The rising global burden of cancer disproportionately affects low- and middle-income countries (LMICs), which account for over half of new patients and cancer deaths worldwide. However, LMIC health systems face profound challenges in implementing comprehensive cancer control programs because of limited health care resources and infrastructure. This analytical review explores contemporary evidence on barriers undermining cancer control efforts in resource-constrained LMIC settings. We conducted a comprehensive literature review of peer-reviewed evidence on cancer control challenges and solutions tailored to resource-limited settings. We provide a conceptual framework categorizing these barriers across the cancer care continuum, from raising public awareness to palliative care. We also appraise evidence-based strategies proposed to overcome identified obstacles to cancer control in the published literature, including task-shifting to nonspecialist health workers, strategic prioritization of high-impact interventions, regional collaborations, patient navigation systems, and novel financing mechanisms. Developing strong primary care delivery platforms integrated with specialized oncology care, alongside flexible and resilient health system models tailored to local contexts, will be critical to curb the rising tide of cancer in resource-limited settings. Urgent global commitments and investments are needed to dismantle barriers and expand access to prevention, early detection, diagnosis, treatment, and palliation services for all patients with cancer residing in LMICs as an ethical imperative. The review elucidates priority areas for policy actions, health systems strengthening, and future research to guide international efforts toward more equitable cancer control globally.

Immune checkpoint inhibitors have led to the development of new approaches for cancer treatment with positive outcomes. However, checkpoint blockade is associated with a unique spectrum of immune-related adverse events (irAEs), which may cause irreversible neurological deficits and even death. We presented a case of a 57-year-old man with non-small-cell lung cancer.who developed ptosis, dyspnea, and muscle weakness as initial symptoms with progression after the treatment with ipilimumab and nivolumab. Myasthenia gravis was confirmed by serum acetylcholine receptor antibody and single fiber electromyography. Myositis was identified by high level of serum creatine phosphokinase and electromyography. Polyneuropathy was identified by nerve conduction study. The patient underwent treatment with steroid and pyridostigmine. Respiratory rehabilitation was also performed. Dyspnea and muscle weakness improved gradually. Ipilimumab and nivolumab were permanently discontinued. This case has increased the clinical awareness by indicating that the checkpoint inhibitors-related neurological irAEs could be complicated and simultaneously involve multiple neurological systems. Early recognition and complete evaluation are critical in clinical practice.

An all-inclusive and accurate prediction of outcomes for patients with acute ischemic stroke (AIS) is crucial for clinical decision-making. This study developed extreme gradient boosting (XGBoost)-based models using three simple factors—age, fasting glucose, and National Institutes of Health Stroke Scale (NIHSS) scores—to predict the three-month functional outcomes after AIS. We retrieved the medical records of 1848 patients diagnosed with AIS and managed at a single medical center between 2016 and 2020. We developed and validated the predictions and ranked the importance of each variable. The XGBoost model achieved notable performance, with an area under the curve of 0.8595. As predicted by the model, the patients with initial NIHSS score > 5, aged over 64 years, and fasting blood glucose > 86 mg/dL were associated with unfavorable prognoses. For patients receiving endovascular therapy, fasting glucose was the most important predictor. The NIHSS score at admission was the most significant predictor for those who received other treatments. Our proposed XGBoost model showed a reliable predictive power of AIS outcomes using readily available and simple predictors and also demonstrated the validity of the model for application in patients receiving different AIS treatments, providing clinical evidence for future optimization of AIS treatment strategies.

Background: The most established pathognomonic protein of Parkinson’s disease (PD), α-synuclein, is extensively investigated for disease diagnosis and prognosis; however, investigations into whether the free form of α-synuclein in the blood functions as a PD biomarker have not been fruitful. Extracellular vesicles (EVs) secreted from cells and present in blood transport molecules are novel platforms for biomarker identification. In blood EVs, α-synuclein originates predominantly from the brain without the interference of the blood–brain barrier. The present study investigated the role of plasma EV-borne α-synuclein as a biomarker of PD. Methods: Patients with mild to moderate stages of PD (n = 116) and individuals without PD (n = 46) were recruited to serve as the PD study group and the control group, respectively. Plasma EVs were isolated, and immunomagnetic reduction–based immunoassay was used to assess EV α-synuclein levels. Conventional statistical analysis was performed using SPSS 25.0, and p < 0.05 was considered significant. Results: Compared with controls, we observed significantly lower plasma EV α-synuclein levels in the patients with PD (PD: 56.0 ± 3.7 fg/mL vs. control: 74.5 ± 4.3 fg/mL, p = 0.009), and the significance remained after adjustment for age and sex. Plasma EV α-synuclein levels in the patients with PD did not correlate with age, disease duration, Part I and II scores of the Unified Parkinson’s Disease Rating Scale (UPDRS), or the Mini-Mental State Examination scores. However, such levels were significantly correlated with UPDRS Part III score, which assesses motor dysfunction. Furthermore, the severity of akinetic-rigidity symptoms, but not tremor, was inversely associated with plasma EV α-synuclein level. Conclusion: Plasma EV α-synuclein was significantly different between the control and PD group and was associated with akinetic-rigidity symptom severity in patients with PD. This study corroborates the possible diagnostic and subtyping roles of plasma EV α-synuclein in patients with PD, and it further provides a basis for this protein’s clinical relevance and feasibility as a PD biomarker.

Plasma extracellular vesicles (EVs) containing various molecules, including cytokines, can reflect the intracellular condition and participate in cell-to-cell signaling, thus emerging as biomarkers for Parkinson’s disease (PD). Inflammation may be a crucial risk factor for PD development and progression. The present study investigated the role of plasma EV cytokines as the biomarkers of PD. This cross-sectional study recruited 113 patients with PD, with mild to moderate stage disease, and 48 controls. Plasma EVs were isolated, and the levels of cytokines, including pro-interleukin (IL)-1β, IL-6, IL-10, tumor necrosis factor (TNF)-α, and transforming growth factor (TGF)-β1, were evaluated. Patients with PD had significantly increased plasma EV pro-IL-1β and TNF-α levels compared with controls after adjustment for age and sex. Despite the lack of a significant association between plasma EV cytokines and motor symptom severity in patients with PD, cognitive dysfunction severity, assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment, was significantly associated with plasma EV pro-IL-1β, IL-6, IL-10, and TNF-α levels. This association was PD specific and not found in controls. Furthermore, patients with PD cognitive deficit (MMSE < 26) exhibited a distinguished EV cytokine profile compared to those without cognitive deficit. The findings support the concept of inflammatory pathogenesis in the development and progression of PD and indicate that plasma EV cytokines may serve as PD biomarkers in future.

Age, sex, and body mass index (BMI) were associated with obstructive sleep apnea (OSA). Although various methods have been used in OSA prediction, this study aimed to develop predictions using simple and general predictors incorporating machine learning algorithms. This single-center, retrospective observational study assessed the diagnostic relevance of age, sex, and BMI for OSA in a cohort of 9,422 patients who had undergone polysomnography (PSG) between 2015 and 2020. The participants were randomly divided into training, testing, and independent validation groups. Multivariable logistic regression (LR) and artificial neural network (ANN) algorithms used age, sex, and BMI as predictors to develop risk-predicting models for moderate-and-severe OSA. The training-testing dataset was used to assess the model generalizability through five-fold cross-validation. We calculated the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the independent validation set to assess the performance of the model. The results showed that age, sex, and BMI were significantly associated with OSA. The validation AUCs of the generated LR and ANN models were 0.806 and 0.807, respectively. The independent validation set's accuracy, sensitivity, specificity, PPV, and NPV were 76.3%, 87.5%, 57.0%, 77.7%, and 72.7% for the LR model, and 76.4%, 87.7%, 56.9%, 77.7%, and 73.0% respectively, for the ANN model. The LR-and ANN-boosted models with the three simple parameters effectively predicted OSA in patients referred for PSG examination and improved insight into risk stratification for OSA diagnosis.