Platelet distribution width as an useful indicator of influenza severity in children.

PURPOSE: The present study aims to investigate the potential of platelet distribution width as an useful parameter to assess the severity of influenza in children. METHODS: Baseline characteristics and laboratory results were collected and analyzed. Receiver operating characteristic (ROC) curve analysis was used to joint detection of inflammatory markers for influenza positive children, and the scatter-dot plots were used to compare the differences between severe and non-severe group. RESULTS: Influenza B positive children had more bronchitis and pneumonia (P < 0.05), influenza A infected children had more other serious symptoms (P = 0.007). Neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio (NLR), and platelet parameters performed differently among < 4 years and ≥ 4 years children with influenza. Combined detection of platelet parameters and other indicators could better separate healthy children from influenza infected children than single indicator detection. The levels of platelet distribution width of children with severe influenza (A and B) infection was significantly dropped, compared with non-severe group (P < 0.05). CONCLUSIONS: Platelet distribution width could be a very useful and economic indicator in distinction and severity assessment for children with influenza.

The value of the systemic immune-inflammation index in assessing disease severity in autoimmune encephalitis.

BACKGROUND: Autoimmune encephalitis (AE) is a group of autoimmune diseases targeting the central nervous system, characterized by severe clinical symptoms and substantial consumption of medical resources. Neuroinflammation plays a crucial role in disease progression, and detecting inflammatory responses can provide insights into disease status and disease severity. The systemic immune-inflammation index (SII), a novel marker of inflammatory status, has been rarely studied in AE. METHODS: Retrospective analysis of data from AE patients admitted to the First Affiliated Hospital of Zhengzhou University between January 2019 and September 2023 was conducted. Univariate analysis and logistic regression were used to assess the association between SII and patient severity. Nomograms for predicting AE severity were established, and receiver operating characteristic (ROC) curves, concordance index (C-index), calibration curves, and decision curve analysis were employed to evaluate predictive accuracy. Additionally, the Clinical Assessment Scale in Autoimmune Encephalitis (CASE) score was used to assess patient severity. RESULTS: This study enrolled 157 patients, of whom 57 were classified as severe according to the CASE score. SII, cerebrospinal fluid (CSF) cell counts, disturbance of consciousness, and behavioural abnormalities independently associated with the occurrence of severe cases. The C-index of the nomograms was 0.87, indicating strong association with disease severity, as supported by the calibration. Additionally, SII levels were highest within seven days of onset and decreased after one month. In subgroup analyses of different antibodies, SII also associations with severe cases in NMDAR encephalitis. CONCLUSIONS: Higher SII levels are associated with an increased likelihood of developing severe AE, peaking within 7 days of disease onset and decreasing thereafter, potentially offering a prognostic marker to assess disease progression early in its course.

Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning.

INTRODUCTION: Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study's aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO2) breath records captured with TidalSense's N-TidalTM capnometer. METHOD: For COPD diagnosis, machine learning algorithms were trained and evaluated on 294 COPD (including GOLD stages 1-4) and 705 non-COPD participants. A logistic regression model was also trained to distinguish GOLD 1 from GOLD 4 COPD with the output probability used as an index of severity. RESULTS: The best diagnostic model achieved an AUROC of 0.890, sensitivity of 0.771, specificity of 0.850 and positive predictive value (PPV) of 0.834. Evaluating performance on all test capnograms that were confidently ruled in or out yielded PPV of 0.930 and NPV of 0.890. The severity determination model yielded an AUROC of 0.980, sensitivity of 0.958, specificity of 0.961 and PPV of 0.958 in distinguishing GOLD 1 from GOLD 4. Output probabilities from the severity determination model produced a correlation of 0.71 with percentage predicted FEV1. CONCLUSION: The N-TidalTM device could be used alongside interpretable machine learning as an accurate, point-of-care diagnostic test for COPD, particularly in primary care as a rapid rule-in or rule-out test. N-TidalTM also could be effective in monitoring disease progression, providing a possible alternative to spirometry for disease monitoring.

Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining.

Multi-layer complex structures are widely used in large-scale engineering structures because of their diverse combinations of properties and excellent overall performance. However, multi-layer complex structures are prone to interlaminar debonding damage during use. Therefore, it is necessary to monitor debonding damage in engineering applications to determine structural integrity. In this paper, a damage information extraction method with ladder feature mining for Lamb waves is proposed. The method is able to optimize and screen effective damage information through ladder-type damage extraction. It is suitable for evaluating the severity of debonding damage in aluminum-foamed silicone rubber, a novel multi-layer complex structure. The proposed method contains ladder feature mining stages of damage information selection and damage feature fusion, realizing a multi-level damage information extraction process from coarse to fine. The results show that the accuracy of damage severity assessment by the damage information extraction method with ladder feature mining is improved by more than 5% compared to other methods. The effectiveness and accuracy of the method in assessing the damage severity of multi-layer complex structures are demonstrated, providing a new perspective and solution for damage monitoring of multi-layer complex structures.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

A novel ROS-Related chemiluminescent semiconducting polymer nanoplatform for acute pancreatitis early diagnosis and severity assessment.

Acute pancreatitis (AP) is a common and potentially life-threatening inflammatory disease of the pancreas. Reactive oxygen species (ROS) play a key role in the occurrence and development of AP. With increasing ROS levels, the degree of oxidative stress and the severity of AP increase. However, diagnosing AP still has many drawbacks, including difficulties with early diagnosis and undesirable sensitivity and accuracy. Herein, we synthesized a semiconducting polymer nanoplatform (SPN) that can emit ROS-correlated chemiluminescence (CL) signals. The CL intensity increased in solution after optimization of the SPN. The biosafety of the SPN was verified in vitro and in vivo. The mechanism and sensitivity of the SPN for AP early diagnosis and severity assessment were evaluated in three groups of mice using CL intensity, serum marker evaluations and hematoxylin and eosin staining assessments. The synthetic SPN can be sensitively combined with different concentrations of ROS to produce different degrees of high-intensity CL in vitro and in vivo. Notably, the SPN shows an excellent correlation between CL intensity and AP severity. This nanoplatform represents a superior method to assess the severity of AP accurately and sensitively according to ROS related chemiluminescence signals. This research overcomes the shortcomings of AP diagnosis in clinical practice and provides a novel method for the clinical diagnosis of pancreatitis in the future.

Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4): A novel tool to assess the severity of hidradenitis suppurativa using artificial intelligence.

BACKGROUND: Hidradenitis suppurativa (HS) is a painful chronic inflammatory skin disease that affects up to 4% of the European adult population. International Hidradenitis Suppurativa Severity Score System (IHS4) is a dynamic scoring tool that was developed to be incorporated into the doctor's daily clinical practice and clinical studies. This helps measure disease severity and guides the therapeutic strategy. However, IHS4 assessment is a time-consuming and manual process, with high inter-observer variability and high dependence on the observer's expertise. MATERIALS AND METHODS: We introduce the Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4), an automatic equivalent of IHS4 that deploys a deep learning model for lesion detection, called Legit.Health-IHS4net, based on the YOLOv5 architecture. AIHS4 was trained on Legit.Health-HS-IHS4, a collection of HS images manually annotated by six specialists and processed by a novel knowledge unification algorithm. RESULTS: Our results show that, with the current dataset size, our tool assesses the severity of HS cases with a performance comparable to that of the most expert physician. Furthermore, the model can be implemented into CADx systems to support doctors in their clinical practice and act as a new endpoint in clinical trials. CONCLUSION: Our work proves the potential usefulness of artificial intelligence in the practice of evidence-based dermatology: models trained on the consensus of large clinical boards have the potential to empower dermatologists in their daily practice and replace current standard clinical endpoints.

Severity Assessment in Rats Undergoing Subarachnoid Hemorrhage Induction by Endovascular Perforation or Corresponding Sham Surgery.

INTRODUCTION: Animal models for preclinical research of subarachnoid hemorrhage (SAH) are widely used as much of the pathophysiology remains unknown. However, the burden of these models inflicted on the animals is not well characterized. The European directive requires severity assessment-based allocation to categories. Up to now, the classification into predefined categories is rather subjective and often without underlying scientific knowledge. We therefore aimed at assessing the burden of rats after SAH or the corresponding sham surgery to provide a scientific assessment. METHODS: We performed a multimodal approach, using different behavior tests, clinical and neurological scoring, and biochemical markers using the common model for SAH of intracranial endovascular filament perforation in male Wistar rats. Up to 7 days after surgery, animals with SAH were compared to sham surgery and to a group receiving only anesthesia and analgesia. RESULTS: Sham surgery (n = 15) and SAH (n = 16) animals showed an increase in the clinical score the first days after surgery, indicating clinical deterioration, while animals receiving only anesthesia without surgery (n = 5) remained unaffected. Body weight loss occurred in all groups but was more pronounced and statistically significant only after surgery. The analysis of burrowing, open field (total distance, erections), balance beam, and neuroscore showed primarily an effect of the surgery itself in sham surgery and SAH animals. Only concerning balance beam and neuroscore, a difference was visible between sham surgery and SAH. The outcome of the analysis of systemic and local inflammatory parameters and of corticosterone in blood and its metabolites in feces was only robust in animals suffering from larger bleedings. Application of principal component analysis resulted in a clear separation of sham surgery and SAH animals from their respective baseline as well as from the anesthesia-only group at days 1 and 3, with the difference between sham surgery and SAH being not significant. DISCUSSION/CONCLUSION: To our knowledge, we are the first to publish detailed clinical score sheet data combined with advanced behavioral assessment in the endovascular perforation model for SAH in rats. The tests chosen here clearly depict an impairment of the animals within the first days after surgery and are consequently well suited for assessment of the animals' suffering in the model. A definitive classification into one of the severity categories named by the EU directive is yet pending and has to be performed in the future by including the assessment data from different neurological and nonneurological disease models.

Implementation of the Surgical Apgar Score in Laboratory Animal Science: A Showcase Pilot Study in a Porcine Model and a Review of the Literature.

INTRODUCTION: In an attempt to further improve surgical outcomes, a variety of outcome prediction and risk-assessment tools have been developed for the clinical setting. Risk scores such as the surgical Apgar score (SAS) hold promise to facilitate the objective assessment of perioperative risk related to comorbidities of the patients or the individual characteristics of the surgical procedure itself. Despite the large number of scoring models in clinical surgery, only very few of these models have ever been utilized in the setting of laboratory animal science. The SAS has been validated in various clinical surgical procedures and shown to be strongly associated with postoperative morbidity. In the present study, we aimed to review the clinical evidence supporting the use of the SAS system and performed a showcase pilot trial in a large animal model as the first implementation of a porcine-adapted SAS (pSAS) in an in vivo laboratory animal science setting. METHODS: A literature review was performed in the PubMed and Embase databases. Study characteristics and results using the SAS were reported. For the in vivo study, 21 female German landrace pigs have been used either to study bleeding analogy (n = 9) or to apply pSAS after abdominal surgery in a kidney transplant model (n = 12). The SAS was calculated using 3 criteria: (1) estimated blood loss during surgery; (2) lowest mean arterial blood pressure; and (3) lowest heart rate. RESULTS: The SAS has been verified to be an effective tool in numerous clinical studies of abdominal surgery, regardless of specialization confirming independence on the type of surgical field or the choice of surgery. Thresholds for blood loss assessment were species specifically adjusted to >700 mL = score 0; 700-400 mL = score 1; 400-55 mL score 2; and <55 mL = score 3 resulting in a species-specific pSAS for a more precise classification. CONCLUSION: Our literature review demonstrates the feasibility and excellent performance of the SAS in various clinical settings. Within this pilot study, we could demonstrate the usefulness of the modified SAS (pSAS) in a porcine kidney transplantation model. The SAS has a potential to facilitate early veterinary intervention and drive the perioperative care in large animal models exemplified in a case study using pigs. Further larger studies are warranted to validate our findings.

Welfare Assessment on Healthy and Tumor-Bearing Mice after Repeated Ultrasound Imaging.

INTRODUCTION: Ultrasound (US) imaging enables tissue visualization in high spatial resolution with short examination times. Thus, it is often applied in preclinical research. Diagnostic US, including contrast-enhanced US (CEUS), is considered to be well-tolerated by laboratory animals although no systematic study has been performed to confirm this claim. Therefore, the aim of this study was to screen for possible effects of US and CEUS examinations on welfare of healthy mice. Additionally, the potential influence of CEUS and molecular CEUS on well-being and therapy response to regorafenib was investigated in breast cancer-bearing mice. MATERIAL AND METHODS: Forty healthy Balb/c mice were randomly assigned for examination with US or CEUS (3×/week) for 4 weeks. Untreated healthy mice and mice receiving only isoflurane anesthesia served as controls (n = 10/group). Ninety-four 4T1 tumor-bearing Balb/c mice were allocated randomly to the following groups: no imaging, isoflurane anesthesia, CEUS, and molecular CEUS. They either received 10 mg/kg regorafenib or vehicle solution daily by oral gavage. Animals were examined three times within 2 weeks. CEUS measurements were performed using phospholipid microbubbles, and phospholipid microbubbles targeting the vascular endothelial growth factor receptor-2 were applied for molecular CEUS. Welfare evaluation was performed by daily observational score sheets, measuring the heart rate, Rotarod performance, and fecal corticosterone metabolites twice a week. On the last day, pathological changes in serum corticosterone concentrations, hemograms, and organ weights were obtained. Moreover, a potential influence of isoflurane anesthesia, CEUS, and molecular CEUS on regorafenib response in tumor-bearing mice was examined. Analysis of variance and Dunnett's post hoc test were performed as statistical analyses. RESULTS: Severity parameters were not altered after repeated US and CEUS examinations of healthy mice, but spleen sizes were significantly lower after isoflurane anesthesia. In tumor-bearing mice, no effect on animal welfare after repeated CEUS and molecular CEUS could be observed. However, leukocyte counts and spleen weights of tumor-bearing mice were significantly lower in animals examined with CEUS and molecular CEUS compared to the control groups. This effect was not visible in regorafenib-treated animals. CONCLUSIONS: Repeated US and (molecular) CEUS have no detectable impact on animal welfare in healthy and tumor-bearing mice. However, CEUS and molecular CEUS in combination with isoflurane anesthesia might attenuate immunological processes in tumor-bearing animals and may consequently affect responses to antitumor therapy.

Does Sex Matter in Liver Surgery? Comparison of Severity Assessments between Female and Male Rats after Partial Hepatectomy: A Pilot Study.

INTRODUCTION: Current animal-based biomedical research, including studies on liver function and disease, is conducted almost exclusively on male animals to mitigate confounding effects of the estrous cycle. However, liver diseases afflict both men and women, so translational research findings should also be applicable to female patients. This pilot study investigated sex differences in objective and subjective severity assessment parameters in rats following 50% partial hepatectomy. MATERIALS AND METHODS: This study was performed using Wistar Han rats, in which measurements of body weight, spontaneous motor activity in the open field (OF) (movement distance, movement velocity, rearing frequency), and fecal corticosterone metabolites were conducted at baseline and at multiple times after partial hepatectomy. Subjective postsurgical severity assessments were conducted using modified score sheets. Blood parameters such as leukocyte count and serum aspartate aminotransferase, as well as estrogens and testosterone were measured from samples obtained during partial hepatectomy and at sacrifice. In addition, the amount of resected liver tissue was measured at partial hepatectomy, and the proliferated liver was weighed at sacrifice. RESULTS: Fecal corticosterone metabolite concentrations differed significantly between males and females at baseline and following hepatectomy. Also, leukocyte counts and estrogen concentrations were significantly different between sexes before partial hepatectomy. Alternatively, there were no sex differences in severity assessments, body weight changes, and behavior in the OF at any measurement time point. Liver weight was significantly different in males and females at the time point of partial hepatectomy and sacrifice. CONCLUSION: The results of this pilot study suggest that males and females respond similarly following partial hepatectomy. Examination of both sexes is very important for translation to humans, where both men and women suffer from liver disease. Furthermore, the use of both sexes in animal-based research would improve the utilization of the animal breeding in terms of the 3 Rs. However, due to some limitations, larger scale investigations including a broader spectrum of pathophysiolological, behavioral, and pharmacokinetic measures are planned.

Artificial Intelligence-Based Psoriasis Severity Assessment: Real-world Study and Application.

BACKGROUND: Psoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current lack of reliable tools for objective severity assessments is overcome. Psoriasis Area and Severity Index (PASI) has a prominent level of subjectivity and is rarely used in real practice, although it is the most widely accepted metric for measuring psoriasis severity currently. OBJECTIVE: This study aimed to develop an image-artificial intelligence (AI)-based validated system for severity assessment with the explicit intention of facilitating long-term management of patients with psoriasis. METHODS: A deep learning system was trained to estimate the PASI score by using 14,096 images from 2367 patients with psoriasis. We used 1962 patients from January 2015 to April 2021 to train the model and the other 405 patients from May 2021 to July 2021 to validate it. A multiview feature enhancement block was designed to combine vision features from different perspectives to better simulate the visual diagnostic method in clinical practice. A classification header along with a regression header was simultaneously applied to generate PASI scores, and an extra cross-teacher header after these 2 headers was designed to revise their output. The mean average error (MAE) was used as the metric to evaluate the accuracy of the predicted PASI score. By making the model minimize the MAE value, the model becomes closer to the target value. Then, the proposed model was compared with 43 experienced dermatologists. Finally, the proposed model was deployed into an app named SkinTeller on the WeChat platform. RESULTS: The proposed image-AI-based PASI-estimating model outperformed the average performance of 43 experienced dermatologists with a 33.2% performance gain in the overall PASI score. The model achieved the smallest MAE of 2.05 at 3 input images by the ablation experiment. In other words, for the task of psoriasis severity assessment, the severity score predicted by our model was close to the PASI score diagnosed by experienced dermatologists. The SkinTeller app has been used 3369 times for PASI scoring in 1497 patients from 18 hospitals, and its excellent performance was confirmed by a feedback survey of 43 dermatologist users. CONCLUSIONS: An image-AI-based psoriasis severity assessment model has been proposed to automatically calculate PASI scores in an efficient, objective, and accurate manner. The SkinTeller app may be a promising alternative for dermatologists' accurate assessment in the real world and chronic disease self-management in patients with psoriasis.

Evaluation of score parameters for severity assessment of surgery and liver cirrhosis in rats.

Severity assessment in animals is an ongoing field of research. In particular, the question of objectifiable and meaningful parameters of score-sheets, as well as their best combination, arise. This retrospective analysis investigates the suitability of a score-sheet for assessing severity and seeks to optimise it for predicting survival in 89 male Sprague Dawley rats (Rattus norvegicus), during an experiment evaluating the influence of liver cirrhosis by bile duct ligation (BDL) on vascular healing. The following five parameters were compared for their predictive power: (i) overall score; (ii) relative weight loss; (iii) general condition score; (iv) spontaneous behaviour score; and (v) the observer's assessment whether pain might be present. Suitable cut-off values of these individual parameters and the combination of multiple parameters were investigated. A total of ten rats (11.2%; 10/89) died or had to be sacrificed at an early stage due to pre-defined humane endpoints. Neither the overall score nor any individual parameter yielded satisfactory results for predicting survival. Using retrospectively calculated cut-off values and combining the overall score with the observer's assessment of whether the animal required analgesia (dipyrone) for pain relief resulted in an improved prediction of survival on the second post-operative day. This study demonstrates that combining score parameters was more suitable than using single ones and that experienced human judgement of animals can be useful in addition to objective parameters in the assessment of severity. By optimising the score-sheet and better understanding the burden of the model on rats, this study contributes to animal welfare.

A cell phone app for facial acne severity assessment.

Acne vulgaris, the most common skin disease, can cause substantial economic and psychological impacts to the people it affects, and its accurate grading plays a crucial role in the treatment of patients. In this paper, we firstly proposed an acne grading criterion that considers lesion classifications and a metric for producing accurate severity ratings. Due to similar appearance of acne lesions with comparable severities and difficult-to-count lesions, severity assessment is a challenging task. We cropped facial skin images of several lesion patches and then addressed the acne lesion with a lightweight acne regular network (Acne-RegNet). Acne-RegNet was built by using a median filter and histogram equalization to improve image quality, a channel attention mechanism to boost the representational power of network, a region-based focal loss to handle classification imbalances and a model pruning and feature-based knowledge distillation to reduce model size. After the application of Acne-RegNet, the severity score is calculated, and the acne grading is further optimized by the metadata of the patients. The entire acne assessment procedure was deployed to a mobile device, and a phone app was designed. Compared with state-of-the-art lightweight models, the proposed Acne-RegNet significantly improves the accuracy of lesion classifications. The acne app demonstrated promising results in severity assessments (accuracy: 94.56%) and showed a dermatologist-level diagnosis on the internal clinical dataset.The proposed acne app could be a useful adjunct to assess acne severity in clinical practice and it enables anyone with a smartphone to immediately assess acne, anywhere and anytime.

Behavioral phenotyping of young Scn1a haploinsufficient mice.

Dravet syndrome is a rare, severe, infancy-onset epileptic encephalopathy associated with a high premature mortality. In most patients, Dravet syndrome is caused by a heterozygous loss-of-function mutation in the SCN1A gene encoding the alpha 1 subunit of the sodium channel. Of the variety of SCN1A variants identified in patients with Dravet syndrome, SCN1A missense mutations occur in one-third of cases. The novel Scn1a-A1783V mouse model of Dravet syndrome carries the human Ala1783Val missense variant. Recently, the behavioral phenotype of Scn1a-A1783V haploinsufficient adult mice has been characterized, which may provide a valuable basis for assessment of novel therapeutic approaches. However, there is still limited information on the developmental course of behavioral alterations in the Scn1a-A1783V mouse model, which is of particular relevance for conclusions about face validity and severity classification of the model. Based on reference data from young wildtype mice, we analyzed selected behavioral parameters and fecal corticosterone metabolites in the Scn1a-A1783V mouse model during post-weaning development. Differences in the preference for a sweet saccharin solution between Dravet mice and wildtype mice were observed once mice reached sexual maturity. Nest building behavior was already influenced by the Scn1a genotype during prepubescence. Sexually mature Dravet mice showed a significantly reduced burrowing performance as compared to their wildtype littermates. In the open-field test, pronounced hyperactivity and increased thigmotactic behavior were evident in prepubescent and sexually mature Dravet mice. Analysis of Irwin scores revealed several genotype-dependent changes in handling-associated parameters during the course of adolescence. The information obtained provides insight into the age-dependence of behavioral patterns in the novel Scn1a-A1783V mouse model of Dravet syndrome. In addition, the dataset confirms the suitability of the applied behavioral composite measure scheme for evidence-based assessment of cumulative severity in genetic mouse lines.

COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment.

There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. Radiology and nucleic acid test (NAT) are complementary COVID-19 diagnosis methods. In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19. COVID-MTL learns different COVID-19 tasks in parallel through our novel random-weighted loss function, which assigns learning weights under Dirichlet distribution to prevent task dominance; our new 3D real-time augmentation algorithm (Shift3D) introduces space variances for 3D CNN components by shifting low-level feature representations of volumetric inputs in three dimensions; thereby, the MTL framework is able to accelerate convergence and improve joint learning performance compared to single-task models. By only using chest CT scans, COVID-MTL was trained on 930 CT scans and tested on separate 399 cases. COVID-MTL achieved AUCs of 0.939 and 0.846, and accuracies of 90.23% and 79.20% for detection of COVID-19 against radiology and NAT, respectively, which outperformed the state-of-the-art models. Meanwhile, COVID-MTL yielded AUC of 0.800 ± 0.020 and 0.813 ± 0.021 (with transfer learning) for classifying control/suspected, mild/regular, and severe/critically-ill cases. To decipher the recognition mechanism, we also identified high-throughput lung features that were significantly related (P < 0.001) to the positivity and severity of COVID-19.

A Cyber-Security Risk Assessment Methodology for Medical Imaging Devices: the Radiologists' Perspective.

Medical imaging devices (MIDs) are exposed to cyber-security threats. Currently, a comprehensive, efficient methodology dedicated to MID cyber-security risk assessment is lacking. We propose the Threat identification, ontology-based Likelihood, severity Decomposition, and Risk assessment (TLDR) methodology and demonstrate its feasibility and consistency with existing methodologies, while being more efficient, providing details regarding the severity components, and supporting organizational prioritization and customization. Using our methodology, the impact of 23 MIDs attacks (that were previously identified) was decomposed into six severity aspects. Four Radiology Medical Experts (RMEs) were asked to assess these six aspects for each attack. The TLDR methodology's external consistency was demonstrated by calculating paired T-tests between TLDR severity assessments and those of existing methodologies (and between the respective overall risk assessments, using attack likelihood estimates by four healthcare cyber-security experts); the differences were insignificant, implying externally consistent risk assessment. The TLDR methodology's internal consistency was evaluated by calculating the pairwise Spearman rank correlations between the severity assessments of different groups of two to four RMEs and each of their individual group members, showing that the correlations between the severity rankings, using the TLDR methodology, were significant (P < 0.05), demonstrating that the severity rankings were internally consistent for all groups of RMEs. Using existing methodologies, however, the internal correlations were insignificant for groups of less than four RMEs. Furthermore, compared to standard risk assessment techniques, the TLDR methodology is also sensitive to local radiologists' preferences, supports a greater level of flexibility regarding risk prioritization, and produces more transparent risk assessments.

Translation into Spanish and field-testing of a new score for evaluating psoriasis severity: The Simplified Psoriasis Index (SPI). / Traducción al español y ensayos de campo de una nueva escala destinada a la valoración global de la gravedad de la psoriasis: el índice de psoriasis simplificado (SPI).

BACKGROUND: The simplified psoriasis index (SPI) was developed in the United Kingdom to provide a simple summary measure for monitoring changes in psoriasis severity and associated psychosocial impact as well as for obtaining information about past disease behavior and treatment. Two complementary versions of the SPI allow for self-assessment by the patient or professional assessment by a doctor or nurse. Both versions have proven responsive to change, reliable, and interpretable, and to correlate well with assessment tools that are widely used in clinical trials - the Psoriasis Area and Severity Index and the Dermatology Quality of Life Index. The SPI has already been translated into several languages, including French, Brazilian Portuguese, Dutch, Arabic, and Thai. OBJECTIVE: To translate the professional and self-assessment versions of the SPI to Spanish and to field test the translations. METHOD: A medically qualified native Spanish speaker translated both versions of the SPI into Spanish. The Spanish translations were discussed by comparing them to blinded back translations into English undertaken by native English speakers; the Spanish texts were then revised in an iterative process involving the translators, 4 dermatologists, and 20 patients. The patients scored their own experience of psoriasis with the self-assessment version and commented on it. The process involved checking the conceptual accuracy of the translation, language-related differences, and subtle gradations of meaning in a process involving all translators and a panel of both Spanish- and English-speaking dermatologists, including a coauthor of the SPI. RESULTS: The final self-assessment and professional Spanish versions of the SPI are presented in this manuscript. CONCLUSIONS: Castilian Spanish translations of both versions of the SPI are now available for monitoring disease changes in Spanish-speaking patients with psoriasis under routine clinical care.

[Safety and efficacy of laparoscopic approach for widespread appendicular peritonitis]. / Vozmozhnosti laparoskopicheskogo metoda v lechenii rasprostranennogo appendikulyarnogo peritonita.

OBJECTIVE: To analyze treatment outcomes in patients with acute appendicitis complicated by widespread peritonitis. MATERIAL AND METHODS: The study included 165 patients acute appendicitis complicated by widespread peritonitis. Inclusion criteria: acute appendicitis complicated by widespread peritonitis MIP grade 1-2 in reactive or toxic phase (grading system by Simonyan K.S.), abdominal cavity index ≤16. Exclusion criteria: MIP grade 3, terminal phase, abdominal cavity index ≥17. RESULTS: Analysis of postoperative data revealed no correlation between surgical approach and incidence of postoperative intra-abdominal abscesses and infiltrates. In the main group, intra-abdominal abscesses occurred in 4.9% of patients (n=5), infiltrates - 12.8% (n=13). In the control group, these parameters were 4.6% (n=2) and 18.2% (n=8), respectively. We have developed and introduced into clinical practice a differentiated approach to surgical treatment of widespread appendicular peritonitis based on laparoscopic data. Abdominal cavity was intraoperatively assessed. The proposed method included 5 criteria with establishment of appropriate points (min 3, max 14). In case of total score 3-8, laparoscopic approach was preferred. Overall score 9-11 required laparoscopic surgery with subsequent elective repeated laparoscopy, ≥12 scores - intraoperative conversion and open surgery. Thus, subject to the rules of surgical intervention, the number of intra-abdominal complications between laparoscopic and open methods is equalized. CONCLUSION: The developed differentiated surgical strategy for patients with appendicular peritonitis is effective and reduces the incidence of wound infection, extra-abdominal complications, and hospital-stay, as well as contributes to early rehabilitation of patients.

Missed opportunities to use rapid influenza testing and severity assessment to avoid hospital admission: A cohort study from an East London District General Hospital.

Annual outbreaks of seasonal influenza cause a substantial health burden. The aim of this study was to compare patient demographic/clinical data in two influenza patient groups presenting to hospital; those requiring O2 or critical care admission and those requiring less intensive treatment. The study was conducted from 1 December 2017 until 1 April 2019 at a district general hospital in East London. Patient demographic and clinical information was collected for all patients who had tested influenza positive by near-patient testing. χ2 test was used for categorical variables to see if there were significant differences for those admitted and the Wilcoxon rank-sum test to compare the length of inpatient stay. Of 127 patients, 56 (44.1%) required oxygen or critical care. There were significant increases in National Early Warning Score (NEWS) observations (P %3C .001), Charlson comorbidity index (P = .049), length of inpatient stay (P %3C .001), and a strong association with increasing age (P = .066) when the more intensive treatment group was compared with the less intensive treatment group. A total of 13 (18.3%) of 71 patients not requiring oxygen or critical care were not admitted to the hospital. Following rapid influenza testing, NEWS scores, comorbidities, and age should be incorporated into a decision tool in Accident and Emergency to aid hospital admission or discharge decisions.