RESUMEN
BACKGROUND: Obesity rates have been increasing globally, leading to a higher incidence of knee osteoarthritis and a surge in primary and revision total knee arthroplasty (TKA). The debate continues on the impact of obesity on TKA success, particularly regarding the use of stemmed tibial components in obese patients. This systematic review aimed to compare the effectiveness of stemmed tibial components versus standard keeled tibial components in obese patients undergoing TKA. METHODS: A systematic review was conducted using databases including PubMed, Embase, Scopus, and Web of Science from inception to December 2023. The eligibility criteria were Participants: Patients who have obesity undergoing TKA; Intervention: stemmed TKA; Comparator: standard keeled tibial TKA; Outcomes: aseptic loosening, patient-reported outcome measures (PROMs), and overall revision. Data extraction and quality assessment were performed using the Newcastle-Ottawa Scale for cohort studies and the Cochrane risk-of-bias tool for randomized trials. RESULTS: The search yielded 470 studies, with 10 studies (42,533 knees) meeting the inclusion criteria. These studies included 3 randomized controlled trials and 7 retrospective cohorts. The primary outcomes measured were aseptic loosening and overall revision rates, while secondary outcomes included PROMs. Results indicated mixed findings, with some studies suggesting improved outcomes with stemmed components in cases of aseptic loosening and mechanical failure, while others showed no significant difference. The PROMs did not show a significant difference between groups post-TKA. The certainty of the evidence was graded as "very low" using the Grading of Recommendations, Assessment, Development, and Evaluations framework. CONCLUSIONS: Current literature does not provide conclusive evidence to support the routine use of stemmed tibial components in TKA for obese patients. The decision to use stem extensions should not solely rely on the patient's obesity status. Further high-quality studies are needed to clarify the role of stemmed components in TKA for this patient population. LEVEL OF EVIDENCE: III.
RESUMEN
Alzheimer's disease (AD) is the most widespread and irreversible form of dementia and accounts for more than half of dementia cases. The most significant risk factors for AD are aging-related exacerbations, degradation of anatomical pathways, environmental variables and mitochondrial dysfunction. Finding a decisive therapeutic solution is a major current issue. Nuanced interactions between major neuropathological mechanisms in AD in patients and microbiome have recently gained rising attention. The presence of bacterial amyloid in the gut triggers the immune system, resulting in increased immune feedbacks and endogenous neuronal amyloid within the CNS. Also, early clinical research revealed that changing the microbiome with beneficial bacteria or probiotics could affect brain function in AD. New approaches focus on the possible neuroprotective action of disease-modifying medications in AD. In the present review, we discuss the impact of the gut microbiota on the brain and review emerging research that suggests a disruption in the microbiota-brain axis can affect AD by mediating neuroinflammation. Such novel methods could help the development of novel therapeutics for AD.
RESUMEN
BACKGROUND: Spinal cord injury (SCI) is a significant public health issue, leading to physical, psychological, and social complications. Machine learning (ML) algorithms have shown potential in diagnosing and predicting the functional and neurologic outcomes of subjects with SCI. ML algorithms can predict scores for SCI classification systems and accurately predict outcomes by analyzing large amounts of data. This systematic review aimed to examine the performance of ML algorithms for diagnosing and predicting the outcomes of subjects with SCI. METHODS: The literature was comprehensively searched for the pertinent studies from inception to May 25, 2023. Therefore, electronic databases of PubMed, Embase, Scopus, and Web of Science were systematically searched with individual search syntax. RESULTS: A total of 9424 individuals diagnosed with SCI across multiple studies were analyzed. Among the 21 studies included, 5 specifically aimed to evaluate diagnostic accuracy, while the remaining 16 focused on exploring prognostic factors or management strategies. CONCLUSIONS: ML and deep learning (DL) have shown great potential in various aspects of SCI. ML and DL algorithms have been employed multiple times in predicting and diagnosing patients with SCI. While there are studies on diagnosing acute SCI using DL algorithms, further research is required in this area.