: A questionnaire was designed to evaluate equipment maintenance and manpower strength based on international and national standards. Thirty-three private DCs were visited in-person with the designed questionnaire to conduct the study. The Chi-square test and t-test were performed. RESULTS: Only 21 dental centres had a scheduled maintenance plan for medical equipment, whereas the rest had no plan. Furthermore, only 22 DCs maintained equipment maintenance records, while 11 DCs did not have any records. Additionally, the study found a lack of trained personnel to handle medical equipment and overall inadequate maintenance practices. Significant differences were observed between the responses for many questions (p< 0.05). CONCLUSION: These findings increase health risks to both the patient and the user. To address these issues, DC administrators should implement stricter measures to adhere to local DCs standards and hire qualified personnel who can periodically maintain and repair medical equipment.

Utilizing adaptive deformable convolution and position embedding for colon polyp segmentation with a visual transformer.

Polyp detection is a challenging task in the diagnosis of Colorectal Cancer (CRC), and it demands clinical expertise due to the diverse nature of polyps. The recent years have witnessed the development of automated polyp detection systems to assist the experts in early diagnosis, considerably reducing the time consumption and diagnostic errors. In automated CRC diagnosis, polyp segmentation is an important step which is carried out with deep learning segmentation models. Recently, Vision Transformers (ViT) are slowly replacing these models due to their ability to capture long range dependencies among image patches. However, the existing ViTs for polyp do not harness the inherent self-attention abilities and incorporate complex attention mechanisms. This paper presents Polyp-Vision Transformer (Polyp-ViT), a novel Transformer model based on the conventional Transformer architecture, which is enhanced with adaptive mechanisms for feature extraction and positional embedding. Polyp-ViT is tested on the Kvasir-seg and CVC-Clinic DB Datasets achieving segmentation accuracies of 0.9891 ± 0.01 and 0.9875 ± 0.71 respectively, outperforming state-of-the-art models. Polyp-ViT is a prospective tool for polyp segmentation which can be adapted to other medical image segmentation tasks as well due to its ability to generalize well.

Time-dependent biomechanical evaluation for corrective planning of scoliosis using finite element analysis - A comprehensive approach.

Scoliosis is a medical condition marked by an abnormal lateral curvature of the spine, typically forming a sideways "S" or "C" shape. Mechanically, it manifests as a three-dimensional deformation of the spine, potentially leading to diverse clinical issues such as pain, diminished lung capacity, and postural abnormalities. This research specifically concentrates on the Adolescent Idiopathic Scoliosis (AIS) population, as existing literature indicates a tendency for this type of scoliosis to deteriorate over time. The principal aim of this investigation is to pinpoint the biomechanical factors contributing to the progression of scoliosis by employing Finite Element Analysis (FEA) on computed tomography (CT) data collected from adolescent patients. By accurately modeling the spinal curvature and related deformities, the stresses and strains experienced by vertebral and intervertebral structures under diverse loading conditions can be simulated and quantified. The transient simulation incorporated damping and inertial terms, along with the static stiffness matrix, to enhance comprehension of the response. The findings of this study indicate a significant reduction in the Cobb angle, halving from its initial value, decreasing from 35° to 17°. In degenerative scoliosis, failure was predicted at 109 cycles, with the Polypropylene brace deforming by 10.34 mm, while the Nitinol brace exhibited significantly less deformation at 7.734 mm. This analysis contributes to a better understanding of the biomechanical mechanisms involved in scoliosis development and can assist in the formulation of more effective treatment strategies. The FEA simulation emerges as a valuable supplementary tool for exploring various hypothetical scenarios by applying diverse loads at different locations to enhance comprehension of the effectiveness of proposed interventions.

Unsupervised local center of mass based scoliosis spinal segmentation and Cobb angle measurement.

Scoliosis is a medical condition in which a person's spine has an abnormal curvature and Cobb angle is a measurement used to evaluate the severity of a spinal curvature. Presently, automatic Existing Cobb angle measurement techniques require huge dataset, time-consuming, and needs significant effort. So, it is important to develop an unsupervised method for the measurement of Cobb angle with good accuracy. In this work, an unsupervised local center of mass (LCM) technique is proposed to segment the spine region and further novel Cobb angle measurement method is proposed for accurate measurement. Validation of the proposed method was carried out on 2D X-ray images from the Saudi Arabian population. Segmentation results were compared with GMM-Based Hidden Markov Random Field (GMM-HMRF) segmentation method based on sensitivity, specificity, and dice score. Based on the findings, it can be observed that our proposed segmentation method provides an overall accuracy of 97.3% whereas GMM-HMRF has an accuracy of 89.19%. Also, the proposed method has a higher dice score of 0.54 compared to GMM-HMRF. To further evaluate the effectiveness of the approach in the Cobb angle measurement, the results were compared with Senior Scoliosis Surgeon at Multispecialty Hospital in Saudi Arabia. The findings indicated that the segmentation of the scoliotic spine was nearly flawless, and the Cobb angle measurements obtained through manual examination by the expert and the algorithm were nearly identical, with a discrepancy of only ± 3 degrees. Our proposed method can pave the way for accurate spinal segmentation and Cobb angle measurement among scoliosis patients by reducing observers' variability.

Amitriptyline-Induced Insomnia in a Young Lady Diagnosed With Cyclic Vomiting Syndrome: A Case Report.

The author reports a 19-year-old woman suffering from repeated episodes of non-bloody vomiting for 18 months. All routine and special investigations were normal. She was labeled as a case of cyclic vomiting syndrome (CVS), and she developed insomnia after the initiation of amitriptyline as a prophylactic treatment. The case was reported to increase awareness regarding the importance of monitoring medication side effects among clinicians when using different classes of medications to treat CVS.

Microscopic Colitis: A Review Article.

Microscopic colitis (MC) is a chronic inflammatory disease that affects the older population. Its clinical presentation includes a variety of gastrointestinal manifestations. The main symptom is chronic watery, nonbloody diarrhea. The disease has a female predominance. The diagnosis might be challenging since the symptoms are similar to other differential diagnoses, such as celiac disease, irritable bowel syndrome, Crohn's disease, bacterial overgrowth, and infectious colitis. The golden diagnostic tool for diagnosis is performing colonoscopy to obtain the colonic biopsy, which demonstrates the characteristic histological evidence needed for diagnosis. The treatment starts with an accurate diagnosis and trial of any possible offending medications. Alternatively, there are many medications, such as bismuth or budesonide, which are very effective in treating this disease. The primary objective of this detailed review is to enhance knowledge and understanding of this condition among healthcare providers to guide them with detailed information regarding epidemiology, clinical presentation, diagnosis, and appropriate management. In the assessment of individuals presenting with persistent chronic diarrhea, it is essential for healthcare providers to consider MC as a probable differential diagnosis.

Prevalence and Risk Factors for Gastroesophageal Reflux Disease (GERD) Among Visitors to the Health Center of Imam Mohammad Ibn Saud Islamic University.

BACKGROUND: A considerable majority of people have gastroesophageal reflux disease (GERD), a common gastrointestinal ailment. Globally, the prevalence of GERD has been rising, and it is linked to several risk factors. In this study, the incidence of GERD in a sample of the population was examined, along with the associated factors that may have an impact on it. METHODOLOGY:  The Gastroesophageal Reflux Disease Questionnaire (GERD-Q) was included in a self-administered survey given to 490 participants in a cross-sectional study to help determine who was more likely to have GERD. The questionnaire collected data on demographic elements, health-related traits, and past GERD diagnoses. RESULTS: The findings revealed that 32.7% of the individuals had previously received a GERD diagnosis. Of the patients, 17.1% had a GERD-Q score of 8 or above, which indicates a higher likelihood of having GERD. Participants who had previously been diagnosed with GERD had a noticeably greater incidence of GERD, and females had a higher incidence of GERD than males. The frequency of caffeine consumption was substantially correlated with the occurrence of GERD. CONCLUSION: Our study emphasizes the value of early GERD diagnosis and therapy to reduce problems and enhance the quality of life for those who are affected. According to our research, coffee use, gender, and prior GERD diagnoses are all linked to an increased risk of developing GERD. The GERD-Q is a trustworthy and proven tool for GERD diagnosis and might be used in clinical practice to recognize GERD patients and offer suitable treatment. Additional research is required to determine how additional risk variables affect the prevalence of GERD.

Knowledge and Awareness About Risk Factors, Clinical Manifestations, and Prevention of Thyroid Disorders in the Era of COVID-19 and Their Association With Socioeconomic Status Among the General Population in Riyadh, Saudi Arabia.

Background Thyroid dysfunction represents the most commonly observed endocrine illness within the population of Saudi Arabia. Thyroid cancer has been recognized as the second most commonly occurring malignant neoplasm among women in Saudi Arabia. Furthermore, there is evidence suggesting that COVID-19 and, to a certain degree, immunization may have an impact on thyroid function. The aim of this study was to evaluate the level of public knowledge, awareness, and attitudes pertaining to the manifestations and risk factors of thyroid disease. Additionally, the study sought to examine the potential role of COVID-19 as a risk factor and explore preventive measures in the context of Riyadh, Saudi Arabia. Methods A cross-sectional online survey was conducted targeting the Saudi population living in Riyadh aged 18 years and older. A self-administered questionnaire constructed on Google Forms was distributed to the general population using an online platform. The questionnaire consisted of five sections: demographic data, risk factors for thyroid disorders, clinical manifestations, prevention, and history of thyroid disease. Binary logistic regression analysis was used to identify predictors of better knowledge of thyroid diseases.  Results Among the 693 participants enrolled, 57.7% were female, and 31.7% were aged between 18 and 25 years. The overall mean knowledge score was 12.2 (SD = 6.57) out of 23 points. Poor knowledge of the risk factors, clinical manifestations, and prevention was observed in 50.4% of the participants. A total of 27.6% had moderate knowledge, and 22.1% had good knowledge levels. Furthermore, only 33.9% of the participants believed that COVID-19 infection was a risk factor. The results of the binary logistic regression analysis revealed that individuals within the age range of 36-45 years, females, and students had a significantly higher level of knowledge compared to other participants (p<0.05). Conclusion This study revealed that the general population of Riyadh, Saudi Arabia, lacked adequate knowledge, awareness, and attitudes regarding the risk factors, clinical symptoms, and prevention of thyroid problems. However, middle-aged individuals, females, and those who were enrolled as students showed a higher level of knowledge. Regarding the pathogenesis of COVID-19, it was observed that all participants had a limited understanding and a lack of awareness. Insufficient public awareness may result in misunderstandings, insufficient identification, and potential oversight of COVID-19-infected patients with thyroid dysfunction. Therefore, it is imperative that healthcare authorities intensify their efforts to broaden the dissemination of information throughout the population.

Software Application toward Accessible Hearing Care Assessment: Gap in Noise Test.

Currently, several methods are being applied to assess auditory temporal resolution in a controlled clinical environment via the measurements of gap detection thresholds (GDTs). However, these methods face two issues: the relatively long time required to perform the gap detection test in such settings and the potential of inaccessibility to such facilities. This article proposes a fast, affordable, and reliable application-based method for the determination of GDT either inside or outside the soundproof booth. The proposed test and the acoustic stimuli were both developed using the MATLAB® programming platform. GDT is determined when the subject is able to distinguish the shortest silent gap inserted randomly in one of two segments of white noise. GDTs were obtained from 42 normal-hearing subjects inside and outside the soundproof booth. The results of this study indicated that average GDTs measured inside the booth (5.12 ± 1.02 ms) and outside (4.78 ± 1.16 ms) were not significantly different. The measured GDTs were also comparable to that reported in the literature. In addition, the GDT screening time of the proposed method was approximately 5 minutes, a screening time that is much less than that reported by the literature. Data show that the proposed application was fast and reliable to screen GDT compared to the standard method currently used in clinical settings.

Bedsores Management: Efficiency Simulation of a New Mattress Design.

Bedsores, also known as pressure ulcers, are wounds caused by the applied external force (pressure) on body segments, thereby preventing blood supply from delivering the required elements to the skin tissue. Missing elements hinder the skin's ability to maintain its health. It poses a significant threat to patients that have limited mobility. A new patented mattress design and alternative suggested designs aimed to reduce pressure are investigated in this paper for their performance in decreasing pressure. A simulation using Ansys finite element analysis (FEA) is carried out for comparison. Three-dimensional models are designed and tested in the simulation for a mattress and human anthropometric segments (Torso and Hip). All designs are carried out in solidworks. Results show that the original design can redistribute the pressure and decrease it up to 17% less than the normal mattress. The original design shows better ability to decrease the absolute amount of pressure on the body. However, increasing the surface area of the movable parts results in less pressure applied to the body parts. Thus, this work suggests changing the surface area of the cubes from 25 to 100 cm2.

Increasing the efficiency of mechanical ventilators during pandemics through additive manufacturing.

The COVID-19 pandemic tested medical facilities' readiness in terms of the number of available mechanical ventilators. Most countries raced to stock up on ventilators, which created a surge in demand and short in supply. Furthermore, other means of coping with the demand were proposed, such as using additive manufacturing. The purpose of this paper was to test whether the addition of 3D-printed splitters would help deliver required tidal volume to each patient, while supporting four patients on a single ventilator for 24 hours on pressure mode at 25-cm H2O, and to determine whether a fifth patient can be ventilated. The ventilation of four human lungs was simulated using 3D printed parts, a single ventilator, four test-lungs, and standard tubing. Peak pressure, positive end-expiratory pressure, total tidal volume, individual tidal volume, total minute volume, and individual tidal volume data were collected. Usage of a 3D printed small size splitter enabled a 26% increase in individual tidal volume compared to standard tubing and a series of two-way splitters. The ventilator was able to supply the required pressure and tidal volume for 24 hours. A single ventilator with a four-way splitter can ventilate four patients experiencing respiratory failure for at least 24 hours without interruption. The equipment cannot sustain ventilating a fifth patient owing to minute volume limitation. This study expands on an earlier study that tested similar circuitry and reveals that the desired individual tidal volume is achieved. However, further research is required to provide the monitoring ability of individual patient parameters and prevention of cross-contamination.

Towards Drug Delivery Control Using Iron Oxide Nanoparticles in Three-Dimensional Magnetic Resonance Imaging.

The purpose of this paper was to detect and separate the cluster intensity provided by Iron oxide nanoparticles (IO-NPs), in the MRI images, to investigate the drug delivery effectiveness. IO-NPs were attached to the macrophages and inserted into the eye of the inflamed mouse's calf. The low resolution of MRI and the tiny dimension of the IO-NPs made the situation challenging. IO-NPs serve as a marker, due to their strong intensity in the MRI, enabling us to follow the track of the macrophages. An image processing procedure was developed to estimate the position and the amount of IO-NPs spreading inside the inflamed mouse leg. A fuzzy Clustering algorithm was adopted to select the region of interest (ROI). A 3D model of the femoral region was used for the detection and then the extraction IO-NPs in the MRI images. The results achieved prove the effectiveness of the proposed method to improve the control process of targeted drug delivered. It helps in optimizing the treatment and opens a promising novel research axis for nanomedicine applications.