The effectiveness of short-term dynamic/interpersonal group therapy on perfectionism; assessment of anxiety, depression and interpersonal problems

Introduction: Perfectionism is acknowledged as a core vulnerability and a perpetuating factor in several psychopathologies. The purpose of the present study was to investigate the effectiveness of short-term dynamic/interpersonal group therapy on perfectionism and perfectionism-related distress such as anxiety, depression, and interpersonal problems. Method: This study is a quasi-experimental study applying clinical trial method and contains pre-test, post-test, follow-up periods and control group. The study population included students and the sample consisted of 30 people with extreme perfectionism, who were assigned in two groups of 15 people, experimental and waiting list groups using randomized block design. Research instruments included TMPS, PSPS, PCI, BDI-II, BAI and IIP-32 scales. In order to analyze the collected data, mixed analysis of variance and Repeated Measures Analysis of Variance were used in SPSS software version 22. Findings: The results show that the intervention in the experimental group compared to the waiting list group caused a clinically and statistically significant decrease in the mean scores. This result is observable and evident in all levels of perfectionism and psychological distress (anxiety, depression and interpersonal problems), except for the subscale of non-display of imperfection from the PSPS scale. These results were preserved through the follow-up periods. Discussion: These results show that short-term dynamic/interpersonal group therapy is effective in treating most of the components of perfectionism, and concerning its effectiveness; it reduced psychological distress and showed that the components pertaining to perfectionism are factors of vulnerability in this regard.

The efficiency of collected biological samples from crime scene on crime detection

AIMS: The present study investigates the efficiency of biological samples taken from the crime scene on crime detection. METHODS. This study is a descriptive, documentary and applied. The statistical population of this research includes 60 employees of Zanjan State Criminal Police, including crime scene investigators, lab experts of forensic chemistry and biology, forensic detectives, medical examiners and examining magistrates and prosecuting attorneys of Zanjan province’s Judicature with at least 5 years of executive experience in crime scene investigation. The questionnaire is used as a tool in this study. The validity of the questionnaire was confirmed by the experts and the reliability of the questionnaire, calculated by the Cronbach's alpha coefficient, was 0.939. For analyzing, descriptive and inferential statistics Kolmogorov-Smirnov, Friedman and one sample t-tests were used. The Kolmogorov-Smirnov test showed normal distribution of the data; so the parametric tests were used to examine the variables. FINDINGS. The results of one sample t-test showed that biological samples of blood, saliva, semen, and hair strand make a significant contribution to the scientific detection of crime. Friedman test for ranking the importance of various factors related to different samples on crime detection rate from the view of the respondents, showed that using modern equipments and technologies, sample quality and officer scientific and technical knowledge have the most priorities. CONCLUSION. According to the results the best biologic test, from the view of the respondents, in crime detection is DNA typing

Challenges of Using Biometric Evidence in Identification

INTRODUCTION ... [1]. Recognizing people traditionally based on identifying them using their physical characteristics (fingerprints, iris, face, way of walking, and DNA) or their behavioral characteristics is called the biometric method [2, 3]. Face recognition is a common way to identify people using their facial features. However, like other biometric methods, in environments with restrictions, the results' quality decreases significantly [4, 5]. ... [6]. A biometric system is a pattern recognition system. A simple biometric system has four essential parts [7]: 1- sensor block (receiving biometric information), 2- feature extraction block (feature vector extraction), 3- comparison block (comparing the vector with templates), 4- decision block ( Identification). ... [8-10]. Although biometric markers are widely used in developed countries today, due to the complexity of hardware and software, extraction of biometric markers such as DNA profile [11], fingerprint characteristics [12], facial characteristics [13], iris and voice characteristics [12], and also the costs of their construction and commissioning face specific challenges in developing countries [14-16]. Challenges that are effective in using biometric evidence: 1- Challenges of providing organizational financial resources, 2- Challenges of providing expert human resources, 3- Challenges of training human resources. The current research investigates the challenges of using biometric evidence in identity detection. Among the research conducted in this field, Garud and Agrawal's research can be mentioned as one that sought to solve the problem of fake faces in recognition technology. Their suggested solutions are blinking, counting based on movement, microtexture extraction, Fourier spectrum analysis, element descriptors, and face recognition focusing on the forehead and image backgrounds [17]. Also, Nogueira, Alencar Lotufo, and Machado have used complex neural networks in their research to detect fingerprint biometrics, which shows that if these networks are trained in advance, they can obtain new results without the need for extensive parameters and expensive designs. Also, this method is highly accurate on minimal training sets [18]. In another study, Andrey 2020 states that in the context of expanding the tools of credit institutions related to the management of the risk of involvement in suspicious transactions, there is a need to introduce new technologies to combat money laundering and the financing of terrorism. Biometric identification technologies are effective in minimizing risk and protecting corporate information systems of banks. ... [19]. AIM(S) This study aimed to investigate the challenges of using biometric evidence in identification. RESEARCH TYPE The current research is a descriptive survey type and method, applied in terms of purpose and nature, and a documentary survey in data collection. RESEARCH SOCIETY, PLACE & TIME Questionnaires were distributed and collected in the winter of 2021. The detectives and investigation police officers of Gilan province in Iran were considered the statistical population with 109 people. SAMPLING METHOD AND NUMBER Considering the number of the statistical population, the total population was considered in this research (109 people). The criterion for entering the study was at least five years of work experience, and the exclusion criterion from the study was an unwillingness to continue attending. USED DEVICES & MATERIALS A researcher-made questionnaire was used as a research tool (Table 1). The scale was used to measure the biometric evidence using seven questions. They measured the challenges of providing organizational financial resources using five questions and the challenges of providing expert human resources using seven questions. Finally, five questions were compiled to measure the challenges of training human resources, and their validity and reliability were checked. The questions were closed-ended and scaled based on a five-point Likert scale from shallow (5 points) to very much (1 point). ETHICAL PERMISSION Ethical considerations were observed in conducting this research, including confidentiality of questionnaires, informed consent, and voluntary withdrawal of participants from the research. STATISTICAL ANALYSIS For data analysis, Pearson's R, t-test, and one-way analysis of variance were used with SmartPLS version 3 software. A confidence factor of 95% and an error level of 5% were considered for all routes. FINDING by TEXT In this study, 84 detectives and police officers of Gilan province in Iran (14 women and 70 men) had completed the questionnaires correctly; 23 of them had a high school diploma, 19 had an associate degree, 38 had a bachelor's degree, and 4 had a master's degree. By performing the Kolmogorov-Smirnov test on the data distribution (variables of providing organizational financial resources, providing expert human resources, and training human resources according to the respondents), the p-value was more significant than 0.05, which indicated the normal distribution of the data. In the questionnaire examination, the factor loading of all items was above 0.4, which confirmed the internal correlation. The average value of the extracted variance of the variables was higher than 0.5, and the convergence validity of the measured variables was favorable. Composite reliability was obtained as more significant than the average variance extracted, which was reported due to the convergence validity of desired measurement models. To check the discriminated validity or divergence, the average value of the extracted variance was more significant than the values of the squared covariance and the maximum squared covariance. The internal reliability of the tools was also confirmed (Table 2). According to Table 3, since the mean square of the extracted variance of all variables was higher than the correlation of the constructs with other constructs in the model, the existence of discriminated validity among the research variables was confirmed, and it showed that the measurement tool had a suitable validity. The impact of the challenges of using biometric evidence in identification was obtained using standard coefficient parameters and significant numbers with SmartPLS software (Figures 1 and 2). The impact and t value of the variable of providing organizational financial resources is 0.817 and 12.78, and the provision of expert human resources was respectively 0.658 and 9.24. Also, the human resources training was obtained as 0.508 and 27.6 in using biometric evidence in identification (Table 4). The t values of the three components showed their confirmation. To investigate the impact of three variables of providing organizational financial resources, providing expert human resources, and the challenges of training human resources in using biometric evidence in identification, a one-sample t-test was used, and the results are presented in Table 4. The value of the t statistic for all variables was more significant than the critical value of 1.96, so all the variables of this research were in good condition. The result of calculating Spearman's correlation coefficient (Spearman's rho) to investigate the relationship between biometric evidence and human resources training was 0.482, and the significance level was p<0.001. Also, the Spearman correlation coefficient for examining the relationship between the use of biometric evidence and the allocation of financial resources was 0.353, and the lack of human resources was 0.519 (p<0.001). Therefore, with 95% certainty, there was a significant relationship between using biometric evidence and human resource training, financial resource allocation, and lack of human resources. The results of the Friedman test to rank the averages showed that the opinions of the respondents were as follows; the variables of human resources training with an average rating of 1.35, allocation of human resources with an average rating of 2.46, and lack of human resources with an average rating of 2.19. Considering that the calculated significance level was lower than the intended significance level (p-value=0.05), it can be said that there was a significant difference between the variables in terms of ranking, and the rank of the variable of human resource allocation was higher than other variables. MAIN COMPARISON to the SIMILAR STUDIES According to Figure 2, the research population and the challenges of providing financial resources had the most significant impact on the use of biometric evidence. In other words, in the current situation, using biometric evidence and new methods and tools requires financing and investment in this sector, indicating a need for more attention on the part of the authorities. These results are consistent with the research conducted by Andrey in 2020 [19] and Pari and Hamidi [20]. Also, the challenge of providing expert human resources had a more significant impact on the use of biometric evidence than the challenge of training expert human resources. The use of expert forces in the analysis of laboratory samples (such as the use of biometric evidence in identity authentication) leads to the improvement of correct and accurate evaluation results, which is consistent with the research conducted by Shirzad et al. [21], Zohreh Nedayee [22] as well as Ebrahimi and Sadeghinejad [23]. The results of this research indicated that there needs to be more expert human resources. This issue originates from the need for more recruitment of expert personnel in recent years or the lack of scientific improvement of the existing personnel. The third challenge in this research was training the human forces employed in using biometric evidence in identity authentication, which was a lower priority than the two challenges of providing financial resources and allocating expert personnel. Cultivating skilled people, referred to as human resource development, is an inevitable necessity for organizations to survive and progress in today's ever-changing world. For this reason, training is considered one of the main tasks of human resources management and is always considered an essential factor in formulating development plans or organizational changes. In any case, simply providing training in the form of courses and training programs cannot be an influential factor in improving human resources in the organization. The research by Nasiri in this field also shows the importance of training [24]. LIMITATIONS The limitations of this research were the non-cooperation of some detectives and police officers of the province, despite sufficient explanations by the researchers. As a result, they did not participate in this research and did not complete the questionnaire. SUGGESTIONS It is suggested to increase this department's budget due to the urgent need for identification methods and the increase in security sensitivities. It is also suggested to provide and reduce the costs by exploiting private investment departments. CONCLUSIONS Based on the research, the lack of adequate financial resources plays a significant role in using biometric evidence for identification. Also, the variable of providing expert human resources and training employed human resources are in the following steps. The use of biometric evidence and the use of new methods and tools require financing and investment, which indicates a lack of attention on the part of the authorities, and the lack of expert human resources can originate from the lack of recruitment of specialists in recent years or the lack of scientific improvement of the existing forces. Also, providing training in the form of courses and training programs cannot be an influential factor in improving human resources in the organization. ACKNOWLEDGMENTS We appreciate all the detectives, intelligence officers, and officials of Gilan province in Iran who cooperated in conducting this research. CONFLICT of INTEREST The authors state that the present study has no conflict of interest. FUNDING SOURCES The present study did not have any financial support

Bilateral primary synovial chondromatosis in the knee joint

Abstract Primary synovial chondromatosis is a disorder characterized by the metaplasia of the synovial membrane and the formation of loose bodies floating in the joint. We described a 30‐year‐old woman without any past medical history complaining of bilateral progressive knee pain who was later discovered to have bilateral synovial chondromatosis

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning : A large multicentric cohort study

To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients: COVID-19 prognostic modeling using CT radiomics and machine learning

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95: 0.81�0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95: 0.81�0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients. © 2022 The Author

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.</p

Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019

Summary Background Reducing the burden of death due to infection is an urgent global public health priority. Previous studies have estimated the number of deaths associated with drug-resistant infections and sepsis and found that infections remain a leading cause of death globally. Understanding the global burden of common bacterial pathogens (both susceptible and resistant to antimicrobials) is essential to identify the greatest threats to public health. To our knowledge, this is the first study to present global comprehensive estimates of deaths associated with 33 bacterial pathogens across 11 major infectious syndromes. Methods We estimated deaths associated with 33 bacterial genera or species across 11 infectious syndromes in 2019 using methods from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, in addition to a subset of the input data described in the Global Burden of Antimicrobial Resistance 2019 study. This study included 343 million individual records or isolates covering 11 361 study-location-years. We used three modelling steps to estimate the number of deaths associated with each pathogen: deaths in which infection had a role, the fraction of deaths due to infection that are attributable to a given infectious syndrome, and the fraction of deaths due to an infectious syndrome that are attributable to a given pathogen. Estimates were produced for all ages and for males and females across 204 countries and territories in 2019. 95% uncertainty intervals (UIs) were calculated for final estimates of deaths and infections associated with the 33 bacterial pathogens following standard GBD methods by taking the 2·5th and 97·5th percentiles across 1000 posterior draws for each quantity of interest. Findings From an estimated 13·7 million (95% UI 10·9–17·1) infection-related deaths in 2019, there were 7·7 million deaths (5·7–10·2) associated with the 33 bacterial pathogens (both resistant and susceptible to antimicrobials) across the 11 infectious syndromes estimated in this study. We estimated deaths associated with the 33 bacterial pathogens to comprise 13·6% (10·2–18·1) of all global deaths and 56·2% (52·1–60·1) of all sepsis-related deaths in 2019. Five leading pathogens—Staphylococcus aureus, Escherichia coli, Streptococcus pneumoniae, Klebsiella pneumoniae, and Pseudomonas aeruginosa—were responsible for 54·9% (52·9–56·9) of deaths among the investigated bacteria. The deadliest infectious syndromes and pathogens varied by location and age. The age-standardised mortality rate associated with these bacterial pathogens was highest in the sub-Saharan Africa super-region, with 230 deaths (185–285) per 100 000 population, and lowest in the high-income super-region, with 52·2 deaths (37·4–71·5) per 100 000 population. S aureus was the leading bacterial cause of death in 135 countries and was also associated with the most deaths in individuals older than 15 years, globally. Among children younger than 5 years, S pneumoniae was the pathogen associated with the most deaths. In 2019, more than 6 million deaths occurred as a result of three bacterial infectious syndromes, with lower respiratory infections and bloodstream infections each causing more than 2 million deaths and peritoneal and intra-abdominal infections causing more than 1 million deaths. Interpretation The 33 bacterial pathogens that we investigated in this study are a substantial source of health loss globally, with considerable variation in their distribution across infectious syndromes and locations. Compared with GBD Level 3 underlying causes of death, deaths associated with these bacteria would rank as the second leading cause of death globally in 2019; hence, they should be considered an urgent priority for intervention within the global health community. Strategies to address the burden of bacterial infections include infection prevention, optimised use of antibiotics, improved capacity for microbiological analysis, vaccine development, and improved and more pervasive use of available vaccines. These estimates can be used to help set priorities for vaccine need, demand, and development