Linking autism spectrum disorders and parkinsonism: clinical and genetic association.

BACKGROUND: Autism spectrum disorders (ASD) comprise many complex and clinically distinct neurodevelopmental conditions, with increasing evidence linking them to parkinsonism. METHODS: We searched Medline and Embase from inception to 21 March 2022 and reviewed the bibliographies of relevant articles. Studies were screened and reviewed comprehensively by two independent authors. RESULTS: Of 863 references from our search, we included eight clinical studies, nine genetic studies, and five case reports. Regardless of age group, Parkinson's disease (PD) and parkinsonian syndromes were more frequently observed in patients with ASD, though the evidence for increased rates of parkinsonism is less clear for children and adolescents. Parkinsonian features and hypokinetic behavior were common in Rett syndrome, with prevalence estimates ranging from 40% to 80%. Frequently observed parkinsonian features include bradykinesia, rigidity, hypomimia, and gait freezing. PD gene PARK2 copy number variations appear more frequently in ASD cases than controls. Evidence suggests that RIT2 and CD157/BST1 are implicated in ASD and PD, while the evidence for other PD-related genes (DRD2, GPCR37, the SLC gene family, and SMPD1) is less clear. Rare mutations, such as ATP13A2, CLN3, and WDR45, could result in autistic behavior and concomitant parkinsonism. CONCLUSION: The prevalence of parkinsonism in ASD is substantially greater than in the general population or matched controls. Various PD-associated gene loci, especially PARK2, could confer susceptibility to ASD as well. Important future directions include conducting prospective cohort studies to understand how parkinsonian symptoms may progress, genetic studies to reveal relevant gene loci, and pathophysiologic studies to identify potential therapeutic targets.

The genetic basis of multiple system atrophy.

Multiple system atrophy (MSA) is a heterogenous, uniformly fatal neurodegenerative ɑ-synucleinopathy. Patients present with varying degrees of dysautonomia, parkinsonism, cerebellar dysfunction, and corticospinal degeneration. The underlying pathophysiology is postulated to arise from aberrant ɑ-synuclein deposition, mitochondrial dysfunction, oxidative stress and neuroinflammation. Although MSA is regarded as a primarily sporadic disease, there is a possible genetic component that is poorly understood. This review summarizes current literature on genetic risk factors and potential pathogenic genes and loci linked to both sporadic and familial MSA, and underlines the biological mechanisms that support the role of genetics in MSA. We discuss a broad range of genes that have been associated with MSA including genes related to Parkinson's disease (PD), oxidative stress, inflammation, and tandem gene repeat expansions, among several others. Furthermore, we highlight various genetic polymorphisms that modulate MSA risk, including complex gene-gene and gene-environment interactions, which influence the disease phenotype and have clinical significance in both presentation and prognosis. Deciphering the exact mechanism of how MSA can result from genetic aberrations in both experimental and clinical models will facilitate the identification of novel pathophysiologic clues, and pave the way for translational research into the development of disease-modifying therapeutic targets.

Learning during the pandemic: Perspectives of medical students in Singapore.

The COVID-19 pandemic has significantly disrupted medical education, particularly affecting clinical-year students. Educational institutions often had to halt, shorten or impose significant restrictions on their hospital rotations due to strict infection control and social-distancing guidelines implemented in tertiary healthcare institutions, as well as manpower and logistical constraints amid the pandemic. Thus, distance-based learning platforms such as online lectures and case-based teaching were increasingly adopted in place of bedside and face-to-face tutorials. While interactive virtual case-based discussions are generally useful in imparting clinical reasoning skills to medical students, they are unfortunately not able to fully replicate the experience of clerking, examining and managing real patients in the wards, which is a quintessential process towards building clinical acumen and attaining core clinical competencies. Therefore, for final year medical students who are preparing for their Bachelor of Medicine and Bachelor of Surgery (MBBS) examinations, many are naturally concerned by how learning in this "new normal" may affect their ability to make the transition to become competent junior doctors. As such, we seek to share our learning experiences as the first batch of medical students to have completed our entire final year of clinical education amid the COVID-19 pandemic, and offer 4 practical suggestions to future batches of students on how to adapt and optimise clinical learning under these circumstances: actively engaging in virtual learning, making the most of every clinical encounter, learning how to construct peer teaching/practice sessions, and maintaining physical and psychological well-being.

Predicting Clinical Outcomes in Acute Ischemic Stroke Patients Undergoing Endovascular Thrombectomy with Machine Learning : A Systematic Review and Meta-analysis.

PURPOSE: Conventional predictive models are based on a combination of clinical and neuroimaging parameters using traditional statistical approaches. Emerging studies have shown that the machine learning (ML) prediction models with multiple pretreatment clinical variables have the potential to accurately prognosticate the outcomes in acute ischemic stroke (AIS) patients undergoing thrombectomy, and hence identify patients suitable for thrombectomy. This article summarizes the published studies on ML models in large vessel occlusion AIS patients undergoing thrombectomy. METHODS: We searched electronic databases including PubMed from 1 January 2000 up to 14 October 2019 for studies that evaluated ML algorithms for the prediction of outcomes in stroke patients undergoing thrombectomy. We then used random-effects bivariate meta-analysis models to summarize the studies. RESULTS: We retained a total of five studies that evaluated ML (4 support vector machine, 1 decision tree model) with a combined cohort of 802 patients. The prevalence of good functional outcome defined by 90-day mRS of 0-2 when available. Random effects model demonstrated that the AUC was 0.846 (95% confidence interval, CI 0.686-0.902). A pooled diagnostic odds ratio of 12.6 was computed. The pooled sensitivity and specificity were 0.795 (95% CI 0.651-0.889) and 0.780 (95% CI 0.634-0.879), respectively. CONCLUSION: ML may be useful as an adjunct to clinical assessment to predict functional outcomes in AIS patients undergoing thrombectomy, and hence identify suitable patients for treatment. Further studies validating ML models in large multicenter cohorts are necessary to explore this further.

Multiple System Atrophy (MSA) and smoking: a meta-analysis and mechanistic insights.

BACKGROUND: The association between cigarette smoking and multiple system atrophy (MSA) has been debated. We conducted a systematic review and a meta-analysis to investigate this link. RESULTS: We identified 161 articles from database searching and bibliographic review. Five case-control studies satisfied the inclusion and exclusion criteria, and 435 and 352 healthy controls and MSA patients were examined. The prevalence of MSA amongst ever smokers was lower compared to never smokers (aOR=0.57; 95% CI, 0.29-1.14), although this result did not reach statistical significance. This was also observed for current and former smokers, with a stronger association for current smokers (aOR=0.63 vs aOR=0.96). CONCLUSIONS: There is a suggestion that smoking protects against MSA. Prospective studies in larger patient cohorts are required to further evaluate the cause-effect relationship and functional studies in cellular and animal models will provide mechanistic insights on their potential etiologic links. METHODS: PubMed and Cochrane Library were searched from inception to July 7, 2019 to identify case-control studies that analyzed smoking as an environmental risk or protective factor for MSA. Two authors independently extracted data and performed risk-of-bias and quality assessment. The random-effects model was assumed to account for between-study variance when pooling the crude and adjusted odds ratios.

A systematic review and meta-regression analysis of nonoperative management of blunt traumatic thoracic aortic injury in 2897 patients.

BACKGROUND: Thoracic endovascular aortic repair has transformed the management of blunt traumatic thoracic aortic injuries (BTTAI). Recent studies have suggested that the nonoperative management (NOM) of BTTAI may be a viable alternative. We investigated the NOM of BTTAI by conducting a systematic review and meta-analysis of the mortality proportions and incidence of complications. METHODS: We searched PubMed through June 22, 2017, and referenced lists of included studies without language restriction, with the assistance of a trained librarian. We included studies that reported the NOM of BTTAI (≥5 participants). Two authors independently screened titles, abstracts, and performed data extraction. Pooled prevalence of mortality (aortic related, in hospital) were obtained based on binomial distribution with Freeman-Tukey double-arcsine transformation and continuity correction. The random-effects model was used for all analyses to account for variation between studies. Meta-regression was performed to explore sources of heterogeneity, including Injury Severity Score, age, and gender. RESULTS: We included 35 studies comprising 2897 participants. The pooled prevalence of all-cause in-patient mortality in the overall, grade I, grade II, grade III, and grade IV populations are as follows: 29.0% (95% confidence interval [CI], 19.3%-39.6%; I2 = 95%; P < .01), 6.8% (95% CI, 0.6%-19.3%; I2 = 52%; P = .03), 0% (95% CI, 0%-2.0%; I2 = 0%; P = .81), 29.2% (95% CI, 17%-42.5%; I2 = 3%; P = .41), and 87.4% (95% CI, 16.4%-100%; I2 = 48%; P = .14), respectively. The combined incidence of aortic-related in-patient mortality in the overall, grade I, grade II, and grade III populations are: 2.4% (95% CI, 0.4%-5.5%; I2 = 60%; P < .01), 0.93% (95% CI, 0%-14.2%; I2 = 65%; P < .01), 0% (95% CI, 0%-1.8%; I2 = 0%; P = .99), and 0.13% (95% CI, 0%-6.4%; I2 = 14%; P = .33), respectively. The total proportion of postdischarge aortic-related mortality is 0% (95% CI, 0%-0.5%; I2 = 0%; P = .91). Meta-regression showed a decreased risk of in-hospital mortality as age increases (ß = .99; 95% CI, 0.98-1.00), an increased risk of in-hospital mortality with a higher Injury Severity Score (ß = 1.02; 95% CI, 1.00-1.04), and a decreased risk of in-hospital mortality among male patients (ß = .54; 95% CI, 0.3-0.90). CONCLUSIONS: This study provides, to our knowledge, the most up-to-date pooled estimate of mortality rates after the NOM of BTTAI. However, its interpretation is limited by the paucity of data and substantial quantitative heterogeneity. If patients are to be managed nonoperatively, we would recommend the judicious use of active surveillance in a select group of patients in the short, mid, and long term.