The objective of this study is to empirically test the effects of disasters which include natural as well as human made disasters such as armed conflict on women’s reproductive health in developing countries. Data from 128 developing countries are used. It was found that average number of deaths from natural disasters and armed conflict in the East Asia and Pacific region was not significantly different from the rest of the developing world. The data are examined using structural equation analysis. This study finds that ‘armed conflict’ in developing countries presents significant reproductive health risks. The implications are discussed.

Over the past decade, the Diagnostic and Statistical Manual 's method of prescribing medications based on presenting symptoms has been challenged. The shift toward precision medicine began with the National Institute of Mental Health and culminated with the World Psychiatric Association's posit that a paradigm shift is needed. This study supports that shift by providing evidence explaining the high rate of psychiatric medication failure and suggests a possible first step toward precision medicine. A large psychiatric practice began collecting electroencephalograms (EEGs) for this study in 2012. The EEGs were analyzed by the same neurophysiologist (board certified in electroencephalography) on 1,233 patients. This study identified 4 EEG biomarkers accounting for medication failure in refractory patients: focal slowing, spindling excessive beta, encephalopathy, and isolated epileptiform discharges. Each EEG biomarker suggests underlying brain dysregulation, which may explain why prior medication attempts have failed. The EEG biomarkers cannot be identified based on current psychiatric assessment methods, and depending upon the localization, intensity, and duration, can all present as complex behavioral or psychiatric issues. The study highlights that the EEG biomarker identification approach can be a positive step toward personalized medicine in psychiatry, furthering the clinical thinking of “testing the organ we are trying to treat.”

This manuscript examines the pivotal role of neuroinflammation in the central nervous system (CNS), particularly considering the impact of the COVID-19 pandemic. Neuroinflammation serves as a defense mechanism against various insults, including toxins, infections, and trauma. However, if left untreated, neuroinflammation can become chronic, leading to significant symptomatic and structural brain damage. Notably, neuroinflammation can mimic psychological disorders, complicating diagnosis and treatment. Current diagnostic methods for neuroinflammation—such as lumbar punctures, MRIs, brain biopsies, blood tests, and PET scans—are often hindered by inaccuracy, invasiveness, and cost. This study posits that electroencephalography (EEG), particularly identifying spindling excessive beta (SEB) activity, offers a promising, non-invasive, and cost-effective alternative for detecting neuroinflammation. This study investigates the relationship between SEB activity and neuroinflammation, focusing on traumatic brain injury (TBI). Through statistical analysis of EEG data from 1,233 psychiatric patients, we identified and compared two groups: 75 non-benzodiazepine-using adults without TBI and 79 non-benzodiazepine using adults with TBI exhibiting SEB activity. We identified a significant prevalence of SEB in individuals with refractory psychiatric conditions, underscoring the significance of this biomarker for neuroinflammation. Furthermore, we examine the therapeutic implications of reducing SEB through interventions such as guanfacine combined with N-Acetyl Cysteine (NAC), photobiomodulation, and hyperbaric oxygen therapy, all of which have demonstrated efficacy in mitigating neuroinflammation. These findings suggest that EEG could play a transformative role in the early detection and management of neuroinflammatory conditions, paving the way for more personalized and effective treatments for mental health disorders.

To investigate whether the temporal organization of resting-state electroencephalography (EEG) is consistent with statistical properties emphasized in crucial-event (CE) theory by examining the quiescent periods between neuronal avalanches, known as inter-avalanche intervals (IAI). Resting-state EEG from healthy adults and traumatic brain injury (TBI) patients was transformed into avalanche rasters using a subject-specific z-score threshold treated as an inferred model parameter. For each subject, IAI distributions were fitted with power law, lognormal, Weibull, and exponential models using maximum likelihood estimation with Monte Carlo Kolmogorov-Smirnov (KS) testing and strict tail selection. Renewal behavior was evaluated through autocorrelation analysis, Ljung-Box testing, and phase shuffled surrogates. Modified Diffusion Entropy Analysis (MDEA) provided an independent estimate of the CE exponent μ. Simulated avalanche rasters with known scaling were also used to test whether the threshold optimization procedure could recover IAI statistics under varying noise conditions. Optimized thresholds revealed stable power-law scaling (1 < τ < 3, KS p ≥ 0.2) with near-zero autocorrelation, consistent with renewal-like temporal organization. Across subjects, μ fell within a similar numerical range to τ but showed weaker separation between cohorts, indicating that the two metrics capture complementary aspects of temporal structure rather than numerically interchangeable estimates. In contrast, τ captured systematic differences between TBI and healthy groups, reflecting compressed quiescent periods and reduced temporal variability in TBI. IAI timing exhibits temporal statistics consistent with CE theory. The framework offers a threshold-optimized approach for quantifying temporal scaling in EEG across cohorts and recording conditions.