METHOD: A cross-sectional study using convenience sampling recruited 230 caregivers of children with ASD aged 4 to 18 years from selected autism centres in Kuching, Sarawak. The caregivers completed the Aberrant Behaviour Checklist-2 and the Zarit Burden Interview.
RESULTS: Univariate analysis revealed a significant difference in caregiver burden for children with ASD receiving medications (p = 0.013), registered with the Social Welfare Department (p = 0.036), and having siblings with ASD (p = 0.046). About 40% of the children exhibited at least one domain of problem behaviour. More than half of the caregivers (53.9%) experienced burden, with the majority experiencing mild burden. Positive associations were seen between irritability (r = 0.458, p
METHODS: The cross sectional study was conducted on the sample recruited from three drug treatment centers in Pakistan. Face-to-face interviews were conducted with participants who met ICD-10 criteria for prescription drug dependence. Several aspects like substance use histories, negative health outcomes, patient attitude, pharmacy and physician practices also collected to predict the determinants of (PDD). Binomial logistic regression models examined the factors associated with PDD and PIDU.
RESULTS: Of the 537 treatment seeking individuals interviewed at baseline, close to one third (178, 33.3%) met criteria for dependence on prescription drugs. The majority of the participants were male (93.3%), average age of 31 years, having urban residence (67.4%). Among participants who met criteria for dependence on prescription drugs (71.9%), reported benzodiazepines as the most frequently used drug, followed by narcotic analgesics (56.8%), cannabis/marijuana (45.5%), and heroin (41.5%). The patients reported alprazolam, buprenorphine, nalbuphine, and pentazocin use as alternatives to illicit drugs. PDD was significantly negatively associated with injectable route (OR = 0.281, 95% CI, 0.079-0.993) and psychotic symptoms (OR = 0.315, 95% CI, 0.100, 0.986). This implies that PDD is less likely to be associated with an injectable route and psychotic symptoms in contrast to PIDU. Pain, depression and sleep disorder were primary reasons for PDD. PDD was associated with the attitude that prescription drugs are safer than illicit drugs (OR = 4.057, 95%CI, 1.254-13.122) and PDD was associated with being on professional terms (i.e., having an established relationship) with pharmaceutical drugs retailers for acquisition of prescription drugs.
DISCUSSION AND CONCLUSION: The study found benzodiazepine and opioid dependence in sub sample of addiction treatment seekers. The results have implications for drug policy and intervention strategies for preventing and treating drug use disorders.
Methods: Based on the morphine withdrawal model, rats were morphine treated with increasing doses from 10 to 50 mg/kg twice daily over a period of 6 days. The treatment was discontinued on day 7 in order to induce a spontaneous morphine abstinence. The withdrawal signs were measured daily after 24 h of the last morphine administration over a period of 28 abstinence days. In rats that developed withdrawal signs, a drug replacement treatment was given using mitragynine, methadone, or buprenorphine and the global withdrawal score was evaluated.
Results: The morphine withdrawal model induced profound withdrawal signs for 16 days. Mitragynine (5-30 mg/kg; i.p.) was able to attenuate acute withdrawal signs in morphine dependent rats. On the other hand, smaller doses of methadone (0.5-2 mg/kg; i.p.) and buprenorphine (0.4-1.6 mg/kg; i.p.) were necessary to mitigate these effects.
Conclusions: These data suggest that mitragynine may be a potential drug candidate for opiate withdrawal treatment.
AIMS: This study was aimed to explore the prevalence of mental health problems among teachers in Bangladesh and to identify the associated risk factors.
METHODS: This web-based cross-sectional study was conducted during the second wave of COVID-19 pandemic in Bangladesh. Data were collected from 381 teachers working at schools, colleges, and universities between 01 August and 29 August 2021 by administering a self-reported e-questionnaire using Google Form, where the mental health of teachers was assessed by depression, anxiety, and stress scale. Data were analyzed using IBM SPSS Statistics (Version 26) and STATA Version 16, and multiple linear regression was executed to predict mental health problems among teachers.
RESULTS: The findings indicate that the overall prevalence of depression, anxiety, and stress among teachers was 35.4%, 43.7%, and 6.6%, respectively. The prevalence was higher among male and older teachers than among their female and younger colleagues. The findings further showed that place of residence, institution, self-reported health, usage of social and electronic media, and fear of COVID-19 significantly influenced the mental health status of teachers.
CONCLUSION: It is strongly recommended that the government and policymakers provide proper mental health services to teachers in order to reduce mental health problems and thus sustain the quality of education during and after the pandemic.
METHODS: We quantified the directed information flow using partial directed coherence (PDC) and the topological networks by graph theory measures at four frequency bands (delta, theta, alpha, and beta). The PDC assesses the causal interactions between neuronal units of the brain network. Besides, the graph theory of the complex network identifies the topological structure of the network. Resting-state electroencephalogram (EEG) data were recorded for 66 patients with different severities of SAD (22 severe, 22 moderate, and 22 mild) and 22 demographically matched healthy controls (HC).
RESULTS: PDC results have found significant differences between SAD groups and HCs in theta and alpha frequency bands (p < 0.05). Severe and moderate SAD groups have shown greater enhanced information flow than mild and HC groups in all frequency bands. Furthermore, the PDC and graph theory features have been used to discriminate three classes of SAD from HCs using several machine learning classifiers. In comparison to the features obtained by PDC, graph theory network features combined with PDC have achieved maximum classification performance with accuracy (92.78%), sensitivity (95.25%), and specificity (94.12%) using Support Vector Machine (SVM).
DISCUSSION: Based on the results, it can be concluded that the combination of graph theory features and PDC values may be considered an effective tool for SAD identification. Our outcomes may provide new insights into developing biomarkers for SAD diagnosis based on topological brain networks and machine learning algorithms.