My Kidney Is Fine, Can't You Cystatin C?

Background: Independent of age, sex, and body composition, individuals of African American race and individuals with high muscle mass have elevated serum creatinine (sCr) levels on average that may result in overestimation of chronic kidney disease (CKD). We present a misdiagnosed case of CKD based on sCr levels, illustrating the utility of cystatin C (CysC) confirmation testing to answer the question: Can confirmation screening of kidney function with CysC in African American patients and patients with high muscle mass reduce the misdiagnosis of CKD? Case Presentation: A 35-year-old African American man with a history of well-controlled HIV was found to have consistently elevated creatinine (Cr). We diagnosed CKD stage 3A based on the estimated glomerular filtration rate (eGFR). Further evaluation showed isolated elevation of sCr with unremarkable urinalysis and other laboratory tests. sCr elevation predated diagnosis and HIV treatment. A CysC-based eGFR (eGFRcys) test confirmed the absence of CKD. Conclusions: The 2009 CKD Epidemiology Collaboration calculation of eGFR based on sCr concentration uses age, sex, and race, with an updated recommendation in 2021 to exclude race. Both equations are less accurate in African American patients, individuals taking medications that interfere with sCr secretion and assay, and patients taking creatine supplements or high protein intake. These clinical scenarios decrease sCr-based eGFR (eGFRCr) but do not change measured eGFR or eGFRCys. Using sCr and serum cystatin C (eGFRCr-Cys) yields better concordance to measured eGFR across all races than does eGFR estimation based on Cr alone. Confirmation with CysC can avoid misdiagnosis, incorrect dosing of drugs, and inaccurate representation of the fitness for duty.

A new lineage nomenclature to aid genomic surveillance of dengue virus.

Dengue virus (DENV) is currently causing epidemics of unprecedented scope in endemic settings and expanding to new geographical areas. It is therefore critical to track this virus using genomic surveillance. However, the complex patterns of viral genomic diversity make it challenging to use the existing genotype classification system. Here we propose adding two sub-genotypic levels of virus classification, named major and minor lineages. These lineages have high thresholds for phylogenetic distance and clade size, rendering them stable between phylogenetic studies. We present an assignment tool to show that the proposed lineages are useful for regional, national and sub-national discussions of relevant DENV diversity. Moreover, the proposed lineages are robust to classification using partial genome sequences. We provide a standardized neutral descriptor of DENV diversity with which we can identify and track lineages of potential epidemiological and/or clinical importance. Information about our lineage system, including methods to assign lineages to sequence data and propose new lineages, can be found at: dengue-lineages.org.

Social robots in research on social and cognitive development in infants and toddlers: A scoping review.

There is currently no systematic review of the growing body of literature on using social robots in early developmental research. Designing appropriate methods for early childhood research is crucial for broadening our understanding of young children's social and cognitive development. This scoping review systematically examines the existing literature on using social robots to study social and cognitive development in infants and toddlers aged between 2 and 35 months. Moreover, it aims to identify the research focus, findings, and reported gaps and challenges when using robots in research. We included empirical studies published between 1990 and May 29, 2023. We searched for literature in PsychINFO, ERIC, Web of Science, and PsyArXiv. Twenty-nine studies met the inclusion criteria and were mapped using the scoping review method. Our findings reveal that most studies were quantitative, with experimental designs conducted in a laboratory setting where children were exposed to physically present or virtual robots in a one-to-one situation. We found that robots were used to investigate four main concepts: animacy concept, action understanding, imitation, and early conversational skills. Many studies focused on whether young children regard robots as agents or social partners. The studies demonstrated that young children could learn from and understand social robots in some situations but not always. For instance, children's understanding of social robots was often facilitated by robots that behaved interactively and contingently. This scoping review highlights the need to design social robots that can engage in interactive and contingent social behaviors for early developmental research.

Reemergence of Cosmopolitan Genotype Dengue Virus Serotype 2, Southern Vietnam.

We performed phylogenetic analysis on dengue virus serotype 2 Cosmopolitan genotype in Ho Chi Minh City, Vietnam. We document virus emergence, probable routes of introduction, and timeline of events. Our findings highlight the need for continuous, systematic genomic surveillance to manage outbreaks and forecast future epidemics.

Practical Tips for Reporting Adnexal Lesions Using O-RADS MRI.

The Ovarian-Adnexal Reporting and Data System (O-RADS) MRI risk stratification system provides a standardized lexicon and evidence-based risk score for evaluation of adnexal lesions. The goals of the lexicon and risk score are to improve report quality and communication between radiologists and clinicians, reduce variability in the reporting language, and optimize management of adnexal lesions. The O-RADS MRI risk score is based on the presence or absence of specific imaging features, including the lipid content, enhancing solid tissue, number of loculi, and fluid type. The probability of malignancy ranges from less than 0.5% when there are benign features to approximately 90% when there is solid tissue with a high-risk time-intensity curve. This information can aid in optimizing management of patients with adnexal lesions. The authors present an algorithmic approach to the O-RADS MRI risk stratification system and highlight key teaching points and common pitfalls. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Novel Baseline Facial Muscle Database Using Statistical Shape Modeling and In Silico Trials toward Decision Support for Facial Rehabilitation.

Backgrounds and Objective: Facial palsy is a complex pathophysiological condition affecting the personal and professional lives of the involved patients. Sudden muscle weakness or paralysis needs to be rehabilitated to recover a symmetric and expressive face. Computer-aided decision support systems for facial rehabilitation have been developed. However, there is a lack of facial muscle baseline data to evaluate the patient states and guide as well as optimize the rehabilitation strategy. In this present study, we aimed to develop a novel baseline facial muscle database (static and dynamic behaviors) using the coupling between statistical shape modeling and in-silico trial approaches. Methods: 10,000 virtual subjects (5000 males and 5000 females) were generated from a statistical shape modeling (SSM) head model. Skull and muscle networks were defined so that they statistically fit with the head shapes. Two standard mimics: smiling and kissing were generated. The muscle strains of the lengths in neutral and mimic positions were computed and recorded thanks to the muscle insertion and attachment points on the animated head and skull meshes. For validation, five head and skull meshes were reconstructed from the five computed tomography (CT) image sets. Skull and muscle networks were then predicted from the reconstructed head meshes. The predicted skull meshes were compared with the reconstructed skull meshes based on the mesh-to-mesh distance metrics. The predicted muscle lengths were also compared with those manually defined on the reconstructed head and skull meshes. Moreover, the computed muscle lengths and strains were compared with those in our previous studies and the literature. Results: The skull prediction's median deviations from the CT-based models were 2.2236 mm, 2.1371 mm, and 2.1277 mm for the skull shape, skull mesh, and muscle attachment point regions, respectively. The median deviation of the muscle lengths was 4.8940 mm. The computed muscle strains were compatible with the reported values in our previous Kinect-based method and the literature. Conclusions: The development of our novel facial muscle database opens new avenues to accurately evaluate the facial muscle states of facial palsy patients. Based on the evaluated results, specific types of facial mimic rehabilitation exercises can also be selected optimally to train the target muscles. In perspective, the database of the computed muscle lengths and strains will be integrated into our available clinical decision support system for automatically detecting malfunctioning muscles and proposing patient-specific rehabilitation serious games.

REDCap mobile data collection: Using implementation science to explore the potential and pitfalls of a digital health tool in routine voluntary medical male circumcision outreach settings in Zimbabwe.

Background: Digital data collection tools improve data quality but are limited by connectivity. ZAZIC, a Zimbabwean consortium focused on scaling up male circumcision (MC) services, provides MC in outreach settings where both data quality and connectivity is poor. ZAZIC implemented REDCap Mobile app for data collection among roving ZAZIC MC nurses. To inform continued scale-up or discontinuation, this paper details if, how, and for whom REDCap improved data quality using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Methods: Data were collected for this retrospective, cross-sectional study for nine months, from July 2019 to March 2020, before COVID-19 paused MC services. Data completeness was compared between paper- and REDCap-based tools and between two ZAZIC partners using two sample, one-tailed t-tests. Results: REDCap reached all roving nurses who reported 26,904 MCs from 1773 submissions. REDCap effectiveness, as measured by data completeness, decreased from 89.2% in paper to 76.6% in REDCap app for Partner 1 (p < 0.001, 95% CI: -0.24, -0.12) but increased modestly from 86.2% to 90.3% in REDCap for Partner 2 (p = 0.05, 95% CI: -.007, 0.12). Adoption of REDCap was 100%; paper-based reporting concluded in October 2019. Implementation varied by partner and user. Maintenance appeared high. Conclusion: Although initial transition from paper to REDCap showed mixed effectiveness, post-hoc analysis from service resumption found increased REDCap data completeness across partners, suggesting locally-led momentum for REDCap-based data collection. Staff training, consistent mentoring, and continued technical support appear critical for continued use of digital health tools for quality data collection in rural Zimbabwe and similar low connectivity settings.

OXSR1 inhibits inflammasome activation by limiting potassium efflux during mycobacterial infection.

Pathogenic mycobacteria inhibit inflammasome activation to establish infection. Although it is known that potassium efflux is a trigger for inflammasome activation, the interaction between mycobacterial infection, potassium efflux, and inflammasome activation has not been investigated. Here, we use Mycobacterium marinum infection of zebrafish embryos and Mycobacterium tuberculosis infection of THP-1 cells to demonstrate that pathogenic mycobacteria up-regulate the host WNK signalling pathway kinases SPAK and OXSR1 which control intracellular potassium balance. We show that genetic depletion or inhibition of OXSR1 decreases bacterial burden and intracellular potassium levels. The protective effects of OXSR1 depletion are at least partially mediated by NLRP3 inflammasome activation, caspase-mediated release of IL-1ß, and downstream activation of protective TNF-α. The elucidation of this druggable pathway to potentiate inflammasome activation provides a new avenue for the development of host-directed therapies against intracellular infections.

Enhanced head-skull shape learning using statistical modeling and topological features.

Skull prediction from the head is a challenging issue toward a cost-effective therapeutic solution for facial disorders. This issue was initially studied in our previous work using full head-to-skull relationship learning. However, the head-skull thickness topology is locally shaped, especially in the face region. Thus, the objective of the present study was to enhance our head-to-skull prediction problem by using local topological features for training and predicting. Head and skull feature points were sampled on 329 head and skull models from computed tomography (CT) images. These feature points were classified into the back and facial topologies. Head-to-skull relations were trained using the partial least square regression (PLSR) models separately in the two topologies. A hyperparameter tuning process was also conducted for selecting optimal parameters for each training model. Thus, a new skull could be generated so that its shape was statistically fitted with the target head. Mean errors of the predicted skulls using the topology-based learning method were better than those using the non-topology-based learning method. After tenfold cross-validation, the mean error was enhanced 36.96% for the skull shapes and 14.17% for the skull models. Mean error in the facial skull region was especially improved with 4.98%. The mean errors were also improved 11.71% and 25.74% in the muscle attachment regions and the back skull regions respectively. Moreover, using the enhanced learning strategy, the errors (mean ± SD) for the best and worst prediction cases are from 1.1994 ± 1.1225 mm (median: 0.9036, coefficient of multiple determination (R2): 0.997274) to 3.6972 ± 2.4118 mm (median: 3.9089, R2: 0.999614) and from 2.0172 ± 2.0454 mm (median: 1.2999, R2: 0.995959) to 4.0227 ± 2.6098 mm (median: 3.9998, R2: 0.998577) for the predicted skull shapes and the predicted skull models respectively. This present study showed that more detailed information on the head-skull shape leads to a better accuracy level for the skull prediction from the head. In particular, local topological features on the back and face regions of interest should be considered toward a better learning strategy for the head-to-skull prediction problem. In perspective, this enhanced learning strategy was used to update our developed clinical decision support system for facial disorders. Furthermore, a new class of learning methods, called geometric deep learning will be studied.

vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding.

BACKGROUND: As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allow non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria. RESULTS: In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis bacteria using our vCOMBAT tool. CONCLUSIONS: The vCOMBAT online tool is publicly available at https://combat-bacteria.org/ .

Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models.

Mechanistic pharmacodynamic models that incorporate the binding kinetics of drug-target interactions have several advantages in understanding target engagement and the efficacy of a drug dose. However, guidelines on how to build and interpret mechanistic pharmacodynamic drug-target binding models considering both biological and computational factors are still missing in the literature. In this chapter, current approaches of building mechanistic PD models and their advantages are discussed. We also present a methodology on how to select a suitable model considering both biological and computational perspectives, as well as summarize the challenges of current mechanistic PD models.

Common anti-haemostatic medications increase the severity of systemic infection by uropathogenic Escherichia coli.

Uropathogenic Escherichia coli (UPEC) causes urinary tract infections that can result in sepsis. The haemostatic system is protective in the pyelonephritis stage of ascending UPEC infection, but the role of the haemostatic system has not been investigated during sepsis. Here we utilize a zebrafish-UPEC systemic infection model to visualize infection-induced coagulation and examine the effects of commonly prescribed anti-haemostatic medications on the infection severity. Treatment of systemically infected zebrafish with warfarin, aspirin, or ticagrelor reduced host survival, while stabilization of clots with aminocaproic acid increased host survival. Anti-haemostatic drug treatment increased UPEC burden. Our findings provide evidence that commonly prescribed anti-haemostatic medications may worsen the outcome of severe UPEC infection.

Rapid and Sensitive SERS-Based Lateral Flow Test for SARS-CoV2-Specific IgM/IgG Antibodies.

As an immune response to COVID-19 infection, patients develop SARS-CoV-2-specific IgM/IgG antibodies. Here, we compare the performance of a conventional lateral flow assay (LFA) with a surface-enhanced Raman scattering (SERS)-based LFA test for the detection of SARS-CoV-2-specific IgM/IgG in sera of COVID-19 patients. Sensitive detection of IgM might enable early serological diagnosis of acute infections. Rapid detection in serum using a custom-built SERS reader is at least an order of magnitude more sensitive than the conventional LFAs with naked-eye detection. For absolute quantification and the determination of the limit of detection (LOD), a set of reference measurements using purified (total) IgM in buffer was performed. In this purified system, the sensitivity of SERS detection is even 7 orders of magnitude higher: the LOD for SERS was ca. 100 fg/mL compared to ca. 1 µg/mL for the naked-eye detection. This outlines the high potential of SERS-based LFAs in point-of-care testing once the interference of serum components with the gold conjugates and the nitrocellulose membrane is minimized.

Untrained networks for compressive lensless photography.

Compressive lensless imagers enable novel applications in an extremely compact device, requiring only a phase or amplitude mask placed close to the sensor. They have been demonstrated for 2D and 3D microscopy, single-shot video, and single-shot hyperspectral imaging; in each case, a compressive-sensing-based inverse problem is solved in order to recover a 3D data-cube from a 2D measurement. Typically, this is accomplished using convex optimization and hand-picked priors. Alternatively, deep learning-based reconstruction methods offer the promise of better priors, but require many thousands of ground truth training pairs, which can be difficult or impossible to acquire. In this work, we propose an unsupervised approach based on untrained networks for compressive image recovery. Our approach does not require any labeled training data, but instead uses the measurement itself to update the network weights. We demonstrate our untrained approach on lensless compressive 2D imaging, single-shot high-speed video recovery using the camera's rolling shutter, and single-shot hyperspectral imaging. We provide simulation and experimental verification, showing that our method results in improved image quality over existing methods.

Anti-inflammatory genes in PBMCs post-spontaneous intracerebral hemorrhage.

BACKGROUND: Neuroinflammation is important in the pathophysiology of spontaneous intracerebral hemorrhage (ICH) and peripheral inflammatory cells play a role in the clinical evolution and outcome. METHODOLOGY: Blood samples from ICH patients (n = 20) were collected at admission for 5 consecutive days for peripheral blood mononuclear cells (PBMCs). Frozen PBMCs were used for real-time PCR using Taqman probes (NFKB1, SOD1, PPARG, IL10, NFE2L2, and REL) and normalized to GAPDH. Data on hospital length of stay and modified Rankin score (MRS) were collected with 90-day MRS ≤ 3 as favorable outcome. Statistical analysis of clinical characteristics to temporal gene expression from early to delayed timepoints was compared for MRS groups (favorable vs unfavorable) and hematoma volume. PRINCIPLE FINDINGS AND RESULTS: IL10, SOD1, and REL expression were significantly higher at delayed timepoints in PBMCs of ICH patients with favorable outcome. PPARG and REL increased between timepoints in patients with favorable outcome. NFKB1 expression was not sustained, but significantly decreased from higher levels at early onset in patients with unfavorable outcome. IL10 expression showed a negative correlation in patients with high hematoma volume (>30 mL). CONCLUSIONS AND SIGNIFICANCE: Anti-inflammatory, pro-survival regulators were highly expressed at delayed time points in ICH patients with a favorable outcome, and IL10 expression showed a negative correlation to high hematoma volume.

Sustained Low-Efficiency Dialysis is Associated with Worsening Cerebral Edema and Outcomes in Intracerebral Hemorrhage.

BACKGROUND/OBJECTIVES: We postulated that renal replacement therapy (RRT) in ICH patients with advanced chronic kidney disease (CKD) is associated with increased frequency and size of perihematomal edema (PHE) expansion and worse patient outcomes. METHODS: The Get With the Guidelines-Stroke Registry was queried for all patients admitted with ICH (N = 1089). Secondary causes, brainstem ICH, and initial HV < 7 cc were excluded. We identified patients with advanced CKD with and without RRT following admission for ICH. ABC/2 formula was used to measure hematoma volume (HV) and PHE. Patient outcomes were 30-day mortality, 90-day modified Rankin Scale score, and discharge disposition. We used propensity scores and optimal matching to adjust for multiple covariates. RESULTS: At 48 h post-ICH, PHE expansion was a significant predictor of poor patient outcomes in our cohort. Patients with CKD who received sustained low-efficacy dialysis (SLED) treatment had larger 48 h PHE growth compared to both untreated CKD group (average treatment effect (ATE), 11.5; 95% CI, 4.9-18.1; p < 0.01) and all untreated patients (ATE, 7.43; 95% CI, 4.7-10.2; p < 0.01). Moreover, patients with RRT had significantly worse functional and mortality outcomes. CONCLUSIONS: SLED treatment in ICH patients with CKD was associated with significant increase in rate and frequency of PHE expansion. Absolute increase in PHE during 48-h post-ICH was associated with increased mortality and worse functional outcomes. Further prospective and multicenter evaluation is needed to differentiate the effects of RRT on hematoma dynamics and patient outcomes from those attributed to CKD.

Tattoo visualization using cross-polarized lighting and infrared photography.

Identification of the deceased is a critical responsibility of the death investigation system. If visual identification is inconclusive, tattoos can provide secondary identification but may be difficult to visualize at various stages of decomposition. We describe the case of a 35-year-old male found submerged underwater by police after swimming at a pier. The decedent was last seen earlier that day. Signs of immersion including sodden hands, feet, and clothing, wet sand on the torso and legs, and heavy edematous legs were observed. The post-mortem blood alcohol concentration was 427 mg/100 mL; signs of recent traumatic injury were not present. The immediate cause of death was drowning as a consequence of ethanol intoxication. When pulled from the water, the decedent's shoulder tattoo was not visible. Cross-polarized lighting and infrared photography visualized the tattoo to help confirm identity. These photographic methods were compared to hydrogen peroxide and optical coherence tomography techniques and described in detail to assist with future cases.

Preventive behavior of Vietnamese people in response to the COVID-19 pandemic.

We sought to evaluate the adherence of Vietnamese adults to Coronavirus Disease 2019 (COVID-19) preventive measures, and gain insight into the effects of the epidemic on the daily lives of Vietnamese people. An online questionnaire was administered from March 31 to April 6, 2020. The questionnaire assessed personal preventive behavior (such as physical distancing, wearing a face mask, cough etiquette, regular handwashing and using an alcohol hand sanitizer, body temperature check, and disinfecting mobile phones) and community preventive behavior (such as avoiding meetings, large gatherings, going to the market, avoiding travel in a vehicle/bus with more than 10 persons, and not traveling outside of the local area during the lockdown). A total adherence score was calculated by summing the scores of the 9 personal and the 11 community prevention questions. In total, 2175 respondents completed the questionnaire; mean age: 31.4 ± 10.7; (range: 18-69); 66.9% were women; 54.2% were health professionals and 22.8% were medical students. The mean adherence scores for personal and community preventive measures were 7.23 ± 1.63 (range 1-9) and 9.57 ± 1.12 (range 1-11), respectively. Perceived adaptation of the community to lockdown (Beta (ß) = 2.64, 95% Confidence Interval (CI) 1.25-4.03), fears/worries concerning one's health (ß = 2.87, 95% CI 0.04-5.70), residing in large cities (ß = 19.40, 95% CI 13.78-25.03), access to official COVID-19 information sources (ß = 16.45, 95% CI 6.82-26.08), and working in healthcare/medical students (ß = 22.53, 95% CI 16.00-29.07) were associated with a higher adherence score to anti-COVID instructions. In conclusion, this study confirmed a high degree of adherence to personal and community preventive behavior among Vietnamese people. Our findings are consistent with the epidemiology of COVID-19 in Vietnam, where there have been few infections and no recorded deaths up to the first week of July 2020.

A statistical shape modeling approach for predicting subject-specific human skull from head surface.

Human skull is an important body structure for jaw movement and facial mimic simulations. Surface head can be reconstructed using 3D scanners in a straightforward way. However, internal skull is challenging to be generated when only external information is available. Very few studies in the literature focused on the skull generation from outside head information, especially in a subject-specific manner with a complete skull. Consequently, this present study proposes a novel process for predicting a subject-specific skull with full details from a given head surface using a statistical shape modeling approach. Partial least squared regression (PLSR)-based method was used. A CT image database of 209 subjects (genders-160 males and 49 females; ages-34-88 years) was used for learning head-to-skull relationship. Heads and skulls were reconstructed from CT images to extract head/skull feature points, head/skull feature distances, head-skull thickness, and head/skull volume descriptors for the learning process. A hyperparameter turning process was performed to determine the optimal numbers of head/skull feature points, PLSR components, deformation control points, and appropriate learning strategies for our learning problem. Two learning strategies (point-to-thickness with/without volume descriptor and distance-to-thickness with/without volume descriptor) were proposed. Moreover, a 10-fold cross-validation procedure was conducted to evaluate the accuracy of the proposed learning strategies. Finally, the best and worst reconstructed skulls were analyzed based on the best learning strategy with its optimal parameters. The optimal number of head/skull feature points and deformation control points are 2300 and 1300 points, respectively. The optimal number of PLSR components ranges from 4 to 8 for all learning configurations. Cross-validation showed that grand means and standard deviations of the point-to-thickness, point-to-thickness with volumes, distance-to-thickness, and distance-to-thickness with volumes learning configurations are 2.48 ± 0.27 mm, 2.46 ± 0.19 mm, 2.46 ± 0.15 mm, and 2.48 ± 0.22 mm, respectively. Thus, the distance-to-thickness is the best learning configuration for our head-to-skull prediction problem. Moreover, the mean Hausdorff distances are 2.09 ± 0.15 mm and 2.64 ± 0.26 mm for the best and worst predicted skull, respectively. A novel head-to-skull prediction process based on the PLSR method was developed and evaluated. This process allows, for the first time, predicting 3D subject-specific human skulls from head surface information with a very good accuracy level. As perspective, the proposed head-to-skull prediction process will be integrated into our real-time computer-aided vision system for facial animation and rehabilitation. Graphical abstract.