Esophageal Surveillance Practices in Esophageal Atresia Patients: A Survey by the Eastern Pediatric Surgery Network

IntroductionEndoscopic surveillance guidelines for patients with repaired esophageal atresia (EA) rely primarily on expert opinion. Prior to embarking on a prospective EA surveillance registry, we sought to understand EA surveillance practices within the Eastern Pediatric Surgery Network (EPSN).MethodsAn anonymous, 23-question Qualtrics survey was emailed to 181 physicians (surgeons and gastroenterologists) at 19 member institutions. Likert scale questions gauged agreement with international EA surveillance guideline-derived statements. Multiple-choice questions assessed individual and institutional practices.ResultsThe response rate was 77%. Most respondents (80%) strongly agree or agree that EA surveillance endoscopy should follow a set schedule, while only 36% claimed to perform routine upper GI endoscopy regardless of symptoms. Many institutions (77%) have an aerodigestive clinic, even if some lack a multi-disciplinary EA team. Most physicians (72%) expressed strong interest in helping develop evidence-based guidelines.ConclusionsOur survey reveals physician agreement with current guidelines but weak adherence. Surveillance methods vary greatly, underscoring the lack of evidence-based data to guide EA care. Aerodigestive clinics may help implement surveillance schedules. Respondents support evidence-based protocols, which bodes well for care standardization. Results will inform the first multi-institutional EA databases in the United States (US), which will be essential for evidence-based care.Level of EvidenceThis is a prognosis study with level 4 evidence.     

Inter‐serotype reassortment among epizootic haemorrhagic disease viruses in the United States

First described in 1955 in New Jersey, epizootic haemorrhagic disease (EHD) causes a severe clinical disease in wild and domestic ruminants worldwide. Epizootic haemorrhagic disease outbreaks occur in deer populations each year from summer to late autumn. The etiological agent is EHD virus (EHDV) which is a double‐stranded segmented icosahedral RNA virus. EHD virus utilizes point mutations and reassortment strategies to maintain viral fitness during infection. In 2018, EHDV serotype 2 was predominantly detected in deer in Illinois. Whole genome sequencing was conducted for two 2018 EHDV2 isolates (IL41747 and IL42218) and the sequence analyses indicated that IL42218 was a reassortant between different serotypes whereas IL41747 was a genetically stable strain. Our data suggest that multiple strains contribute to outbreaks each year.     

从“肾毒”论治慢性肾脏病＊

慢性肾脏病是一种以肾脏功能的损伤为主要表现，并可累及全身多脏器的慢性进展性疾病。中医辨证治疗本病在延缓病情、改善预后等方面具有显著的优势。通过中医理论研究及临床经验总结，笔者提出从“肾毒”论治慢性肾脏病，认为其不仅是本病的致病因素，更是重要的病理产物，是本病发生发展、迁延难愈的关键。在治疗上提出以“益肾元、解肾毒”为大法，并根据不同兼夹辨证施治，采用药对，长治缓图。以“肾毒”立论拟定的经验方苏茵解毒方，在临床治疗和实验研究上均显示出明显的效果，并已制备成院内制剂临床使用，在疗效及经济效益方面均具有显著的优势。     

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.