Machine learning-Based model for prediction of Narcolepsy Type 1 in Patients with Obstructive Sleep Apnea with Excessive Daytime Sleepiness.

Background: Excessive daytime sleepiness (EDS) forms a prevalent symptom of obstructive sleep apnea (OSA) and narcolepsy type 1 (NT1), while the latter might always be overlooked. Machine learning (ML) models can enable the early detection of these conditions, which has never been applied for diagnosis of NT1. Objective: The study aimed to develop ML prediction models to help non-sleep specialist clinicians identify high probability of comorbid NT1 in patients with OSA early. Methods: Totally, clinical features of 246 patients with OSA in three sleep centers were collected and analyzed for the development of nine ML models. LASSO regression was used for feature selection. Various metrics such as the area under the receiver operating curve (AUC), calibration curve, and decision curve analysis (DCA) were employed to evaluate and compare the performance of these ML models. Model interpretability was demonstrated by Shapley Additive explanations (SHAP). Results: Based on the analysis of AUC, DCA, and calibration curves, the Gradient Boosting Machine (GBM) model demonstrated superior performance compared to other machine learning (ML) models. The top five features used in the GBM model, ranked by feature importance, were age of onset, total limb movements index, sleep latency, non-REM (Rapid Eye Movement) sleep stage 2 and severity of OSA. Conclusion: The study yielded a simple and feasible screening ML-based model for the early identification of NT1 in patients with OSA, which warrants further verification in more extensive clinical practices.

Development and validation of a machine learning model for prediction of comorbid major depression disorder among narcolepsy type 1.

BACKGROUND: Major depression disorder (MDD) forms a common psychiatric comorbidity among patients with narcolepsy type 1 (NT1), yet its impact on patients with NT1 is often overlooked by neurologists. Currently, there is a lack of effective methods for accurately predicting MDD in patients with NT1. OBJECTIVE: This study utilized machine learning (ML) algorithms to identify critical variables and developed the prediction model for predicting MDD in patients with NT1. METHODS: The study included 267 NT1 patients from four sleep centers. The diagnosis of comorbid MDD was based on Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5). ML models, including six full models and six compact models, were developed using a training set. The performance of these models was compared in the testing set, and the optimal model was evaluated in the testing set. Various evaluation metrics, such as Area under the receiver operating curve (AUC), precision-recall (PR) curve and calibration curve were employed to assess and compare the performance of the ML models. Model interpretability was demonstrated using SHAP. RESULT: In the testing set, the logistic regression (LG) model demonstrated superior performance compared to other ML models based on evaluation metrics such as AUC, PR curve, and calibration curve. The top eight features used in the LG model, ranked by feature importance, included social impact scale (SIS) score, narcolepsy severity scale (NSS) score, total sleep time, body mass index (BMI), education years, age of onset, sleep efficiency, sleep latency. CONCLUSION: The study yielded a straightforward and practical ML model for the early identification of MDD in patients with NT1. A web-based tool for clinical applications was developed, which deserves further verification in diverse clinical settings.

Novel compound heterozygous mutation and phenotype in the tetratricopeptide repeat-like domain of the GEMIN5 gene in two Chinese families.

BACKGROUND: GEMIN5 is an RNA-binding protein that regulates multiple molecular functions, including splicing, localisation, translation, and mRNA stability. GEMIN5 mutations present a syndrome centred on cerebellar dysplasia, including motor dysfunction, developmental delay, cerebellar atrophy, and hypotonia. CASES: We report three patients from two families with novel compound heterozygous mutations in the tetratricopeptide repeat-like domain of the GEMIN5 gene who presented with motor dysfunction, developmental delay, and ataxia syndrome. Novel variants were identified: c.2551_c.2552delCT (Leu851Glufs*30) and c.2911 C > G (Gln971Glu) in Family 1, and c.3287 T > C (Leu1096Pro) and c.2882 G > C (Trp961 Ser) in Family 2, which were inherited from their parents. Moreover, infantile spasms syndrome(ISs) was diagnosed in the family. CONCLUSION: We report the first case of ISs caused by GEMIN5 gene mutations. Our cases expand on GEMIN5 variants and neurological phenotypes, reinforcing the crucial impact of tetratricopeptide repeat-like domain variants in the GEMIN5 gene.

Synthesis and biological activity of organotin 4-methyl-1,2,3-thiadiazole-5-carboxylates and benzo[1,2,3]thiadiazole-7-carboxylates.

A series of organotin 4-methyl-1,2,3-thiadiazole-5-carboxylates and benzo[1,2,3]thiadiazole-7-carboxylates have been synthesized and characterized by NMR ((1)H, (13)C, and (119)Sn), IR, and elemental analyses. The structure of the dimeric complex {[(BTHCO(2))SnEt(2)](2)O}(2) (BTH represents benzo[1,2,3]thiadiazol-7-yl) has been further confirmed by X-ray diffraction crystallography. Assessment for fungicidal activity indicates that all of the newly synthesized compounds exhibit good growth inhibition against Alternaria solani , Cercospora arachidicola , Gibberella zeae , Physalospora piricola , and Botrytis cinerea . High growth inhibition percentage at 50 microg/mL was obtained in vitro in the case of triorganotin 4-methyl-1,2,3-thiadiazole-5-carboxylates and benzo[1,2,3]thiadiazole-7-carboxylates. The corresponding EC(50) values of these triorganotin carboxylates have been detected, and values of EC(50) as low as 0.12 microg/mL against P. piricola and 0.16 microg/mL against G. zeae, respectively, were observed for triethyltin benzo[1,2,3]thiadiazole-7-carboxylate.