Dyspnea, respiratory muscle strength, cough capacity, and sleep quality in individuals with substance use disorder.

There is evidence that substance use and smoking cause some adverse effects on the respiratory system. The aim of this study was to assess dyspnea severity, respiratory muscle strength, cough capacity, and sleep quality in people with substance use disorder (SUD). Forty eight individuals with SUD and 28 active cigarette smokers participated in the study. Participants' dyspnea severity was assessed using the Modified Medical Research Council Scale, respiratory muscle strength was measured with a portable electronic mouth pressure device, peak cough flow was assessed with a Peak Flow Meter, and sleep quality was determined using the Pittsburgh Sleep Quality Index (PSQI). The amount of daily cigarette smoking and dyspnea severity were significantly higher in individuals with SUD (p < .001). Peak cough flow values, maximum inspiratory pressure (MIP), maximum expiratory pressure (MEP), MIP (%predicted), and MEP (%predicted) were not significantly different between the SUD patients and the active cigarette smokers (p > .05). However, PSQI sub-parameters such as subjective sleep quality, sleep latency, habitual sleep efficiency, use of sleeping medication, and total scores showed significant differences between the SUD patients and the active cigarette smokers (p < .05, p < .001, p = .03, p < .001, p < .001, respectively). Individuals with SUD were found to have higher dyspnea and poorer sleep quality than active smokers. However, respiratory muscle strength and cough capacities were similar.

HCV Genotype Distribution Among People Who Inject Drug in Turkey: Findings from Multicenter and Cross-Sectional Study.

Background: Hepatitis C virus (HCV) infection is very common in people who inject drugs (PWID). Studies about the prevalence and genotype distribution of the HCV among PWID are very crucial for developing strategies to manage HCV infection. This study's objective is to map the distribution of HCV genotypes among PWID from various regions of Turkey. Method: This prospective, multicenter, cross-sectional study involved 197 PWID who tested positive for anti-HCV antibodies from 4 different addiction treatment facilities in Turkey. Interviews were done with people who had anti-HCV antibodies, and blood samples were taken to check the HCV RNA viremia load and genotyping. Results: This study was conducted on 197 individuals with a mean age of 30.3 ± 8.6 years. 9.1% (136/197 patients) had a detectable HCV-RNA viral load. Genotype 3 was the most commonly observed genotype by 44.1%, followed by genotype 1a by 41.9%, genotype 2 by 5.1%, genotype 4 by 4.4%, and genotype 1b by 4.4%. Whereas genotype 3 was dominant with 44.4% at the central Anatolia region of Turkey, the frequencies of genotypes 1a and 3, which were predominantly detected in the south and northwest regions of Turkey, were very close to each other. Conclusion: Although genotype 3 is the predominant genotype in the PWID population in Turkey, the prevalence of HCV genotype varied across the country. To eliminate HCV infection in the PWID, treatment and screening strategies that differ by genotype are essentially required. Especially identification of genotypes will be useful in developing individualized treatments and determining national prevention strategies.

Investigation of hand functions in individuals with cannabis and its derivatives use disorder.

BACKGROUND: The present study was aimed to compare the grip/pinch strengths and manual dexterity of individuals with and without the use of cannabis and its derivatives. METHODS: A cross-sectional prospective study was conducted with 66 individuals, including 33 cases with the use of cannabis (and its derivatives) and 33 age- and sex-matched controls. Grip and pinch strengths were evaluated with a dynamometer. The Nine-Hole Peg Test (9HPT), Minnesota Manual Dexterity Test (MMDT), and Michigan Hand Outcomes Questionnaire (MHQ) were used to assess the hand function. RESULTS: The hand grip strength and dominant hand 2-point pinch (2PP) grip strength were less in individuals with substance use disorder (SUD) (p < 0.05). The 9HPT duration of the SUD patients was higher (p < 0.05). On the other hand, the MMDT insertion and rotation test results were different between the groups (p < 0.05). Grip strength was related with the MMDT insertion and rotation tests (r = -0.411 to -0.480). There was significant correlation between grip strength with dominant hand 9HPT (r = -0.370) and between dominant hand 3-point pinch (3PP) strength with MMDT insertion (r = -0.378). In addition, dominant hand 2PP strength was correlated with overall hand function of MHQ (r = 0.382). CONCLUSION: The individuals with cannabis use disorder showed reduced grip strength on both sides and decreased 2PP strength on the dominant side compared to healthy individuals. In addition, there is a decrease in the hand skills of individuals with cannabis use disorder. Decreased grip strength of individuals with cannabis use disorder affected their hand skills negatively.

Health anxiety during the early phases of COVID-19 pandemic in Turkey and its relationship with postpandemic attitudes, hopelessness, and psychological resilience.

PURPOSE: To investigate health anxiety-related factors in the early stages of pandemic in Turkey. DESIGN AND METHODS: This study included 1046 participants who responded to the online survey anonymously between 28 March and 04 April 2020. FINDINGS: Demographic data, postpandemic attitudes towards the elderly and precaution-taking behaviors were health anxiety-related factors. The main predictors of the health anxiety were the level of hopelessness, perception of self, time spent on social media, and following COVID-19 pandemic-related news. PRACTICE IMPLICATIONS: The results of this study are important in terms of understanding the health anxiety during the pandemic and providing data support for the proper interventions.

Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls.

Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response-based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.

Evaluation of Plantar Foot Sensation, Balance, Physical Performance, and Fear of Movement in Substance Use Disorders.

BACKGROUND: Neuropathologic changes may occur in the nervous system due to long-term substance use, leading to functional disability with altering of balance. We know little about substance-related mechanisms that can cause movement disorders. This study investigated the effects of plantar foot sensation and balance on physical performance as an effect of substance use in detoxified patients. METHODS: Twenty-three users of cannabis, volatile agents, or narcotic/stimulant agents alone or in combination for at least 1 year (mean age, 27.6 years) and 20 healthy volunteers (mean age, 24.6 years) were included. Participant evaluations were implemented immediately after the detoxification process with psychiatrist approval. Depression, state-trait anxiety, and fear of movement levels were evaluated with the Beck Depression Inventory, State-Trait Anxiety Inventory, and Tampa Scale for Kinesiophobia, respectively. Plantar foot sensations were evaluated with light touch, two-point discrimination, and vibration examinations. Balance was assessed with balance software and a balance board and force platform. Balance path, balance path distance, and center of pressure were recorded. Physical performance was evaluated with the Timed Up and Go (TUG) test in the final step. RESULTS: There was a significant difference in two-point discrimination of patients versus controls (P < .05). Significant differences were also found in balance values, particularly in the sagittal direction (P < .05). TUG test results of patients compared with controls showed a negative influence on physical function (P < .05). CONCLUSIONS: Detailed examination should be performed to understand movement disorders in substance users. Herein, substance users had impaired two-point discrimination and sagittal balance reciprocally. Thus, customized physiotherapy approaches to substance users should be considered to improve their movement disorders.

Binomial Logistic Regression and Artificial Neural Network Methods to Classify Opioid-Dependent Subjects and Control Group Using Quantitative EEG Power Measures.

Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in medical data classification studies. LR, a statistical fitting model, is suggested in medical problems because of its well-established methodology and coefficients contributing to the evaluation of clinical interpretations. ANNs are graphical models structured with node networks interconnected with arcs each of which is expressed in terms of weights discovered throughout the modeling process. Since ANNs have a complex structure with its layers and nodes in the layers, which provides ANNs the ability to model any data with complex relationships. Among the various models having origins in statistics and computer science, LR and ANNs have prevailed in the area of mass medical data classification. In this study, we introduce the 2 aforementioned approaches in order to generate a model dichotomizing 75 opioid-dependent patients and 59 control subjects from each other. Quantitative electroencephalography (QEEG) absolute power value of each electrode were calculated for 4 consecutive frequency bands namely delta, theta, alpha, and beta with the frequencies, 0.5 to 4, 4 to 8, 8 to 12, and 12 to 20 Hz, respectively. Significant independent variables contributing to the classification were underlined in LR while a feature selection (FS) method, genetic algorithm, is being applied to the ANN model to reveal more informative features. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores, and Gini coefficient. Although ANN-based classifier outperformed compared with LR, both models performed satisfactorily for absolute power measure in beta frequency band. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies.

Volumetric brain abnormalities in polysubstance use disorder patients.

AIM: Polysubstance users represent the largest group of patients seeking treatment at addiction and rehabilitation clinics in Turkey. There is little knowledge about the structural brain abnormalities seen in polysubstance users. This study was conducted to examine the structural brain differences between polysubstance use disorder patients and healthy control subjects using voxel-based morphometry. METHODS: Forty-six male polysubstance use disorder patients in the early abstinence period and 30 healthy male controls underwent structural magnetic resonance imaging scans. Voxel-based morphometry analysis was performed to examine gray matter (GM) abnormality differences. RESULTS: Polysubstance use disorder patients displayed significantly smaller GM volume in the thalamus, temporal pole, superior frontal gyrus, cerebellum, gyrus rectus, occipital lobe, anterior cingulate cortex, superior temporal gyrus, and postcentral gyrus. CONCLUSION: A widespread and smaller GM volume has been found at different regions of the frontal, temporal, occipital, and parietal lobes, cerebellum, and anterior cingulate cortex in polysubstance users.

Therapeutic drug monitoring: perspectives of psychiatrists in Turkey.

OBJECTIVES: Although the medical and economic implications of therapeutic drug monitoring have been intensely discussed over the past years, little is known about the experiences and attitudes of psychiatrists in their clinical practice. The aim of this study was to investigate psychiatrists' daily practice with therapeutic drug monitoring in Turkey. METHODS: A nation-wide cross-sectional survey among adult and child psychiatry specialist psychiatrists in Turkey was conducted. RESULTS: We found that 98.4% (n = 380) of the study participants used TDM in clinical practice and 1.6% (n = 6) did not. However, TDM use is limited to mood stabilizers (lithium 96.3%, valproate 97.6%) to a great extent. Only a small number of psychiatrists perform TDM for other psychotropic drugs, e.g., clozapine 2.4%, tricyclic antidepressants 1.3%, benzodiazepines 1.1%, and selective serotonin reuptake inhibitors 0,8%. CONCLUSIONS: Most of the psychiatrists in Turkey have a positive attitude toward use of therapeutic drug monitoring although there is also a considerable difficulty to reach services for the therapeutic drug monitoring of psychotropics other than mood stabilizers.