Biomarkers for disease activity are lacking in chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). We aimed to investigate whether motor nerve conduction studies (NCSs) and nerve ultrasound and their follow-up changes could predict steroid-dependency and treatment refractoriness. Sixty-three CIDP patients were followed up with both nerve ultrasound and NCS. Cross-sectional areas (CSAs) were measured on the bilateral median, ulnar nerves and brachial plexus. NCSs were performed on the median and ulnar nerves. Patients with normal or mildly slow MCV at the first visit were less likely to be steroid-dependent and had lower INCAT at the last follow-up (median 0 [0,1]), whereas those with dramatically slow MCV were more likely to be steroid-dependent and had higher INCAT at the last follow-up (median 2[2,2]) (p = 0.009 for steroid dependent, p = 0.004 for INCAT). None of the patients whose MCV improved above the lower normal limit were steroid-dependent, whereas nearly half of those whose MCV decreased or remained unchanged were steroid-dependent (p = 0.005). A two-step method had a sensitivity of 85% and specificity of 80% for distinguishing patients with steroid dependency. First, we divided patients into three groups according to the MCV change. Second, we explored the trend of steroid-dependent and treatment-refractory based on the CSA at admission and change in CSA. For patients whose MCV improved beyond the threshold, the risk of relapse was low, and we suggest more rapid tapering of steroid. For those with decreased MCV, the risk of relapse was greater and slower steroid tapering or immunosuppressant use is suggested.

This dataset contains annotated interictal epileptiform discharge (IED) from 84 patients (Peking Union Medical College Hospital, China), each contributing 20 minutes of continuous raw EEG recordings, using MAT format. The IEDs are categorized into five types based on occurrence regions. The states of consciousness (wake/sleep) are annotated.
Note on version 2:The dataset has been updated. Specifically, a total of 37 annotations (31 deletions and 6 updates) were adjusted to ensure accuracy and consistency for analysis. These annotations were modified due to their atypical characteristics, differing from conventional interictal epileptiform discharges (IEDs). This refined dataset represents the final version used for training and validation in our associated paper entitled “An EEG dataset for interictal epileptiform discharges with spatial distribution information”.
The specific changes of annotations are listed as follow:MAT_Files:DA00103C.mat  Delete:['305.846', '0', '!']DA00100Z.mat  Delete:['142.686', '0', '!']DA00102T.mat  Delete:['213.124', '0', '!'], ['388.27', '0', '!'], Update:['213.78', '0', '!end'] -> ['211.78', '0', '!end']DA00102W.mat  Delete:['48.274', '0', '!'], ['438.406', '0', '!'], ['516.94', '0', '!'], ['576.554', '0', '!']DA00102Y.mat  Delete:['605.436', '0', '!']DA00103B.mat  Delete:['1173.344', '0', '!']DA00103I.mat  Update:['1128.746', '0', '!end'] -> ['1127.746', '0', '!end']DA00103K.mat  Delete:['485.026', '0', '!']DA00103M.mat  Delete:['45.006', '0', '!'], ['76.166', '0', '!'], ['108.226', '0', '!'], ['189.608', '0', '!'], ['537.642', '0', '!']DA00103N.mat  Update:['1196.27', '0', '!end'] -> ['1195.27', '0', '!end']DA00103Q.mat  Delete:['696.692', '0', '!'], ['1213.206', '0', '!'], Update:['632.1', '0', '!'] -> ['632.2', '0', '!']DA00103U.mat  Delete:['1076.12', '0', '!'], ['1208.146', '0', '!'], ['1210.474', '0', '!'], ['1211.242', '0', '!']DA00100S.mat  Delete:['1204.8', '0', '!']DA00103O.mat  Delete:['12.542', '0', '!']DA00103S.mat  Delete:['1173.22', '0', '!']DA001010.mat  Delete:['1185.496', '0', '!']DA001031.mat  Delete:['0.684', '0', '!'], ['222.106', '0', '!']DA00103E.mat  Delete:['862.716', '0', '!']DA00100V.mat  Delete:['768.154', '0', '!']，['768.532', '0', '!']DA00102R.mat  Update:['552.244', '0', '!end'] →['551.244', '0', '!end']，['704.172', '0', '!end'] →['703.172', '0', '!end']
The changes in MAT_Files result in alterations in the npy_files: DA00103C_152000_154000_500__5.npy(Occipital-IED) -> DA00103C_152000_154000_500__0.npy(Non-IED)DA00100Z_70000_72000_500__2.npy(Frontal-IED) -> DA00100Z_70000_72000_500__0.npy (Non-IED)DA00102W_24000_26000_500__3.npy(Temporal-IED) -> DA00102W_24000_26000_500__0.npy (Non-IED)DA00102W_218000_220000_500__3.npy(Temporal-IED) -> DA00102W_218000_220000_500__0.npy (Non-IED)DA00102W_258000_260000_500__3.npy(Temporal-IED) -> DA00102W_258000_260000_500__0.npy(Non-IED)DA00102Y_302000_304000_500__4.npy(Centro-Parietal-IED) -> DA00102Y_302000_304000_500__0.npy(Non-IED)DA00103M_22000_24000_500__2.npy(Frontal-IED) -> DA00103M_22000_24000_500__0.npy(Non-IED)DA00103M_94000_96000_500__2.npy(Frontal-IED) -> DA00103M_94000_96000_500__0.npy(Non-IED)DA00103M_268000_270000_500__2.npy(Frontal-IED) -> DA00103M_268000_270000_500__0.npy(Non-IED)DA00103U_604000_606000_500__3.npy(Temporal-IED) -> DA00103U_604000_606000_500__0.npy(Non-IED)DA00103C_170000_172000_500__0.npy(Non-IED) -> DA00103C_170000_172000_500__5.npy(Occipital-IED)DA00100Z_0_2000_500__0.npy(Non-IED) -> DA00100Z_0_2000_500__2.npy(Frontal-IED)DA00102W_124000_126000_500__0.npy(Non-IED) -> DA00102W_124000_126000_500__3.npy(Temporal-IED)DA00102W_242000_244000_500__0.npy(Non-IED) -> DA00102W_242000_244000_500__3.npy(Temporal-IED)DA00102W_268000_270000_500__0.npy(Non-IED) -> DA00102W_268000_270000_500__3.npy(Temporal-IED)DA00102Y_612000_614000_500__0.npy(Non-IED) -> DA00102Y_612000_614000_500__4.npy(Centro-Parietal-IED)DA00103M_30000_32000_500__0.npy(Non-IED) -> DA00103M_30000_32000_500__2.npy(Frontal-IED)DA00103M_96000_98000_500__0.npy(Non-IED) -> DA00103M_96000_98000_500__2.npy(Frontal-IED)DA00103M_568000_570000_500__0.npy(Non-IED) -> DA00103M_568000_570000_500__2.npy(Frontal-IED)DA00103U_606000_607500_500__0.npy(Non-IED) -> DA00103U_606000_607500_500__3.npy(Temporal-IED)

This dataset contains annotated interictal epileptiform discharge (IED) from 84 patients (Peking Union Medical College Hospital, China), each contributing 20 minutes of continuous raw EEG recordings, using MAT format. The IEDs are categorized into five types based on occurrence regions. The states of consciousness (wake/sleep) are annotated.

This dataset contains annotated interictal epileptiform discharge (IED) from 84 patients (Peking Union Medical College Hospital, China), each contributing 20 minutes of continuous raw EEG recordings, using MAT format. The IEDs are categorized into five types based on occurrence regions. The states of consciousness (wake/sleep) are annotated.
Note on version 2:The dataset has been updated. Specifically, a total of 37 annotations (31 deletions and 6 updates) were adjusted to ensure accuracy and consistency for analysis. These annotations were modified due to their atypical characteristics, differing from conventional interictal epileptiform discharges (IEDs). This refined dataset represents the final version used for training and validation in our associated paper entitled “An EEG dataset for interictal epileptiform discharges with spatial distribution information”.
The specific changes of annotations are listed as follow:MAT_Files:DA00103C.mat  Delete:['305.846', '0', '!']DA00100Z.mat  Delete:['142.686', '0', '!']DA00102T.mat  Delete:['213.124', '0', '!'], ['388.27', '0', '!'], Update:['213.78', '0', '!end'] -> ['211.78', '0', '!end']DA00102W.mat  Delete:['48.274', '0', '!'], ['438.406', '0', '!'], ['516.94', '0', '!'], ['576.554', '0', '!']DA00102Y.mat  Delete:['605.436', '0', '!']DA00103B.mat  Delete:['1173.344', '0', '!']DA00103I.mat  Update:['1128.746', '0', '!end'] -> ['1127.746', '0', '!end']DA00103K.mat  Delete:['485.026', '0', '!']DA00103M.mat  Delete:['45.006', '0', '!'], ['76.166', '0', '!'], ['108.226', '0', '!'], ['189.608', '0', '!'], ['537.642', '0', '!']DA00103N.mat  Update:['1196.27', '0', '!end'] -> ['1195.27', '0', '!end']DA00103Q.mat  Delete:['696.692', '0', '!'], ['1213.206', '0', '!'], Update:['632.1', '0', '!'] -> ['632.2', '0', '!']DA00103U.mat  Delete:['1076.12', '0', '!'], ['1208.146', '0', '!'], ['1210.474', '0', '!'], ['1211.242', '0', '!']DA00100S.mat  Delete:['1204.8', '0', '!']DA00103O.mat  Delete:['12.542', '0', '!']DA00103S.mat  Delete:['1173.22', '0', '!']DA001010.mat  Delete:['1185.496', '0', '!']DA001031.mat  Delete:['0.684', '0', '!'], ['222.106', '0', '!']DA00103E.mat  Delete:['862.716', '0', '!']DA00100V.mat  Delete:['768.154', '0', '!']，['768.532', '0', '!']DA00102R.mat  Update:['552.244', '0', '!end'] →['551.244', '0', '!end']，['704.172', '0', '!end'] →['703.172', '0', '!end']
The changes in MAT_Files result in alterations in the npy_files: DA00103C_152000_154000_500__5.npy(Occipital-IED) -> DA00103C_152000_154000_500__0.npy(Non-IED)DA00100Z_70000_72000_500__2.npy(Frontal-IED) -> DA00100Z_70000_72000_500__0.npy (Non-IED)DA00102W_24000_26000_500__3.npy(Temporal-IED) -> DA00102W_24000_26000_500__0.npy (Non-IED)DA00102W_218000_220000_500__3.npy(Temporal-IED) -> DA00102W_218000_220000_500__0.npy (Non-IED)DA00102W_258000_260000_500__3.npy(Temporal-IED) -> DA00102W_258000_260000_500__0.npy(Non-IED)DA00102Y_302000_304000_500__4.npy(Centro-Parietal-IED) -> DA00102Y_302000_304000_500__0.npy(Non-IED)DA00103M_22000_24000_500__2.npy(Frontal-IED) -> DA00103M_22000_24000_500__0.npy(Non-IED)DA00103M_94000_96000_500__2.npy(Frontal-IED) -> DA00103M_94000_96000_500__0.npy(Non-IED)DA00103M_268000_270000_500__2.npy(Frontal-IED) -> DA00103M_268000_270000_500__0.npy(Non-IED)DA00103U_604000_606000_500__3.npy(Temporal-IED) -> DA00103U_604000_606000_500__0.npy(Non-IED)DA00103C_170000_172000_500__0.npy(Non-IED) -> DA00103C_170000_172000_500__5.npy(Occipital-IED)DA00100Z_0_2000_500__0.npy(Non-IED) -> DA00100Z_0_2000_500__2.npy(Frontal-IED)DA00102W_124000_126000_500__0.npy(Non-IED) -> DA00102W_124000_126000_500__3.npy(Temporal-IED)DA00102W_242000_244000_500__0.npy(Non-IED) -> DA00102W_242000_244000_500__3.npy(Temporal-IED)DA00102W_268000_270000_500__0.npy(Non-IED) -> DA00102W_268000_270000_500__3.npy(Temporal-IED)DA00102Y_612000_614000_500__0.npy(Non-IED) -> DA00102Y_612000_614000_500__4.npy(Centro-Parietal-IED)DA00103M_30000_32000_500__0.npy(Non-IED) -> DA00103M_30000_32000_500__2.npy(Frontal-IED)DA00103M_96000_98000_500__0.npy(Non-IED) -> DA00103M_96000_98000_500__2.npy(Frontal-IED)DA00103M_568000_570000_500__0.npy(Non-IED) -> DA00103M_568000_570000_500__2.npy(Frontal-IED)DA00103U_606000_607500_500__0.npy(Non-IED) -> DA00103U_606000_607500_500__3.npy(Temporal-IED)

Biomarkers for disease activity are lacking in chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). We aimed to investigate whether motor nerve conduction studies (NCSs) and nerve ultrasound and their follow-up changes could predict steroid-dependency and treatment refractoriness. Sixty-three CIDP patients were followed up with both nerve ultrasound and NCS. Cross-sectional areas (CSAs) were measured on the bilateral median, ulnar nerves and brachial plexus. NCSs were performed on the median and ulnar nerves. Patients with normal or mildly slow MCV at the first visit were less likely to be steroid-dependent and had lower INCAT at the last follow-up (median 0 [0,1]), whereas those with dramatically slow MCV were more likely to be steroid-dependent and had higher INCAT at the last follow-up (median 2[2,2]) (p = 0.009 for steroid dependent, p = 0.004 for INCAT). None of the patients whose MCV improved above the lower normal limit were steroid-dependent, whereas nearly half of those whose MCV decreased or remained unchanged were steroid-dependent (p = 0.005). A two-step method had a sensitivity of 85% and specificity of 80% for distinguishing patients with steroid dependency. First, we divided patients into three groups according to the MCV change. Second, we explored the trend of steroid-dependent and treatment-refractory based on the CSA at admission and change in CSA. For patients whose MCV improved beyond the threshold, the risk of relapse was low, and we suggest more rapid tapering of steroid. For those with decreased MCV, the risk of relapse was greater and slower steroid tapering or immunosuppressant use is suggested.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Pathogenic variants in hnRNPA1 have been reported in amyotrophic lateral sclerosis (ALS) patients. However, studies on hnRNPA1 mutant spectrum and pathogenicity of variants were rare. We performed whole exome sequencing of ALS-associated genes and subsequent verification of rare variants in hnRNPA1 in our ALS patients. The hnRNPA1 mutations reported in literature were reviewed and combined with our results to determine the genotype-phenotype relationship. Functional analysis of the novel variant p.G195A was performed in vitro by transfection of mutant hnRNPA1 into 293T cell. Among 207 ALS patients recruited, 3 rare hnRNPA1 variants were identified (mutant frequency 1.45%), including two recurrent mutations (p.P340S and p.G283R), and a novel rare variant p.G195A. In combination with previous reports, there are 27 ALS patients with 15 hnRNPA1 mutations identified. Disease onset age was 47.90 ± 1.52 years with predominant limb onset. The p.P340S mutation caused flail arm syndrome (FAS) in two independent families with extended life expectancy. The newly identified p.G195A mutation, lying at the start of the PrLD (“prion-like” domain)/LCD (low-complexity domain), causes local structural changes in 3D protein prediction. Upon sodium arsenite exposure, mutant hnRNPA1 retained in the nucleus but deficit of cytoplasmic G3BP1-positive stress granule clearance was observed. This is different from the p.P340S mutation which caused both cytoplasmic translocation and stress granule formation. No cytoplasmic TDP-43 translocation was observed. Mutations in hnRNPA1 are overall minor in ALS patients. The p.P340S mutation is associated with manifestation of FAS. Mutations in LCD of hnRNPA1 cause stress granule misprocessing.

Pathogenic variants in hnRNPA1 have been reported in amyotrophic lateral sclerosis (ALS) patients. However, studies on hnRNPA1 mutant spectrum and pathogenicity of variants were rare. We performed whole exome sequencing of ALS-associated genes and subsequent verification of rare variants in hnRNPA1 in our ALS patients. The hnRNPA1 mutations reported in literature were reviewed and combined with our results to determine the genotype-phenotype relationship. Functional analysis of the novel variant p.G195A was performed in vitro by transfection of mutant hnRNPA1 into 293T cell. Among 207 ALS patients recruited, 3 rare hnRNPA1 variants were identified (mutant frequency 1.45%), including two recurrent mutations (p.P340S and p.G283R), and a novel rare variant p.G195A. In combination with previous reports, there are 27 ALS patients with 15 hnRNPA1 mutations identified. Disease onset age was 47.90 ± 1.52 years with predominant limb onset. The p.P340S mutation caused flail arm syndrome (FAS) in two independent families with extended life expectancy. The newly identified p.G195A mutation, lying at the start of the PrLD (“prion-like” domain)/LCD (low-complexity domain), causes local structural changes in 3D protein prediction. Upon sodium arsenite exposure, mutant hnRNPA1 retained in the nucleus but deficit of cytoplasmic G3BP1-positive stress granule clearance was observed. This is different from the p.P340S mutation which caused both cytoplasmic translocation and stress granule formation. No cytoplasmic TDP-43 translocation was observed. Mutations in hnRNPA1 are overall minor in ALS patients. The p.P340S mutation is associated with manifestation of FAS. Mutations in LCD of hnRNPA1 cause stress granule misprocessing.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.