原发性免疫缺陷病和脊髓性肌萎缩症临床特点及新生儿早期联合筛查实践

原发性免疫缺陷病(PID)及脊髓性肌萎缩症(SMA)均是由基因突变引起的能严重影响儿童健康甚至导致死亡的遗传性出生缺陷,早期发现和及时诊断治疗是影响预后的关键,新生儿筛查是目前实现早期诊断和干预的有效手段。近年来部分国家和地区已将SMA和PID中最为严重的重症联合免疫缺陷病(SCID)和最常见的B细胞缺乏症X连锁无丙种球蛋白血症(XLA)纳入新生儿筛查范围。浙江省在国内首次开展对SCID、XLA和SMA三种遗传性疾病的新生儿早期联合筛查实践,极大地提高了筛查效率,使出生缺陷三级预防关口前移,有助于显著改善患者的预后、提高生活质量、降低死亡率。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Are risk factors necessary for pretest probability assessment of coronary artery disease? A patient similarity network analysis of the PROMISE trial

BackgroundPretest probability (PTP) calculators utilize epidemiological-level findings to provide patient-level risk assessment of obstructive coronary artery disease (CAD). However, their limited accuracies question whether dissimilarities in risk factors necessarily result in differences in CAD. Using patient similarity network (PSN) analyses, we wished to assess the accuracy of risk factors and imaging markers to identify ≥50% luminal narrowing on coronary CT angiography (CCTA) in stable chest-pain patients.MethodsWe created four PSNs representing: patient characteristics, risk factors, non-coronary imaging markers and calcium score. We used spectral clustering to group individuals with similar risk profiles. We compared PSNs to a contemporary PTP score incorporating calcium score and risk factors to identify ≥50% luminal narrowing on CCTA in the CT-arm of the PROMISE trial. We also conducted subanalyses in different age and sex groups.ResultsIn 3556 individuals, the calcium score PSN significantly outperformed patient characteristic, risk factor, and non-coronary imaging marker PSNs (AUC: 0.81 vs. 0.57, 0.55, 0.54; respectively, p ?< ?0.001 for all). The calcium score PSN significantly outperformed the contemporary PTP score (AUC: 0.81 vs. 0.78, p ?< ?0.001), and using 0, 1–100 and ?> ?100 cut-offs provided comparable results (AUC: 0.81 vs. 0.81, p ?= ?0.06). Similar results were found in all subanalyses.ConclusionCalcium score on its own provides better individualized obstructive CAD prediction than contemporary PTP scores incorporating calcium score and risk factors. Risk factors may not be able to improve the diagnostic accuracy of calcium score to predict ≥50% luminal narrowing on CCTA.     

New era for myelofibrosis treatment with novel agents beyond Janus kinase-inhibitor monotherapy: Focus on clinical development of BCL-XL/BCL-2 inhibition with navitoclax

Myelofibrosis is a heterogeneous myeloproliferative neoplasm characterized by chronic inflammation, progressive bone marrow failure, and hepatosplenic extramedullary hematopoiesis. Treatments like Janus kinase inhibitor monotherapy (e.g., ruxolitinib) provide significant spleen and symptom relief but demonstrate limited ability to lead to a durable disease modification. There is an urgent unmet medical need for treatments with a novel mechanism of action that can modify the underlying pathophysiology and affect the disease course of myelofibrosis. This review highlights the role of B-cell lymphoma (BCL) protein BCL-extra large (BCL-X_L) in disease pathogenesis and the potential role that navitoclax, a BCL-extra large/BCL-2 inhibitor, may have in myelofibrosis treatment.