Toxicity assessment of SARS-CoV-2-derived peptides in combination with a mix of pollutants on zebrafish adults: A perspective study of behavioral, biometric, mutagenic, and biochemical toxicity.

The dispersion of SARS-CoV-2 in aquatic environments via the discharge of domestic and hospital sewage has been confirmed in different locations. Thus, we aimed to evaluate the possible impacts of zebrafish (Danio rerio) exposure to SARS-CoV-2 peptide fragments (PSPD-2001, 2002, and 2003) alone and combined with a mix of emerging pollutants. Our data did not reveal the induction of behavioral, biometric, or mutagenic changes. But we noticed an organ-dependent biochemical response. While nitric oxide and malondialdehyde production in the brain, gills, and muscle did not differ between groups, superoxide dismutase activity was reduced in the "PSPD", "Mix", and "Mix+PSPD" groups. An increase in catalase activity and a reduction in DPPH radical scavenging activity were observed in the brains of animals exposed to the treatments. However, the "Mix+PSPD" group had a higher IBRv2 value, with NO levels (brain), the reduction of acetylcholinesterase activity (muscles), and the DPPH radical scavenging activity (brain and muscles), the most discriminant factors for this group. The principal component analysis (PCA) and hierarchical clustering analysis indicated a clear separation of the "Mix+PSPD" group from the others. Thus, we conclude that exposure to viral fragments, associated with the mix of pollutants, induced more significant toxicity in zebrafish adults than in others.

Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data.

With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains sensitive patient-related information and is therefore usually anonymized by removing patient identifiers, e.g., patient names before publication. To the best of our knowledge, we are the first to show that a well-trained deep learning system is able to recover the patient identity from chest X-ray data. We demonstrate this using the publicly available large-scale ChestX-ray14 dataset, a collection of 112,120 frontal-view chest X-ray images from 30,805 unique patients. Our verification system is able to identify whether two frontal chest X-ray images are from the same person with an AUC of 0.9940 and a classification accuracy of 95.55%. We further highlight that the proposed system is able to reveal the same person even ten and more years after the initial scan. When pursuing a retrieval approach, we observe an mAP@R of 0.9748 and a precision@1 of 0.9963. Furthermore, we achieve an AUC of up to 0.9870 and a precision@1 of up to 0.9444 when evaluating our trained networks on external datasets such as CheXpert and the COVID-19 Image Data Collection. Based on this high identification rate, a potential attacker may leak patient-related information and additionally cross-reference images to obtain more information. Thus, there is a great risk of sensitive content falling into unauthorized hands or being disseminated against the will of the concerned patients. Especially during the COVID-19 pandemic, numerous chest X-ray datasets have been published to advance research. Therefore, such data may be vulnerable to potential attacks by deep learning-based re-identification algorithms.

Biometric recognition through gait analysis.

The use of people recognition techniques has become critical in some areas. For instance, social or assistive robots carry out collaborative tasks in the robotics field. A robot must know who to work with to deal with such tasks. Using biometric patterns may replace identification cards or codes on access control to critical infrastructures. The usage of Red Green Blue Depth (RGBD) cameras is ubiquitous to solve people recognition. However, this sensor has some constraints, such as they demand high computational capabilities, require the users to face the sensor, or do not regard users' privacy. Furthermore, in the COVID-19 pandemic, masks hide a significant portion of the face. In this work, we present BRITTANY, a biometric recognition tool through gait analysis using Laser Imaging Detection and Ranging (LIDAR) data and a Convolutional Neural Network (CNN). A Proof of Concept (PoC) has been carried out in an indoor environment with five users to evaluate BRITTANY. A new CNN architecture is presented, allowing the classification of aggregated occupancy maps that represent the people's gait. This new architecture has been compared with LeNet-5 and AlexNet through the same datasets. The final system reports an accuracy of 88%.

A PNU-Based Methodology to Improve the Reliability of Biometric Systems.

Face recognition is an important application of pattern recognition and image analysis in biometric security systems. The COVID-19 outbreak has introduced several issues that can negatively affect the reliability of the facial recognition systems currently available: on the one hand, wearing a face mask/covering has led to growth in failure cases, while on the other, the restrictions on direct contact between people can prevent any biometric data being acquired in controlled environments. To effectively address these issues, we designed a hybrid methodology that improves the reliability of facial recognition systems. A well-known Source Camera Identification (SCI) technique, based on Pixel Non-Uniformity (PNU), was applied to analyze the integrity of the input video stream as well as to detect any tampered/fake frames. To examine the behavior of this methodology in real-life use cases, we implemented a prototype that showed two novel properties compared to the current state-of-the-art of biometric systems: (a) high accuracy even when subjects are wearing a face mask; (b) whenever the input video is produced by deep fake techniques (replacing the face of the main subject) the system can recognize that it has been altered providing more than one alert message. This methodology proved not only to be simultaneously more robust to mask induced occlusions but also even more reliable in preventing forgery attacks on the input video stream.

Remote sensory assessment of beer quality based on visual perception of foamability and biometrics compared to standard emotional responses from affective images.

The social isolation settings derived from the COVID-19 pandemic affected the standard sensory evaluation techniques used in the food and beverage industry. This situation forced companies and researchers to assess other options to continue conducting these tests in remote contactless locations. This study aimed to evaluate two sets of samples (i) six images from Geneva affective picture database (GAPED) and (ii) six videos of beer pouring using traditional self-reported sensory data and emotional response from consumers biometrics. Specifically, four research questions (RQ) arouse from this study: RQ1: are there significant differences between GAPED images and beers in unconscious and self-reported responses from consumers?, RQ2: are there any correlations between subconscious and self-reported responses from consumers when assessing beer?, RQ3: can consumers differentiate positive, neutral and negative images based on subconscious and self-reported responses?, RQ4: are there any relationships between subconscious and self-reported responses when assessing GAPED images and beers, and how are samples associated with variables? A total of 113 Mexican beer consumers participated in the virtual sensory session using an online videoconference software to record videos of participants during the session. Results showed there were significant differences (p < 0.05) between samples, especially for self-reported responses (RQ1), and several correlations between variables, such as positive correlations between the perceived quality of beers and happy emoji (r = 0.84), and negative correlation with confused emoji (r = -0.97; RQ2). Besides, using the proposed methods, consumers were able to correctly differentiate through elicited emotions the positive, neutral and negative GAPED images (RQ3). Regarding RQ4, several relationships were found between variables in both GAPED images and beers; however, it was found that different emotions were elicited depending of the stimuli used. The proposed method showed to be a reliable and practical option to conduct visual and potentially tasting sensory tests in isolation and recruit participants from different countries without travelling to collect their responses.

Extracting Behavior Identification Features for Monitoring and Managing Speech-Dependent Smart Mental Illness Healthcare Systems.

Speech is one of the major communication tools to share information among people. This exchange method has a complicated construction consisting of not the best imparting of voice but additionally consisting of the transmission of many-speaker unique information. The most important aim of this research is to extract individual features through the speech-dependent health monitoring and management system; through this system, the speech data can be collected from a remote location and can be accessed. The experimental analysis shows that the proposed model has a good efficiency. Consequently, in the last 5 years, many researchers from this domain come in front to explore various aspects of speech which includes speech analysis using mechanical signs, human system interaction, speaker, and speech identification. Speech is a biometric that combines physiological and behavioural characteristics. Especially beneficial for remote attack transactions over telecommunication networks, the medical information of each person is quite a challenge, e.g., like COVID-19 where the medical team has to identify each person in a particular region that how many people got affected by some disease and took a quick measure to get protected from such diseases and what are the safety measure required. Presently, this task is the most challenging one for researchers. Therefore, speech-based mechanisms might be useful for tracking his/her voice quality or throat getting affected. By collecting the database of people matched and comparing with his/her original database, it can be identified in such scenarios. This provides the better management system without touching and maintains a safe distance data that can be gathered and processed for further medical treatment. Many research studies have been done but speech-dependent approach is quite less and it requires more work to provide such a smart system in society, and it may be possible to reduce the chances to come into contact with viral effected people in the future and protect society for the same.

Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models.

This work addresses the challenge of building an accurate and generalizable periocular recognition model with a small number of learnable parameters. Deeper (larger) models are typically more capable of learning complex information. For this reason, knowledge distillation (kd) was previously proposed to carry this knowledge from a large model (teacher) into a small model (student). Conventional KD optimizes the student output to be similar to the teacher output (commonly classification output). In biometrics, comparison (verification) and storage operations are conducted on biometric templates, extracted from pre-classification layers. In this work, we propose a novel template-driven KD approach that optimizes the distillation process so that the student model learns to produce templates similar to those produced by the teacher model. We demonstrate our approach on intra- and cross-device periocular verification. Our results demonstrate the superiority of our proposed approach over a network trained without KD and networks trained with conventional (vanilla) KD. For example, the targeted small model achieved an equal error rate (EER) value of 22.2% on cross-device verification without KD. The same model achieved an EER of 21.9% with the conventional KD, and only 14.7% EER when using our proposed template-driven KD.

Distribution, Progression, and Associated Factors of Refractive Status of Children in Lhasa, Tibet, after COVID-19 Quarantine.

INTRODUCTION: Uncorrected refractive error is one of the major causes of visual impairment in children and adolescents worldwide. During the COVID-19 epidemic, home isolation is considered a boost to the progression of children's myopia. Under geographical conditions of high altitude and strong sunshine, the Tibetan plateau is the main residence of the Tibetan population, where little information is available about the refractive status and developmental trajectory. Therefore, this article aimed to evaluate the distribution, progression, and associated factors of the refractive status in second-grade children in Lhasa after COVID-19 quarantine. MATERIALS AND METHODS: Students from 7 elementary schools completed comprehensive ocular examinations in the Lhasa Childhood Eye Study. Data regarding cycloplegic refraction and corneal biometry parameters, including axial length (AL), corneal power, anterior chamber depth (ACD), and other demographic factors, were analyzed. RESULTS: A total of 1,819 students were included, with a mean age of 7.9 ± 0.5 years, of which 961 were boys (52.8%), and 95.1% were Tibetan. The prevalence of myopia, emmetropia, mild hyperopia, and hyperopia was 10.94%, 24.02%, 60.80%, and 4.24%, respectively. Besides, the average cycloplegic spherical equivalent refraction (SER) was +1.07 ± 0.92 diopter (D) before the COVID-19 quarantine and +0.59 ± 1.08D after the quarantine (p < 0.05), with a growth rate of 7%. Moreover, the prevalence of hyperopia in girls was significantly higher than that of boys (p < 0.001). Nonetheless, the proportion of myopia and emmetropia was similar (p = 0.75). Meanwhile, children in suburban schools had a significantly lower proportion of myopia (p < 0.001). The average AL, ACD, lens power (LP), and AL-to-corneal radius (AL/CR) ratio were 22.79 ± 0.78 mm, 3.54 ± 0.21 mm, 25.12 ± 1.48D, and 2.93 ± 0.08, respectively. The results of AL, ACD, and AL/CR for girls were significantly lower than for boys, while the result of LP is the opposite (p < 0.001). Finally, multivariate regression analysis revealed that SER was negatively correlated with AL, LP, and AL/CR ratio, while positively correlated with CR and ACD (p < 0.001). CONCLUSION: This study found that after the COVID-19 confinement, myopia progressed faster in Lhasa children but was still significantly lower than that of plain cities in China. Compared to short-term confinement, this acceleration was more likely related to the growth and general trend of myopia in children. Collectively, these findings help to explore the differences in ocular growth and development among children of different ethnic groups.

Impact of a Virtual Culinary Medicine Curriculum on Biometric Outcomes, Dietary Habits, and Related Psychosocial Factors among Patients with Diabetes Participating in a Food Prescription Program.

Culinary medicine is an evidence-based approach that blends the art of cooking with the science of medicine to inculcate a healthy dietary pattern. Food prescription programs are gaining popularity in the Unites States, as a means to improve access to healthy foods among patient populations. The purpose of this paper is to describe the implementation and preliminary impact of A Prescription for Healthy Living (APHL) culinary medicine curriculum on biometric and diet-related behavioral and psychosocial outcomes among patients with diabetes participating in a clinic-led food prescription (food Rx) program. We used a quasi-experimental design to assess APHL program impact on patient biometric outcome data obtained from electronic health records, including glycosylated hemoglobin (HbA1c), body mass index (BMI), and blood pressure (n = 33 patients in the APHL group, n = 75 patients in the food Rx-only group). Pre-post surveys were administered among those in the APHL group to monitor program impact on psychosocial and behavioral outcomes. Results of the outcome analysis showed significant pre-to-post reduction in HbA1c levels among participants within the APHL group (estimated mean difference = -0.96% (-1.82, -0.10), p = 0.028). Between-group changes showed a greater decrease in HbA1c among those participating in APHL as compared to food Rx-only, albeit these differences were not statistically significant. Participation in APHL demonstrated significant increases in the consumption of fruits and vegetables, fewer participants reported that cooking healthy food is difficult, increased frequency of cooking from scratch, and increased self-efficacy in meal planning and cooking (p < 0.01). In conclusion, the results of our pilot study suggest the potential positive impact of a virtually-implemented culinary medicine approach in improving health outcomes among low-income patients with type 2 diabetes, albeit studies with a larger sample size and a rigorous study design are needed.

Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data.

Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.

A smart computer mouse with biometric sensors for unobtrusive office work-related stress monitoring.

Health disorders related to the prolonged exposure to stress are very common among office workers. The need for an automated and unobtrusive method of detecting and monitoring occupational stress is imperative and intensifies in the current conditions, where the pandemic COVID-19 causes changes in the working norms globally. In this study, we present a smart computer mouse with biometric sensors integrated in such a way that its structure and functionality remain unaffected. Photoplethysmography (PPG) signal is collected from user's thumb by a PPG sensor placed on the side wall of the mouse, while galvanic skin response (GSR) is measured from the palm through two electrodes placed on the top surface of the mouse. Biosignals are processed by a microcontroller and can be transferred wirelessly over Wi-Fi connection. Both the sensors and the microcontroller have been placed inside the mouse, enabling its plug and play use, without any additional equipment. The proposed module has been developed as part of a system that infers about the stress levels of office workers, based on their interactions with the computer and its peripheral devices.

Lifestyle and mental health 1 year into COVID-19.

In previous work, Giuntella et al. (Proc Natl Acad Sci 118:e2016632118, 2021), we documented large disruptions to physical activity, sleep, time use and mental health among young adults at the onset of the COVID-19 pandemic in Spring 2020. This study explores the trends 1 year into COVID-19, as vaccines began to roll out, COVID-19 deaths declined, and social distancing measures eased in the United States. We combine biometric and survey data from multiple cohorts of college students spanning Spring 2019 through Spring 2021 (N = 1179). Our results show persistent impacts of the pandemic on physical activity and mental health. One year into the pandemic, daily steps averaged about 6300 per day compared to about 9800 per day prior to the pandemic, a 35% decline. Almost half of participants were at risk of clinical depression compared to a little over one-third prior to the pandemic, a 36% increase. The impacts on screen time, social interactions and sleep duration at the onset of COVID-19 largely dissipated over the course of the pandemic, though screen time remained significantly higher than pre-pandemic levels. In contrast to the sharp changes in lifestyle and mental health documented as the pandemic emerged in March 2020, we do not find evidence of behavioral changes or improvements in mental well-being over the course of Spring 2021 as the pandemic eased.

Comparing the clinical efficacy of COVID-19 vaccines: a systematic review and network meta-analysis.

New Coronavirus Disease 2019 (COVID-19) vaccines are available to prevent the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. We compared the efficacy of new COVID-19 vaccines to prevent symptomatic and severe disease in the adult population and to prevent symptomatic COVID-19 among the elderly. Leading medical databases were searched until August 30, 2021. Published phase 3 randomized controlled trials (RCTs) evaluated efficacy of the vaccine to prevent symptomatic and sever COVID-19 in adults were included. Two reviewers independently evaluated the literature search results and independently extracted summary data. The risk of bias was evaluated using the Cochrane Risk of Bias Assessment Tool. We performed a network meta-analysis (NMA) according to PRISMA-NMA 2015 to pool indirect comparisons between different vaccines regarding their relative efficacy. The primary outcomes were the efficacy of the vaccine against symptomatic COVID-19 in adults (PROSPERO registration number: CRD42021235364). Above 200,000 adult participants from eight phase 3 RCTs were included in NMA, of whom 52% received the intervention (active COVID-19 vaccine). While each of nine vaccines was tested in the unique clinical trial as compared to control, based on indirect comparison, BNT162b2 and mRNA-1273 vaccines were ranked with the highest probability of efficacy against symptomatic COVID-19 (P-scores 0.952 and 0.843, respectively), followed by Gam-COVID-Vac (P-score 0.782), NVX-CoV23730 (P-score 0.700), CoronaVac (P-score 0.570), BN02 (P-score 0.428), WIV04 (P-score 0.327), and Ad26.COV2.S (P-score 0.198). No statistically significant difference was seen in the ability of the vaccines to prevent symptomatic disease in the elderly population. No vaccine was statistically significantly associated with a decreased risk for severe COVID-19 than other vaccines, although mRNA-1273 and Gam-COVID-Vac have the highest P-scores (0.899 and 0.816, respectively), indicating greater protection against severe disease than other vaccines. In our indirect comparison, the BNT162b2 and mRNA-1273 vaccines, which use mRNA technology, were associated with the highest efficacy to prevent symptomatic COVID-19 compared to other vaccines. This finding may have importance when deciding which vaccine to use, together with other important factors as availability of the vaccines, costs, logistics, side effects, and patient acceptability.

Detection of Iris Presentation Attacks Using Feature Fusion of Thepade's Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features.

Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade's sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade's SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade's SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human-computer interaction and security in the cyber-physical space by improving person validation.

Air seal performance of personalized and statistically shaped 3D-printed face masks compared with market-available surgical and FFP2 masks.

The ongoing COVID-19 pandemic has revealed alarming shortages of personal protective equipment for frontline healthcare professionals and the general public. Therefore, a 3D-printable mask frame was developed, and its air seal performance was evaluated and compared. Personalized masks (PM) based on individual face scans (n = 8) and a statistically shaped mask (SSM) based on a standardized facial soft tissue shape computed from 190 face scans were designed. Subsequently, the masks were additively manufactured, and in a second step, the PM and SSM were compared to surgical masks (SM) and FFP2 masks (FFP2) in terms of air seal performance. 3D-printed face models allowed for air leakage evaluation by measuring the pressure inside the mask in sealed and unsealed conditions during a breathing simulation. The PM demonstrated the lowest leak flow (p < 0.01) of inspired or expired unfiltered air of approximately 10.4 ± 16.4%, whereas the SM showed the highest (p < 0.01) leakage with 84.9 ± 7.7%. The FFP2 and SSM had similar values of 34.9 ± 18.5% leakage (p > 0.68). The developed framework allows for the time- and resource-efficient, on-demand, and in-house production of masks. For the best seal performance, an individually personalized mask design might be recommended.

Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset.

Importance: Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation. Objective: To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus. Design, Setting, and Participants: The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated. Exposures: Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay. Main Outcomes and Measures: The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). Results: A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC). Conclusions and Relevance: This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.

Using excess deaths and testing statistics to determine COVID-19 mortalities.

Factors such as varied definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US from January 2020 until February 2021 is 9[Formula: see text] higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find statistically insignificant or even negative excess deaths for at least most of 2020 in places such as Germany, Denmark, and Norway.

A novel design proposal for low-cost vein-viewer for medical and non-contact biometric applications using NIR imaging.

The vein-viewer is a new revolution in the health industry. In fact, it is one of the must-have gadgets for any medical professional. The vein-viewer is device that helps to access easily veins when trying to collect a blood sample or for administering Intravenous (IV) cannulation. It is also an aid for dermatologist/aesthetic physician to access client's veins for sclerotherapy procedures or avoiding veins in cosmetic procedures. The vein-viewer is highly applicable where vascular positioning is really difficult; examples while canulating infants, obese, hairy/dark skins, dialysis/cancer patients etc. In addition, frequent attempts affect patients, causing trauma and subcutaneous haemorrhage. As palm/finger vein patterns are unique and complex, difficult to duplicate or steal as it is beneath the skin. So, in this Covid19 pandemic time, the vein-viewer finds applications in the secure non-contact bio-metric authentications for secure banking and attendance registering system to identify an individual. In this article I am trying to explain the design overview of vein-viewer system, its design challenges, cost aspects, its availability and also sharing a few inputs for the new compact, low-cost design and implementation.

Effect of SARS-CoV-2 infection during the second half of pregnancy on fetal growth and hemodynamics: A prospective study.

INTRODUCTION: Our objective was to compare the fetal growth velocity and fetal hemodynamics in pregnancies complicated and in those not complicated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. MATERIAL AND METHODS: Prospective case-control study of consecutive pregnancies complicated by SARS-CoV-2 infection during the second half of pregnancy matched with unaffected women. The z scores of head circumference, abdominal circumference, femur length, and estimated fetal weight were compared between the two groups. Fetal growth was assessed by analyzing the growth velocity of head circumference, abdominal circumference, femur length, and estimated fetal weight between the second- and third-trimester scans. Similarly, changes in the pulsatility index of uterine, umbilical, and middle cerebral arteries, and their ratios were compared between the two study groups. RESULTS: Forty-nine consecutive pregnancies complicated, and 98 not complicated, by SARS-CoV-2 infection were included. General baseline and pregnancy characteristics were similar between pregnant women with and those without SARS-CoV-2 infection. There was no difference in head circumference, abdominal circumference, femur length, and estimated fetal weight z scores between pregnancies complicated and those not complicated by SARS-CoV-2 infection at both the second- and third-trimester scans. Likewise, there was no difference in the growth velocity of all these body parameters between the two study groups. Finally, there was no difference in the pulsatility index of both maternal and fetal Doppler scans throughout gestation between the two groups. CONCLUSIONS: Pregnancies complicated by SARS-CoV-2 infection are not at higher risk of developing fetal growth restriction through impaired placental function. The findings from this study do not support a policy of increased fetal surveillance in these women.

Poornima Prabhakaran: balancing development, sustainability and public health.

Poornima Prabhakaran talks to Andréia Azevedo Soares about climate-related health hazards in India, initiatives to address them, and the challenges presented by industrial development.