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Alzheimer’s disease diagnosis from structural MRI remains challenging in clinical practice. While deep learning shows promise for automated dementia detection, comprehensive comparisons of different neural network approaches are lacking. It analyzed T1-weighted MRI scans comprised 14,983 2D grid images derived from 1346 unique patients. Ten coronal brain slices spaced 2mm apart were arranged in 512 × 512-pixel grids using our 2D coronal-10 slicing sMRI methodology to preserve anatomical relationships while reducing computational demands. Ten deep learning architectures were systematically compared, including traditional CNNs, Vision Transformers, and Capsule Networks. Patient-level data splitting prevented information leakage. ECAResNet269 achieved the highest balanced accuracy (63%), with mild performance across all classes: dementia (38% sensitivity/77% specificity), MCI (72% sensitivity/66% specificity), and healthy controls (44% sensitivity/90% specificity). Class imbalance mitigation strategies substantially improved model performance, with combined SMOTE, cost-sensitive learning, and focal loss approaches achieving 74% balanced accuracy and (78% CN, 76%MCI, 69% AD) sensitivity in the ECAResNet269 model. Pretrained CNNs architectures substantially outperformed advanced methods–Vision Transformer and CapsNets showed complete classification failure. The 2D grid method retained 96% of diagnostic information compared to 3D approaches while providing 4.2 × faster processing. Traditional CNNs architectures remain most effective for medical neuroimaging classification. ECAResNet269 achieved clinically relevant performance suitable for dementia screening applications. The 2D grid methodology successfully balances diagnostic accuracy with computational efficiency, enabling deployment on standard clinical hardware.

Background Malnutrition is a nutritional disorder that adversely affects the body from a functional or clinical perspective. It is very often observed in the elderly population. This study aimed to estimate the prevalence of malnutrition among hospitalized elderly patients and its associated factors and outcomes in terms of length of stay and mortality in King Abdulaziz University Hospital, Jeddah, Saudi Arabia. Methods In a cross-sectional study, we evaluated the nutritional status of hospitalized elderly patients using the most recent version of the short form of Mini Nutritional Assessment (MNA-SF). Results A total of 248 hospitalized patients were included (70.0 ± 7.7 years; 60% female). According to the MNA-SF, a total of 76.6% patients were either malnourished or at risk of malnutrition. Malnourished patients had significantly lower levels of serum albumin (28.2 ± 7.7), hemoglobin (10.5 ± 1.8), and lymphocyte (1.7 ± 0.91). They had increased tendency to stay in the hospital for longer durations (IQR, 5-11 days; median = 7 days) and had a mortality rate of 6.9%. Conclusion Malnutrition was highly prevalent among hospitalized elderly and was associated with increased length of stay and mortality.

Deep neural network architectures have transformed medical imaging, particularly in structural MRI (sMRI) classification. However, existing state-of-the-art deep learning models face limitations in preprocessing and feature extraction when classifying dementia-related conditions. This study addresses these challenges by evaluating multiple architectures for dementia diagnosis. This study assessed eight pretrained convolutional neural networks (CNNs), a Vision Transformer (ViT), a multimodal attention model, and a capsule network (CapsNet) for classifying three classes: dementia, mild cognitive impairment (MCI), and healthy controls. The dataset, obtained from ADNI, was balanced across classes and comprised 10,000 training images per class, 3,000 validation images per class, and 850 test images per class. Classification was performed using 2D slices from sMRI scans. Performance metrics included accuracy, specificity, and sensitivity. Among all evaluated models, the 3D-CNN and multimodal attention models achieved the highest performance, with accuracies of 84% and 86%, specificities of 83% and 86%, and sensitivities of 84% and 86%, respectively. The ViT and CapsNet models achieved 100% sensitivity for Alzheimer's disease (AD) but demonstrated low precision for AD (43%) and 0% for other classes, indicating class imbalance effects. All models showed reduced performance and bias toward certain classes. The findings highlight the limitations of current architectures in sMRI dementia classification, including suboptimal feature extraction and class-specific biases. While certain models, such as multimodal attention and 3D-CNN, performed better overall, precision and generalization remain challenges. Future work should focus on improved data representation through advanced computer vision methods and architectural modifications to enhance diagnostic accuracy and computational efficiency.

Malnutrition is prevalent among hospitalized older patients. Early identification is therefore essential to implementing appropriate therapeutic interventions. This study aimed to explore the correlation between handgrip strength (HGS) and nutritional status in hospitalized older adults. This observational cross-sectional study was conducted at King Abdulaziz University Hospital, where a consecutive cohort of older adult inpatients was enrolled for participation. Shortly after admission, HGS and nutritional status were assessed using a dynamometer and the most recent version of the Mini-Nutritional Assessment Short Form (MNA-SF) test, respectively. Key anthropometric and biochemical indicators were also collected. A total of 135 consecutive patients were evaluated. Among participants with low HGS, 18 (16.22%) were malnourished, 43 (38.74%) were at risk of malnutrition, and 50 (45.05%) had normal nutrition status. The participants with low HGS had low hemoglobin, low lymphocyte levels, high creatinine levels, high BUN levels, high CRP levels, high HbA1c levels, and high vitamin B12 levels. Multiple logistic regression analysis showed that age, hemoglobin, and HbA1C were independently associated with low HGS. Based on the cut-off values for the HGS by the European Working Group on Sarcopenia in Older People-2 (EWGSOP2), low HGS showed high sensitivity to detect \"malnourished and at risk of malnutrition\" as well as \"malnourished alone;\" however, the specificity was low to exclude \"malnourished and at risk of malnutrition\" as well as \"malnutrition alone.\" Age over 75 years, low hemoglobin, and elevated HbA1C levels were independent risk factors for low HGS. While HGS was sensitive in detecting malnutrition or risk, its specificity was low. Therefore, HGS may not be adequate as a standalone tool to assess nutritional status in hospitalized older adults. Replication of this study using locally reliable and validated HGS cut-off values is warranted to confirm these findings.

As the age of an individual progresses, they are prone to more diseases; dementia is one of these age-related diseases. Regarding the detection of dementia, traditional cognitive testing is currently one of the most accurate tests. Nevertheless, it has many disadvantages, e.g., it does not measure the extent of the brain damage and does not take the patient’s intelligence into consideration. In addition, traditional assessment does not measure dementia under real-world conditions and in daily tasks. It is therefore advisable to investigate the newest, more powerful applications that combine cognitive techniques with computerized techniques. Virtual reality worlds are one example, and allow patients to immerse themselves in a controlled environment. This study created the Medical Visuospatial Dementia Test (referred to as the “MVD Test”) as a non-invasive, semi-immersive, and cognitive computerized test. It uses a 3D virtual environment platform based on medical tasks combined with AI algorithms. The objective is to evaluate two cognitive domains: visuospatial assessment and memory assessment. Using multiple machine learning algorithms (MLAs), based on different voting approaches, a 3D system classifies patients into three classes: patients with normal cognition, patients with mild cognitive impairment (MCI), and patients with severe cognitive impairment (dementia). The model with the highest performance was derived from voting approach named Ensemble Vote, where accuracy was 97.22%. Cross-validation accuracy of Extra Tree and Random Forest classifiers, which was greater than 99%, indicated a greater discriminate capacity than that of other classes.

Young-onset dementia (YOD) is influenced by various risk factors, including substance abuse. In this report, we present the case of a 54-year-old man who developed YOD following prolonged abuse of amphetamine-type stimulants. The patient exhibited insidious cognitive decline over a three-year period before seeking medical attention. Neuroimaging revealed atrophy of the temporal lobe, suggesting a connection between amphetamine-induced neurotoxicity and the cognitive abnormalities observed in the patient condition. Our case highlights the importance of considering amphetamine-type stimulants as potential risk factors for YOD and emphasizes the need to recognize cognitive impairment resulting from substance abuse. Additionally, we look into relevant literature to provide further context and insights. doi: https://doi.org/10.12669/pjms.40.4.8737 How to cite this: Alamri SH. Young-onset dementia following chronic abuse of amphetamine-type stimulants. Pak J Med Sci. 2024;40(4):779-781.  doi: https://doi.org/10.12669/pjms.40.4.8737 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Smart health technology includes physical sensors, intelligent sensors, and output advice to help monitor patients’ health and adjust their behavior. Virtual reality (VR) plays an increasingly larger role to improve health outcomes, being used in a variety of medical specialties including robotic surgery, diagnosis of some difficult diseases, and virtual reality pain distraction for severe burn patients. Smart VR health technology acts as a decision support system in the diseases diagnostic test of patients as they perform real world tasks in virtual reality (e.g., navigation). In this study, a non-invasive, cognitive computerized test based on 3D virtual environments for detecting the main symptoms of dementia (memory loss, visuospatial defects, and spatial navigation) is proposed. In a recent study, the system was tested on 115 real patients of which thirty had a dementia, sixty-five were cognitively healthy, and twenty had a mild cognitive impairment (MCI). The performance of the VR system was compared with Mini-Cog test, where the latter is used to measure cognitive impaired patients in the traditional diagnosis system at the clinic. It was observed that visuospatial and memory recall scores in both clinical diagnosis and VR system of dementia patients were less than those of MCI patients, and the scores of MCI patients were less than those of the control group. Furthermore, there is a perfect agreement between the standard methods in functional evaluation and navigational ability in our system where P-value in weighted Kappa statistic= 100% and between Mini-Cog-clinical diagnosis vs. VR scores where P-value in weighted Kappa statistic= 93%.

Objectives: Falls are one of the major health issues faced by older adults, and they can result in physical harm, eventual loss of independence, and even death. Herein, we investigated the prevalence, alongside the main risk factors and resulting injuries, of falls among older adults. Methods: We employed a descriptive cross-sectional approach. Data were collected between February and July 2021 from 403 older adults aged 60 years or above via an online self-reported questionnaire. Basic activities of daily living (BADLs) and instrumental activities of daily living (IADLs) were also recorded. Results: The prevalence of falls among community-dwelling older adults was 47.4%. Among those who had experienced a fall, 36.2% incurred injuries, 25.3% had fractures, and 23.1% required walking aids. Age between 95-104 years, female sex, participants on anti-hypertensive medications, history of hip or knee replacement surgery, and presence of a caregiver, were significantly more likely to have had a previous history of falls (p < 0.05). Furthermore, having a previous history of stroke, osteoporosis, lower limb weakness, dizziness, using wheelchairs as walking aids, and living with the fear of stumbling or slipping were significantly associated with history of previous falls (p < 0.05). Conclusions: The prevalence of falls is high among community-dwelling older adults in Jeddah. Physicians should identify older adults with higher falling risk and provide them with appropriate interventions. Public health strategies could significantly reduce falls and fall-related injuries in older adults. List of Abbreviations: KSA: Kingdom of Saudi Arabia, BADLs: Basic activities of daily living, IADLs: Instrumental activities of daily living, SPSS: Statistical Package for the Social Sciences, χ2: Chi-squared test. doi: https://doi.org/10.12669/pjms.39.1.6993 How to cite this: Alamri SH, Ghamri RA, Alshehri WH, Alhuthayli RS, Alamoudi NM, Alnufaei RD, et al. Falls and correlations among community-dwelling older adults: A Cross-sectional study in Jeddah, Saudi Arabia. Pak J Med Sci. 2023;39(1):---. doi: https://doi.org/10.12669/pjms.39.1.6993 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Alzheimer’s disease (AD) is a common neurodegenerative disorder that presents a significant health concern, often leading to substantial cognitive decline among older adults. A prominent feature of AD is progressive dementia, which eventually disrupts daily functioning and the ability to live independently. A major challenge in addressing AD is its prolonged pre-symptomatic phase, which makes early detection difficult. Moreover, the disease’s complexity and the inefficiency of current diagnostic methods impede the development of targeted therapies. Therefore, there is an urgent need to enhance diagnostic methodologies for detection and treating AD even before clinical symptoms appear. Exosomes are nanoscale biovesicles secreted by cells, including nerve cells, into biofluids. These exosomes play essential roles in the central nervous system (CNS) by facilitating neuronal communication and thus influencing major physiological and pathological processes. Exosomal cargo, particularly microRNAs (miRNAs), are critical mediators in this cellular communication, and their dysregulation affects various pathological pathways related to neurodegenerative diseases, including AD. This review discusses the significant roles of exosomal miRNAs in the pathological mechanisms related to AD, focusing on the promising use of exosomal miRNAs as diagnostic biomarkers and targeted therapeutic interventions for this devastating disease.

Metabolic syndrome (MetS) has become a global health concern and is a reliable predictor of long-term adverse health outcomes. This study aimed to determine the prevalence of MetS and its components in a group of Saudi adults with type 2 diabetes using the World Health Organization (WHO) and Adult Treatment Panel (ATP) III definitions, and to examine agreement between both definitions. This cross-sectional study included adults with type 2 diabetes who were followed up at the family medicine and endocrinology clinics of King Abdulaziz University Hospital (KAUH) from January to March 2018. An interview-administered questionnaire was designed to collect demographic data, anthropometric measurements, and medical history. We used the 1999 WHO and 2001 ATP III definitions for diagnosing MetS. The study included 155 diabetes patients. The overall prevalence of MetS components (three of more components) among patients was 80% according to the WHO criteria and 85.8% according to the ATP III criteria. The kappa statistics demonstrated good agreement between both definitions (κ = 0.751, p < 0.001). The sensitivity and specificity of diagnosing MetS using the WHO versus ATP III criteria were 92.5% and 95.5%, respectively. There was weak positive association between the number of MetS components and the number of diabetic complications. MetS was highly prevalent among Saudi adults with type 2 diabetes regardless of the diagnostic criteria. It is, therefore, imperative that clinicians identify MetS in this patient population and educate them on the importance of adherence to treatment and therapeutic lifestyle changes.