Background: Dust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission is important for devising effective mitigation strategies, ensuring a safer working environment, and minimizing environmental impact. This study focuses on developing an artificial neural network (ANN) model to predict dust emission values in the machining of black poplar (Populus nigra L.), oriental beech (Fagus orientalis L.), and medium-density fiberboards. Methods: The multilayer feed-forward ANN model is developed using a customized application built with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades, and cutting depth, whereas the output is the dust emission. Model performance is assessed through graphical and statistical comparisons. Results: The results reveal that the developed ANN model can provide adequate predictions for dust emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study predicts intermediate dust emission values for different cutting widths and cutting depths, which are not considered in the experimental work. It is observed that dust emission tends to decrease with reductions in cutting width and cutting depth. Conclusion This study introduces an alternative approach to optimize machining-process conditions for minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission values without the need for additional experimental activities, thereby reducing experimental time and costs.

Background: Dust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission is important for devising effective mitigation strategies, ensuring a safer working environment, and minimizing environmental impact. This study focuses on developing an artificial neural network (ANN) model to predict dust emission values in the machining of black poplar (Populus nigra L.), oriental beech (Fagus orientalis L.), and medium-density fiberboards. Methods: The multilayer feed-forward ANN model is developed using a customized application built with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades, and cutting depth, whereas the output is the dust emission. Model performance is assessed through graphical and statistical comparisons. Results: The results reveal that the developed ANN model can provide adequate predictions for dust emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study predicts intermediate dust emission values for different cutting widths and cutting depths, which are not considered in the experimental work. It is observed that dust emission tends to decrease with reductions in cutting width and cutting depth. Conclusion This study introduces an alternative approach to optimize machining-process conditions for minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission values without the need for additional experimental activities, thereby reducing experimental time and costs.

Background: Dust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission is important for devising effective mitigation strategies, ensuring a safer working environment, and minimizing environmental impact. This study focuses on developing an artificial neural network (ANN) model to predict dust emission values in the machining of black poplar (Populus nigra L.), oriental beech (Fagus orientalis L.), and medium-density fiberboards. Methods: The multilayer feed-forward ANN model is developed using a customized application built with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades, and cutting depth, whereas the output is the dust emission. Model performance is assessed through graphical and statistical comparisons. Results: The results reveal that the developed ANN model can provide adequate predictions for dust emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study predicts intermediate dust emission values for different cutting widths and cutting depths, which are not considered in the experimental work. It is observed that dust emission tends to decrease with reductions in cutting width and cutting depth. Conclusion This study introduces an alternative approach to optimize machining-process conditions for minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission values without the need for additional experimental activities, thereby reducing experimental time and costs.

Background: Dust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission is important for devising effective mitigation strategies, ensuring a safer working environment, and minimizing environmental impact. This study focuses on developing an artificial neural network (ANN) model to predict dust emission values in the machining of black poplar (Populus nigra L.), oriental beech (Fagus orientalis L.), and medium-density fiberboards. Methods: The multilayer feed-forward ANN model is developed using a customized application built with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades, and cutting depth, whereas the output is the dust emission. Model performance is assessed through graphical and statistical comparisons. Results: The results reveal that the developed ANN model can provide adequate predictions for dust emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study predicts intermediate dust emission values for different cutting widths and cutting depths, which are not considered in the experimental work. It is observed that dust emission tends to decrease with reductions in cutting width and cutting depth. Conclusion This study introduces an alternative approach to optimize machining-process conditions for minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission values without the need for additional experimental activities, thereby reducing experimental time and costs.

Seizures are believed to be a dose-dependent side effect of clozapine. In this case report, we describe a patient who had tonic-clonic seizures after using a low dose clozapine who did not have any seizure risk. The 29-year-old male patient had been followed-up with a diagnosis of schizophrenia for about 5 years. When using clozapine 200 mg/day he had a tonic-clonic seizure with bilateral diffuse epileptic activity in electroencephalography (EEG). In the literature, there are a few case reports about low-dose clozapine-induced seizure. Seizures were observed in our case with a low dose of clozapine (200 mg/day) making this case remarkable. EEG monitoring at regular intervals and examination of plasma levels of clozapine could be useful in preventing the development of seizures.
Adult ; Clozapine ; Diagnosis ; Electroencephalography ; Humans ; Male ; Plasma ; Schizophrenia ; Seizures*

OBJECTIVE: Bipolar disorder (BD) is a chronic mood disorder characterized by recurrent episodes that has a lifetime prevalence of 0.4–5.5%. The neurochemical mechanism of BD is not fully understood. Oxidative stress in neurons causes lipid peroxidation in proteins associated with neuronal membranes and intracellular enzymes and it may lead to dysfunction in neurotransmitter reuptake and enzyme activities. These pathological processes are thought to occur in brain regions associated with affective functions and emotions in BD. The relationship between the number of manic episodes and total oxidant-antioxidant capacity was investigated in this study. METHODS: Eighty-two BD patients hospitalized due to manic symptoms and with no episodes of depression were enrolled in the study. Thirty of the 82 patients had had their first episode of mania, and the other 52 patients had had two or more manic episodes. The control group included 45 socio-demographically matched healthy individuals. Serum total antioxidant capacity (TAC) and total oxidant capacity (TOC) measurements of the participants were performed. The oxidative stress index (OSI) was calculated by TOC/TAC. RESULTS: There were no significant differences in OSI scores between BD patients with first-episode mania and BD patients with more than one manic episode. However, OSI scores in both groups were significantly higher than in the control group. TOC levels of BD patients with first-episode mania were found to be significantly higher than TOC levels of BD patients with more than one manic episode and healthy controls. There were no significant differences in TAC levels between BD patients with first-episode mania and BD patients with more than one manic episode. TAC levels in both groups were significantly higher than in the control group. CONCLUSION: Significant changes in oxidative stress indicators were observed in this study, confirming previous studies. Increased levels of oxidants were shown with increased disease severity rather than with the number of manic episodes. Systematic studies, including of each period of the disorder, are needed for using the findings indicating deterioration of oxidative parameters.
Bipolar Disorder* ; Brain ; Depression ; Humans ; Lipid Peroxidation ; Membranes ; Mood Disorders ; Neurons ; Neurotransmitter Agents ; Oxidants ; Oxidative Stress* ; Pathologic Processes ; Prevalence

OBJECTIVE: Findings about inflammatory processes in schizophrenia are increasing day by day. Inflammatory processes in schizophrenia are associated with both its etiology and clinical symptoms. Serum high-sensitivity C-reactive protein (hsCRP) is also one of these inflammatory processes. Particularly, it is thought to be closely related to clinical findings of patients with schizophrenia. METHODS: In this study, the relationship between clinical findings of hsCRP levels of patients with drug-naïve first-episode psychosis (FEP) and patients with schizophrenia in acute exacerbation phase is investigated. Clinical findings, psychometric properties (the Scale for the Assessment of Positive Symptoms, the Scale for the Assessment of Negative Symptoms, Brief Psychiatric Rating Scale), and hsCRP levels of patients were compared. RESULTS: Forty-eight patients with FEP, 74 patients with schizophrenia in acute exacerbation phase and 54 healthy controlled volunteers are included in the study. The most substantial finding in the study is that there is a positive correlation between hsCRP levels and severity of positive symptoms of both patient groups, with FEP and with schizophrenia. The second most substantial finding is there is no significant difference between patients with FEP and schizophrenia, in terms of hsCRP. CONCLUSION: The relationship between hsCRP and positive symptom severity in two groups of patients supports the inflammatory hypothesis in the etiopathogenesis of schizophrenia. This finding is supportive of close relation between inflammatory processes and clinical findings of patient with schizophrenia.
C-Reactive Protein ; Humans ; Polytetrafluoroethylene ; Psychometrics ; Psychotic Disorders ; Schizophrenia ; Volunteers

BACKGROUND: Although regional anesthesia is the first choice for patients undergoing total knee arthroplasty (TKA), it may not be effective and the risk of complications is greater in patients who are obese or who have spinal deformities. We compared the success of ultrasound-guided femoral and sciatic nerve blocks with sedoanalgesia versus spinal anesthesia in unilateral TKA patients in whom spinal anesthesia was difficult. METHODS: We enrolled 30 patients; 15 for whom spinal anesthesia was expected to be difficult were classified as the block group, and 15 received spinal anesthesia. Regional anesthesia was achieved with bupivacaine 62.5 mg and prilocaine 250 mg to the sciatic nerve, and bupivacaine 37.5 mg and prilocaine 150 mg to the femoral nerve. Bupivacaine 20 mg was administered to induce spinal anesthesia. Hemodynamic parameters, pain and sedation scores, and surgical and patient satisfaction were compared. RESULTS: A sufficient block could not be obtained in three patients in the block group. The arterial pressure was significantly lower in the spinal group (P < 0.001), and the incidence of nausea was higher (P = 0.017) in this group. Saturation and patient satisfaction were lower in the block group (P < 0.028), while the numerical pain score (P < 0.046) and the Ramsay sedation score were higher (P = 0.007). CONCLUSIONS: Ultrasound-guided sciatic and femoral nerve blocks combined with sedoanalgesia were an alternative anesthesia method in selected TKA patients.
Anesthesia ; Anesthesia, Conduction ; Anesthesia, Spinal* ; Arterial Pressure ; Arthroplasty* ; Bupivacaine ; Congenital Abnormalities ; Femoral Nerve ; Hemodynamics ; Humans ; Incidence ; Knee* ; Nausea ; Nerve Block ; Patient Satisfaction ; Prilocaine ; Sciatic Nerve* ; Ultrasonography