Naturally occurring benzophenones and xanthones from Garcinia smeathmannii (Planch. & Triana) Oliv. displayed anti-inflammatory effects by modulating the activities of inflammatory mediators in LPS-stimulated RAW 264.7 macrophages.

Introduction: There is a growing interest in studying natural products for the identification of novel lead compounds for drug development for treating inflammatory diseases. Although some studies have focused anti-inflammatory activity of benzophenones and xanthones, exploring additional targets such as enzymes and cytokines, involved in their inflammatory response could provide more comprehensive understanding of the compounds' anti-inflammatory effects. In this study, four xanthones ananixanthone (1), smeathxanthone A (2), smeathxanthone B (3), and 1,3,5,8-tetrahydroxy-2-(3-methybut-2-enyl)-4-(3,7-dimethyloct-2,6-dienyl) xanthone (4); and three benzophenones guttiferone O (5), guttiferone M (6), and aristophenone A (7) from Garcinia smeathmannii (Planch. & Triana) Oliv. were investigated for their effect on nitric oxide production, cyclooxygenase, lipoxygenase inhibition, and Th1/Th2 cytokines production in activated RAW 264.7 macrophages. Methods: The Griess reagent method and the ferrous oxidation-xylenol orange assay were used to evaluate the inhibition of NO production and the 15-lipoxygenase activity respectively. Cyclooxygenase activity was assessed using the fluorometric COX activity assay kit and measurement of Th1/Th2 cytokines was performed using a flow cytometer. Results: All the tested compounds exhibited a dose-dependent inhibition of NO production with varying degrees of inhibitory effects on 15-LOX activity. Compound (6), displays the best inhibitory effect on COX-1/COX-2 activity. A general trend of the tested compounds on cytokines profiles revealed that compound (5) showed a pronounced enhancement of anti-inflammatory cytokines (IL-4 and IL-10). Conclusion: This observation supports future exploration of ananixanthone (1), guttiferone O (5), and guttiferone (6) as potential candidates for the development of anti-inflammatory drugs.

Chitosan-Coated Azithromycin/Ciprofloxacin-Loaded Polycaprolactone Nanoparticles: A Characterization and Potency Study.

Purpose: Antimicrobial resistance is a major health hazard worldwide. Combining azithromycin (AZ) and ciprofloxacin (CIP) in one drug delivery system was proposed to boost their antibacterial activity and overcome resistance. This study aims to improve azithromycin and ciprofloxacin activity by co-encapsulating them inside chitosan-coated polymeric nanoparticles and evaluating their antibacterial activity. Methods: The double emulsion method was employed to co-encapsulate AZ/CIP inside chitosan-coated polymeric nanoparticles. The formulations were evaluated for their nanoparticle size, size distribution, and zeta potential. Differential scanning calorimetry (DSC) analysis characterized the formula's thermal sustainability. Encapsulation efficiency was measured by HPLC and spectrophotometric analysis. Morphological studies used the Transmission Electron Microscopy (TEM). The in vitro release profiles of both AZ and CIP were monitored utilizing the dialysis membrane bag method. The micro-dilution assay assessed the antimicrobial activity against a clinical isolate of Klebsiella pneumoniae. Results: The prepared AZ/CIP-poly-caprolactone nanoparticles were spherical; their size range was 184.0 ± 3.3-190.4 ± 5.6 nm and had high size uniformity (poly-dispersity index below 0.2). The zeta potential ranged from -21.2 ± 2.4 to -27.0 ± 2.5 mV, while chitosan-coated nanoparticles showed a positive zeta potential value ranging from 8 to 11 mV. The thermal study confirmed the amorphous state of both antibiotics inside the nanoparticles. The results of the in vitro release study indicated a slow and uniform rate of release for both drugs extended over 4-days, with a faster rate in the case of AZ. The MIC values reported for both chitosan-coated NP have been tremendously reduced by at least 15 folds of pure CIP and more than 60 folds of pure AZ. Conclusion: The co-encapsulation of AZ/CIP into chitosan-coated polymeric nanoparticles has been successfully achieved. The produced particles showed many beneficial attributes of uniform particle sizes below 200 nm and high zeta potential values. Chitosan-coated polymeric nanoparticles extensively enhanced the antibacterial activity of both AZ/CIP against bacteria.

Unipolar and Bipolar Depression Detection and Classification Based on Actigraphic Registration of Motor Activity Using Machine Learning and Uniform Manifold Approximation and Projection Methods.

Modern technology frequently uses wearable sensors to monitor many aspects of human behavior. Since continuous records of heart rate and activity levels are typically gathered, the data generated by these devices have a lot of promise beyond counting the number of daily steps or calories expended. Due to the patient's inability to obtain the necessary information to understand their conditions and detect illness, such as depression, objectively, methods for evaluating various mental disorders, such as the Montgomery-Asberg depression rating scale (MADRS) and observations, currently require a significant amount of effort on the part of specialists. In this study, a novel dataset was provided, comprising sensor data gathered from depressed patients. The dataset included 32 healthy controls and 23 unipolar and bipolar depressive patients with motor activity recordings. Along with the sensor data collected over several days of continuous measurement for each patient, some demographic information was also offered. The result of the experiment showed that less than 70 of the 100 epochs of the model's training were completed. The Cohen Kappa score did not even pass 0.1 in the validation set, due to an imbalance in the class distribution, whereas in the second experiment, the majority of scores peaked in about 20 epochs, but because training continued during each epoch, it took much longer for the loss to decline before it fell below 0.1. In the second experiment, the model soon reached an accuracy of 0.991, which is as expected given the outcome of the UMAP dimensionality reduction. In the last experiment, UMAP and neural networks worked together to produce the best outcomes. They used a variety of machine learning classification algorithms, including the nearest neighbors, linear kernel SVM, Gaussian process, and random forest. This paper used the UMAP unsupervised machine learning dimensionality reduction without the neural network and showed a slightly lower score (QDA). By considering the ratings of the patient's depressive symptoms that were completed by medical specialists, it is possible to better understand the relationship between depression and motor activity.

Thrombosis of intraorbital arterio-venous fistula.

A 65-year-old male patient presented with a rare arterio-venous fistula (AFV). The symptoms included congestion, decreased visual acuity, and proptosis. Further investigation revealed a non-traumatic intra orbital AFV with ophthalmic vein thrombosis. The management strategy was craniotomy and the prescription of anticoagulants. The patient recovered 2 months after surgery demonstrating successful resolution of his presenting symptoms and an alternative approach to complicated cases of embolization.

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures.

Dementia affects the patient's memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a clear indication of dementia and plays a crucial role in the recognition process. Even though earlier studies have used speech features to recognize subjects suffering from dementia, they are often used along with other linguistic features obtained from transcriptions. This study explores significant standalone speech features to recognize dementia. The primary contribution of this work is to identify a compact set of speech features that aid in the dementia recognition process. The secondary contribution is to leverage machine learning (ML) and deep learning (DL) models for the recognition task. Speech samples from the Pitt corpus in Dementia Bank are utilized for the present study. The critical speech feature set of prosodic, voice quality and cepstral features has been proposed for the task. The experimental results demonstrate the superiority of machine learning (87.6 percent) over deep learning (85 percent) models for recognizing Dementia using the compact speech feature combination, along with lower time and memory consumption. The results obtained using the proposed approach are promising compared with the existing works on dementia recognition using speech.

Magnitude of diabetic peripheral neuropathy in Saudi Arabia: a systematic review and meta-analysis.

BACKGROUND: Diabetic peripheral neuropathy (DPN), due to its potential for causing morbidity and disability from foot ulcers and amputations, is increasingly becoming a source of concern in Saudi Arabia and worldwide. However, wide variability exists in the prevalence of DPN reported in previous studies in Saudi Arabia, limiting the utility of existing data in national public health policy. Therefore, the aim of this study was to systematically evaluate the magnitude of DPN in patients living with DM in Saudi Arabia in order to inform policymakers during the implementation of appropriate preventive and treatment strategies for DPN. METHODS: PubMed, Google Scholar, African Journals Online, Scopus, Web of Science, Embase, and Wiley Online Library were searched systematically to acquire relevant articles based on preset criteria. We evaluated heterogeneity and publication bias and employed a random-effects model to estimate the pooled prevalence of DPN from the included studies. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines in conducting the meta-analysis. Analysis was performed using the STATA Version 12 software. RESULTS: Twelve studies with a total of 4,556 participants living with DM, of whom 2,081 were identified as having DPN were included in the meta-analysis. The overall prevalence of DPN was 39% (95% CI [30%, 49%]). Subgroup analysis based on diagnostic method showed that prevalence estimates for DPN using screening questionnaires and clinical examination were 48% (95% CI [46%, 50%]) and 40% (95% CI: [38%, 42%]), respectively, while the estimated prevalence using nerve conduction studies was 26% (95% CI [15%, 36%]). CONCLUSION: This study showed a high magnitude of DPN in Saudi Arabia (39%), thus highlighting the need for sustained efforts to reduce the prevalence of diabetes mellitus and DPN in the kingdom.

A Case of a Female Patient Presenting With Idiopathic Granulomatous Mastitis With Superimposed Enterococcus Avium Infection.

Idiopathic granulomatous mastitis (IGM) is defined as an uncommon, benign, chronic inflammatory disease of unknown etiology that affects the breast and can mimic breast cancer. It usually manifests as a solid, ill-defined breast lump in postpartum women of reproductive age. Furthermore, because it lacks a particular radiographic finding, core biopsy and histology are the only ways to make a conclusive diagnosis. There is no agreement on the best way to treat IGM. Ideally, a multidisciplinary approach should be used to weigh the benefits and drawbacks of each treatment option, with options such as observation, antibiotics, surgery, and medication therapy being examined (steroids and immunosuppressants). In this report, we review a case of a patient who had IGM with superimposed Enterococcus avium infection. To our knowledge, such a report is considered unprecedented in the Middle East. A literature review on IGM will be presented, as well as the clinical presentation, association with bacterial infection, treatment, and pathological and radiographic findings.

A Lethal Manifestation of Chronic Active Epstein-Barr Virus Infection: A Case Report.

Chronic active Epstein-Barr virus infection (CAEBV) is a rare and lethal condition caused by persistent Epstein-Barr virus (EBV) infection. Signs and symptoms of CAEBV infection include fever, lymphadenopathy, and hepatosplenomegaly. Due to life-threatening consequences such as multiple organ failure, hemophagocytic syndrome, EBV-positive lymphoproliferative illness, and coagulopathy, early identification is important for successful therapy. However, because of the wide range of clinical symptoms, it might be difficult to diagnose the disease due to limited clinical experience and a low number of reports. We report a case of CAEBV in a 59-year-old woman from Saudi Arabia. We present the hospital course of the patient from admission until the patient's death as well as the clinical and pathological findings with a review of the literature. This is a rare case of CAEBV in Saudi Arabia.

System, Space, Staff, and Stuff framework in establishing a new pediatric critical care unit (PICU) (4S Framework).

By virtue of being in a developing country with ongoing expanding of the healthcare system, establishing or at least renovating a Pediatric critical care unit (PICU) has become a necessity. As intensivists and healthcare providers, we excel at our job as clinicians; however, we perform less than perfect when it comes to participating in establishing new PICUs and deliberately building and designing an EBM and patient-centered PICU with a complete understanding of the technical and non-clinical processes during commissioning or operational phases like construction, physical layout (blueprint), Biomedical engineering aspects, equipment, supply, and work-environment enhancement. If all healthcare providers -and especially intensivists- avoid being involved actively in PICUs designing process at their institution, they will miss an opportunity to gain a new perspective as well as they might contribute to a fragmented process of ICU design and a suboptimal result that might impact the PICU environment, patient journey and eventually the quality of care in that ICU. The PICU designing processes should be handled via a multi-professional team approach in an integrated -not parallel- manner that includes clinical and non-clinical personnel. Therefore, the processes will be more integrated, and they will finish the project efficiently, effectively, safely, and patient-centered way. This paper is an expert opinion and literature review that describes a conceptual framework to guide simple and practical mental processes in establishing and designing processes for new PICUs in developing countries. We called this preparedness tool: the 4S framework (system, space, staff, and stuff). It is a well-known preparedness tool that is commonly used in planning new projects by project leaders. Therefore, we utilized it in establishing a new PICU intended to meet the national and international accreditation standards and requirements. This unique preparedness tool will help establish an easy conceptual framework for all healthcare providers to grasp the complex -clinical and non-clinical- processes of establishing new PICUs and develop a holistic approach to this complex project. Note: The authors had leading roles in establishing or renovating many PICUs in Saudi Arabia, in both private and governmental hospitals, and would like to share their novel conceptual framework for establishing new PICUs in developing countries.

An Analytical Study of Speech Pathology Detection Based on MFCC and Deep Neural Networks.

Diseases of internal organs other than the vocal folds can also affect a person's voice. As a result, voice problems are on the rise, even though they are frequently overlooked. According to a recent study, voice pathology detection systems can successfully help the assessment of voice abnormalities and enable the early diagnosis of voice pathology. For instance, in the early identification and diagnosis of voice problems, the automatic system for distinguishing healthy and diseased voices has gotten much attention. As a result, artificial intelligence-assisted voice analysis brings up new possibilities in healthcare. The work was aimed at assessing the utility of several automatic speech signal analysis methods for diagnosing voice disorders and suggesting a strategy for classifying healthy and diseased voices. The proposed framework integrates the efficacy of three voice characteristics: chroma, mel spectrogram, and mel frequency cepstral coefficient (MFCC). We also designed a deep neural network (DNN) capable of learning from the retrieved data and producing a highly accurate voice-based disease prediction model. The study describes a series of studies using the Saarbruecken Voice Database (SVD) to detect abnormal voices. The model was developed and tested using the vowels /a/, /i/, and /u/ pronounced in high, low, and average pitches. We also maintained the "continuous sentence" audio files collected from SVD to select how well the developed model generalizes to completely new data. The highest accuracy achieved was 77.49%, superior to prior attempts in the same domain. Additionally, the model attains an accuracy of 88.01% by integrating speaker gender information. The designed model trained on selected diseases can also obtain a maximum accuracy of 96.77% (cordectomy × healthy). As a result, the suggested framework is the best fit for the healthcare industry.

Sign Language Recognition for Arabic Alphabets Using Transfer Learning Technique.

Sign language is essential for deaf and mute people to communicate with normal people and themselves. As ordinary people tend to ignore the importance of sign language, which is the mere source of communication for the deaf and the mute communities. These people are facing significant downfalls in their lives because of these disabilities or impairments leading to unemployment, severe depression, and several other symptoms. One of the services they are using for communication is the sign language interpreters. But hiring these interpreters is very costly, and therefore, a cheap solution is required for resolving this issue. Therefore, a system has been developed that will use the visual hand dataset based on an Arabic Sign Language and interpret this visual data in textual information. The dataset used consists of 54049 images of Arabic sign language alphabets consisting of 1500\ images per class, and each class represents a different meaning by its hand gesture or sign. Various preprocessing and data augmentation techniques have been applied to the images. The experiments have been performed using various pretrained models on the given dataset. Most of them performed pretty normally and in the final stage, the EfficientNetB4 model has been considered the best fit for the case. Considering the complexity of the dataset, models other than EfficientNetB4 do not perform well due to their lightweight architecture. EfficientNetB4 is a heavy-weight architecture that possesses more complexities comparatively. The best model is exposed with a training accuracy of 98 percent and a testing accuracy of 95 percent.

Assessment of Emergency Department Satisfaction Level in Saudi Arabia General Hospital.

Background and objectives: An overcrowded emergency department (ED) cannot meet the patients' growing demand. This situation harms employees' performance and, alternatively, causes anxiety and dissatisfaction among patients since the quality of healthcare outcomes fall below their expectations. This study aimed at improving and validating a scale for assessing patient satisfaction in the ED. Methods: In this study, 134 participants from Wadi Al-Dawasir General Hospital were enrolled using a convenient sampling technique. A cross-sectional survey was conducted using 5-point Likert scales. Results: All tested hypotheses showed statistical significance (P < 0.05). Our results show that male employees were more satisfied compared with their female counterparts. Furthermore, Saudi employees were more satisfied with the health services than non-Saudi ones. Conclusion: The findings of this study brought to the fore that patients and their families were satisfied with the healthcare services and their quality. This means better service delivery played a crucial role in enhancing satisfaction levels. Nevertheless, this study also highlights that overcrowding is a significant problem for healthcare organizations. Wadi al-Dawasir General Hospital's ED should continually improve its quality to meet the growing needs of its clients.

Assessing Pediatric Inter-Hospital Transfer: A single-center, Retrospective, Observational Study of Saudi Arabia's National Life-Saving Protocol.

Objective: To examine the accuracy of our national Life-Saving Protocol (LSP). To the best of our knowledge, this is the first study addressing this issue in Saudi Arabia. Background: LSP was created to facilitate triaging patients with LIFE or LIMB threatening conditions in peripheral hospitals with limited services to large regional hospitals to receive definitive care. Method: This is a retrospective single-center observational study over 12 months studying the patients who arrived via LSP to our Emergency room (ED), at the only regional pediatric hospital. For the subgroup of patients who were admitted to PICU through LSP, we further assessed their outcomes like mortality and length of stay (LOS) through a matched case-control study of 1:1 with similar patients who were admitted to our PICU via other routes rather than LSP. The primary outcome is to assess the accuracy of the LSP in triaging pediatric patients with LIFE of LIMB conditions. Secondary outcomes include assessing the association between LSP and (mortality, LOS) for those who were admitted to the regional PICU via LSP compared to patients admitted to PICU via other sources of admission. Results: During the study period, 118 patients arrived at our ED via LSP. Only 43 patients (36 %) were admitted to the PICU with LIFE or LIMB conditions. A total of 64 patients (54%) of the patients were admitted directly to the general pediatric ward from ED level due to absence of LIFE of LIMB threatening condition and 8% (n=9) were discharged immediately home from the ED level due to lack of any significant illness. One patient died at ED level, and one was referred to another hospital with a minor orthopedic injury. For those who were admitted to the PICU via LSP, the mortality rate was (13.9%) (6/43), and the control group was (4.6%) (2/43) with a p-value of 0.08. Conclusion: LSP is an excellent initiative and essential tool in our healthcare system; however, our study showed huge variation in the ability of the system to recognize true pediatric patients with LIFE or LIMB conditions. Our study might form a stepping-stone in future studies assessing the LSP at the national level.

Convolution-Based Encoding of Depth Images for Transfer Learning in RGB-D Scene Classification.

Classification of indoor environments is a challenging problem. The availability of low-cost depth sensors has opened up a new research area of using depth information in addition to color image (RGB) data for scene understanding. Transfer learning of deep convolutional networks with pairs of RGB and depth (RGB-D) images has to deal with integrating these two modalities. Single-channel depth images are often converted to three-channel images by extracting horizontal disparity, height above ground, and the angle of the pixel's local surface normal (HHA) to apply transfer learning using networks trained on the Places365 dataset. The high computational cost of HHA encoding can be a major disadvantage for the real-time prediction of scenes, although this may be less important during the training phase. We propose a new, computationally efficient encoding method that can be integrated with any convolutional neural network. We show that our encoding approach performs equally well or better in a multimodal transfer learning setup for scene classification. Our encoding is implemented in a customized and pretrained VGG16 Net. We address the class imbalance problem seen in the image dataset using a method based on the synthetic minority oversampling technique (SMOTE) at the feature level. With appropriate image augmentation and fine-tuning, our network achieves scene classification accuracy comparable to that of other state-of-the-art architectures.

Exposure to cell phones reduces heart rate variability in both normal-weight and obese normotensive medical students.

BACKGROUND: We investigated and compared the effect of the radiofrequency electromagnetic field (RF-EM) emitted by a cell phone on the electrocardiogram and heart rate variability (HRV) of normotensive normal-weight and obese medical students. METHOD: Twenty medical student volunteers, normal weight (age = 23 ± 2, BMI = 23.05 ± 1.72) or obese (age = 24 ± 2, BMI = 32.39 ± 4.78), were exposed to a cell phone (1) close to the heart in silent mode, no ringing or vibrating; (2) close to the heart in ring and vibration mode; (3) next to the ear (brain) while listening; and (4) next to the ear while listening and speaking. RESULTS: The average basal HR of obese students significantly increased, while the PR interval; time domains, including standard deviation (SD) of all normal R-R intervals (SDNN), mean of the SD of all normal R-R intervals (SDNNi), SD of the average of normal R-R intervals (SDANN), and percentage of R-R intervals at least 50 ms different from the previous interval (pNN50); and high-power frequency (HF) decreased. The LF/HF ratio also significantly increased. The SDNN, SDNNi, SDANN, pNN50, and HF levels significantly decreased and the LF/HF significantly increased in normal-weight and obese individuals only when the phone was near the apex of the heart in ring and vibration mode. All changes were more profound in obese students. CONCLUSION: Keeping the phone in a chest pocket reduced the HRV of normal-weight and obese medical students and exaggerated the effect of obesity on sympathetic activation.

To Decipher the Mycoplasma hominis Proteins Targeting into the Endoplasmic Reticulum and Their Implications in Prostate Cancer Etiology Using Next-Generation Sequencing Data.

Cancer was initially considered a genetic disease. However, recent studies have revealed the connection between bacterial infections and growth of different types of cancer. The enteroinvasive strain of Mycoplasma hominis alters the normal behavior of host cells that may result in the growth of prostate cancer. The role of M. hominis in the growth and development of prostate cancer still remains unclear. The infection may regulate several factors that influence prostate cancer growth in susceptible individuals. The aim of this study was to predict M. hominis proteins targeted into the endoplasmic reticulum (ER) of the host cell, and their potential role in the induction of prostate cancer. From the whole proteome of M. hominis, 19 proteins were predicted to be targeted into the ER of host cells. The results of our study predict that several proteins of M. hominis may be targeted to the host cell ER, and possibly alter the normal pattern of protein folding. These predicted proteins can modify the normal function of the host cell. Thus, the intercellular infection of M. hominis in host cells may serve as a potential factor in prostate cancer etiology.