-
Sleep and Biological Rhythms Oct 2022The objectives of this study were to describe prevalence, incidence, and medications among patients who were diagnosed with narcolepsy in Japan using a claims database....
UNLABELLED
The objectives of this study were to describe prevalence, incidence, and medications among patients who were diagnosed with narcolepsy in Japan using a claims database. Patients diagnosed with narcolepsy were identified from January 2010 to December 2019 using an employment-based health insurance claims database compiled by JMDC Inc. The prevalence and incidence of narcolepsy were estimated annually in the overall population and by age and sex among employees and their dependents aged < 75 years. Medications, examined for each quarter in the overall population, were modafinil, methylphenidate, pemoline, tricyclic antidepressants, selective serotonin reuptake inhibitors, and serotonin-norepinephrine reuptake inhibitors. We identified 1539 patients with narcolepsy. The overall annual prevalence increased from 5.7 to 18.5/100,000 persons in 2010 and 2019, respectively. Large increases were found from 2010 to 2019 in patients aged 20-29 years and 10-19 years, with the highest prevalence in 2019 (9.7-37.5/100,000 persons and 5.0-27.1/100,000 persons). The overall incidence slightly increased from 3.6 to 4.3/100,000 person-year from 2010 to 2019, and the highest incidence was found in patients aged 20-29 years and 10-19 years (5.8-11.3/100,000 person-year, and 3.8-7.4/100,000 person-year from 2010 to 2019, respectively). Methylphenidate and modafinil were commonly prescribed in 2010 (27.3-38.9% and 17.5-45.5%, respectively). Methylphenidate prescriptions declined during the 10 years, whereas modafinil prescriptions increased (15.6-17.1% and 43.8-45.8% in 2019, respectively). The estimated prevalence and incidence of narcolepsy appeared to increase from 2010 to 2019, especially in teenagers and 20-year olds.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s41105-022-00406-4.
PubMed: 38468628
DOI: 10.1007/s41105-022-00406-4 -
PeerJ 2024Various segmentation networks based on Swin Transformer have shown promise in medical segmentation tasks. Nonetheless, challenges such as lower accuracy and slower...
Various segmentation networks based on Swin Transformer have shown promise in medical segmentation tasks. Nonetheless, challenges such as lower accuracy and slower training convergence have persisted. To tackle these issues, we introduce a novel approach that combines the Swin Transformer and Deformable Transformer to enhance overall model performance. We leverage the Swin Transformer's window attention mechanism to capture local feature information and employ the Deformable Transformer to adjust sampling positions dynamically, accelerating model convergence and aligning it more closely with object shapes and sizes. By amalgamating both Transformer modules and incorporating additional skip connections to minimize information loss, our proposed model excels at rapidly and accurately segmenting CT or X-ray lung images. Experimental results demonstrate the remarkable, showcasing the significant prowess of our model. It surpasses the performance of the standalone Swin Transformer's Swin Unet and converges more rapidly under identical conditions, yielding accuracy improvements of 0.7% (resulting in 88.18%) and 2.7% (resulting in 98.01%) on the COVID-19 CT scan lesion segmentation dataset and Chest X-ray Masks and Labels dataset, respectively. This advancement has the potential to aid medical practitioners in early diagnosis and treatment decision-making.
Topics: Humans; COVID-19; Electric Power Supplies; Health Personnel; Pemoline; Thorax
PubMed: 38435997
DOI: 10.7717/peerj.17005 -
The Journal of Clinical Psychiatry Oct 2022The cognitive adverse effects (AEs) of electroconvulsive therapy (ECT) limit the wider use of the treatment. These AEs can be attenuated by changing the way ECT is... (Meta-Analysis)
Meta-Analysis
The cognitive adverse effects (AEs) of electroconvulsive therapy (ECT) limit the wider use of the treatment. These AEs can be attenuated by changing the way ECT is administered; however, such changes may reduce the response rate, the speed of response, or both. A recent systematic review and meta-analysis identified more than a dozen pharmacologic interventions in 26 randomized controlled trials (RCTs) that sought to reduce ECT-induced cognitive AEs. Because of large differences across RCTs, only a few outcomes for a few interventions could be pooled in meta-analysis, and most pooled analyses included only 2-3 RCTs. Important findings were that acetylcholinesterase inhibitors, ketamine, memantine, and liothyronine were associated with improved global cognitive functioning at 1-14 days post-ECT. Anti-inflammatory treatments and opioid receptor antagonists were not associated with improvement in general cognitive outcome at 1-14 days post-ECT. Meta-analysis was not possible for the remaining interventions, including piracetam, melatonin, pemoline, nortriptyline, herbal agents, drugs acting on the cortisol pathway, opioid receptor antagonists, l-tryptophan, vasopressin analogs, calcium channel blockers, and others; in individual RCTs, some of these interventions attenuated some cognitive measures as some time points after ECT. Regrettably, none of the RCTs examined clinically meaningful outcomes such as subjective cognitive impairment, impairments in daily life, and persistent autobiographical memory deficits. Future research should study such clinically meaningful outcomes (rather than laboratory tests), using pharmacologic interventions, perhaps in combination, for ECT procedures that are associated with higher cognitive AE burden. A risk is that whatever attenuates ECT-induced cognitive AEs may also attenuate ECT-related therapeutic benefits.
Topics: Calcium Channel Blockers; Cholinesterase Inhibitors; Cognition; Electroconvulsive Therapy; Humans; Hydrocortisone; Ketamine; Melatonin; Memantine; Narcotic Antagonists; Nortriptyline; Pemoline; Piracetam; Treatment Outcome; Triiodothyronine; Tryptophan
PubMed: 36198062
DOI: 10.4088/JCP.22f14668