Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity that is diagnosed visually by analyzing electroencephalography (EEG) signals. Background: Surgical operations are the only option for epilepsy treatment when patients are refractory to treatment, which highlights the role of classifying focal and generalized epilepsy syndrome. Therefore, developing a model to be used for diagnosing focal and generalized epilepsy automatically is important. Methods: A classification model based on longitudinal bipolar montage (LB), discrete wavelet transform (DWT), feature extraction techniques, and statistical analysis in feature selection for RNN combined with long short-term memory (LSTM) is proposed in this work for identifying epilepsy. Initially, normal and epileptic LB channels were decomposed into three levels, and 15 various features were extracted. The selected features were extracted from each segment of the signals and fed into LSTM for the classification approach. Results: The proposed algorithm achieved a 96.1% accuracy, a 96.8% sensitivity, and a 97.4% specificity in distinguishing normal subjects from subjects with epilepsy. This optimal model was used to analyze the channels of subjects with focal and generalized epilepsy for diagnosing purposes, relying on statistical parameters. Conclusions: The proposed approach is promising, as it can be used to detect epilepsy with satisfactory classification performance and diagnose focal and generalized epilepsy.

Posterior reversible encephalopathy syndrome (PRES) is a rare neurological disorder which is increasingly recognized to occur in systemic lupus erythematosus (SLE). The purpose of this study was to identify the characteristics of SLE patients with PRES and the associated factors of the poor outcome among them. We investigated SLE patients who developed PRES between 2005–2011 at the Universiti Kebangsaan Malaysia Medical Centre. A comprehensive literature search was done to find all published cases of PRES in SLE. Pooled analysis was conducted to identify the factors associated with poor outcome. There were 103 cases of PRES in SLE published in the literature but only 87 cases were included in the analysis in view of incomplete individual data in the remaining cases. The majority of the cases were Asians (74.2%), female (95.4%) with mean age of 26.3 ± 8.8 years. PRES was highly associated with active disease (97.5%), hypertension (91.7%) and renal involvement (85.1%). We found that 79 patients had a full recovery (90.8%) with a mean onset of full clinical recovery in 5.6 ± 4.1 days. On univariate analysis and logistic regression analysis the predictors of poor outcome, defined as incomplete clinical recovery or death, were intracranial hemorrhage, odds ratio (OR) 14 (1.1–187.2), p = 0.04 and brainstem involvement in PRES, OR 10.9 (1.3–90.6), p = 0.003. Intracranial hemorrhage and brainstem involvement were the two important predictors of poor outcome of PRES. Larger prospective studies are needed to further delineate the risk of poor outcome among them.

Background: Dengue is a common illness in the tropics. Equally common are neurological complications that stem from dengue infection. However, to date, parkinsonism following dengue has not been reported in medical literature. Case presentation: A previously well 18-year old man developed parkinsonism, in addition to other neurological symptoms following serologically confirmed dengue fever. Alternative etiologies were excluded by way of imaging and blood investigations. Conclusions: The authors detail the first reported case of parkinsonism complicating dengue fever. Keeping rare presentations of common illnesses in mind, it behoves clinicians to consider parkinsonism as a complication following dengue infection. This would prevent injudicious treatment with L-dopa and dopamine agonists. Immunosuppression with steroids has been shown to be helpful in certain cases.

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources. In this research, the capabilities of multiple platforms are evaluated for the real-time deployment of diagnostic tools. MRI classification and segmentation applications developed in previous studies are used for testing the performance using different hardware and software configurations. Cost–benefit analysis is carried out using a workstation with a NVIDIA Graphics Processing Unit (GPU), Jetson Xavier NX, Raspberry Pi 4B, and Android phone, using MATLAB, Python, and Android Studio. The mean computational times for the classification app on the PC, Jetson Xavier NX, and Raspberry Pi are 1.2074, 3.7627, and 3.4747 s, respectively. On the low-cost Android phone, this time is observed to be 0.1068 s using the Dynamic Range Quantized TFLite version of the baseline model, with slight degradation in accuracy. For the segmentation app, the times are 1.8241, 5.2641, 6.2162, and 3.2023 s, respectively, when using JPEG inputs. The Jetson Xavier NX and Android phone stand out as the best platforms due to their compact size, fast inference times, and affordability.

Leptospirosis is a spirochetal zoonotic disease with a wide clinical spectrum, often underdiagnosed especially when presented as an acute neurological manifestation. We report a case of a 24-year-old man with serologically positive leptospirosis, who presented with altered sensorium, seizures and subsequently developed cortical blindness. His brain MRI revealed bilateral occipital and later parietal lobe cerebritis.

Oxidative stress is a contributing factor that leads to the vascular complications of diabetes mellitus. Diabetic peripheral neuropathy (DPN) is one of the microvascular complications with rising concern as the disease progresses despite strict glucose control and monitoring. Thus, there is an ongoing need for an early intervention that is effective in halting or slowing the progression of DPN where antioxidants have been proposed as potential therapeutic agents. This systematic review aims to evaluate the existing evidence on the antioxidant effect in DPN and provide insight on the role of antioxidants in the progression of DPN in a rat model. A comprehensive literature search was conducted on Web of Science, EBSCOhost, and Scopus to identify the effects and role of antioxidants in DPN. Data extraction was performed and SYRCLE’s risk of bias (RoB) tool was used for risk assessment. This systematic review was written following the PRISMA 2020 statements. From the literature search, 1268 articles were screened, and a total of 101 full-text articles were further screened before 33 were analyzed. These findings collectively suggest that antioxidants can play a crucial role in managing and potentially reversing the effects of diabetic neuropathy by targeting oxidative stress and improving nerve function.