The Subjective Visual Vertical and Electrocochleography Tests in Individuals with Meniere's Disease.

Introduction: Meniere's disease is an inner ear disorder not associated with central brain structure involvement. Although the ECochG test has been commonly used to diagnose Meniere's disease recently, it has not demonstrated high sensitivity. Therefore, it is recommended that other complementary tests alongside the ECochG test be used to diagnose Meniere's disease. The SVV test has gained popularity recently for this reason, and in this study, it was decided to use the SVV test in both static and dynamic modes, along with the ECochG test, as a tool for diagnosing Meniere's disease and evaluating its diagnostic features. Materials and Methods: The study was conducted on 53 patients with confirmed unilateral Meniere's disease and a normal group. Means were calculated with a 95% confidence interval for the groups, and the corresponding graphs were plotted. Independent t-tests were used to examine the difference in SVV results between the normal and Meniere's groups. The ROC curve was then used to determine the cutoff point and calculate. Results: After investigating the cutoff point for the three SVV conditions (tilted towards the lesion), a cutoff point of 2.1 degrees with a sensitivity and specificity of approximately 0.7 was identified as the best condition for distinguishing the Meniere's group from the healthy group. Conclusion: Finally, based on the findings, it can be concluded that the SVV test has relatively low sensitivity for diagnosing Meniere's disease. Therefore, more than relying solely on its results to identify Meniere's disease is required, and it is suggested that other measures alongside the ECochG and SVV tests be included in future studies for further investigation.

Prevalence of Ear-Related Problems in Individuals Recovered From COVID-19.

Introduction: The aim was to assess prevalence of tinnitus, hyperacusis, hearing and balance problems among patients recovered from COVID-19 infection. Self-reported ear and hearing symptoms were compared in three groups comprising: confirmed COVID-19, possible COVID-19, and non-COVID-19. Materials and Methods: 1649 participants completed the survey in this cross-sectional study. The mean age was 34 years and 65% were female. Participants with confirmed and possible COVID-19 were asked if after their infection (compared to the past) they experienced hearing loss, ringing or whistling noises, fullness or blockage in their ears, loudness of the sounds that are normal to other people bother them more (an indication of hyperacusis), dizziness, giddiness, or imbalance. Results: Among participants with confirmed COVID-19, 16% reported that compared to the past their hearing has decreased, 21.5% noticed tinnitus, 22.5% aural fullness, 26.1% hyperacusis and 17.3% balance problems. Regression models showed that compared to the non-COVID-19 group, participants with confirmed COVID-19 had odds ratios (ORs) of significantly greater than 1 in predicting presence of self-reported symptoms of hearing loss, tinnitus, aural fullness, hyperacusis and balance problems, OR=1.96 (p=0.001), OR=1.63 (p=0.003), OR=1.8 (p<0.001), OR=2.2 (p<0.001), and OR=2.99 (p<0.001), respectively. Conclusions: There seem to be higher prevalence of self-report symptoms of ear-related problems among individuals with confirmed COVID-19 infection compared to a non-COVID-19 group during the pandemic.

The latent factor structure and assessment of childbirth-related PTSD: psychometric characteristics of the City Birth Trauma Scale-Persian version (City-BiTS-P).

Objective: There is no validated Persian questionnaire to measure childbirth-related PTSD (CB-PTSD) symptoms. To cover this gap, the present study aimed to provide a Persian version of the City Birth Trauma Scale (CityBiTS-Pr) and to determine its psychometric properties. Method: Since this is a cross-sectional study, sampling was done using a convenient sampling method. In total, 300 Persian-speaking women took part in this study and completed the City Birth Trauma Scale (CityBiTS-Pr), the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), the Edinburgh Postnatal Depression Scale (EPDS), the Anxiety Subscale of the Depression, and the Anxiety and Stress Scale (DASS-21). In addition, sociodemographic information was completed. A confirmatory factor analysis of two- and four-factor models and a bi-factor model with a general factor and two specific factors were tested. Fit indices were calculated for all three models. Reliability, convergent, divergent, and discriminant validity also were examined. R v4.2.1 and SPSS v23 were used for data analysis. Results: The four-factor model comprised intrusion, avoidance, negative cognitions and mood, and hyper-arousal showed a poor fit. The two-factor model composed of "birth-related symptoms" and "general symptoms" provided the best results based on all fit indices. The bi-factor result was relatively good, but the loadings indicated that the general symptoms factor is not well defined. Conclusion: The Persian version of the City Birth Trauma Scale (CityBiTS-Pr) is a valid and reliable questionnaire for evaluating postpartum PTSD.

Development, validity, and reliability of Neural Circuits Questionnaire (NCQ).

After a prolonged period of using the cortico-centric models of brain function, scientists developed a parallel perspective with an emphasis on all neural structures of the brain. Therefore, the present study aims to create a valid and reliable assessment based on the parallel perspective of brain function. First, a basic assessment was made. Then, a sample size of 183 participants was recruited from graduates and undergraduates at the Ferdowsi University of Mashhad over a 2-year period (2020 and 2021). The measures were the Neural Circuits Questionnaire (NCQ) and the Demographic Data Questionnaire. Data analyses were performed by using exploratory factor analysis. Using a receiver operating characteristic (ROC) curve, cutting points for the quality index of the questionnaire were calculated. The results showed that the questionnaire has a three-factor structure and a Cronbach's alpha of 0.617. Based on the ROC curve, the valid range for the quality index is 10.5-15.5. The cerebro-cerebellar index is higher for Ph.D. students than for students with lower degrees (F = 8.60, p = 0.001). In conclusion, it appears that the Neural Circuits Questionnaire is a valid and reliable questionnaire to assess cognitive function following the parallel perspective of brain function.

Deep Learning-Based Transfer Learning for Classification of Skin Cancer.

One of the major health concerns for human society is skin cancer. When the pigments producing skin color turn carcinogenic, this disease gets contracted. A skin cancer diagnosis is a challenging process for dermatologists as many skin cancer pigments may appear similar in appearance. Hence, early detection of lesions (which form the base of skin cancer) is definitely critical and useful to completely cure the patients suffering from skin cancer. Significant progress has been made in developing automated tools for the diagnosis of skin cancer to assist dermatologists. The worldwide acceptance of artificial intelligence-supported tools has permitted usage of the enormous collection of images of lesions and benevolent sores approved by histopathology. This paper performs a comparative analysis of six different transfer learning nets for multi-class skin cancer classification by taking the HAM10000 dataset. We used replication of images of classes with low frequencies to counter the imbalance in the dataset. The transfer learning nets that were used in the analysis were VGG19, InceptionV3, InceptionResNetV2, ResNet50, Xception, and MobileNet. Results demonstrate that replication is suitable for this task, achieving high classification accuracies and F-measures with lower false negatives. It is inferred that Xception Net outperforms the rest of the transfer learning nets used for the study, with an accuracy of 90.48. It also has the highest recall, precision, and F-Measure values.

Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study.

COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have focused on multidimensional opportunities and solutions in encountering the COVID-19 pandemic. This study focuses on mental health and sentiment validations caused by the global lockdowns across the countries, resulting in a mental disability among individuals. This paper discusses a technique for identifying the mental state of an individual by sentiment analysis of feelings such as anxiety, depression, and loneliness caused by isolation and pauses to the normal chains of operations in daily life. The research uses a Neural Network (NN) to resolve and extract patterns and validate threshold trained datasets for decision making. This technique was used to validate 2,173 global speech samples, and the resulting accuracy of mental state and sentiments are identified with 93.5% accuracy in classifying the behavioral patterns of patients suffering from COVID-19 and pandemic-influenced depression.

The prevalence of Bartonella, hemoplasma, and Rickettsia felis infections in domestic cats and in cat fleas in Ontario.

The prevalence of persistent bacteremic Bartonella spp. and hemoplasma infections was determined in healthy pet cats in Ontario. Blood samples from healthy cats sent to a diagnostic laboratory for routine health assessment over the course of 1 y were tested for Bartonella spp. using both polymerase chain reaction (PCR) and blood culture, and for the presence of hemoplasma by PCR. The overall prevalence of Bartonella spp. by PCR and by culture combined was 4.3% (28/646) [3.7% (24/646) Bartonella henselae, 0.6% (4/646) Bartonella clarridgeiae]. The novel B. henselae PCR developed for this study demonstrated nearly twice the sensitivity of bacterial isolation. The overall prevalence of hemoplasma was 4% (30/742) [3.3% (25/742) Candidatus Mycoplasma haemominutum, 0.7% (5/742) Mycoplasma haemofelis]. There was no significant difference between the prevalence of infection by season or by age (< or = 2 y, > 2 y). Candidatus Mycoplasma turicensis was identified, for the first time in Canada, in 1 cat. The prevalence of Bartonella (58%) and hemoplasma (47% M. haemofelis, 13% M. haemominutum) in blood from a small sampling (n = 45) of stray cats was considerably higher than that found in healthy pet cats. The prevalence of Rickettsia felis in cat fleas was also assessed. A pool of fleas from each of 50 flea-infested cats was analyzed for the presence of R. felis by PCR. Rickettsia felis was confirmed, for the first time in Canada, in 9 of the 50 samples. Therefore, the prevalence of Bartonella and hemoplasma infection in healthy pet cats is relatively low. Further, the control of cat fleas is important because of the public health significance of Bartonella and R. felis infection.