UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.

The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable of accurately distinguishing COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority. Such automatic systems are usually based on traditional machine learning or deep learning methods. Differently from most of the existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a new, simple but efficient deep learning feature fusion model, called U n c e r t a i n t y F u s e N e t , which is able to classify accurately large datasets of both of these types of images. We argue that the uncertainty of the model's predictions should be taken into account in the learning process, even though most of the existing studies have overlooked it. We quantify the prediction uncertainty in our feature fusion model using effective Ensemble Monte Carlo Dropout (EMCD) technique. A comprehensive simulation study has been conducted to compare the results of our new model to the existing approaches, evaluating the performance of competing models in terms of Precision, Recall, F-Measure, Accuracy and ROC curves. The obtained results prove the efficiency of our model which provided the prediction accuracy of 99.08% and 96.35% for the considered CT scan and X-ray datasets, respectively. Moreover, our U n c e r t a i n t y F u s e N e t model was generally robust to noise and performed well with previously unseen data. The source code of our implementation is freely available at: https://github.com/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification.

DSM-5 Posttraumatic Stress Disorder Symptoms in Nonclinical Samples of Chinese and Pakistani Trauma-Exposed Adults: Factor Structure and Invariance Across Culture.

ABSTRACT: The purpose of the current study was to examine the latent structure and cross-cultural measurement validity of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) posttraumatic stress disorder (PTSD) symptoms assessed by the PTSD Checklist for DSM-5. Participants comprised trauma-exposed Chinese and Pakistani undergraduate students (N = 495 and N = 186, respectively). Confirmatory factor analysis (CFA) indicated that a seven-factor hybrid model involving intrusion, avoidance, negative affect, anhedonia, externalizing behaviors, anxious arousal, and dysphoric arousal factors provided good fit in both samples. This model fit significantly better than three alternative models including the DSM-5 four-factor model and six-factor anhedonia and externalizing behaviors models. The subsequent multigroup CFA showed that the best-fitting hybrid model demonstrated cross-cultural measurement invariance. Our findings provide further empirical support for the seven-factor PTSD hybrid model and its cross-cultural invariance, and have implications for understanding and application of DSM-5's PTSD symptoms.

Big five inventory-version 10: A two-minute personality measurement tool in Urdu.

OBJECTIVE: To translate into Urdu and validate the Big Five Inventory 10. METHODS: The study was conducted at a tertiary care hospital in Kharian Cantonment, and a university in Gilgit, Pakistan, from October to December 2020. Online video meetings were held for the translation process related to the Big Five Inventory 10. A systematic six-step process was followed for translation and validation. The volunteers recruited for the pilot and validation phases were from various different administrative regions of the country. Convergent and discriminant validity to assess construct validity, and Cronbach's alpha was calculated to assess the reliability of the scale. Data was analysed using SPSS 23. RESULTS: Of the 500 subjects, 358(71.6%) were males and 142(28.4%) were females. The overall age range was 18-48 years. The Urdu version was of the Big Five Inventory 10 was found to have a high level of construct validity supported by convergent and discriminant validity (p<0.05). The Cronbach alpha for all the sub-scales fell in the conventional range (0.71-0.88). Females scored higher on the 'agreeableness' subscale than the males (p<0.0). CONCLUSIONS: The Big Five Inventory 10 Urdu version was found to be a valid and reliable tool for researchers and clinicians having time constraints.

Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients.

Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help in identifying critically ill patients, providing appropriate treatment, and preventing mortality. We conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in Iran between March 3, 2020, and April 8, 2020. Patients with COVID-19 were followed up after two months to check their health condition. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon rank-sum test. Three hundred and nineteen patients (mean age 45.48 ± 18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein, fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating, and age were the most important symptoms of COVID-19 infection. Traveling in the past 3 months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not show any relationship with COVID-19. To the best of our knowledge, a number of factors associated with mortality due to COVID-19 have been investigated for the first time in this study. Our results might be helpful in early prediction and risk reduction of mortality in patients infected with COVID-19.

Prevalence and risk factors of domestic violence and its impacts on women's mental health in Gilgit-Baltistan, Pakistan.

OBJECTIVE: To assess the prevalence of domestic violence, associated risk factors, and its impacts on women's mental health in Gilgit-Baltistan (GB), Pakistan. METHODS: This is a sequential explanatory strategy that is a mixed-method research design was conducted at Department of Behavioral Sciences, Karakoram International University Gilgit from January 2017 to June 2018 on 160 married women. Quantitative data were collected using Karachi domestic violence screening scale and mental health inventory and qualitative data were collected through interview guides. Descriptive and inferential statistical techniques were applied to analyze quantitative data while qualitative data were analyzed using thematic analysis. RESULTS: Married women in GB reported higher levels of domestic violence (88.8%; psychological (69.4%), physical (37.5%) & sexual (21.2%). Abused women reported lower levels of mental health (t=3.19, p=0.00); psychological wellbeing (t=2.03, p=0.04), general positive affect (t=2.09, p=0.03), and life satisfaction (t=2.39, p=0.01) and higher levels of psychological distress (t=3.27, p=0.00), anxiety (t=3.06, p=0.00), depression (t=2.60, p=0.01), and loss of emotional/behavioral control (t=3.05, p=0.00) as compared to non-abused women. Risk factors behind domestic violence were identified as; poverty, the influence of in-laws, second marriage, stepchildren, forceful intimate relationships, husband's irresponsibility, and addiction, and handicapped children. CONCLUSIONS: We found higher level of domestic violence, associated risk factors, and poor mental health of abused women in GB.

IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment.

Wart disease (WD) is a skin illness on the human body which is caused by the human papillomavirus (HPV). This study mainly concentrates on common and plantar warts. There are various treatment methods for this disease, including the popular immunotherapy and cryotherapy methods. Manual evaluation of the WD treatment response is challenging. Furthermore, traditional machine learning methods are not robust enough in WD classification as they cannot deal effectively with small number of attributes. This study proposes a new evolutionary-based computer-aided diagnosis (CAD) system using machine learning to classify the WD treatment response. The main architecture of our CAD system is based on the combination of improved adaptive particle swarm optimization (IAPSO) algorithm and artificial immune recognition system (AIRS). The cross-validation protocol was applied to test our machine learning-based classification system, including five different partition protocols (K2, K3, K4, K5 and K10). Our database consisted of 180 records taken from immunotherapy and cryotherapy databases. The best results were obtained using the K10 protocol that provided the precision, recall, F-measure and accuracy values of 0.8908, 0.8943, 0.8916 and 90%, respectively. Our IAPSO system showed the reliability of 98.68%. It was implemented in Java, while integrated development environment (IDE) was implemented using NetBeans. Our encouraging results suggest that the proposed IAPSO-AIRS system can be employed for the WD management in clinical environment.

Urdu translation and validation of PHQ-9, a reliable identification, severity and treatment outcome tool for depression.

OBJECTIVE: To translate and validate the self-report brief version of Patient Health Questtionaire-9 in Urdu. METHODS: The descriptive study was carried out at the Combined Military Hospitals in Gilgit and Lahore, and Pakistan Naval Ship Shifa Hospital in Karachi, from February to May 2016, and comprised of patients recruited from primary healthcare centres of the three cities Standardised procedures including forward-translation, back-translation, expert panel discussion, face validation, pilot testing, and target population validation were done. SPSS 21 was used for statistical analysis. RESULTS: Of the 293 patients, 164(56%) were males and 129(44%) were females. Exploratory factor analysis revealed a single factor solution with minimum factor loading being 0.63. Cronbach's alpha for the scale was 0.91 and split-half reliability was 0.77. Females were more likely to have depressive symptoms compared to male participants (p<0.01). Participants' area of dwelling also influenced their reported symptoms (p<0.01). CONCLUSIONS: Patient Health Questtionaire-9 Urdu scale was found to be a valid and reliable tool to screen, rate and monitor outcomes of depressive illness in primary healthcare settings in Pakistan.

Assessment of Freshwater Springs, Associated Diseases and Indigenous Perception in Ghizer, Gilgit-Baltistan, Pakistan.

OBJECTIVE: To assess the quality of freshwater aquifers (springs) associated diseases, and indigenous perception in Ghizer, Gilgit-Baltistan. METHODS: This was a cross-sectional study conducted at the department of environmental science, Karakoram International University Gilgit after approval from departmental research committee. In order to get the most accurate results, 18 water samples in triplicates were collected according to our lab own set of sampling standard operating procedures (SOPs) using pre-sterilized bottles of 100 ml from June 2016 to August 2016 along with associated diseases record and structured interviews from indigenous population. For physicochemical and microbial assessment, samples were tested before five hours after collection and associated diseases and indigenous perception was analyzed using descriptive statistical techniques. RESULTS: Findings revealed that levels of pH, EC, phosphates, TDS, and nitrite, fall within WHO standards except nitrate, temperature and turbidity. The highest concentration (mgL-1± SE) of Hg in Barkolti and Barsat springs were (0.01±0.005) and (0.02±0.001) while Zn was (0.04±0.02) respectively. The concentration of Cu in Barkolti spring was (0.2± 0.03) while in Barsat spring below the detection level. The concentration of Cd, Pb, Cr, and Fe in both springs fall within WHO permissible limits. The mean value of E. coli recorded in both Barkolti and Barsat aquifers were (1.08cfu ±0.45) and (2.11cfu±0.56) respectively. The prevalence of water-associated diseases recorded in the order diarrhea, dysentery, typhoid, cholera, hepatitis and their incidence increases at high peak in summer. Irrespective of physicochemical and microbial components, indigenous population believed that the spring water has curative properties. CONCLUSIONS: Physiochemical and microbial parameters of spring's water fall within WHO standards except nitrate, turbidity, temperature, and E. coli, and incidence of certain associated diseases. However, indigenous population is unaware about the fact and believed that spring water very safe.

Mental health of cardiac patients in Gilgit, Pakistan: A cross-sectional study.

OBJECTIVE: To assess and compare mental health of cardiac patients with disease-free individuals. METHODS: This cross-sectional study was carried out at the Combined Military Hospital, Gilgit-Baltistan, Pakistan, from August to December 2016, and comprised cardiac patients in group A and healthy controls in group B. Data was collected using mental health inventory, a 38-item tool to measure general psychological distress and well-being. Cardiac patients' mental health was compared with illness-free individuals by applying descriptive and inferential statistical techniques. SPSS 21 was used for data analysis. RESULTS: Of the 80 participants, 40(50%) were patients and the rest were controls. The mean mental health inventory score in group B was 150.72±19.94 compared to 140.35±22.83in group A (p<0.05). Cardiac patients' mean score on psychological distress was 82.4±14.85 compared to 75.87±15.34in controls (p<0.05). The mean level of anxiety was 31.75±8.01 and that of depression was 14.58±3.58 in patients compared to 26.70±6.74 (p<0.01) and 12.65±4.34 (p<0.05) in the controls. CONCLUSIONS: Cardiac patients experienced higher level of depression and anxiety.

Urdu translation and validation of GAD-7: A screening and rating tool for anxiety symptoms in primary health care.

OBJECTIVE: To translate and validate Generalized Anxiety Disorder -7scale in Urdu, for use in Pakistan in the primary healthcare setups. METHODS: The validation study was conducted at the Combined Military Hospital, Gilgit, Pakistan, from February to May 2016.We followed a systematic six-step process to validate the Generalized Anxiety Disorder-7 scale in the target population. The instrument was translated independently and then fused together. Back-translation was followed by recommendations by an expert committee, and face validity improvement by a language expert. A pilot study was done to get user's feedback on the construct. Volunteers were administered the questionnaire for validation procedure, along with a well-being scale, at three different cities representing volunteers from four different administrative regions of Pakistan. RESULTS: There were 285 volunteers in the study. Principal component exploratory factor analysis supported unidimensional structure of the scale with an eigenvalue of 5.18 and it explained 64.8% of the total variance. Total score on the scale was negatively correlated with positive effect (r = -0.44, p<0.001) and life satisfaction (r = -0.49, p<0.001) subscales of a the well-being scale, while it was positively correlated with the negative affect (r = 0.63, p<0.001) subscale of the same, indicating a good level of convergent and discriminate validity. Cronbach's alpha for the scale was 0.92 and split-half reliability was 0.82, revealing a good level of reliability. CONCLUSIONS: The Generalized Anxiety Disorder -7 scale was found to be a validated, brief, self-administered Urdu tool to screen, rate, and monitor outcome of anxiety disorders in primary healthcare setups.

Mood changes at very high altitudes in Pakistan.

OBJECTIVE: To screen out psychiatric 'cases' and find the frequency of anxiety and depression symptoms in military volunteers performing duties at very high altitudes in the Karakoram ranges of Pakistan. METHODS: This was a descriptive study lasting from Jan 2015 to June 2015, on volunteers serving at very high altitude, using General Health Questionnaire-12 (GHQ-12) and Hospital Anxiety and Depression Scale (HADS), Urdu versions. Analysis involved descriptive, inferential techniques and Bonferroni test. Demographic variables were compared to the scores. RESULTS: A high percentage of the military volunteers screened positive for psychiatric 'caseness' and symptoms of anxiety and depression; mostly in the mild to moderate range, while very few of them reported severe symptoms. Demographic variables such as marital status, number of children, positive family psychiatric history, past medical history, duration at high altitude and educational levels were found to be significant risk factors for developing symptoms of anxiety and depression. CONCLUSIONS: Individuals performing duties at very high altitude, experience symptoms of anxiety and depression, their demographics are important in understanding their emotional problems.

Symptoms of Depression and Anxiety among Women attending Primary Health Care in Gilgit-Baltistan (GB), Pakistan.

OBJECTIVE: To assess symptoms of anxiety and depression among women reporting to primary health care (PHC women) in Gilgit Baltistan (GB), Pakistan. METHODS: This was a cross-sectional study conducted on PHC women belonging GB including other three provinces of Pakistan. PHQ-9 and GAD-7 were used to assess anxiety and depression. Descriptive and inferential statistical techniques were applied to analyze the collected data. RESULTS: PHC women from GB reported higher level of depressive symptoms (t=7.58, p=0.00) and lower level of anxiety symptoms (t=8.3, p=0.00) when compared with cut-off score. Insignificant differences were found in depressive (t=1.5, p>.05) and anxiety (t=1.2, p>.05) scores between GB women and women from rest of Pakistan. However, inter-province differences in depressive (F=5.78, p= 00) and anxiety (F=4.5, p=0.00) symptoms were significant. Increasing age and lack of education were found significant risk factors for GB PHC women's depressive and anxiety symptoms. CONCLUSIONS: PHC women from GB have higher level of depressive and lower level of anxiety symptoms like women from other provinces of Pakistan. Their demographics should be considered while addressing their emotional problems.

Automated quantitative multiplex immunofluorescence in situ imaging identifies phospho-S6 and phospho-PRAS40 as predictive protein biomarkers for prostate cancer lethality.

BACKGROUND: We have witnessed significant progress in gene-based approaches to cancer prognostication, promising early intervention for high-risk patients and avoidance of overtreatment for low-risk patients. However, there has been less advancement in protein-based approaches, even though perturbed protein levels and post-translational modifications are more directly linked with phenotype. Most current, gene expression-based platforms require tissue lysis resulting in loss of structural and molecular information, and hence are blind to tumor heterogeneity and morphological features. RESULTS: Here we report an automated, integrated multiplex immunofluorescence in situ imaging approach that quantitatively measures protein biomarker levels and activity states in defined intact tissue regions where the biomarkers of interest exert their phenotype. Using this approach, we confirm that four previously reported prognostic markers, PTEN, SMAD4, CCND1 and SPP1, can predict lethal outcome of human prostate cancer. Furthermore, we show that two PI3K pathway-regulated protein activities, pS6 (RPS6-phosphoserines 235/236) and pPRAS40 (AKT1S1-phosphothreonine 246), correlate with prostate cancer lethal outcome as well (individual marker hazard ratios of 2.04 and 2.03, respectively). Finally, we incorporate these 2 markers into a novel 5-marker protein signature, SMAD4, CCND1, SPP1, pS6, and pPRAS40, which is highly predictive for prostate cancer-specific death. The ability to substitute PTEN with phospho-markers demonstrates the potential of quantitative protein activity state measurements on intact tissue. CONCLUSIONS: In summary, our approach can reproducibly and simultaneously quantify and assess multiple protein levels and functional activities on intact tissue specimens. We believe it is broadly applicable to not only cancer but other diseases, and propose that it should be well suited for prognostication at early stages of pathogenesis where key signaling protein levels and activities are perturbed.

An adaptive ensemble deep learning framework for reliable detection of pandemic patients.

Nurses, often considered the backbone of global health services, are disproportionately vulnerable to COVID-19 due to their front-line roles. They conduct essential patient tests, including blood pressure, temperature, and complete blood counts. The pandemic-induced loss of nursing staff has resulted in critical shortages. To address this, robotic solutions offer promising avenues. To solve this problem, we developed an ensemble deep learning (DL) model that uses seven different models to detect patients. Detected images are then used as input for the soft robot, which performs basic assessment tests. In this study, we introduce a deep learning-based approach for nursing soft robots, and propose a novel deep learning model named Deep Ensemble of Adaptive Architectures. Our method is twofold: firstly, an ensemble deep learning technique detects COVID-19 patients; secondly, a soft robot performs basic assessment tests on the identified patients. We evaluate the performance of various deep learning-based object detectors for patient detection, examining implementations of You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), Region-Based Convolutional Neural Network (RCNN), and Region-Based Fully Convolutional Network (R-FCN) on a proprietary dataset comprising 32,668 hospital surveillance images. Our results indicate that while YOLO and VGG facilitate rapid detection, Faster-RCNN (Inception ResNet-v2) and our proposed Ensemble-DL achieve the highest accuracy. Ensemble-DL offers accurate results in a reasonable timeframe, making it apt for patient detection on embedded platforms. Through real-world experiments, our method outperforms baseline approaches (including Faster-RCNN, R-FCN variants, CNN+LSTM, etc.) in terms of both precision and recall. Achieving an impressive accuracy of 98.32%, our deep learning-based model for nursing soft robots presents a significant advancement in the identification and assessment of COVID-19 patients, ultimately enhancing healthcare efficiency and patient care.

Rural and urban differences in the prevalence and determinants of Type-2 diabetes in Bangladesh.

OBJECTIVE: To estimate the prevalence of Type 2 Diabetes (T2D) in urban and rural settings and identify the specific risk factors for each location. METHOD: We conducted this study using data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS), sourced from the DHS website. The survey employed a stratified two-stage sampling method, which included 7,658 women and 7,048 men aged 18 and older who had their blood glucose levels measured. We utilized chi-square tests and ordinal logistic regression to analyze the association between various selected variables in both urban and rural settings and their relationship with diabetes and prediabetes. RESULTS: The prevalence of T2D was 10.8% in urban areas and 7.4% in rural areas, while pre-diabetes affected 31.4% and 27% of the populations in these respective settings. The study found significant factors influencing diabetes in both urban and rural regions, particularly in the 55-64 age group (Urban: AOR = 1.88, 95% CI [1.46, 2.42]; Rural: AOR = 1.87, 95% CI [1.54, 2.27]). Highly educated individuals had lower odds of T2D, while wealthier and overweight participants had higher odds in both areas. In rural regions, T2D risk was higher among caffeinated drink consumers and those not engaged in occupation-related physical activity, while these factors did not show significant influence in urban areas. Furthermore, urban participants displayed a significant association between T2D and hypertension. CONCLUSION: Our study outlines a comprehensive strategy to combat the increasing prevalence of T2D in both urban and rural areas. It includes promoting healthier diets to control BMI level, encouraging regular physical activity, early detection through health check-ups, tailored awareness campaigns, improving healthcare access in rural regions, stress management in urban areas, community involvement, healthcare professional training, policy advocacy like sugary drink taxation, research, and monitoring interventions. These measures collectively address the T2D challenge while accommodating the distinct features of urban and rural settings.

Progress and trends in neurological disorders research based on deep learning.

In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.

Risk factors of retinopathy in type 2 diabetes mellitus at a tertiary care hospital, Bahawalpur Pakistan.

OBJECTIVES: To find out the risk factors of diabetic retinopathy in type 2 diabetes mellitus. METHODOLOGY: It was a cross-sectional study involving 300 patients of type 2 diabetes. Clinical history, relevant examination including fundoscopy and lab investigations were done. Data was analysed with SPSS 17.0. T-test and chi square/Fischer exact were applied to determine significance. RESULTS: Mean age of the patients was 49.04 ± 0.69 years with slight female predominance with male to female ratio of 3:4. Average duration of disease was 7.17 ± 0.38 years. Diabetic retinopathy was diagnosed in (74, 23.9%). Mean HbA1c was 8.15% in patients with retinopathy and 8.884% in those who had no retinopathy (p=0.08). However, duration of DM, age of patients, male gender, high total cholesterol, high LDL and microalbuminuria were significantly associated with the development of retinopathy. CONCLUSIONS: Diabetic retinopathy was found in 23.9% of type 2 diabetics. It was associated with duration of disease, age at presentation, male gender, high total cholesterol, high LDL and microalbuminuria. A single high level of HbA1c was not associated with retinopathy.

Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data.

The automated extraction of critical information from electronic medical records, such as oncological medical events, has become increasingly important with the widespread use of electronic health records. However, extracting tumor-related medical events can be challenging due to their unique characteristics. To address this difficulty, we propose a novel approach that utilizes Generative Adversarial Networks (GANs) for data augmentation and pseudo-data generation algorithms to improve the model's transfer learning skills for various tumor-related medical events. Our approach involves a two-stage pre-processing and model training process, where the data is cleansed, normalized, and augmented using pseudo-data. We evaluate our approach using the i2b2/UTHealth 2010 dataset and observe promising results in extracting primary tumor site size, tumor size, and metastatic site information. The proposed method has significant implications for healthcare and medical research as it can extract vital information from electronic medical records for oncological medical events.

Runx2 protein expression utilizes the Runx2 P1 promoter to establish osteoprogenitor cell number for normal bone formation.

The Runt-related transcription factor, Runx2, is essential for osteogenesis and is controlled by both distal (P1) and proximal (P2) promoters. To understand Runx2 function requires determination of the spatiotemporal activity of P1 and P2 to Runx2 protein production. We generated a mouse model in which the P1-derived transcript was replaced with a lacZ reporter allele, resulting in loss of P1-derived protein while simultaneously allowing discrimination between the activities of the two promoters. Loss of P1-driven expression causes developmental defects with cleidocranial dysplasia-like syndromes that persist in the postnatal skeleton. P1 activity is robust in preosteogenic mesenchyme and at the onset of bone formation but decreases as bone matures. Homozygous Runx2-P1(lacZ/lacZ) mice have a normal life span but exhibit severe osteopenia and compromised bone repair in adult mice because of osteoblastic defects and not increased osteoclastic resorption. Gene expression profiles of bone, immunohistochemical studies, and ex vivo differentiation using calvarial osteoblasts and marrow stromal cells identified mechanisms for the skeletal phenotype. The findings indicate that P1 promoter activity is necessary for generating a threshold level of Runx2 protein to commit sufficient osteoprogenitor numbers for normal bone formation. P1 promoter function is not compensated via the P2 promoter. However, the P2 transcript with compensatory mechanisms from bone morphogenetic protein (BMP) and Wnt signaling is adequate for mineralization of the bone tissue that does form. We conclude that selective utilization of the P1 and P2 promoters enables the precise spatiotemporal expression of Runx2 necessary for normal skeletogenesis and the maintenance of bone mass in the adult.

Definitive hematopoiesis requires Runx1 C-terminal-mediated subnuclear targeting and transactivation.

Runx1 is a key hematopoietic transcription factor required for definitive hematopoiesis and is a frequent target of leukemia-related chromosomal translocations. The resulting fusion proteins, while retaining DNA binding activity, display loss of subnuclear targeting and associated transactivation functions encoded by the C-terminus of the protein. To define the precise contribution of the Runx1 C-terminus in development and leukemia, we created a knock-in mouse with a C-terminal truncation by introducing a single nucleic acid substitution in the native Runx1 locus. This mutation (Runx1(Q307X)) models genetic lesions observed in patients with leukemia and myeloproliferative disorders. The Runx1(Q307X) homozygous mouse exhibits embryonic lethality at E12.5 due to central nervous system hemorrhages and a complete lack of hematopoietic stem cell function. While able to bind DNA, Runx1(Q307X) is unable to activate target genes, resulting in deregulation of various hematopoietic markers. Thus, we demonstrate that the subnuclear targeting and transcriptional regulatory activities of the Runx1 C-terminus are critical for hematopoietic development. We propose that compromising the C-terminal functions of Runx1 is a common mechanism for the pathological consequences of a variety of somatic mutations and Runx1-related leukemic fusion proteins observed in human patients.