United States


Neuropathology holds the potential to advance our understanding of Alzheimer&#39;s disease and related dementias. Artificial intelligence (AI) models through well-labeled datasets can effectively perform complex tasks at or above human performance. Recent studies have analyzed neuropathological biomarkers like amyloid beta plaques and neurofibrillary tangles in whole slide images through AI models. However, appropriately labeled images are needed to detect which brain region the neuropathological biomarkers originate from for analytical purposes. The study aims to develop automated detection of gray matter regions in WSIs for downstream testing of established AI pipelines in neuropathology.The study involves manual annotations of gray matter regions on Bielschowsky silver stained WSIs from the Emory ADRC digital cohort. These annotations are used to train UNET semantic segmentation models for automated detection of gray matter regions on a larger cohort of non-annotated WSIs. Co-registration to immunohistochemically stained WSIs (amyloid beta, tau, TDP-43) is used to define gray matter annotations across stains. Established AI pipelines are then deployed in these regions to assess improvement on original results. 300 gray matter masks have been generated and an initial classifier has been trained using the YOLOv8 (You Only Look Once) model developed by Ultralytics. While the project is ongoing, our initial results have demonstrated the feasibility of developing an automated gray matter segmentation model to further understanding of biomarkers associated with clinical care.


AMA Research Challenge 2024 

Artificial Intelligence Powered Neuropathology: Automated Gray Matter Segmentation For Biomarker Analysis

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AMA Research Challenge 2023

Join the AMA Research Challenge poster symposium and semifinals to explore top research posters, score posters in the semifinals (an AMA member exclusive opportunity) and attend education sessions with experienced physicians to learn tips to advance your research and career. 


Neuropathology holds the potential to advance our understanding of Alzheimer's disease and related dementias. Artificial intelligence (AI) models through well-labeled datasets can effectively perform complex tasks at or above human performance. Recent studies have analyzed neuropathological biomarkers like amyloid beta plaques and neurofibrillary tangles in whole slide images through AI models. However, appropriately labeled images are needed to detect which brain region the neuropathological biomarkers originate from for analytical purposes. The study aims to develop automated detection of gray matter regions in WSIs for downstream testing of established AI pipelines in neuropathology.The study involves manual annotations of gray matter regions on Bielschowsky silver stained WSIs from the Emory ADRC digital cohort. These annotations are used to train UNET semantic segmentation models for automated detection of gray matter regions on a larger cohort of non-annotated WSIs. Co-registration to immunohistochemically stained WSIs (amyloid beta, tau, TDP-43) is used to define gray matter annotations across stains. Established AI pipelines are then deployed in these regions to assess improvement on original results. 300 gray matter masks have been generated and an initial classifier has been trained using the YOLOv8 (You Only Look Once) model developed by Ultralytics. While the project is ongoing, our initial results have demonstrated the feasibility of developing an automated gray matter segmentation model to further understanding of biomarkers associated with clinical care.


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### Grand Prize Sponsor

AMA would like to thank Laurel Road for sponsoring a $10,000 grand prize for the winner of the AMA Research Challenge. Laurel Road is not involved in winner selection process. The winner will be announced on February 20, 2025 during the AMA Research Challenge finals. Payment of the grand prize will be directly from Laurel Road. See the AMA Research Challenge Official Rules for terms and conditions.

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Migraine is a common, disabling medical condition affecting over 40 million people in the United States. Cervical pain has been reported to be more prevalent among migraine sufferers than nausea (Calhoun et al., 2010), yet there is limited research describing the characteristics of the neck pain experienced by persons with migraine. We designed a 10-question survey to analyze the types and characteristics of neck pain experienced by patients with episodic migraine (EM, defined as 14 or fewer headache days per month.)
Subjects were recruited via patient portal message at the New England Institute of Neurology and Headache (NEINH, Stamford, CT) or through school newsletter at the Frank H Netter MD School of Medicine at Quinnipiac University (North Haven, CT). Subjects who met the inclusion criteria (diagnosed with EM, age 18-55, self-report of cervical pain with migraines) and had no history of cervicogenic headache or cervical trauma were interviewed. 
The majority of patients (58%) described their neck pain using the terms “tight or stiff”. The VAS score rating for those whose neck pain began before the migraine (M = 6.7, SD = 2.1) was significantly higher than those whose pain began during the headache (M = 4.2, SD = 1.3, p < 0.05). Patients with aura rated the neck pain (M = 6.7, SD = 1.9) significantly higher than those who do not have aura (M = 5.2, SD = 2.0, p <0.05). There was a strong, positive correlation between age and how much the neck pain contributed to overall disability (Pearson = 0.648, p<0.001). 
Our study demonstrates a small portion of the complex interplay between neck pain and migraine. The significantly higher VAS rating by those whose neck pain begins before their migraine may suggest the pain as a trigger. The difference in VAS scores based on aura may also suggest some relationship between neck pain and aura. Though we attempted to eliminate confounding neck pain that may arise due to age, the significant positive correlation may suggest that we set the age cut off too high. We recognize that our study had a small sample size and our survey only captured a small snapshot of the interplay that exists between neck pain and migraine. The study should be expanded to investigate if similar relationships between neck pain and migraine exist with a larger, more diverse sample size.

Incidence and Characteristics of Neck Pain in Episodic Migraine

Background
The International Mouse Phenotyping Consortium (IMPC) is a database that characterizes the function of over 20,000 protein-encoding genes in mice through knockout. Each knockout gene and its associated phenotype can be accessed through their website. For this project, we used the IMPC to explore the Atlastin GTPase 3 gene (Atl3), a gene related to Hereditary Sensory Neuropathy Type 1F (HSN1F). HSN1F is a rare neurological genetic disorder, characterized by the loss of sensation to the hands and feet due to a mutation in Atl3. When Atl3 is knocked out in mice, the IMPC revealed seven phenotypes: enlarged prostate gland, abnormal liver morphology, small adrenal glands, small kidney, enlarged lymph nodes, cataract, and enlarged urinary bladder.

Methods
The National Organization for Rare Disorders (NORD) was used to identify the disease of interest and its associated gene, Atl3. Using the IMPC database, seven phenotypes associated with the Atl3 knockout mice were identified and compiled into a well-known analytical tool, Microsoft Excel. The genes that showed that phenotype when knocked out in mice were compiled for each phenotype. Using variations of a common algorithm and conditional formatting in Microsoft Excel, the specific number of times a gene showed up and the number of phenotypes each gene was associated with were sorted visually with a specific color. Further analysis, totaling the number of times each specific gene appeared in multiple times was formatted graphically.

Results
The seven phenotypes associated with the Atl3 knockout mice were enlarged prostate gland, abnormal liver morphology, small adrenal glands, small kidney, enlarged lymph nodes, cataract, and enlarged urinary bladder. Our analysis revealed four genes, dynein cytoplasmic 1 light intermediate chain 1 (dync1li1), piggyBac transposable element derived 1 (Pgbd1), MAS-related GPR, member B2 (Mrgprb2), and ADNP homeobox 2 (Adnp2), that when knocked out in mice shared five of the seven phenotypes associated with the Atl3 knockout mouse.

Conclusion
Through the IMPC database, we identified dync1li1, Pgbd1, Mrgprb2, and Adnp2 as genes that potentially have an interaction or could be involved in pathways with Atl3 because they share five of the seven phenotypes. Based on our data, further research is recommended into these genes and how they relate to Atl3 as it shares these phenotypes. Utilizing currently existing tools such as the IMPC, genes and their interactions can be identified to further find genes that may have not been extensively studied yet that mimic the loss of Atl3.

Investigating Atl3-Related Genes Utilizing Data from the International Mouse Phenotyping Consortium

Background: Brain metastases, secondary brain tumors originating from cancers elsewhere in the body, can present with focal neurologic deficits without systemic symptoms and are usually evaluated using magnetic resonance imaging (MRI). Multimodal imaging methods are usually used to determine the primary cancer source. Radiomic analysis involves extracting quantitative features from medical images using data-characterization algorithms. Recent advances in deep learning and radiomics, which quantitatively analyze imaging data, offer the potential for improved prediction of primary tumor origin through MR imaging analysis. Using longitudinal MRI data, we developed and validated a mixed-effects neural network model for predicting the primary origin of brain metastases.

Methods: We used the open-source dataset from 5 different institutions made available by Ocaña-Tienda et al. (2023) to build the model. We extracted 2446 radiomic features, out of which 60 features were retained after feature selection using wrapper- and filter-based techniques. For the nested mixed-effects neural network model, we incorporated a dual-layer structure with ReLU activation for the fixed effects and unique embeddings for each cluster, leading to a nested structure. The model was compiled using an Adam optimizer and trained across several epochs with a steadily declining learning rate facilitated by exponential decay. Regularization was implicitly managed through embedding and learning rate adjustments. Model performance was evaluated using receiver operating characteristic curve (ROC) analysis.

Results: Our mixed-effects model demonstrated high accuracy (90.5%), precision (92.7%), recall (87.8%), and F1 score (90.2%), with an area under the ROC (AUC-ROC) curve of 0.944 in identifying the primary tumor site. The nested mixed-effects model was superior to the fixed-effects model with an AUC-ROC of 0.719.

Conclusion: Our mixed-effects neural network model accurately predicts the primary origin of brain metastases with high internal and external validity. Using advanced radiomic feature selection and deep learning techniques. Our model achieved high-performance metrics, thus providing a promising tool for enhancing diagnostic capability and tailoring patient-specific management strategies. We are exploring it's clinical utility further by adding more data from a tertiary cancer hospital. We have also deployed a patented prototype software for research use to study the effect of integrating this model in clinical workflows to optimize patient care.

Mixed-effects deep-learning based quantitative radiomic analysis of longitudinal magnetic resonance imaging of brain metastases predict primary tumor origin

Introduction:
Traumatic brain injury (TBI) from closed-head trauma is a leading cause of disability, with limited effective interventions. Many murine models for researching TBI impact the brain parenchyma directly and are limited by the fact that these forces do not recapitulate clinically relevant closed head injury. An alternative model applying clinically relevant injury mechanics to the intact skull may lead to variable results and as a result, preclinical modeling of TBI remains a challenge. Current models often do not explore sex differences in TBI, which is critically important for translation to clinical practice. 

Objective:
The current study aims to characterize a clinically relevant murine model of closed-head TBI and analyze the impact of system parameters on motor coordination, spatial learning, and neuronal damage, with comparisons made between sexes.

Methods:
We systematically investigated sources of variability in a murine model of closed-head TBI and developed a framework to reduce variability across severity and sex. We manipulated pressure, dwell time, and displacement to determine effects on motor coordination, spatial learning, and neuronal damage in 10-week-old male and female mice. 

Results:
Increasing pressure beyond 70 psi had a ceiling effect on cellular and behavioral outcomes, while manipulating dwell time only affected behavioral performance. Increasing displacement precisely graded injury severity in both sexes across all outcomes. Physical signs of trauma occurred more frequently at higher displacements. Stratifying severity based on day-1 rotarod performance retained histological relationships and separated both sexes into injury severity cohorts with distinct patterns of behavioral recovery. Utilizing this stratification strategy, within-group rotarod variability over 6 days post-injury was reduced by 50%. 

Conclusions:
These results have important implications for translational research in TBI and provide a framework for using this clinically relevant model in both male and female mice.

Optimization of a translational murine model of closed-head traumatic brain injury

Abstract Title
Psychiatric outcomes in patients with mild concussion following differential treatment timing by age
Background
The temporary pathophysiological changes after a mild concussion may offer a therapeutic window in which timely intervention can influence long-term post-concussion sequelae. We sought to understand if the risk of depression or anxiety, two well-established sequelae following mild concussion, could be affected by the choice of immediate treatment versus delayed treatment. Our study provides quantifiable risks to different treatment windows across multiple ages and treatment time periods.

Our goal was to examine if patients who received an immediate treatment within one week of mild concussion had different risks for subsequent depression or anxiety compared with those who received delayed treatment after one week of the event, stratified by age groups.

Methods
This was a nationwide multicenter retrospective cohort study that used the TriNetX Analytics platform to access de-identified electronic health records of over 100 million patients. The study population was comprised of a total of 9,652 patients with a diagnosis of mild concussion, who received either immediate treatment defined as within 1 week (n = 5,266), or delayed treatment which was defined as 1 week to 6 months (n = 4,386) following the diagnosis of mild concussion. Each treatment group was separated into 3 age groups: <=25, 26-64, and 65+ years. Treatment was defined by CPT code and included one or more of the following neuro-rehabilitative services: CPT96132, CPT92507, CPT1013510, and CPT97165.

Results
Among patients aged <=25, those who received immediate treatment had a significantly higher lower risk for depression (HR: 0.782, 95% CI 0.613 –0.998) and anxiety (HR: 0.68, 95% CI: 0.564–0.819) compared with those who received delayed treatment. For those aged 26–64 years, there was a significantly reduced risk of depression (HR: 0.726, 95% CI: 0.593–0.89) and anxiety (HR: 0.635, 95% CI: 0.54–0.747) in the immediate treatment group. However, among patients in the oldest cohort of 65+, there was no significant decrease in risk of depression (HR: 0.735, 95% CI: 0.538–1.004), but there was a significant decrease in anxiety (HR: 0.608, 95% CI: 0.444–0.831) in the immediate treatment group compared to delayed treatment.

Conclusion

This study indicates that patients with a mild concussion who received treatment within one week had significantly reduced risk of depression and anxiety disorders than those who received treatment after one week. Prompt treatment of even mild concussions may mitigate adverse psychiatric outcomes following mild concussion.

Psychiatric Outcomes in Patients with Mild Concussion Following Differential Treatment Timing by Age

Introduction: Cannabis use among adults in the United States is growing, and while case reports and animal studies suggest a relationship between its use and delirium, no clinical studies have assessed the potential association. Therefore, this study aims to determine whether there is an association between a history of cannabis use disorder and delirium. 
Methods: The TriNetX Analytics Network, a research platform containing de-identified electronic health records from over 116 million patients and 63 healthcare organizations across the United States was utilized. History of cannabis use disorder was defined as at least 1 diagnosis of Cannabis related disorders (ICD-10-CM F12) within 1 year prior to hospitalization or surgery. Propensity score matching based on demographic characteristics (age, sex, race, and ethnicity) and comorbidities (diabetes, cardiovascular disease, neurological disease, dementia, depression, substance use, physical disabilities) was performed. The primary outcome is a diagnosis of delirium due to a known physiological condition (ICD-10-CM F05) within either 2 weeks (in-patient hospitalization analyses) or 1 week (post-operative analyses) of the respective hospital event.
Results: Following a propensity score match, 178,275 individuals were included in the 18-64 years in-patient cohort, 16,307 individuals in the 65+ in-patient cohort, 85,314 individuals in the 18-64 post-operative cohort, and 11,136 individuals in the 65+ post-operative cohort. In the in-patient hospitalization analysis, there was a significant association between cannabis use and delirium in the 18-64 cohort (HR: 1.43, 95% CI: 1.11 – 1.85) but not in the 65+ cohort (HR: 1.19, 95% CI: 0.66 – 2.12). In the postoperative setting, there was a significantly increased risk of delirium in the cannabis cohort in both the 18-64 (HR: 3.37, 95% CI: 2.59 – 4.39) group and 65+ years old group (HR: 2.37, 95% CI: 1.51 – 3.72).
Conclusions and Relevance: The findings of this investigation suggest that documented cannabis-use disorder within one year of hospitalization or surgery is associated with a significantly increased risk of delirium. Delirium is associated with considerable patient morbidity, which can be mitigated through early diagnosis. Documented cannabis use may, therefore, warrant additional delirium screening measures and clinical discretion.



The Association between Cannabis Use and Delirium in the Postoperative and Inpatient Settings

Background
Stroke ranks as the fifth leading cause of death in the United States and is the primary contributor to neurological impairments and cognitive deficits, responsible for 5.2% of global deaths. Approximately half of stroke survivors suffer from long-term disabilities, making it a leading cause of disability worldwide. Currently approved treatments for ischemic stroke focus on restoring cerebral blood flow through intravenous thrombolysis and mechanical thrombectomy. Minocycline, a lipophilic protease inhibitor capable of crossing the blood-brain barrier, exhibits anti-inflammatory, antioxidant, and antiapoptotic properties both centrally and peripherally. This literature review aims to assess the efficacy and safety of Minocycline as an adjunctive therapy in treating ischemic stroke and improving post-stroke functional outcomes.
Methods
We searched PubMed, Google Scholar, and clinicaltrials.gov databases. We used a combination of keywords: acute ischemic stroke, Minocycline, neuroprotection, and neuroinflammation. We included full-text peer-reviewed scholarly articles published within the last decade, randomized controlled trials, and systematic reviews with meta-analyses. We excluded articles in languages other than English, experimental studies and animal studies.
Results
We systematically reviewed nine articles that met our predefined eligibility criteria and involved 1,010 participants. These studies assessed the effectiveness and safety of Minocycline by comparing groups treated with Minocycline to control groups using the National Institute of Health Stroke Scale. Across the nine studies, there was consistent evidence of substantial clinical improvement among patients treated with Minocycline. However, two of the studies indicated that although Minocycline was deemed safe, it did not result in improved outcomes for patients with acute stroke.
Conclusion
Minocycline shows promise in mitigating both immediate and prolonged effects of ischemic stroke, demonstrating efficacy with minimal adverse effects even at high dosages. Nonetheless, conflicting results in two studies suggest that small sample sizes might contribute to varying conclusions. Despite the scarcity of data from clinical trials, Minocycline could serve as a valuable adjunctive therapy, particularly for patients in remote areas lacking access to specialized stroke care or those unsuitable for intravenous thrombolysis. Additional randomized clinical studies on a large scale are needed to verify the Minocycline's neuroprotective effects and its potential to improve outcomes and reduce long-term disabilities in ischemic stroke patients.


The Role of Minocycline as Adjunctive Therapy in Acute Ischemic Stroke: Literature Review

Background
Single CGRP regimens may not improve migraine outcomes and could worsen symptoms in some patients. Combining small molecule antagonists (SMA) and ligand monoclonal antibodies (L-mAb) targets CGRP molecules and receptors, potentially providing increased synergistic relief. Our study aims to assess the effects of this dual-CGRP approach.

Methods
A retrospective matched cohort study was conducted at a neurological care center in Hawaii, analyzing 90 chronic migraine patients aged ≥18 years treated with CGRP inhibitors (L-mAbs: fremanezumab, galcanezumab, eptinezumab; SMAs: ubrogepant, rimegepant, atogepant; or a combination). The study compared dual L-mAb and SMA CGRP treatments with mono-L-mAb or mono-SMA CGRP treatments, matched by age and sex. Variables included age, age at diagnosis, sex, onabotulinumtoxinA use, headache frequency, duration, severity, and associated symptoms before and three months post-treatment. Adverse events were recorded for the dual-treatment group at three-month follow-up. Statistical analyses were made using Wilcoxon, Kruskal-Wallis, and Fisher's exact tests, with significance set at < 0.05.

Results
Patients on dual-CGRP therapy had an average reduction of four headache days, with some experiencing up to 14 fewer days, while mono-CGRP patients experienced no change (p = 0.112). Headache severity was reduced by 20% for dual-CGRP patients and 10% for mono-CGRP patients (p = 0.039). Aura symptoms were significantly reduced in the dual-CGRP group, with 48% (13 patients) becoming aura-free compared to 20% in the mono-CGRP group (p = 0.004). Adverse events in the dual-CGRP group were mild, with three patients experiencing fatigue, drowsiness, or mild constipation. No patients discontinued treatment, and no serious adverse events were reported.

Conclusion
Dual-CGRP regimens may provide improved effectiveness for controlling migraine symptoms by significantly reducing headache severity and aura symptoms without significant adverse events.

The Safety and Effectiveness of Dual Calcitonin Gene-Related Peptide (CGRP) Therapies for Migraine Treatment: A Focus on Small Molecule Antagonist and Ligand Monoclonal Combinations

Background
Neurodegenerative diseases (NDs), such as Alzheimer’s disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS), significantly affect the daily lives of patients. Shared risk factors for this group of diseases include age, genetic background, socioeconomic status, metabolic conditions, and environmental toxin exposure. Veterans are particularly vulnerable to these conditions due to their unique military experiences and exposures. This study aims to identify distinct risk factors for NDs in the veteran population, providing insights for tailoring clinical interventions to this unique patient population.

Methods
This systematic review was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In June 2024, a comprehensive search was conducted using the following electronic databases: PubMed, MEDLINE, EMBASE, Cochrane Database, and Scopus, focusing on studies published between 2000 and 2024. Search terms included combinations of MeSH terms and keywords such as “neurodegenerative diseases,” “veterans,” “military,” “risk factors,” “Alzheimer’s disease,” “Parkinson’s disease,” “ALS,” and “MS.” Inclusion criteria focused on peer-reviewed articles, cohort studies, case-control studies, and case series investigating ND risk factors in veterans. Publications not in English, opinion pieces, and non-human studies were excluded. 

Results
Unique risk factors for NDs in veterans include environmental toxin exposure, combat-related physical injuries, and psychological impact of military service. Environmental agents, like Agent Orange, herbicides, neurotoxins, chlorinated solvents, and other chemical warfare agents were significant factors. Physical military injuries, such as traumatic brain injuries (TBI), hearing loss, and visual impairments, have been linked to a higher incidence in NDs and neurotrauma. Veterans have long been known to be affected by the psychiatric effects of military experiences, specifically post-traumatic stress disorder (PTSD). Recent research has suggested a connection between the PTSD and eventual cognitive decline associated with NDs. 

Conclusion
Identifying the military-related risk factors in developing NDs underscores the multifactorial nature of NDs while highlighting the unique life experiences of veterans that contribute to their neurological decline. This study emphasizes the importance of understanding patient backgrounds to devise effective preventive measures and therapeutic approaches. Future research should prioritize longitudinal studies to further elucidate these associations and enhance intervention strategies.

Unique Military-Related Risk Factors Contributing to Neurodegenerative Diseases Among Veterans: A Systematic Review

Background
Encephalitis (inflammation of the brain) is primarily divided into Autoimmune Encephalitis (AE) and Infectious Encephalitis (IE). Encephalitis poses a substantial burden on the U.S. healthcare system with an annual prevalence of 5-10 patients per 100,000 individuals and an inpatient mortality of nearly 10% [1-3]. Additionally, most patients are discharged with sequelae of impaired cognitive function [4]. Previous studies suggest both autoimmune (e.g. multiple sclerosis) and infectious (e.g. SARS-CoV-2) condition severity is associated with Vitamin D (VitD), where it can modify disease activity or where hypovitaminosis is a causal risk [5-6]. VitD supplementation, an extremely accessible and cost-effective intervention, in some cases resulted in disease prevention and risk reduction [5,6]. Despite these findings, the relationship between VitD and both autoimmune- and infection-mediated encephalitis is unknown. We sought to assess this relationship between VitD and clinical measures and outcomes in patients with encephalitis. 

Methods
373 adult encephalitis patient charts from 03-31-2013 to 03-31-2023 were reviewed from the Texas Medical Center/Greater Houston area. VitD levels, demographics, physical exam findings, treatments, imaging findings, diagnostics, and outcome variables were collected. VitD values were trichotomized into “Deficient”, “Insufficient”, and “Sufficient” [7]. 

Results 
Chi Square Tests of Independence analysis revealed significant associations between VitD Status and Disposition (p = 0.01), Cause of Death (p < 0.001), and # Rituximab therapies (p = 0.006). Additionally, statistically significant associations existed between low VitD levels (VitD Deficiency and Insufficiency) and immunocompromised state (p = 0.008), poorer outcomes referencing the Glasgow Outcome Scale (GOS) (p = 0.014), Hospital Readmission (p < 0.001), severe comorbid conditions referencing the Charlson Comorbidity Score (p = 0.032), and Mortality (p < 0.001). Binary Regression analysis revealed confounds; African-American patients and those receiving mechanical ventilation had poorer outcomes based on the GOS (p = 0.008, 0.048).

Conclusion 
Our study provides evidence that VitD status has a significant relationship with AE and IE clinical measures and outcomes post-hospitalization. However, we were limited by lack of VitD documentation; ~10% of study patients had documented measurements taken during their hospital stay. This illuminates the need for clinicians to 1) collect VitD data for all AE/IE patients and 2) consider VitD supplementation, an accessible and cost-effective intervention, for AE/IE patients. Future studies, currently being pursued, include incorporating 300 additional AE/IE patients from the Johns Hopkins medical system to further define the relationship between VitD status with clinical outcomes. 



1. Graus F, Titulaer MJ, Balu R, Benseler S, Bien CG, Cellucci T, Cortese I, Dale RC, Gelfand JM, Geschwind M, Glaser CA, Honnorat J, Höftberger R, Iizuka T, Irani SR, Lancaster E, Leypoldt F, Prüss H, Rae-Grant A, Reindl M, Rosenfeld MR, Rostásy K, Saiz A, Venkatesan A, Vincent A, Wandinger KP, Waters P, Dalmau J. A clinical approach to diagnosis of autoimmune encephalitis. Lancet Neurol. 2016 Apr;15(4):391-404. doi: 10.1016/S1474-4422(15)00401-9. 
2. Ellul M, Solomon T. Acute encephalitis - diagnosis and management. Clin Med (Lond). 2018 Mar;18(2):155-159. doi: 10.7861/clinmedicine.
3. Hansen MA, Samannodi MS, Hasbun R. Predicting Inpatient Mortality Among Encephalitis Patients: A Novel Admission Risk Score. Open Forum Infect Dis. 2020 Oct 7;7(11):ofaa471. doi: 10.1093/ofid/ofaa471.
4. Kvam KA, Stahl JP, Chow FC, Soldatos A, Tattevin P, Sejvar J, Mailles A. Outcome and Sequelae of Infectious Encephalitis. J Clin Neurol. 2024 Jan;20(1):23-36. doi: 10.3988/jcn.2023.0240. PMID: 38179629; PMCID: PMC10782093.
5. Sintzel MB, Rametta M, Reder AT. Vitamin D and Multiple Sclerosis: A Comprehensive Review. Neurol Ther. 2018 Jun;7(1):59-85. doi: 10.1007/s40120-017-0086-4. Epub 2017 Dec 14. PMID: 29243029; PMCID: PMC5990512.
6. Xu Y, Baylink DJ, Chen CS, Reeves ME, Xiao J, Lacy C, Lau E, Cao H. The importance of vitamin d metabolism as a potential prophylactic, immunoregulatory and neuroprotective treatment for COVID-19. J Transl Med. 2020 Aug 26;18(1):322. doi: 10.1186/s12967-020-02488-5. PMID: 32847594; PMCID: PMC7447609.
7. Holick MF. Vitamin D status: measurement, interpretation, and clinical application. Ann Epidemiol. 2009 Feb;19(2):73-8. doi: 10.1016/j.annepidem.2007.12.001. Epub 2008 Mar 10. PMID: 18329892; PMCID: PMC2665033.

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