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Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Confrontation on Borneo tests a thesis on counterinsurgency: winning hearts and minds succeeds if object place/people win over the hearts and minds of subject counterinsurgency soldiers. This psychological transformation depends on fixed objects that counterinsurgency cannot easily change: the counterinsurgency destination and the counterinsurgents' place of origin that formed soldiers' unconscious selves. Soldiers encountered on Borneo a transformative, attractive, alien destination that changed their behavior. Soldiers then ratified unconscious behavior by asserting that the cause was their innate decency and official hearts and minds policy. But Borneo had formed the unconscious self that gave form to hearts and minds. This article argues that altered states of being shape counterinsurgency. [ABSTRACT FROM AUTHOR]

Introduction: Access to emergency care is an essential part of the health system. Improving access to emergency services in low- and middle-income countries (LMIC) decreases mortality and reduces global disparities; however, few studies have assessed emergency services resources in LMICs. To guide future improvements in care, we performed a comprehensive assessment of the emergency services capacity of a rural community in Guatemala serving a mostly indigenous population.Methods: We performed an exhaustively sampled cross-sectional survey of all healthcare facilities providing urgent and emergent care in the four largest cities surrounding Lake Atitlán using the Emergency Services Resource Assessment Tool (ESRAT). Results: Of 17 identified facilities, 16 agreed to participate and were surveyed: nine private hospitals; four public clinics; and three public hospitals, including the region’s public departmental hospital. All facilities provided emergency services 24/7, and a dedicated emergency unit was available at 67% of hospitals and 75% of clinics. A dedicated physician was present in the emergency unit during the day at 67% of hospitals and 75% of clinics. Hospitals had a significantly higher percentage of available equipment compared to clinics (85% vs 54%, mean difference 31%; 95% confidence interval (CI) 23-37%; P = 0.004). There was no difference in availability of laboratory tests between public and private hospitals or between cities. Private hospitals had access to a significantly higher percentage of medications compared to clinics (56% vs 27%, mean difference 29%; 95% CI 9-49%; P = 0.024). Conclusion: We found a high availability of emergency services and universal availability of personal protective equipment but a severe shortage of critical medications in clinics, and widespread shortage of pediatric equipment.