In recent years, point-of-care testing (POCT) has become a topical issue. Lateral flow immunoassay strategies based on magnetic nanoparticles (MNPs) are important POCT elements due to their sensitive quantification of biological materials via MNP magnetic field measurement. In this study, we designed a magnetic flux sensor for use in immunomagnetic biosensing platforms, incorporating a mathematical model and computer simulation strategy. The system used field programmable gate array (FPGA) as the control chip, synthesized excitation signals and excited coils to generate excitation magnetic fields. Also, the stepping motor was controlled to drive the test strip at a uniform speed through the sensor detection area. A differential configuration strategy was used for sensor pick-up coils to assess MNP influence on the magnetic flux, which was insensitive to background magnetic interference and common-mode noise. These factors significantly enhanced the signal-to-noise ratio of the sensor. The magnetic flux sensor structure was optimized, and response magnetic field characteristics of MNP on test strips analyzed using finite element analysis (FEA) simulations. System performance was evaluated by testing human chorionic gonadotropin (HCG), which demonstrated a linear performance, with a limit of detection of 0.0098 mIU/mL. This system may be used to identify other target analytes in different application settings.

We report a case of a 52-year old patient complaining of palpitations and syncope. His heart rate trends from his Apple Watch SE suggested two types of tachycardia, and we successfully performed ablation for both of them in a single session. Our case demonstrates the cardiological potential of smart watches.

Background and Objectives: Cystic fibrosis-related diabetes is the major comorbidity in cystic fibrosis and reduces life expectancy, highlighting the need for early diagnosis and treatment. This study aimed to verify whether abnormal continuous glucose monitoring results predict cystic fibrosis-related diabetes onset and/or a decline in the forced expiratory volume in the 1st second or body mass index of patients with cystic fibrosis. Methods: Thirty-nine patients with cystic fibrosis (age 10–19 years) were screened for cystic fibrosis-related diabetes using the oral glucose tolerance test. Patients without diabetes diagnose underwent 3 days of continuous glucose monitoring, spirometry and body mass index measurements and were reassessed at the end of the study with a second spirometry, body mass index evaluation and oral glucose tolerance test. Results: Thirty-four patients completed the follow-up (3.1±0.51 years). None of the study variables predicted cystic fibrosis-related diabetes progression or were associated with hypoglycemic events. Continuous glucose monitoring could detect glucose abnormalities not revealed in the oral glucose tolerance test. Patients with glucose level of >140 mg/dL on continuous glucose monitoring showed significantly lower body mass index z scores at both study initiation (-1.55±1.68 vs. -0.17±0.88; p-value=0.02) and completion (-1.65±1.55 vs. -0.42±1.08; p-value=0.039). Conclusions: Continuous glucose monitoring can identify glucose abnormalities not detected by oral glucose tolerance test that are related to early stage decreases in body mass index. However, it was ineffective in predicting the onset of diabetes in this cystic fibrosis population. Different diagnostic criteria for diabetes may be required for individuals with cystic fibrosis.

Merkel Cell Carcinoma (MCC) is a rare dermatologic malignancy with propensity for metastasis to lymph nodes, skin, lung, liver, bone, and brain. Metastasis to the stomach is rare with only 13 reported cases. We discuss the outcomes of a partial gastrectomy for the treatment of symptomatic MCC with gastric metastasis.

Objective: To evaluate group differences in social adjustment in survivors of pediatric ALL compared to survivor siblings, and controls; identify disease-related predictors of social adjustment in survivors; and explore whether executive functioning explained differences in social adjustment across groups and between disease-related predictors. Methods: Survivors of pediatric ALL (n=38, average age at diagnosis=4.27 years [SD=1.97]; average time off treatment=4.83 years [SD=1.52]), one sibling (if available, n=20), and one parent from each family were recruited from a long-term survivor clinic. Healthy age- and sex-matched controls (n=38) and one parent from each family were recruited from the community. Parents completed the Behavioral Assessment System for Children, Parent Rating Scale (BASC-3) Social Withdrawal subscale as a measure of social adjustment and the Behavior Rating Inventory of Executive Functions (BRIEF-2) as a measure of executive function for each of their children. Results: Parents reported that survivors had significantly worse social adjustment compared to controls (b=6.34, p=.004), but not survivor siblings. Among survivors, greater time off treatment (b=2.06, p=.058) and poorer executive functioning (b=0.42, p=.006) were associated with worse social adjustment. Executive function did not mediate differences in social withdrawal between survivors and controls or the relationship between time off treatment and social withdrawal among survivors. Conclusions: Survivors of pediatric ALL presenting to follow-up programs should be screened for difficulties with social adjustment. Future research should examine treatment- and non-treatment-related factors contributing to poorer social outcomes.

Rapidly Destructive Coxopathy is a rare orthopedic manifestation that results in the damage of the hip joint in a very limited timespan causing severe hip pain and disability. It is difficult to differentiate from osteoarthritis upon initial examination and can only be distinguished through frequent follow-ups and radiographic evaluation.

Driving for a long time leads to physical, psychological stress, which increases life at higher risk and puts health in all hostile areas. Spending more time behind the wheel leads to uninterrupted sitting, resulting in obesity, mental health risk, poor sleep, and bad habits such as drug addiction. In this research, reliable emergency services ensure safe driving. Such an experience guarantees continuous monitoring of the driver's health intelligently. An intelligent health system continuously monitors the driver's health status and constantly updates the cloud server to monitor the driver's heart rate. According to a recent study, 60% of road accidents occur due to the driver's restless continuous driving. On motorways, vehicles are faster than those on the city side, and if a vehicle brakes abruptly without notification, it causes severe damage, followed by escort vehicles. In addition, the driver's health is a crucial factor, and the unexpected worsening of health complications such as stroke and cardiac arrest is unpredictable, as the symptoms are prevalent and occur within minutes. To avoid stressful driving, the intelligent assistant must monitor his health and suggests relaxation guidelines.

Efficient adsorption separation of propylene (C3H6) and propane (C3H8) can largely lower the energy consumption compared to the current energy-intensive cryogenic distillation. Herein, we report an isoreticular family of pillar-layer metal-organic frameworks (MOFs), M(AIP)(BPY)0.5 (M = Co, Ni, and Zn), for efficient C3H6/C3H8 separation by exploiting thermodynamic and kinetic effects, circumventing disadvantages of each separation mechanism. The three MOFs feature an open metal site for each metal node and uniform but narrow one-dimensional (1D) channels, offering strong binding sites toward C3H6 via π-complexations while obstructing the diffusion of bulkier C3H8. The Ni-MOF shows the best separation performance based on the highest thermodynamic and kinetic C3H6/C3H8 selectivity, further verified by computational simulations. Ni(AIP)(BPY)0.5 has a moderate C3H6 uptake of 1.94 mmol/g but a remarkably high C3H6/C3H8 uptake ratio of 4.26 at 298 K and 1 bar. Efficient C3H6/C3H8 separation, good recyclability, moisture and water stability of Ni(AIP)(BPY)0.5 are confirmed.

The advanced use of a pH-responsive biomaterial-based injectable liquid implant for effective chemotherapeutic delivery in glioblastoma multiforme brain (GBM) tumour treatment is presented. As an implant, we proposed a water-in-oil-in-water multiple emulsion with encapsulated doxorubicin. The effectiveness of the proposed therapy was evaluated by comparing the cancer cell viability achieved in classical therapy (chemotherapeutic solution). The experimental study included doxorubicin release rates and consumption for two emulsions differing in drop sizes and structures in the presence of GBM-cells (LN229, U87 MG), and a cell viability. The results showed that the multiple emulsion implant was significantly more effective than classical therapy when considering the reduction in cancer cell viability: 85% for the emulsion-implant, and only 43% for the classical therapy. A diffusion-reaction model was adapted to predict doxorubicin release kinetics and elimination by glioblastoma cells. CFD simulations confirmed that the drug release kinetics depends on multiple emulsion structures and drop sizes.

Post-transplant cyclophosphamide (PTCy) has become the most popular approach in haplo-identical hematopoietic cell transplant (haplo-HCT). Although there are reports of a small number of adult patients in the literature who experienced graft failure and were re-transplanted with a haploidentical donor with PTCy prophylaxis, there is still insufficient guidance for patients with specific contraindications/complications to cyclophosphamide and virtually no data in the pediatric setting. Abatacept (Aba), a T cell co-stimulation blockade, has been shown in previous studies to prevent severe acute graft-versus-host disease (GVHD) with minimal toxicity and durable engraftment. We report the efficacy of Aba-based GVHD prophylaxis in four pediatrics patients (ages 2-12 years) who received a haplo-HCT with peripheral blood stem cells (PBSC). Three patients had previous transplants. One patient developed acute GVHD of skin stage 3 and one patient had both stage 3 skin and stage 1 GI acute GVHD. Two patients had mild chronic skin GVHD. All 4 patients are alive with full donor chimerism and without disease at 7-23 months follow up, weaning or off immunosuppressive agents with no complications. Successful haplo-HCT utilizing an Aba- based regimen can result in reliable engraftment and acceptable GVHD. However, our small sample size limits generalizability and encourages the consideration of a larger prospective trial to validate these results in the haplo-HCT setting.

Bacground: We aimed to analyze combination of the schockwave lithotripy (SWL) success predictors. Methods: In this retrospective study, the outcomes of the patients with kidney stones treated with SWL were analyzed. Adult patients (≥18 years) with complete records with non-contrast computed tomography (NCCT), stone analysis, laboratory data were involved in the study. Patients who were with urinary system anomalies, who were receiving alpha-blocker and/or calcium channel blockers and whom with impaired kidney function were excluded. The effect of stone density, skin-to-stone distance (SSD), perirenal tissue density (PTD), subcutaneous tissue density (STD), stone size, stone burden, stone localization, infundibulopelvic angle (IA), as well as body mass index (BMI) and stone analysis results on the success of the treatment was evaluated. Results: SSD, PTD, STD, stone localization, IA, as well as body-mass index, did not have any association with SWL success. Stone size and stone burden had a significant association with treatment success (p = 0.0001), and the cut-off values determined for stone size and stone burden were 12.95 mm (p = 0.0006) and 121.38 mm2 (p = 0.004) respectively. Stone density also had a significant association with treatment success (p = 0.0001), and the cut-off value determined for stone density was 739 HU (Hounsfield Unit) (p = 0.001). Treatment success was significantly lower in cystine and calcium oxalate monohydrate stones compared to other stone types (p = 0.019). Conclusion: Significant markers that determine SWL effectiveness are stone size, stone burden, stone density and, besides, stone type.

The current pandemic caused by Covid-19 triggered intensively the development of high-performance air filters. Polypropylene (PP) is widely used as the raw material of meltblown nonwovens that is the core layer in air filters, such as, masks. In this study, an electret PP meltblown nonwoven with antibacterial activity was developed, and nano boehmite (AlOOH) and nano-ZnO employed as electret and antibacterial agent, respectively. 0.5-2.0 wt% of AlOOH and 1.0 wt% of ZnO were doped into PP matrix using a twin-screw extruder and the resulting masterbatches applied as raw materials to afford nonwovens via a meltblown process. The as-prepared nonwovens were characterized by means of SEM, IR and DSC/TG, and after corona charging, the filtration efficiency, charge decay and antibacterial properties were evaluated. More than 1.0 wt% dosage of AlOOH endowed the nonwoven with high filtration efficiency and 1.0 wt% of ZnO brought about antibacterial activity. Corona charging was an effective means to make the nonwovens electret charged and the charges were quicker to decay in air than in a sealed bag. The as-prepared meltblown nonwoven would be a remarkably promising filter in air filtration.

Deep learning models perform remarkably well for the same task under the assumption that data is always coming from the same distribution. However, this is generally violated in practice, mainly due to the differences in the data acquisition techniques and the lack of information about the underlying source of new data. Domain Generalization targets the ability to generalize to test data of an unseen domain; while this problem is well-studied for images, such studies are significantly lacking in spatiotemporal visual content – videos and GIFs. This is due to (1) the challenging nature of misalignment of temporal features and the varying appearance/motion of actors and actions in different domains, and (2) spatiotemporal datasets being laborious to collect and annotate for multiple domains. We collect and present the first synthetic video dataset of Animated GIFs for domain generalization, Ani-GIFs, that is used to study domain gap of videos vs. GIFs, and animated vs. real GIFs, for the task of action recognition. We provide a training and testing setting for Ani-GIFs, and extend two domain generalization baseline approaches, based on data augmentation and explainability, to the spatiotemporal domain to catalyze research in this direction.

Attention maps, a popular heatmap-based explanation method for Visual Question Answering (VQA), are supposed to help users understand the model by highlighting portions of the image/question used by the model to infer answers. However, we see that users are often misled by current attention map visualizations that point to relevant regions despite the model producing an incorrect answer. Hence, we propose Error Maps that clarify the error by highlighting image regions where the model is prone to err. Error maps can indicate when a correctly attended region may be processed incorrectly leading to an incorrect answer, and hence, improve users’ understanding of those cases. To evaluate our new explanations, we further introduce a metric that simulates users’ interpretation of explanations to evaluate their potential helpfulness to understand model correctness. We finally conduct user studies to see that our new explanations help users understand model correctness better than baselines by an expected 30% and that our proxy helpfulness metrics correlate strongly (rho>0.97) with how well users can predict model correctness.

The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced when the model is encouraged to remember the evidence for previously made decisions. As a first step towards exploring this hypothesis, we propose a simple novel training paradigm, called Remembering for the Right Reasons (RRR), that additionally stores visual model explanations for each example in the buffer and ensures the model has “the right reasons” for its predictions by encouraging its explanations to remain consistent with those used to make decisions at training time. Without this constraint, there is a drift in explanations and increase in forgetting as conventional continual learning algorithms learn new tasks. We demonstrate how RRR can be easily added to any memory or regularization-based approach and results in reduced forgetting, and more importantly, improved model explanations. We have evaluated our approach in the standard and few-shot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. Our code is available at \url{https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons}

In the domain of Visual Question Answering (VQA), studies have shown improvement in users’ mental model of the VQA system when they are exposed to examples of how these systems answer certain Image-Question (IQ) pairs. In this work, we show that showing controlled counterfactual image-question examples are more effective at improving the mental model of users as compared to simply showing random examples. We compare a generative approach and a retrieval-based approach to show counterfactual examples. We use recent advances in generative adversarial networks (GANs) to generate counterfactual images by deleting and inpainting certain regions of interest in the image. We then expose users to changes in the VQA system’s answer on those altered images. To select the region of interest for inpainting, we experiment with using both human-annotated attention maps and a fully automatic method that uses the VQA system’s attention values. Finally, we test the user’s mental model by asking them to predict the model’s performance on a test counterfactual image. We note an overall improvement in users’ accuracy to predict answer change when shown counterfactual explanations. While realistic retrieved counterfactuals obviously are the most effective at improving the mental model, we show that a generative approach can also be equally effective.

COGLE (COmmon Ground Learning and Explanation) is an explainable artificial intelligence (XAI) system for autonomous drones that deliver supplies in mountainous areas to field units. The drone missions have risks that vary with topography, flight decisions, and mission goals in a simulated environment. Users must determine which AI-controlled drone is better for a mission. Narrative explanations identify the advantages of a drone’s plan (“What?”) and reasons that the better drone is able to do them (“Why?”). Visual explanations highlight risks from obstacles that users may have overlooked (“Where?”). A model induction user study showed that post-decision explanations produced a small effect on the participants’ abilities to identify the better of two imperfect drones and their plans for a mission, but they did not teach participants to judge the multiple success factors in complex missions as well as the AI pilots. In a decision support variation of the task, users would receive pre-decision explanations to help them to decide when to trust the XAI’s decision. In a fielded XAI application, every drone available for a mission may lack some competencies. We created a proof-of-concept demonstration of automatic ways to combine knowledge from multiple imperfect AIs to get better solutions that the individual AIs do not find on their own. This paper reports on the research challenges, technical approach, and findings of the project and also reflects on the multidisciplinary journey that we took.

We study a user-guided approach for producing global explanations of deep networks for image recognition. The global explanations are produced with respect to a test data set and give the overall frequency of different “recognition reasons” across the data. Each reason corresponds to a small number of the most significant human-recognizable visual concepts used by the network. The key challenge is that the visual concepts cannot be predetermined and those concepts will often not correspond to existing vocabulary or have labelled data sets. We address this issue via an interactive-naming interface, which allows users to freely cluster significant image regions in the data into visually similar concepts. Our main contribution is a user study on two visual recognition tasks. The results show that the participants were able to produce a small number of visual concepts sufficient for explanation and that there was significant agreement among the concepts, and hence global explanations, produced by different participants.

Objectives: Serum lactate dehydrogenase has been extensively studied in hypertensive disorders of pregnancy. However, to date, its clinical usefulness in the field remains unknown. The present meta-analysis has been designed to evaluate differences in serum LDH values among patients with hypertensive disorders of pregnancy and to help determine its diagnostic accuracy as well its predictive accuracy in determining adverse pregnancy outcomes. Methods: We searched Medline, Scopus, Clinicaltrials.gov, EMBASE, Cochrane Central Register of Controlled Trials CENTRAL and Google Scholar databases from their inception till March 18, 2021. Meta-analysis was performed using the meta and metafor functions in Rstudio. Pooled mean differences (MD) were calculated with the Hartung-Knapp-Sidik-Jonkman. Meta-regression analyses, publication bias assessment and Trim and Fill function were also performed. The adequacy of the sample size was evaluated with Trial Sequential Analysis. Results: Fifty-two articles were included that comprised 5,340 women with hypertensive disorders of pregnancy and 2,890 healthy controls. The meta-analysis revealed significant differences among patients with preeclampsia (either mild or severe) compared to controls, as well as among patients with mild and severe preeclampsia. Significant asymmetry was noted after examining funnel plots, however, and the trim and fill analysis revealed that differences were significant only among cases with severe preeclampsia and healthy controls. Morbidity outcomes increased with LDH levels > 600 IU/L and particularly when these exceeded 800 IU/L. However, confidence and prediction intervals indicated an underpowered sample size for the accurate determination of the odds of developing maternal morbidity. Conclusions: Despite the extensive research in the field of hypertensive disorders of pregnancy, there is still lack of evidence concerning the diagnostic performance of serum LDH in clinical practice; hence, research should expand in this direction to evaluate its clinical utility.

Background Aromatase inhibitors (AI) have positive effects on disease-free life in patients with Breast Cancer (BC); on the other hand, their side effects especially arthralgia can be observed in many of patients. This study aimed to evaluate the effectiveness of Progressive Relaxation Exercises (PRE) on the common side effects of AI in patients with BC. Methods A total of 44 patients receiving AI were allocated to the study and control groups in this randomized controlled, single-blind trial. The study group (n=22) performed a combined (one-day supervised, and 3 days home-based) PRE program 4 days/week, for six weeks. The control group (n=22) was given advice about relaxation in daily life. Data was collected initially and after 6 weeks of the intervention. Pain, quality of life (QoL) and emotional status (ES) were assessed using the Brief Pain Inventory (BPI), Functional Assessment of Chronic Illness Therapy (FACT) and Hospital Anxiety and Depression (HAD) scales, respectively. Results Pain was significantly reduced within the study group in Pain Severity (p=0.001) and Pain Interference (p=0.01) sub-scores. Pain was also reduced between the groups as compared using the Pain Severity (p=0.00) and Patient Pain Experience (p=0.003) sub-scores; QoL and ES remained with no significant difference either within the groups or between the groups (p>0.05). Conclusion The results of this study showed that PRE significantly decreased pain scores in BC patients receiving AI. Although the reduction in pain is a valuable data even in the 6-week period in those cases, the long-term effects of relaxation techniques need to be followed.

Percutaneous transluminal septal myocardial ablation (PTSMA) is an established procedure for treating symptomatic hypertrophic obstructive cardiomyopathy. We report a case of urgent PTSMA for treating refractory heart failure due to exacerbated obstruction of the left ventricular outflow tract after surgical aortic valvular replacement to treat severe aortic stenosis.

The primary goal of this treatment is the restoration of venous outflow and can be achieved by combined endovascular techniques. We report a case of 38 years-old female, with leg pain and edema, skin changes, venous claudication and dyspnea for polmunary embolism.

Machine learning (ML) algorithms are gaining popularity in clinical practice settings due to their ability to process information in ways that augment human reasoning. While tools that rely on output from ML algorithms in the healthcare setting are appealing for their ability to aid in clinical decision making and streamline workflows, their implementation and effectiveness are not well documented. There is an abundance of published ML literature that focuses on whether algorithms can predict an outcome or predict it better than previous algorithms, but a dearth of effort evaluating their implementation or impact on patient outcomes. While developing and validating algorithms is an important first step in research, comprehensive evaluation is needed before application of ML algorithms in new settings is considered. Evaluation should examine both the process of implementation and the outcomes using a mix of qualitative and quantitative methods. This commentary describes a model we developed to guide our institutional ML evaluation efforts.

Objective: To evaluate the efficacy of combined therapy of letrozole and CC compared to the use of letrozole alone to achieve ovulation in infertile women with PCOS. Design: This was a single-centre, double arm, triple-blind randomized controlled trial. The study was conducted after taking approval from Institutional Ethics Committee and was prospectively registered with the clinical trials registry- India. Setting: Women were randomly assigned to receive a combination of 2.5 mg letrozole and placebo daily or a combination of 2.5 mg letrozole and 50 mg CC daily on cycle days 3–7 for one treatment cycle. Methods: 80 participants were enrolled and randomized to either the letrozole-only group (n=40) or the combined letrozole and CC group(n=40). Main outcome measures: The primary outcome measure was documentation of ovulation, confirmed by TVS 48 hours later with or without serum progesterone estimation.Results: In comparison, both Intention to treat (ITT) and per-protocol (PP) analysis showed a statistically significant increase in ovulation rate in the arm treated with combined letrozole and CC. Conclusion: Combination therapy with letrozole and clomipheneshould be considered as a first-line ovulation induction agent for infertile patients with PCOS owing to its low cost and better tolerability. Additional well designed clinical trials are required to determine the efficacy of the combination of letrozole and CC in terms of clinical pregnancy rates and live birth rates. Funding: There is no funding agency for this study. Keywords: Clomiphene, Letrozole, Polycystic ovarian syndrome, ovulation induction