Preoperative Depression is Associated With Increased Complications Following Ankle Fracture Surgery

Background The effects of preoperative depression following ankle fracture surgery remains unknown. The purpose of this study is to investigate the relationship between preoperative depression and outcomes following ankle fracture surgery. Methods This retrospective study used the Truven MarketScan database to identify patients who underwent ankle fracture surgery from January 2009 to December 2018. Patients with and without a diagnosis of preoperative depression were identified based on International Classification of Diseases (ICD) codes. Chi-squared and multivariate analyses were performed to determine the association between preoperative depression and postoperative complications following ankle fracture surgery. Results In total, 107,897 patients were identified for analysis, 13,981 of whom were diagnosed with depression (13%). Preoperative depression was associated with the increased odds for postoperative infection (odds ratio [OR]: 1.33, confidence interval [CI]: 1.20-1.46), wound complications (OR: 1.13, CI: 1.00-1.28), pain-related postoperative emergency department visits (OR: 1.58, CI: 1.30-19.1), 30-day and 90-day readmissions (OR: 1.08, CI: 1.03-1.21 and OR: 1.13, CI: 1.07-1.18), sepsis (OR: 1.39, CI: 1.12-1.72), and postoperative development of complex regional pain syndrome (OR: 1.46, CI: 1.18-1.81). Conclusion Preoperative depression is associated with increased complications following ankle fracture surgery. Further studies are warranted to investigate the degree to which depression is a modifiable risk factor. Level of Evidence: 3

Dapsone is an anticatalysis for Alzheimer’s disease exacerbation

This study investigated leprosy patients with Alzheimer's disease (AD) treated with dapsone (4,4’-diaminodiphenyl sulfone, DDS) as a cytosolic DNA sensor cyclic-GMP-AMP synthase (cGAS)/stimulator of interferon genes (STING) signaling pathway and neuroinflammasome competitor. We searched the Sorokdo National Hospital medical records and the National Health Insurance Service in South Korea with the International Classification of Diseases (ICD)-10 code and Electronic Data Interchange (EDI) from January 2005 to June 2020. Four groups were defined: Treatment (T) 1: DDS prescription (+) AD prevalence (+), T 2: DDS (+) AD nondiagnosed (-), T 3: DDS nonprescription (-) AD (+), T 4: DDS (-) AD (-). The T1:T3 tests demonstrate that the incidence of AD is significantly reduced in the presence of dapsone among AD patients. The T1:T3 tests demonstrate that the incidence of AD is significantly reduced in the presence of dapsone among AD patients. T1 (M = 0.18, SD = 0.074):T2 (M = 0.55, SD = 0.14) and T3 (M = 0.18, SD = 0.074):T4 (M = 0.55, SD = 0.14) explain that dapsone effects on AD can be clearly distinguished according to its presence or absence.The T1:T4 and the T2:T3 test demonstrate a causal relationship in which the presence or absence of dapsone determines the onset of AD. The T1:T3 test proved that the incidence of AD was significantly reduced by dapsone. (The t-value is -23.1, p-value is < .00001, significant at p < .05) The T2:T3 test proved that the prevalence of AD was significantly high without dapsone, and without AD was increased with dapsone. (The t-value is -6.38, p-value is < .00001, significant at p < .05) AD is increased in the absence of dapsone. Our study has demonstrated that dapsone has the potential for the prevention of AD. This study indicates that dapsone is a valid preventive therapeutic for AD. KEYWORD: Neuroinflmmasome, Alzheimer's disease, Dapsone

Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database

<b>Purpose. </b>A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes–related symptoms using a large nationwide electronic health record (EHR) database. <p><b>Methods. </b>We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (<i> n </i>= 1,136,301 patients) was identified using a rule-based phenotype method. A multi-step procedure was then used to identify type 2 diabetes–related symptoms based on <i>International Classification of Diseases</i>,<i> </i>9th and 10th revisions, diagnosis codes. Type 2 diabetes–related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. </p> <p><b>Results.</b> Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21–60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies.</p> <p><b>Conclusion.</b> To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes–related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified. </p>

Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database

OBJECTIVE A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes–related symptoms using a large nationwide electronic health record (EHR) database. Methods We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (n = 1,136,301 patients) was identified using a rule-based phenotype method. A multistep procedure was then used to identify type 2 diabetes–related symptoms based on International Classification of Diseases, 9th and 10th revisions, diagnosis codes. Type 2 diabetes–related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. Results Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21–60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies. Conclusion To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes–related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified.

Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database

<b>Purpose. </b>A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes–related symptoms using a large nationwide electronic health record (EHR) database. <p><b>Methods. </b>We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (<i> n </i>= 1,136,301 patients) was identified using a rule-based phenotype method. A multi-step procedure was then used to identify type 2 diabetes–related symptoms based on <i>International Classification of Diseases</i>,<i> </i>9th and 10th revisions, diagnosis codes. Type 2 diabetes–related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. </p> <p><b>Results.</b> Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21–60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies.</p> <p><b>Conclusion.</b> To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes–related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified. </p>

Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection

Agriculture has becomes an immense area of research and is ascertained as a key element in the area of computer vision. In the agriculture field, image processing acts as a primary part. Cucumber is an important vegetable and its production in Pakistan is higher as compared to the other vegetables because of its use in salads. However, the diseases of cucumber such as Angular leaf spot, Anthracnose, blight, Downy mildew, and powdery mildew widely decrease the quality and quantity. Lately, numerous methods have been proposed for the identification and classification of diseases. Early detection and then treatment of the diseases in plants is important to prevent the crop from a disastrous decrease in yields. Many classification techniques have been proposed but still, they are facing some challenges such as noise, redundant features, and extraction of relevant features. In this work, an automated framework is proposed using deep learning and best feature selection for cucumber leaf diseases classification. In the proposed framework, initially, an augmentation technique is applied to the original images by creating more training data from existing samples and handling the problem of the imbalanced dataset. Then two different phases are utilized. In the first phase, fine-tuned four pre-trained models and select the best of them based on the accuracy. Features are extracted from the selected fine-tuned model and refined through the Entropy-ELM technique. In the second phase, fused the features of all four fine-tuned models and apply the Entropy-ELM technique, and finally fused with phase 1 selected feature. Finally, the fused features are recognized using machine learning classifiers for the final classification. The experimental process is conducted on five different datasets. On these datasets, the best-achieved accuracy is 98.4%. The proposed framework is evaluated on each step and also compared with some recent techniques. The comparison with some recent techniques showed that the proposed method obtained an improved performance.

Simple Excel and ICD-10 based dataset calculator for the Charlson and Elixhauser comorbidity indices

Background The Charlson and Elixhauser Comorbidity Indices are the most widely used comorbidity assessment methods in medical research. Both methods are adapted for use with the International Classification of Diseases, which 10th revision (ICD-10) is used by over a hundred countries in the world. Available Charlson and Elixhauser Comorbidity Index calculating methods are limited to a few applications with command-line user interfaces, all requiring specific programming language skills. This study aims to use Microsoft Excel to develop a non-programming and ICD-10 based dataset calculator for Charlson and Elixhauser Comorbidity Index and to validate its results with R- and SAS-based methods. Methods The Excel-based dataset calculator was developed using the program’s formulae, ICD-10 coding algorithms, and different weights of the Charlson and Elixhauser Comorbidity Index. Real, population-wide, nine-year spanning, index hip fracture data from the Estonian Health Insurance Fund was used for validating the calculator. The Excel-based calculator’s output values and processing speed were compared to R- and SAS-based methods. Results A total of 11,491 hip fracture patients’ comorbidities were used for validating the Excel-based calculator. The Excel-based calculator’s results were consistent, revealing no discrepancies, with R- and SAS-based methods while comparing 192,690 and 353,265 output values of Charlson and Elixhauser Comorbidity Index, respectively. The Excel-based calculator’s processing speed was slower but differing only from a few seconds up to four minutes with datasets including 6250–200,000 patients. Conclusions This study proposes a novel, validated, and non-programming-based method for calculating Charlson and Elixhauser Comorbidity Index scores. As the comorbidity calculations can be conducted in Microsoft Excel’s simple graphical point-and-click interface, the new method lowers the threshold for calculating these two widely used indices. Trial registration retrospectively registered.

Neurochemical and Hormonal Contributors to Compulsive Sexual Behavior Disorder

Purpose of Review Compulsive sexual behavior disorder has been recently included in the 11th revision of the International Classification of Diseases (ICD-11), and the possible contribution of neurochemical and hormonal factors have been reported. However, relatively little is known concerning the neurobiology underlying this disorder. The aim of this article is to review and discuss published findings in the area. Recent Findings Evidence suggests that the neuroendocrine systems are involved in the pathophysiology of compulsive sexual behavior. The hypothalamus-pituitary adrenal axis, the hypothalamus-pituitary–gonadal axis, and the oxytocinergic system have been implicated. Summary Further studies are needed to elucidate the exact involvement of neuroendocrine and hormonal systems in compulsive sexual behavior disorder. Prospective longitudinal studies are particularly needed, especially those considering co-occurring psychiatric disorders and obtaining hormonal assessments in experimental circumstances with appropriate control groups.

A cry for help: Dramatherapists can intervene to change the language and environment in mental health care by engaging critically with the new diagnostic category of Complex PTSD

In funded healthcare settings, access to dramatherapy and other arts therapies is limited. Patients suffering the long-term emotional effects of childhood or prolonged trauma are often not helped by short-term funded therapies. These therapies that engage in the diagnostic model of suffering with disorder specific research speak little to those suffering multiple traumas. This leaves dramatherapists unable to reach those most in need of their skills. At the same time, survivors are left bewildered and shamed again as they ‘fail’ to benefit from the limited symptom management approaches on offer. While the diagnostic model of suffering may be approaching obsolescence, what still seems a long way away is a major overhaul of the mainstream understanding of suffering and mental health that could fuel a reorganisation of how services are delivered and research conducted. In this context, the new diagnostic criteria of Complex Post Traumatic Stress Disorder in the International Classification of Diseases-11 provides an opportunity and perhaps even a rallying cry for dramatherapists to evidence how our skills can provide a framework and method for survivors to re-imagine themselves and understand and claim their place in the world by loosening the chains of fear and shame.