Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiological mechanisms underpinning IA is crucial for enabling an accurate diagnosis and informing treatment and prevention strategies. Despite the recent increase in studies examining the neurophysiological traits of IA, their findings often vary. To enhance the accuracy of identifying key neurophysiological characteristics of IA, this study used the phase lag index (PLI) and weighted PLI (WPLI) methods, which minimize volume conduction effects, to analyze the resting-state electroencephalography (EEG) functional connectivity. We further evaluated the reliability of the identified features for IA classification using various machine learning methods.

Ninety-two participants (42 with IA and 50 healthy controls (HCs)) were included. PLI and WPLI values for each participant were computed, and values exhibiting significant differences between the two groups were selected as features for the subsequent classification task.

Support vector machine (SVM) achieved an 83% accuracy rate using PLI features and an improved 86% accuracy rate using WPLI features. t-test results showed analogous topographical patterns for both the WPLI and PLI. Numerous connections were identified within the delta and gamma frequency bands that exhibited significant differences between the two groups, with the IA group manifesting an elevated level of phase synchronization.

Functional connectivity analysis and machine learning algorithms can jointly distinguish participants with IA from HCs based on EEG data. PLI and WPLI have substantial potential as biomarkers for identifying the neurophysiological traits of IA.

The exploration of molecular characteristics has emerged as a prominent trend to advance precision medicine. The utilization of genetic testing to guide therapy is integral to precision medicine. This study aims to investigate the potential patient populations for the reimbursement of next-generation sequencing (NGS) and assess the budget impact from the perspective of Taiwan’s single insurer, the National Health Insurance Administration.

To comprehend the scope for medicines with companion diagnostics (CDx) involved, we analyze the U.S. Food and Drug Administration-approved/cleared diagnostic tests, conduct a literature review to identify medicines approved by the European Medicines Agency that require a CDx, and identify the medicines with CDx involved covered by the National Health Insurance (NHI) in Taiwan. Subsequently, we explore the potential reimbursement indications for NGS testing and conduct a budget impact analysis to evaluate the expected financial impact for the NHI over a five-year period. Furthermore, sensitivity analyses are conducted to deal with uncertainty.

We have compiled 13 cancer types for which NGS can serve as a companion diagnostic. These encompass non-small-cell lung cancer, colorectal cancer, breast cancer, ovarian cancer, biliary tract cancer, acute myeloid leukemia, acute lymphoblastic leukemia, melanoma, cholangiocarcinoma, prostate cancer, pancreatic cancer, gastrointestinal stromal tumor, and thyroid cancer/medullary thyroid cancer. The implementation of NGS reimbursement in NHI will benefit 25,000 to 30,000 patients undergoing targeted therapies. The projected incremental budget impact ranges from TWD570 million to TWD650 million (USD19 million to USD22 million) over five years.

This study focuses on evaluating the financial impact of incorporating NGS testing into NHI reimbursement for relevant cancer drug indications. The findings can serve as references for the planning of reimbursement policies. However, with the advancement of precision medicine, it is foreseeable that there will be a broader range of applications for NGS, and its cost will gradually decrease.

Certain rhythmic arterial pressure waves in humans and animals have been noticed for over one century. We found the novel and slowest arterial pressure waves in children following surgical repair for CHD, and examined their characteristics and clinical implications.

We enrolled 212 children with 22 types of CHD within postoperative 48 h. We monitored haemodynamics (blood pressure, cardiac cycle efficiency, dP/dTmax), cerebral (ScO2), and renal (SrO2) oxygen saturation every 6 s. Electroencephalogram was continuously monitored. Mean blood flow velocity (Vm) of the middle cerebral artery was measured at 24 h.

We found the waves with a frequency of ∼ 90 s immediately following surgical repair in 46 patients in 12 types of CHD (21.7%), being most prevalent in patients with aortic arch abnormalities (Aorta Group, n = 24, 42.3%) or ventricular septal defect (Ventricular Septal Defect Group, n = 12, 23.5%). In Aorta and Ventricular Septal Defect Groups, the occurrence of the waves was associated with lower blood pressures, dP/dTmax, cardiac cycle efficiency, ScO2, SrO2, Vm, worse electroencephalogram background abnormalities, higher number of electroencephalogram sharp waves, and serum lactate (Ps <0.0001–0.07), and were accompanied with fluctuations of ScO2 and SrO2 in 80.6% and 69.6% of patients, respectively.

The waves observed in children following cardiovascular surgery are the slowest ever reported, occurring most frequently in patients with aortic arch abnormalities or ventricular septal defect. While the occurrence of the waves was associated with statistically worse and fluctuated ScO2 and SrO2, worse systemic haemodynamics, and electroencephalogram abnormalities, at present these waves have no known clinical relevance.

Although maternal mental illnesses have been found to influence child health and development, little is known about the impact of maternal positive well-being on child health and development. Therefore, this longitudinal study investigated the effects of prenatal subjective well-being on birth outcomes and child development by considering the potential modifier effect of parity.

Pregnant women in early stages of pregnancy were recruited at five selected hospitals in Taipei, Taiwan, during their prenatal appointments since 2011. Self-reported evaluations were conducted at seven time points up to 2 years postpartum. Linear regression and generalized estimating equation models were used for examination.

Higher prenatal eudaimonic well-being was associated with longer gestational length (adjusted beta [aβ] = 0.36, 95% confidence interval [CI] = 0.03, 0.68) and higher birth weight (aβ = 124.71, 95% CI = 35.75, 213.66). Higher positive and negative affect were associated with longer gestational length (aβ = 0.38, 95% CI = 0.06, 0.70) and smaller birth weight (aβ = −93.51, 95% CI = −178.35, −8.67), respectively. For child’s outcomes, we found an association between higher prenatal eudaimonic well-being and decreased risks of suspected developmental delay, particularly for children of multiparous mothers (adjusted odds ratio = 0.18, 95% CI = 0.05, 0.70). Higher levels of prenatal depression and anxiety were significantly associated with increased risks of suspected developmental delay for children of primiparous mothers.

Positive prenatal maternal mental health may benefit birth outcomes and child development, particularly for children of multiparous mothers. Interventions for improving prenatal mental health may be beneficial for child development.

COVID-19 has long-term impacts on public mental health, while few research studies incorporate multidimensional methods to thoroughly characterise the psychological profile of general population and little detailed guidance exists for mental health management during the pandemic. This research aims to capture long-term psychological profile of general population following COVID-19 by integrating trajectory modelling approaches, latent trajectory pattern identification and network analyses.

Longitudinal data were collected from a nationwide sample of 18 804 adults in 12 months after COVID-19 outbreak in China. Patient Health Questionnaire-9, Generalised Anxiety Disorder-7 and Insomnia Severity Index were used to measure depression, anxiety and insomnia, respectively. The unconditional and conditional latent growth curve models were fitted to investigate trajectories and long-term predictors for psychological symptoms. We employed latent growth mixture model to identify the major psychological symptom trajectory patterns, and ran sparse Gaussian graphical models with graphical lasso to explore the evolution of psychopathological network.

At 12 months after COVID-19 outbreak, psychological symptoms generally alleviated, and five psychological symptom trajectories with different demographics were identified: normal stable (63.4%), mild stable (15.3%), mild-increase to decrease (11.7%), mild-decrease to increase (4.0%) and moderate/severe stable (5.5%). The finding indicated that there were still about 5% individuals showing consistently severe distress and approximately 16% following fluctuating psychological trajectories, who should be continuously monitored. For individuals with persistently severe trajectories and those with fluctuating trajectories, central or bridge symptoms in the network were mainly ‘motor abnormality’ and ‘sad mood’, respectively. Compared with initial peak and late COVID-19 phase, aftermath of initial peak might be a psychologically vulnerable period with highest network connectivity. The central and bridge symptoms for aftermath of initial peak (‘appetite change’ and ‘trouble of relaxing’) were totally different from those at other pandemic phases (‘sad mood’).

This research identified the overall growing trend, long-term predictors, trajectory classes and evolutionary pattern of psychopathological network of psychological symptoms in 12 months after COVID-19 outbreak. It provides a multidimensional long-term psychological profile of the general population after COVID-19 outbreak, and accentuates the essentiality of continuous psychological monitoring, as well as population- and time-specific psychological management after COVID-19. We believe our findings can offer reference for long-term psychological management after pandemics.