{"results":[{"id":"ss_25617348d0c14a81c5cf292eec5e991d0322e607","title":"Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021","authors":[{"name":"Jaimie D Steinmetz"},{"name":"Katrin Seeher"},{"name":"N. Schiess"},{"name":"Emma Nichols"},{"name":"B. Cao"},{"name":"Chiara Servili"},{"name":"Vanessa Cavallera"},{"name":"Ewerton Cousin"},{"name":"Hailey Hagins"},{"name":"Madeline E Moberg"},{"name":"Max L Mehlman"},{"name":"Yohannes Abate"},{"name":"Jaffar Abbas"},{"name":"M. Abbasi"},{"name":"Mohammadreza Abbasian"},{"name":"H. Abbastabar"},{"name":"Michael Abdelmasseh"},{"name":"Mohammad Abdollahi"},{"name":"Mozhan Abdollahi"},{"name":"M. Abdollahifar"},{"name":"Rami Abd-Rabu"},{"name":"Deldar Morad Abdulah"},{"name":"Auwal Abdullahi"},{"name":"Aidin Abedi"},{"name":"Vida Abedi"},{"name":"Roberto Ariel Abeldaño Zuñiga"},{"name":"Hassan Abidi"},{"name":"Olumide Abiodun"},{"name":"R. Aboagye"},{"name":"Hassan Abolhassani"},{"name":"Victor Aboyans"},{"name":"Woldu Aberhe Abrha"},{"name":"Ahmed Abualhasan"},{"name":"Eman Abu-Gharbieh"},{"name":"Salahdein Aburuz"},{"name":"L. Adamu"},{"name":"Isaac Yeboah Addo"},{"name":"O. Adebayo"},{"name":"Victor Adekanmbi"},{"name":"Tayo Alex Adekiya"},{"name":"W. Adikusuma"},{"name":"Q. Adnani"},{"name":"Saryia Adra"},{"name":"Tsion Afework"},{"name":"A. Afolabi"},{"name":"Ali Afraz"},{"name":"Saira Afzal"},{"name":"Shahin Aghamiri"},{"name":"A. Agodi"},{"name":"Williams Agyemang-Duah"},{"name":"B. Ahinkorah"},{"name":"Aqeel Ahmad"},{"name":"Danish Ahmad"},{"name":"Sajjad Ahmad"},{"name":"A. M. Ahmadzade"},{"name":"Ali Ahmed"},{"name":"Ayman Ahmed"},{"name":"H. Ahmed"},{"name":"J. Ahmed"},{"name":"Luai A. Ahmed"},{"name":"M. Ahmed"},{"name":"Syed Anees Ahmed"},{"name":"Marjan Ajami"},{"name":"Budi Aji"},{"name":"O. Ajumobi"},{"name":"Seyed Esma'il Akade"},{"name":"Morteza Akbari"},{"name":"Hossein Akbarialiabad"},{"name":"Shiva Akhlaghi"},{"name":"K. Akinosoglou"},{"name":"R. Akinyemi"},{"name":"Maxwell Akonde"},{"name":"S. M. Hasan"},{"name":"Fares Alahdab"},{"name":"T. AL-Ahdal"},{"name":"Rasmieh M. Al-amer"},{"name":"M. Albashtawy"},{"name":"Mohammad T. Albataineh"},{"name":"Khalifah A. Aldawsari"},{"name":"Hediyeh Alemi"},{"name":"Sharifullah Alemi"},{"name":"A. Algammal"},{"name":"A. Al-Gheethi"},{"name":"F. Alhalaiqa"},{"name":"R. Alhassan"},{"name":"Abid Ali"},{"name":"Endale Alemayehu Ali"},{"name":"Liaqat Ali"},{"name":"M. Ali"},{"name":"Musa Ali"},{"name":"Rafat Ali"},{"name":"Shahid Ali"},{"name":"Syed Shujait Shujait Ali"},{"name":"Zahid Ali"},{"name":"S. M. Alif"},{"name":"Y. Alimohamadi"},{"name":"Ahmednur Adem Aliyi"},{"name":"M. Aljofan"},{"name":"S. Aljunid"},{"name":"Suvarna Alladi"},{"name":"Joseph Almazan"},{"name":"Sami A. Almustanyir"},{"name":"B. Al-Omari"},{"name":"J. Alqahtani"},{"name":"Ibrahim Alqasmi"},{"name":"A. Alqutaibi"},{"name":"R. Salman"},{"name":"Z. Altaany"},{"name":"J. Al-Tawfiq"},{"name":"K. Altirkawi"},{"name":"N. Alvis-Guzmán"},{"name":"Y. Al-Worafi"},{"name":"Hany Aly"},{"name":"Safwat Aly"},{"name":"K. Alzoubi"},{"name":"Reza Amani"},{"name":"Alireza Amindarolzarbi"},{"name":"Sohrab Amiri"},{"name":"Mohammad Hosein Amirzade-Iranaq"},{"name":"Hubert Amu"},{"name":"D. Amugsi"},{"name":"G. Amusa"},{"name":"J. Amzat"},{"name":"R. Ancuceanu"},{"name":"D. Anderlini"},{"name":"David B Anderson"},{"name":"C. Andrei"},{"name":"S. Androudi"},{"name":"Dhanalakshmi Angappan"},{"name":"Teklit Angesom"},{"name":"Abhishek Anil"},{"name":"A. Ansari-Moghaddam"},{"name":"R. Anwer"},{"name":"M. Arafat"},{"name":"Aleksandr Y. Aravkin"},{"name":"D. Areda"},{"name":"Hany Ariffin"},{"name":"Hidayat Arifin"},{"name":"Mesay Arkew"},{"name":"Johan Ärnlöv"},{"name":"Mahwish Arooj"},{"name":"A. Artamonov"},{"name":"K. D. Artanti"},{"name":"R. T. Aruleba"},{"name":"A. Asadi-Pooya"},{"name":"Tilahun Ferede Asena"},{"name":"M. Asghari-Jafarabadi"},{"name":"M. Ashraf"},{"name":"Tahira Ashraf"},{"name":"Kendalem Asmare Atalell"},{"name":"S. Athari"},{"name":"Bantalem Tilaye Tilaye Atinafu"},{"name":"P. Atorkey"},{"name":"M. Atout"},{"name":"A. Atreya"},{"name":"A. Aujayeb"},{"name":"Abolfazl Avan"},{"name":"B. Quintanilla"},{"name":"H. Ayatollahi"},{"name":"O. Ayinde"},{"name":"S. M. Ayyoubzadeh"},{"name":"S. Azadnajafabad"},{"name":"Z. Azizi"},{"name":"K. Azizian"},{"name":"A. Azzam"},{"name":"Mahsa Babaei"},{"name":"Muhammad Badar"},{"name":"A. Badiye"},{"name":"Soroush Baghdadi"},{"name":"Sara Bagherieh"},{"name":"Ruhai Bai"},{"name":"A. Baig"},{"name":"Senthilkumar Balakrishnan"},{"name":"Shivanthi K. Balalla"},{"name":"Ovidiu Constantin Baltatu"},{"name":"Maciej Banach"},{"name":"Soham Bandyopadhyay"},{"name":"Indrajit Banerjee"},{"name":"Mehmet Fırat Baran"},{"name":"Miguel A Barboza"},{"name":"M. Barchitta"},{"name":"Mainak Bardhan"},{"name":"S. Barker-Collo"},{"name":"T. Bärnighausen"},{"name":"Amadou Barrow"},{"name":"Davood Bashash"},{"name":"Hamideh Bashiri"},{"name":"H. Bashiru"},{"name":"A. Basiru"},{"name":"João Diogo Basso"},{"name":"Sanjay Basu"},{"name":"A. Batiha"},{"name":"Kavita Batra"},{"name":"B. Baune"},{"name":"Neeraj Bedi"},{"name":"Ahmet Begde"},{"name":"Tahmina Begum"},{"name":"Babak Behnam"},{"name":"A. Behnoush"},{"name":"Maryam Beiranvand"},{"name":"Y. Béjot"},{"name":"A. Bekele"},{"name":"M. A. Belete"},{"name":"U. Belgaumi"},{"name":"Maryam Bemanalizadeh"},{"name":"Rose G. Bender"},{"name":"Bright Benfor"},{"name":"Derrick A. Bennett"},{"name":"I. Benseñor"},{"name":"Betyna N Berice"},{"name":"P. J. Bettencourt"},{"name":"Kebede A. Beyene"},{"name":"Abhishek Bhadra"},{"name":"D. Bhagat"},{"name":"K. Bhangdia"},{"name":"Nikha Bhardwaj"},{"name":"Pankaj Bhardwaj"},{"name":"A. Bhargava"},{"name":"Sonu M M Bhaskar"},{"name":"A. Bhat"},{"name":"Vivek Bhat"},{"name":"G. K. Bhatti"},{"name":"Jasvinder Singh Bhatti"},{"name":"Rajbir Bhatti"},{"name":"A. Bijani"},{"name":"B. Bikbov"},{"name":"M. Bilalaga"},{"name":"Atanu Biswas"},{"name":"Saeid Bitaraf"},{"name":"V. Bitra"},{"name":"T. Bjørge"},{"name":"V. Bodolica"},{"name":"Aadam Olalekan Bodunrin"},{"name":"A. Boloor"},{"name":"Dejana Braithwaite"},{"name":"Carol Brayne"},{"name":"H. Brenner"},{"name":"A. Briko"},{"name":"M. Vega"},{"name":"Julie Brown"},{"name":"C. Budke"},{"name":"Danilo Buonsenso"},{"name":"Katrin Burkart"},{"name":"Richard A. Burns"},{"name":"Yasser K. Bustanji"},{"name":"M. H. Butt"},{"name":"Nadeem Shafique Butt"},{"name":"Z. Butt"},{"name":"Lucas Scotta Cabral"},{"name":"F. C. Santos"},{"name":"D. Calina"},{"name":"Ismael Campos-Nonato"},{"name":"Chao Cao"},{"name":"Hélène Carabin"},{"name":"Rosario Cárdenas"},{"name":"G. Carreras"},{"name":"Andre F Carvalho"},{"name":"C. Castañeda-Orjuela"},{"name":"Adriano Casulli"},{"name":"F. Catalá-López"},{"name":"A. Catapano"},{"name":"A. Caye"},{"name":"L. Cegolon"},{"name":"Muthia Cenderadewi"},{"name":"Ester Cerin"},{"name":"P. R. Chacón-Uscamaita"},{"name":"Jeffrey Shi Kai Chan"},{"name":"Gashaw Sisay Chanie"},{"name":"J. Charan"},{"name":"Vijay Kumar Chattu"},{"name":"E. Abebe"},{"name":"Hui Chen"},{"name":"Jianqi Chen"},{"name":"Gerald Chi"},{"name":"Fatemeh Chichagi"},{"name":"S. Chidambaram"},{"name":"Ritesh Chimoriya"},{"name":"Patrick R. Ching"},{"name":"Abdulaal Chitheer"},{"name":"Yuen Yu Chong"},{"name":"Hitesh Chopra"},{"name":"Sonali Gajanan Choudhari"},{"name":"E. Chowdhury"},{"name":"Rajiv Chowdhury"},{"name":"Hanne Christensen"},{"name":"D. Chu"},{"name":"I. Chukwu"},{"name":"Eric Chung"},{"name":"Kaleb Coberly"},{"name":"A. Columbus"},{"name":"J. Comachio"},{"name":"J. Conde"},{"name":"P. Cortesi"},{"name":"V. M. Costa"},{"name":"Rosa A A Couto"},{"name":"M. Criqui"},{"name":"N. Cruz-Martins"},{"name":"M. Ohadi"},{"name":"Sriharsha Dadana"},{"name":"O. Dadras"},{"name":"X. Dai"},{"name":"Zhaoli Dai"},{"name":"Emanuele D’Amico"},{"name":"H. Danawi"},{"name":"L. Dandona"},{"name":"R. Dandona"},{"name":"A. Darwish"},{"name":"Saswati Das"},{"name":"Subasish Das"},{"name":"A. Dascălu"},{"name":"N. Dash"},{"name":"M. Dashti"},{"name":"F. D. L. Hoz"},{"name":"A. Torre-Luque"},{"name":"Diego De Leo"},{"name":"Frances Dean"},{"name":"Amin Dehghan"},{"name":"A. Dehghan"},{"name":"Hiwot Dejene"},{"name":"Daniel Demant"},{"name":"A. K. Demetriades"},{"name":"Solomon Demissie"},{"name":"Xinlei Deng"},{"name":"Hardik D. Desai"},{"name":"Vinoth Gnana Chellaiyan Devanbu"},{"name":"K. Dhama"},{"name":"S. Dharmaratne"},{"name":"M. Dhimal"},{"name":"D. Silva"},{"name":"Daniel Diaz"},{"name":"M. Dibas"},{"name":"Delaney D. Ding"},{"name":"Monica Dinu"},{"name":"M. Dirac"},{"name":"Mengistie Diress"},{"name":"T. Do"},{"name":"Thao Phuong Do"},{"name":"K. Doan"},{"name":"Milad Dodangeh"},{"name":"M. Doheim"},{"name":"K. Dokova"},{"name":"Deepa Dongarwar"},{"name":"Haneil Larson Dsouza"},{"name":"John Dube"},{"name":"Senbagam Duraisamy"},{"name":"O. Durojaiye"},{"name":"S. Dutta"},{"name":"Arkadiusz Marian Dziedzic"},{"name":"H. Edinur"},{"name":"N. Eissazade"},{"name":"Michael Ekholuenetale"},{"name":"T. Ekundayo"},{"name":"N. E. Nahas"},{"name":"I. Sayed"},{"name":"Mohammad Amin Elahi Najafi"},{"name":"I. Elbarazi"},{"name":"N. Elemam"},{"name":"F. Elgar"},{"name":"I. Elgendy"},{"name":"H. Elhabashy"},{"name":"Muhammed Elhadi"},{"name":"Legesse Tesfaye Elilo"},{"name":"R. Ellenbogen"},{"name":"Omar Abdelsadek Abdou Elmeligy"},{"name":"Mohamed A. Elmonem"},{"name":"M. Elshaer"},{"name":"Ibrahim Elsohaby"},{"name":"M. Emamverdi"},{"name":"T. Emeto"},{"name":"Matthias Endres"},{"name":"C. Esezobor"},{"name":"S. Eskandarieh"},{"name":"A. Fadaei"},{"name":"A. Fagbamigbe"},{"name":"A. Fahim"},{"name":"Ali Faramarzi"},{"name":"Jawad Fares"},{"name":"Mohsen Farjoud Kouhanjani"},{"name":"Andre Faro"},{"name":"F. Farzadfar"},{"name":"A. Fatehizadeh"},{"name":"M. Fathi"},{"name":"S. Fathi"},{"name":"Syeda Anum Fatima Fatima"},{"name":"Alireza Feizkhah"},{"name":"S. Fereshtehnejad"},{"name":"A. Ferrari"},{"name":"N. Ferreira"},{"name":"G. Fetensa"},{"name":"Neda Firouraghi"},{"name":"Florian Fischer"},{"name":"Ana Catarina Fonseca"},{"name":"Lisa M. Force"},{"name":"Arianna Fornari"},{"name":"Behzad Foroutan"},{"name":"Takeshi Fukumoto"},{"name":"M. Gadanya"},{"name":"A. Gaidhane"},{"name":"Yaseen Galali"},{"name":"Nasrin Galehdar"},{"name":"Quan Gan"},{"name":"A. Gandhi"},{"name":"B. Ganesan"},{"name":"W. M. Gardner"},{"name":"Naval Garg"},{"name":"Shuo-Yan Gau"},{"name":"R. Gautam"},{"name":"T. Gebre"},{"name":"Mesfin Gebrehiwot"},{"name":"Gebreamlak Gebremedhn Gebremeskel"},{"name":"Haftay Gebremedhin Gebreslassie"},{"name":"Lemma Getacher"},{"name":"B. G. Yazdi"},{"name":"F. Ghadirian"},{"name":"F. Ghaffarpasand"},{"name":"Reza Ghanbari"},{"name":"MohammadReza Ghasemi"},{"name":"R. Ghazy"},{"name":"Sailaja Ghimire"},{"name":"Ali Gholami"},{"name":"Ali Gholamrezanezhad"},{"name":"Elena Ghotbi"},{"name":"S. Ghozy"},{"name":"A. Gialluisi"},{"name":"P. Gill"},{"name":"Logan M. Glasstetter"},{"name":"E. Gnedovskaya"},{"name":"A. Golchin"},{"name":"Mahaveer Golechha"},{"name":"Pouya Goleij"},{"name":"D. Golinelli"},{"name":"M. Gomes-Neto"},{"name":"A. Goulart"},{"name":"Anmol Goyal"},{"name":"Richard Gray"},{"name":"Michale Grivna"},{"name":"Habtamu Alganeh Guadie"},{"name":"Bin Guan"},{"name":"G. Guarducci"},{"name":"S. Guicciardi"},{"name":"D. Gunawardane"},{"name":"Hanbing Guo"},{"name":"Bhawna Gupta"},{"name":"R. Gupta"},{"name":"Sapna Gupta"},{"name":"Veera Gupta"},{"name":"Vivek Gupta"},{"name":"R. A. Gutiérrez"},{"name":"F. Habibzadeh"},{"name":"Vladimir Hachinski"},{"name":"Rasool Haddadi"},{"name":"Mostafa Hadei"},{"name":"Najah R. Hadi"},{"name":"Nils Haep"},{"name":"T. Haile"},{"name":"A. Haj-Mirzaian"},{"name":"Brian J. Hall"},{"name":"R. Halwani"},{"name":"Sajid Hameed"},{"name":"Mohammad Hamiduzzaman"},{"name":"A. Hammoud"},{"name":"Hannah W. Han"},{"name":"Nasrin Hanifi"},{"name":"G. Hankey"},{"name":"M. A. Hannan"},{"name":"Junwei Hao"},{"name":"H. Harapan"},{"name":"Habtamu Endashaw Hareru"},{"name":"A. Hargono"},{"name":"N. I. Harlianto"},{"name":"J. Haro"},{"name":"Nicholas Nathaniel Hartman"},{"name":"Ahmed I Hasaballah"},{"name":"Faizul Hasan"},{"name":"H. Hasani"},{"name":"M. Hasanian"},{"name":"Amr Hassan"},{"name":"Shoaib Hassan"},{"name":"S. Hassanipour"},{"name":"H. Hassankhani"},{"name":"Mohammed Bheser Hassen"},{"name":"Johannes Haubold"},{"name":"S. Hay"},{"name":"Khezar Hayat"},{"name":"M. Hegazy"},{"name":"G. Heidari"},{"name":"Mohammad Heidari"},{"name":"R. Heidari-Soureshjani"},{"name":"Hamed Hesami"},{"name":"Kamal Hezam"},{"name":"Y. Hiraike"},{"name":"Howard J Hoffman"},{"name":"R. Holla"},{"name":"K. Hopf"},{"name":"Nobuyuki Horita"},{"name":"M. Hossain"},{"name":"Md. Belal Hossain"},{"name":"S. Hossain"},{"name":"Hassan Hosseinzadeh"},{"name":"M. Hosseinzadeh"}],"abstract":"Summary Background Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021. Methods We estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined. Findings Globally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer. Interpretation As the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed. Funding Bill \u0026 Melinda Gates Foundation.","source":"Semantic Scholar","year":2024,"language":"en","subjects":["Medicine"],"doi":"10.1016/S1474-4422(24)00038-3","url":"https://www.semanticscholar.org/paper/25617348d0c14a81c5cf292eec5e991d0322e607","pdf_url":"http://www.thelancet.com/article/S1474442224000383/pdf","is_open_access":true,"citations":850,"published_at":"","score":93.5},{"id":"doaj_10.3389/fneur.2026.1724717","title":"Impact of early vs. late tracheostomy on clinical outcomes in mechanically ventilated patients with intracerebral hemorrhage extending into the ventricles: a retrospective cohort study based on quantitative assessment of parenchymal and intraventricular hematoma volumes","authors":[{"name":"Minghui Lu"},{"name":"Jiajun Wei"},{"name":"Qiang Cai"}],"abstract":"BackgroundThe optimal timing for tracheostomy in patients with intracerebral hemorrhage extending into the ventricles who require mechanical ventilation remains controversial, and there is a paucity of evidence to guide clinical practice. This study aimed to elucidate the impact of early vs. late tracheostomy on clinical outcomes and complications in this population, utilizing multivariable models to identify risk factors and define the potential beneficiary population.MethodsThis single-center retrospective cohort study consecutively enrolled 157 patients with severe spontaneous intracerebral hemorrhage extending into the ventricles requiring mechanical ventilation (GCS score ≤8) between January 2020 and December 2023. Based on the timing of tracheostomy, patients were classified into an early group (ET, ≤7 days after mechanical ventilation, n = 81) and a late group (LT, \u0026gt;7 days after mechanical ventilation, n = 76). Baseline characteristics, treatment measures, and outcome data were collected. Hematoma volumes in both the brain parenchyma and ventricles on admission CT scans were precisely quantified using 3D Slicer software. The primary outcome was the 6-month modified Rankin Scale (mRS) score. Secondary outcomes included the duration of mechanical ventilation, ICU length of stay (LOS), and the incidence of short-term complications [ventilator-associated pneumonia (VAP), new-onset arrhythmia, shock, and acute kidney injury (AKI)]. Multivariable logistic regression analysis was employed to identify independent risk factors for complications and to assess the protective effect of early tracheostomy.ResultsIn this cohort of 157 mechanically ventilated patients with severe intraventricular hemorrhage, baseline characteristics were well-balanced between Early (ET, n = 81) and Late Tracheostomy (LT, n = 76) groups. While 6-month functional outcomes (mRS) showed no significant difference (P = 0.360), the ET group demonstrated substantially shorter duration of mechanical ventilation (13 vs. 19 days, P \u0026lt; 0.001) and ICU stay (17 vs. 25 days, P \u0026lt; 0.001). ET was associated with significantly lower incidence of ventilator-associated pneumonia (28.40 vs. 48.68%, P = 0.009), new-onset arrhythmia (18.52 vs. 32.89%, P = 0.039), and shock requiring vasopressors (24.7 vs. 40.79%, P = 0.031). Multivariable analysis identified GCS score \u0026lt;6 (OR 3.588, P = 0.008) and Graeb score ≥8 (OR 8.735, P = 0.037) as independent risk factors for complications, while confirming early tracheostomy as an independent protective factor (aOR 0.306, P = 0.019) after adjustment for confounders.ConclusionIn this single-center retrospective cohort study, early tracheostomy was associated with shorter durations of mechanical ventilation and ICU stay, as well as a lower incidence of major complications, and demonstrates a favorable safety profile. Although it does not improve long-term neurological function, early tracheostomy serves as an independent protective factor. When combined with the identification of risk factors such as GCS \u0026lt;6 and Graeb score ≥8, it provides a basis for individualized treatment. These findings suggest an association that warrants further investigation in prospective studies.","source":"DOAJ","year":2026,"language":"","subjects":["Neurology. Diseases of the nervous system"],"doi":"10.3389/fneur.2026.1724717","url":"https://www.frontiersin.org/articles/10.3389/fneur.2026.1724717/full","is_open_access":true,"published_at":"","score":70},{"id":"ss_d5edbea13317094ca60dedae7da76a02f2a2da8a","title":"Climate change and disorders of the nervous system.","authors":[{"name":"S. Sisodiya"},{"name":"Medine I. Gulcebi"},{"name":"Francesco Fortunato"},{"name":"James D Mills"},{"name":"E. Haynes"},{"name":"E. Bramon"},{"name":"P. Chadwick"},{"name":"O. Ciccarelli"},{"name":"A. S. David"},{"name":"K. De Meyer"},{"name":"Nick C Fox"},{"name":"Joanna Davan Wetton"},{"name":"Martin Koltzenburg"},{"name":"Dimitri M. Kullmann"},{"name":"M. Kurian"},{"name":"Hadi Manji"},{"name":"M. Maslin"},{"name":"Manjit Matharu"},{"name":"Hugh Montgomery"},{"name":"M. Romanello"},{"name":"D. Werring"},{"name":"Lisa Zhang"},{"name":"K. Friston"},{"name":"Michael G Hanna"}],"abstract":"Anthropogenic climate change is affecting people's health, including those with neurological and psychiatric diseases. Currently, making inferences about the effect of climate change on neurological and psychiatric diseases is challenging because of an overall sparsity of data, differing study methods, paucity of detail regarding disease subtypes, little consideration of the effect of individual and population genetics, and widely differing geographical locations with the potential for regional influences. However, evidence suggests that the incidence, prevalence, and severity of many nervous system conditions (eg, stroke, neurological infections, and some mental health disorders) can be affected by climate change. The data show broad and complex adverse effects, especially of temperature extremes to which people are unaccustomed and wide diurnal temperature fluctuations. Protective measures might be possible through local forecasting. Few studies project the future effects of climate change on brain health, hindering policy developments. Robust studies on the threats from changing climate for people who have, or are at risk of developing, disorders of the nervous system are urgently needed.","source":"Semantic Scholar","year":2024,"language":"en","subjects":["Medicine"],"doi":"10.1016/S1474-4422(24)00087-5","url":"https://www.semanticscholar.org/paper/d5edbea13317094ca60dedae7da76a02f2a2da8a","pdf_url":"https://discovery.ucl.ac.uk/10192724/1/Sisodiya_Manuscript_FINAL_clean%20%282%29.pdf","is_open_access":true,"citations":57,"published_at":"","score":69.71000000000001},{"id":"doaj_10.1186/s12888-024-06253-6","title":"The voice of depression: speech features as biomarkers for major depressive disorder","authors":[{"name":"Felix Menne"},{"name":"Felix Dörr"},{"name":"Julia Schräder"},{"name":"Johannes Tröger"},{"name":"Ute Habel"},{"name":"Alexandra König"},{"name":"Lisa Wagels"}],"abstract":"Abstract Background Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patients. This project aimed to identify discriminating speech features between patients with major depressive disorder (MDD) and healthy controls (HCs) by examining associations with symptom severity measures. Methods Forty-four MDD patients from the Psychiatry Department, University Hospital Aachen, Germany and fifty-two HCs were recruited. Participants described positive and negative life events, which were recorded for analysis. The Beck Depression Inventory (BDI-II) and the Hamilton Rating Scale for Depression gauged depression severity. Transcribed audio recordings underwent feature extraction, including acoustics, speech rate, and content. Machine learning models including speech features and neuropsychological assessments, were used to differentiate between the MDD patients and HCs. Results Acoustic variables such as pitch and loudness differed significantly between the MDD patients and HCs (effect sizes 𝜼2 between 0.183 and 0.3, p \u003c 0.001). Furthermore, variables pertaining to temporality, lexical richness, and speech sentiment displayed moderate to high effect sizes (𝜼2 between 0.062 and 0.143, p \u003c 0.02). A support vector machine (SVM) model based on 10 acoustic features showed a high performance (AUC = 0.93) in differentiating between HCs and patients with MDD, comparable to an SVM based on the BDI-II (AUC = 0.99, p = 0.01). Conclusions This study identified robust speech features associated with MDD. A machine learning model based on speech features yielded similar results to an established pen-and-paper depression assessment. In the future, these findings may shape voice-based biomarkers, enhancing clinical diagnosis and MDD monitoring.","source":"DOAJ","year":2024,"language":"","subjects":["Psychiatry"],"doi":"10.1186/s12888-024-06253-6","url":"https://doi.org/10.1186/s12888-024-06253-6","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.1038/s41531-023-00602-0","title":"Resting-state EEG measures cognitive impairment in Parkinson’s disease","authors":[{"name":"Md Fahim Anjum"},{"name":"Arturo I. Espinoza"},{"name":"Rachel C. Cole"},{"name":"Arun Singh"},{"name":"Patrick May"},{"name":"Ergun Y. Uc"},{"name":"Soura Dasgupta"},{"name":"Nandakumar S. Narayanan"}],"abstract":"Abstract Cognitive dysfunction is common in Parkinson’s disease (PD). We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from National Institutes of Health (NIH) Toolbox using cross-validations, regression models, and randomization tests. Finally, we externally validated our approach on 32 PD participants. We observed cognition-related changes in EEG over multiple spectral rhythms. Utilizing only 8 best-performing electrodes, our proposed index strongly correlated with cognition (MoCA: rho = 0.68, p value \u003c 0.001; NIH-Toolbox cognitive tests: rho ≥ 0.56, p value \u003c 0.001) outperforming traditional spectral markers (rho = −0.30–0.37). The index showed a strong fit in regression models (R 2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Notably, our approach was equally effective (rho = 0.68, p value \u003c 0.001; MoCA) in out-of-sample testing. In summary, we introduced a computationally efficient data-driven approach for cross-domain cognition indexing using fewer than 10 EEG electrodes, potentially compatible with dynamic therapies like closed-loop neurostimulation. These results will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.","source":"DOAJ","year":2024,"language":"","subjects":["Neurology. Diseases of the nervous system"],"doi":"10.1038/s41531-023-00602-0","url":"https://doi.org/10.1038/s41531-023-00602-0","is_open_access":true,"published_at":"","score":68},{"id":"ss_8a6555022175b94af566de3afbf6a59049f73d69","title":"Sarcopenia and nervous system disorders","authors":[{"name":"Jie Yang"},{"name":"Feifei Jiang"},{"name":"Ming Yang"},{"name":"Zhizhi Chen"}],"abstract":"","source":"Semantic Scholar","year":2022,"language":"en","subjects":["Medicine"],"doi":"10.1007/s00415-022-11268-8","url":"https://www.semanticscholar.org/paper/8a6555022175b94af566de3afbf6a59049f73d69","is_open_access":true,"citations":61,"published_at":"","score":67.83},{"id":"ss_af95725a05aa827c0d82f2b0fceef6a1c9d45820","title":"Autoimmune Disorders of the Nervous System: Pathophysiology, Clinical Features, and Therapy","authors":[{"name":"S. Bhagavati"}],"abstract":"Remarkable discoveries over the last two decades have elucidated the autoimmune basis of several, previously poorly understood, neurological disorders. Autoimmune disorders of the nervous system may affect any part of the nervous system, including the brain and spinal cord (central nervous system, CNS) and also the peripheral nerves, neuromuscular junction and skeletal muscle (peripheral nervous system, PNS). This comprehensive overview of this rapidly evolving field presents the factors which may trigger breakdown of self-tolerance and development of autoimmune disease in some individuals. Then the pathophysiological basis and clinical features of autoimmune diseases of the nervous system are outlined, with an emphasis on the features which are important to recognize for accurate clinical diagnosis. Finally the latest therapies for autoimmune CNS and PNS disorders and their mechanisms of action and the most promising research avenues for targeted immunotherapy are discussed.","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Medicine"],"doi":"10.3389/fneur.2021.664664","url":"https://www.semanticscholar.org/paper/af95725a05aa827c0d82f2b0fceef6a1c9d45820","pdf_url":"https://www.frontiersin.org/articles/10.3389/fneur.2021.664664/pdf","is_open_access":true,"citations":71,"published_at":"","score":67.13},{"id":"doaj_10.1016/j.xnsj.2023.100261","title":"Impact of preoperative insomnia on poor postoperative pain control after elective spine surgery and the modified Calgary postoperative pain after spine surgery (MCAPPS) score","authors":[{"name":"Michael M.H. Yang, MD, MSc, MBiotech"},{"name":"Jay Riva-Cambrin, MD, MSc"},{"name":"Jonathan Cunningham, MD, MSc"},{"name":"Steven Casha, MD, PhD"}],"abstract":"Background: Approximately 30% to 64% of patients experience inadequate pain control following spine surgery. The Calgary postoperative pain after spine surgery (CAPPS) score was developed to identify this subset of patients. The impact of preoperative insomnia on postoperative pain control is unknown. This study aimed to investigate the relationship between preoperative insomnia and poor pain control after spine surgery, as well as improve the predictive accuracy of the CAPPS score. Methods: A prospective cohort study was conducted in patients undergoing elective spine surgery. Poor pain control was defined as a mean numeric rating scale pain score \u003e4 at rest within the first 24-hours after surgery. Patients were evaluated using the CAPPS score, which included 7 prognostic factors. A multivariable logistic regression model was used to examine the association between preoperative insomnia severity index (ISI) and poor pain control, adjusting for the CAPPS score. The Modified CAPPS score was derived from this model. Results: Of 219 patients, 49.7% experienced poorly controlled pain. Prevalence of clinical insomnia (ISI≥15) was 26.9%. Preoperative ISI was independently associated with poor pain control (odds ratio [OR] 1.09, [95%CI=1.03–1.16], p=.004), after adjusting for the CAPPS score (OR 1.61, [95%CI=1.38–1.89], p\u003c.001). The model exhibited good discrimination (c-statistics 0.80, [95%CI=0.74–0.86]) and calibration (Hosmer-Lemeshow chi-square=8.95, p=.35). The Modified CAPPS score also demonstrated good discrimination (c-statistic 0.78, [95%CI=0.72–0.84]) and calibration (Hosmer-Lemeshow chi-square=2.92, p=.57). Low-, high-, and extreme-risk groups stratified by the Modified CAPPS score had 17.3%, 49.1%, and 80.7% predicted probability of experiencing inadequate pain control compared to 32.0%, 64.0%, and 85.1% in the CAPPS score. Conclusions: Preoperative insomnia is prevalent and is a modifiable risk factor for poor pain control following spine surgery. Early identification and management of preoperative insomnia may lead to improved postoperative pain outcomes. Future external validation is needed to confirm the accuracy of the Modified CAPPS score.","source":"DOAJ","year":2023,"language":"","subjects":["Orthopedic surgery","Neurology. Diseases of the nervous system"],"doi":"10.1016/j.xnsj.2023.100261","url":"http://www.sciencedirect.com/science/article/pii/S266654842300063X","is_open_access":true,"published_at":"","score":67},{"id":"ss_3545e4aaf1aecb2b86821b4acc2ba4e01c20ffd9","title":"Glia‐neuron energy metabolism in health and diseases: New insights into the role of nervous system metabolic transporters","authors":[{"name":"M. Jha"},{"name":"B. Morrison"}],"abstract":"\u0026NA; The brain is, by weight, only 2% the volume of the body and yet it consumes about 20% of the total glucose, suggesting that the energy requirements of the brain are high and that glucose is the primary energy source for the nervous system. Due to this dependence on glucose, brain physiology critically depends on the tight regulation of glucose transport and its metabolism. Glucose transporters ensure efficient glucose uptake by neural cells and contribute to the physiology and pathology of the nervous system. Despite this, a growing body of evidence demonstrates that for the maintenance of several neuronal functions, lactate, rather than glucose, is the preferred energy metabolite in the nervous system. Monocarboxylate transporters play a crucial role in providing metabolic support to axons by functioning as the principal transporters for lactate in the nervous system. Monocarboxylate transporters are also critical for axonal myelination and regeneration. Most importantly, recent studies have demonstrated the central role of glial cells in brain energy metabolism. A close and regulated metabolic conversation between neurons and both astrocytes and oligodendroglia in the central nervous system, or Schwann cells in the peripheral nervous system, has recently been shown to be an important determinant of the metabolism and function of the nervous system. This article reviews the current understanding of the long existing controversies regarding energy substrate and utilization in the nervous system and discusses the role of metabolic transporters in health and diseases of the nervous system. HighlightsGlucose, lactate and acetate are used as metabolic fuels in the nervous systemFunction of energy metabolites dependent on expression of specific metabolic transportersBrain glucose transporters are primarily expressed in blood vessels and gliaMonocarboxylate transporters support axon function in the brain and nerve regeneration in the peripheral nervous systemDisruption of glucose or monocarboxylate transporters leads to cellular dysfunction and neurologic disease","source":"Semantic Scholar","year":2018,"language":"en","subjects":["Medicine","Biology"],"doi":"10.1016/j.expneurol.2018.07.009","url":"https://www.semanticscholar.org/paper/3545e4aaf1aecb2b86821b4acc2ba4e01c20ffd9","pdf_url":"https://europepmc.org/articles/pmc6156776?pdf=render","is_open_access":true,"citations":165,"published_at":"","score":66.95},{"id":"doaj_10.1055/s-0041-1722834","title":"Personal Protective Equipment-Related Nasal Bridge Folliculitis in a Corona Warrior","authors":[{"name":"Rajeev Sharma"},{"name":"Ashish Bindra"},{"name":"Kapil Dev Soni"}],"abstract":"Nasal bridge is a common site suffering personal protective equipment-induced skin damages over face among first-line health care workers in this coronavirus disease 2019 pandemic. We hereby report folliculitis as a complication following regular use of N95 respirator and goggles, unreported in literature till now.","source":"DOAJ","year":2022,"language":"","subjects":["Surgery","Neurology. Diseases of the nervous system"],"doi":"10.1055/s-0041-1722834","url":"http://www.thieme-connect.de/DOI/DOI?10.1055/s-0041-1722834","is_open_access":true,"published_at":"","score":66},{"id":"doaj_10.18863/pgy.972163","title":"Psychological Factors Associated with Fibromyalgia and the Areas of Psychological Intervention","authors":[{"name":"Rumeysa Eda Kanık Tezcan"},{"name":"Özden Yalçınkaya Alkar"}],"abstract":"Fibromyalgia is a musculoskeletal pain syndrome characterized with the presence of sensitive points and widespread chronic pain and restricts one's daily life activities and decreases the quality of life. The etiology of fibromyalgia is unclear, but there are numerous hypotheses regarding the emergence and progression of the disease. Among these, the biopsychosocial model offers a holistic framework in which biological, psychological and social mechanisms play a role in the development of fibromyalgia. Since the etiology of the disease is not yet understood, effective methods for its treatment have not been found, thus, interventions aim to reduce the effect of fibromyalgia and increase psychological and physiological functionality. This article aims to examine the psyhcological intervention areas and methods for fibromyalgia patients. In the literature, it is concluded that pain avoidance beliefs and behaviors, self-efficacy, physical activity, sleep quality, self-compassion, emotional skills, coping strategies, personality, comorbid psychopathology are the factors related with the emergence of the disease, severity of pain, and adherence to treatment in fibromyalgia patients. In this context, Cognitive-Behavioral Therapy (CBT), physical exercises, sleep management Acceptance and Commitment Therapy (ACT), compassion focused psychotherapies, mindfulness based psychotherapies, emotion expression and emotion regulation techniques, and biofeedback are recommended as effective methods which can be included in the treatment plans of fibromyalgia patients. Moreover, considering the psychosocial factors in the assessment processes was essential to establish individualized treatment plans. In addition, the importance of multidisciplinary approaches in the treatment processes of fibromyalgia has been discussed within the framework of the biopsychosocial model.","source":"DOAJ","year":2022,"language":"","subjects":["Psychiatry"],"doi":"10.18863/pgy.972163","url":"https://dergipark.org.tr/tr/download/article-file/1883464","pdf_url":"https://dergipark.org.tr/tr/download/article-file/1883464","is_open_access":true,"published_at":"","score":66},{"id":"doaj_10.1177/17562864221138147","title":"The overlapping relationship among depression, anxiety, and somatic symptom disorder and its impact on the quality of life of people with epilepsy","authors":[{"name":"Sisi Shen"},{"name":"Zaiquan Dong"},{"name":"Qi Zhang"},{"name":"Jing Xiao"},{"name":"Dong Zhou"},{"name":"Jinmei Li"}],"abstract":"Background: Emotional disorder is an important indicator for assessing the quality of life (QOL) of people with epilepsy (PWE). Depression, somatic symptom disorder (SSD) and anxiety are among the most frequently occurring mental disorders and overlap with each other. Objectives: This study examines the overlap of these three emotional disorders and their effects separately and in combination on the QOL of PWE. Design: Cross-sectional study. Data Sources and Methods: Adults attending our epilepsy clinic between 1 July 2020 and 1 May 2022 were consecutively enrolled. They were screened for depression, SSD, and anxiety by structured interviews, and demographic, epilepsy-related and QOL indicators were collected. Multivariate analysis, propensity score matching (PSM) and stratified analysis were used to explore the effects of their respective and combined effects on QOL. Results: Among the 749 patients, 189 patients (25%) were diagnosed with depression, 183 patients (24%) were diagnosed with SSD, and 157 patients (21%) were diagnosed with anxiety. The frequency of occurrence of each emotional disorder together with other emotional disorders was higher than the frequency of occurrence of an emotional disorder alone. Depression, SSD, and anxiety all had an independent effect on QOL of PWE ( p  \u003c 0.001). Depression had the greatest effect, followed by SSD, and then anxiety ( β : multivariate analysis, −11.0 versus –7.8 versus –6.5; PSM, −14.7 versus –9.4 versus –6.8). The QOL of PWE decreased more significantly with the increasing number of comorbid emotional disorders ( β : –12.1 versus –20.7 versus –23.0). Conclusion: It is necessary to screen for three emotional disorders, that is, depression, SSD, and anxiety, in PWE. Attention should be paid to people with multiple comorbid emotional disorders.","source":"DOAJ","year":2022,"language":"","subjects":["Neurology. Diseases of the nervous system"],"doi":"10.1177/17562864221138147","url":"https://doi.org/10.1177/17562864221138147","is_open_access":true,"published_at":"","score":66},{"id":"doaj_10.3389/fpsyt.2022.822519","title":"Decision-Making Within Forensic Psychiatric Investigations: The Use of Various Information Sources by Different Expert Groups to Reach Conclusions on Legal Insanity","authors":[{"name":"Lizel Göranson"},{"name":"Olof Svensson"},{"name":"Olof Svensson"},{"name":"Peter Andiné"},{"name":"Peter Andiné"},{"name":"Sara Bromander"},{"name":"Sara Bromander"},{"name":"Ann-Sophie Lindqvist Bagge"},{"name":"Ann-Sophie Lindqvist Bagge"},{"name":"Malin Hildebrand Karlén"},{"name":"Malin Hildebrand Karlén"},{"name":"Malin Hildebrand Karlén"}],"abstract":"BackgroundWhich type of information experts use to make decisions regarding legal insanity within forensic psychiatric investigations (FPI) is relatively unknown, both in general and when considering variations due to case context. It is important to explore this area to be able to counteract the effects of various kinds of cognitive bias.MethodThe aim was to explore whether FPI expert groups differed regarding case-specific as well as general use of information types required to make decisions on severe mental disorder (SMD). Three FPI case vignettes were presented to three professional groups involved in FPIs in Sweden (n = 41): forensic psychiatrists (n = 15), psychologists (n = 15), and social workers (n = 11). The participants reported which types of information they required to reach conclusions regarding SMD in each case. They also reported which types of information they had used within general FPI praxis during the previous year and the information types’ perceived usefulness.ResultsThe expert groups differed somewhat regarding what type of information they required for the cases (e.g., results from cognitive testing), but some information was required in all cases (e.g., client’s self-report). Regarding the preliminary assessment of SMD in the three cases, minor differences were found. Within the general FPI praxis, experts reported using several information types, while the general perceived usefulness of these sources varied.DiscussionThe professional groups relied partly on a “core” of information sources, but some case-specific adaptations were found. The professional groups’ inclination to suspect SMD also varied somewhat. This indicates a need to explore the potential consequences of these similarities and differences.","source":"DOAJ","year":2022,"language":"","subjects":["Psychiatry"],"doi":"10.3389/fpsyt.2022.822519","url":"https://www.frontiersin.org/articles/10.3389/fpsyt.2022.822519/full","is_open_access":true,"published_at":"","score":66},{"id":"ss_099012e63509b37086c8bf8b2011bfbb85c10e7e","title":"Inflammatory demyelinating diseases of the central nervous system","authors":[{"name":"R. Höftberger"},{"name":"H. Lassmann"}],"abstract":"Inflammatory demyelinating diseases are a heterogeneous group of disorders, which occur against the background of an acute or chronic inflammatory process. The pathologic hallmark of multiple sclerosis (MS) is the presence of focal demyelinated lesions with partial axonal preservation and reactive astrogliosis. Demyelinated plaques are present in the white as well as gray matter, such as the cerebral or cerebellar cortex and brainstem nuclei. Activity of the disease process is reflected by the presence of lesions with ongoing myelin destruction. Axonal and neuronal destruction in the lesions is a major substrate for permanent neurologic deficit in MS patients. The MS pathology is qualitatively similar in different disease stages, such as relapsing remitting MS or secondary or primary progressive MS, but the prevalence of different lesion types differs quantitatively. Acute MS and Balo's type of concentric sclerosis appear to be variants of classic MS. In contrast, neuromyelitis optica (NMO) and spectrum disorders (NMOSD) are inflammatory diseases with primary injury of astrocytes, mediated by aquaporin-4 antibodies. Finally, we discuss the histopathology of other inflammatory demyelinating diseases such as acute disseminated encephalomyelitis and myelin oligodendrocyte glycoprotein antibody-associated demyelination. Knowledge of the heterogenous immunopathology in demyelinating diseases is important, to understand the clinical presentation and disease course and to find the optimal treatment for an individual patient.","source":"Semantic Scholar","year":2017,"language":"en","subjects":["Medicine"],"doi":"10.1016/B978-0-12-802395-2.00019-5","url":"https://www.semanticscholar.org/paper/099012e63509b37086c8bf8b2011bfbb85c10e7e","pdf_url":"https://europepmc.org/articles/pmc7149979?pdf=render","is_open_access":true,"citations":141,"published_at":"","score":65.22999999999999},{"id":"doaj_10.1192/bjo.2021.1054","title":"Incidence of suicidality in people with depression over a 10-year period treated by a large UK mental health service provider","authors":[{"name":"Emma R. Francis"},{"name":"Daniela Fonseca de Freitas"},{"name":"Craig Colling"},{"name":"Megan Pritchard"},{"name":"Giouliana Kadra-Scalzo"},{"name":"Natalia Viani"},{"name":"Jaya Chaturvedi"},{"name":"Tom R. Denee"},{"name":"Cicely Kerr"},{"name":"Mitesh Desai"},{"name":"Gemma Scott"},{"name":"Hitesh Shetty"},{"name":"Mathew Broadbent"},{"name":"David Chandran"},{"name":"Johnny Downs"},{"name":"Sumithra Velupillai"},{"name":"Mizanur Khondoker"},{"name":"Robert Stewart"},{"name":"Rina Dutta"},{"name":"Richard D. Hayes"}],"abstract":"We describe the incidence of suicidality (2007–2017) in people with depression treated by secondary mental healthcare services at South London and Maudsley NHS Trust (n = 26 412). We estimated yearly incidence of ‘suicidal ideation’ and ‘high risk of suicide’ from structured and free-text fields of the Clinical Record Interactive Search system. The incidence of suicidal ideation increased from 0.6 (2007) to 1 cases (2017) per 1000 population. The incidence of high risk of suicide, based on risk forms, varied between 0.06 and 0.50 cases per 1000 adult population (2008–2017). Electronic health records provide the opportunity to examine suicidality on a large scale, but the impact of service-related changes in the use of structured risk assessment should be considered.","source":"DOAJ","year":2021,"language":"","subjects":["Psychiatry"],"doi":"10.1192/bjo.2021.1054","url":"https://www.cambridge.org/core/product/identifier/S2056472421010541/type/journal_article","is_open_access":true,"published_at":"","score":65},{"id":"ss_5c59c65b80c46bb13a34fc8616465104394d6f94","title":"Sarcoidosis of the central nervous system: clinical features, imaging, and CSF results","authors":[{"name":"D. Kidd"}],"abstract":"","source":"Semantic Scholar","year":2018,"language":"en","subjects":["Medicine"],"doi":"10.1007/s00415-018-8928-2","url":"https://www.semanticscholar.org/paper/5c59c65b80c46bb13a34fc8616465104394d6f94","is_open_access":true,"citations":76,"published_at":"","score":64.28},{"id":"doaj_10.3389/fpsyt.2019.00238","title":"Baclofen but Not Diazepam Alleviates Alcohol-Seeking Behavior and Hypothalamic–Pituitary–Adrenal Axis Dysfunction in Stressed Withdrawn Mice","authors":[{"name":"Yolaine Rabat"},{"name":"Nadia Henkous"},{"name":"Marc Corio"},{"name":"Xavier Nogues"},{"name":"Daniel Beracochea"}],"abstract":"This study compares the impact of repeated injections of baclofen (an agonist of GABAB receptors) or diazepam (a benzodiazepine having an agonist action on GABAA receptors) given during the alcohol-withdrawal period on the stress-induced restoration of alcohol-seeking behavior and hypothalamic–pituitary–adrenal (HPA) axis dysfunction after a long (4 weeks) abstinence. Thus, C57BL/6 mice were submitted to a 6-month alcohol consumption [12% volume/volume (v/v)] and were progressively withdrawn to water before testing. Diazepam (Valium®, Roche) and baclofen (Baclofen®, Mylan) were administered intraperitoneally for 15 consecutive days (1 injection/day) during the withdrawal period at decreasing doses ranging from 1.0 mg/kg (Day 15) to 0.25 mg/kg (Day 1) for diazepam and from 1.5 mg/kg (Day 15) to 0.37 mg/kg (Day 1) for baclofen. Alcohol-seeking behavior was evaluated by alcohol-place preference in an odor recognition task. In the stress condition, mice received three electric footshocks 45 min before behavioral testing. Blood was sampled immediately after behavioral testing, and plasma corticosterone concentrations were measured by commercial enzyme immunoassay kits. Results showed that non-stressed withdrawn mice did not exhibit alcohol-place preference or alteration of plasma corticosterone concentrations relative to water controls. After stress, however, withdrawn mice exhibited a significant alcohol-place preference and higher circulating corticosterone concentrations as compared to stressed water controls. Interestingly, repeated administration during the withdrawal phase of baclofen but not diazepam suppressed both the alcohol-place preference and normalized corticosterone levels in stressed withdrawn animals. In conclusion, this study evidences that a pre-treatment with baclofen but not with diazepam during the withdrawal phase normalized, even after a long period of abstinence, the HPA axis response to stress, which contributes to the long-term preventing effects of this compound on alcohol-seeking behavior.","source":"DOAJ","year":2019,"language":"","subjects":["Psychiatry"],"doi":"10.3389/fpsyt.2019.00238","url":"https://www.frontiersin.org/article/10.3389/fpsyt.2019.00238/full","is_open_access":true,"published_at":"","score":63},{"id":"doaj_The+application+of+artificial+intelligence+in+clinical+diagnosis+and+treatment+of+intracranial+hemorrhage","title":"The application of artificial intelligence in clinical diagnosis and treatment of intracranial hemorrhage","authors":[{"name":"Jian-bo CHANG"},{"name":"Ren-zhi WANG"},{"name":"Ming FENG"}],"abstract":"Both manifestations and treatments of intracranial hemorrhage (ICH) are varied and the effect meets bottlenecks. Recently, the artificial intelligence (AI) technology has developed rapidly. This review aims to help clinicians understand AI technology regarding its application in ICH by systematically reviewing the historical and current examples. Hope to stimulate the AI progress and enhance the level of treatment in ICH in the future. Ultimately, the treatment of ICH would be precision and individualization.\nDOI:10.3969/j.issn.1672-6731.2019.09.004","source":"DOAJ","year":2019,"language":"","subjects":["Neurology. Diseases of the nervous system"],"url":"http://www.cjcnn.org/index.php/cjcnn/article/view/2006","is_open_access":true,"published_at":"","score":63},{"id":"ss_8ab222bcefe633293e8f08a4d772ac320d456773","title":"Cell-surface central nervous system autoantibodies: Clinical relevance and emerging paradigms","authors":[{"name":"S. Irani"},{"name":"J. Gelfand"},{"name":"Adam Al‐Diwani"},{"name":"A. Vincent"}],"abstract":"The recent discovery of several potentially pathogenic autoantibodies has helped identify patients with clinically distinctive central nervous system diseases that appear to benefit from immunotherapy. The associated autoantibodies are directed against the extracellular domains of cell‐surface–expressed neuronal or glial proteins such as LGI1, N‐methyl‐D‐aspartate receptor, and aquaporin‐4. The original descriptions of the associated clinical syndromes were phenotypically well circumscribed. However, as availability of antibody testing has increased, the range of associated patient phenotypes and demographics has expanded. This in turn has led to the recognition of more immunotherapy‐responsive syndromes in patients presenting with cognitive and behavioral problems, seizures, movement disorders, psychiatric features, and demyelinating disease. Although antibody detection remains diagnostically important, clinical recognition of these distinctive syndromes should ensure early and appropriate immunotherapy administration. We review the emerging paradigm of cell‐surface–directed antibody–mediated neurological diseases, describe how the associated disease spectrums have broadened since the original descriptions, discuss some of the methodological issues regarding techniques for antibody detection and emphasize considerations surrounding immunotherapy administration. As these disorders continue to reach mainstream neurology and even psychiatry, more cell‐surface–directed antibodies will be discovered, and their possible relevance to other more common disease presentations should become more clearly defined. Ann Neurol 2014;76:168–184","source":"Semantic Scholar","year":2014,"language":"en","subjects":["Medicine"],"doi":"10.1002/ana.24200","url":"https://www.semanticscholar.org/paper/8ab222bcefe633293e8f08a4d772ac320d456773","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ana.24200","is_open_access":true,"citations":161,"published_at":"","score":62.83},{"id":"ss_8783e0bdb498c1b5bc6bda85fa69a0aa8cdc532f","title":"Time Perception Mechanisms at Central Nervous System","authors":[{"name":"R. Fontes"},{"name":"Jéssica Ribeiro"},{"name":"D. Gupta"},{"name":"D. Machado"},{"name":"Fernando Lopes-Júnior"},{"name":"F. Magalhães"},{"name":"V. Bastos"},{"name":"K. Rocha"},{"name":"V. Marinho"},{"name":"G. Lima"},{"name":"B. Velasques"},{"name":"P. Ribeiro"},{"name":"M. Orsini"},{"name":"B. Pessôa"},{"name":"M. Leite"},{"name":"S. Teixeira"}],"abstract":"The five senses have specific ways to receive environmental information and lead to central nervous system. The perception of time is the sum of stimuli associated with cognitive processes and environmental changes. Thus, the perception of time requires a complex neural mechanism and may be changed by emotional state, level of attention, memory and diseases. Despite this knowledge, the neural mechanisms of time perception are not yet fully understood. The objective is to relate the mechanisms involved the neurofunctional aspects, theories, executive functions and pathologies that contribute the understanding of temporal perception. Articles form 1980 to 2015 were searched by using the key themes: neuroanatomy, neurophysiology, theories, time cells, memory, schizophrenia, depression, attention-deficit hyperactivity disorder and Parkinson’s disease combined with the term perception of time. We evaluated 158 articles within the inclusion criteria for the purpose of the study. We conclude that research about the holdings of the frontal cortex, parietal, basal ganglia, cerebellum and hippocampus have provided advances in the understanding of the regions related to the perception of time. In neurological and psychiatric disorders, the understanding of time depends on the severity of the diseases and the type of tasks.","source":"Semantic Scholar","year":2016,"language":"en","subjects":["Medicine"],"doi":"10.4081/ni.2016.5939","url":"https://www.semanticscholar.org/paper/8783e0bdb498c1b5bc6bda85fa69a0aa8cdc532f","pdf_url":"https://www.pagepress.org/journals/index.php/ni/article/download/5939/5879","is_open_access":true,"citations":83,"published_at":"","score":62.49}],"total":5534232,"page":1,"page_size":20,"sources":["CrossRef","DOAJ","Semantic Scholar"],"query":"Neurology. Diseases of the nervous system"}