Prevalence, Outcomes, and Predictors of Prolonged Corrected QT Interval in Hydroxychloroquine-Naïve Hospitalized COVID-19 Patients.

The studies regarding prevalence, outcomes, and predictors of prolonged corrected QT (QTc) among COVID-19 patients not on QTc-prolonging medication are not available in the literature. In this retrospective cohort study, the QTc of 295 hospital-admitted COVID-19 patients was analyzed and its association with in-hospital mortality was determined. The QTc was prolonged in 14.6% (43/295) of the study population. Prolonged QTc was seen in patients with older age (P = 0.018), coronary artery disease (P = 0.001), congestive heart failure (P = 0.042), elevated N-terminal-pro-B-type natriuretic peptide (NT-ProBNP) (P < 0.0001), and on remdesivir (P = 0.046). No episode of torsades de pointes arrhythmia or any arrhythmic death was observed among patients with prolonged QTc. The mortality was significantly high in patients with prolonged QTc (P = 0.003). The multivariate logistic regression analysis showed coronary artery disease (odds ratio (OR): 4.153, 95% CI 1.37-14.86; P = 0.013), and NT-ProBNP (ng/L) (OR: 1.000, 95% CI 1.000-1.000; P = 0.007) as predictors of prolonged QTc. The prolonged QTc was associated with the worst in-hospital survival (p by log-rank 0.001). A significant independent association was observed between prolonged QTc and in-hospital mortality in multivariate cox-regression analysis (adjusted hazard ratio: 3.861; (95% CI 1.719-6.523), P < 0.0001). QTc was found to be a marker of underlying comorbidities among COVID-19 patients. Prolonged QTc in hospitalized COVID-19 patients was independently associated with in-hospital mortality.

Promoting Polysulfide Redox Reactions through Electronic Spin Manipulation.

Catalytic additives able to accelerate the lithium-sulfur redox reaction are a key component of sulfur cathodes in lithium-sulfur batteries (LSBs). Their design focuses on optimizing the charge distribution within the energy spectra, which involves refinement of the distribution and occupancy of the electronic density of states. Herein, beyond charge distribution, we explore the role of the electronic spin configuration on the polysulfide adsorption properties and catalytic activity of the additive. We showcase the importance of this electronic parameter by generating spin polarization through a defect engineering approach based on the introduction of Co vacancies on the surface of CoSe nanosheets. We show vacancies change the electron spin state distribution, increasing the number of unpaired electrons with aligned spins. This local electronic rearrangement enhances the polysulfide adsorption, reducing the activation energy of the Li-S redox reactions. As a result, more uniform nucleation and growth of Li2S and an accelerated liquid-solid conversion in LSB cathodes are obtained. These translate into LSB cathodes exhibiting capacities up to 1089 mA h g-1 at 1 C with 0.017% average capacity loss after 1500 cycles, and up to 5.2 mA h cm-2, with 0.16% decay per cycle after 200 cycles in high sulfur loading cells.

Automated assessment of mental workload from PPG sensor data using cross-wavelet coherence and transfer learning.

Highly complex cognitive works require more brain power. The productivity of a person suffers due to this strain, which is sometimes referred to as a mental burden or psychological load. A person's mental health and safety in high-stress working conditions can be improved with the help of mental workload assessment. A photoplethysmogram (PPG) signal is a non-invasive and easily acquired physiological signal that contains information related to blood volume changes in the micro-vascular bed of tissues and can indicate psychologically relevant information to assess a person's mental workload (MW). An individual under a high MW possesses an increase in sympathetic nervous system activity, which results in morphological changes in the PPG waveform. In this work, a time-frequency analysis framework is developed to capture these distinguishing PPG features for the automatic assessment of MW. In particular, a cross-wavelet coherence (WTC) approach is proposed to extract simultaneous time-frequency information of the PPG during MW relative to the resting PPG. The suggested technique is validated on a publicly available data set of 22 healthy individuals who took part in an N-back task with PPG recording. Under three different fixed window lengths, images are obtained using WTC between PPG records during N-back task activity and rest. The images are used further to obtain PPG classification in two broad classes of low and high MW using a customized pre-trained Inception-V3 model. The best validation and test accuracy of 93.86% and 93.07%, respectively obtained in the window setting of 1200 samples used for WTC image creation.

A robust deep learning system for screening of obstructive sleep apnea using T-F spectrum of ECG signals.

Obstructive sleep apnea (OSA) is a non-communicable sleep-related medical condition marked by repeated disruptions in breathing during sleep. It may induce various cardiovascular and neurocognitive complications. Electrocardiography (ECG) is a useful method for detecting numerous health-related disorders. ECG signals provide a less complex and non-invasive solution for the screening of OSA. Automated and accurate detection of OSA may enhance diagnostic performance and reduce the clinician's workload. Traditional machine learning methods typically involve several labor-intensive manual procedures, including signal decomposition, feature evaluation, selection, and categorization. This article presents the time-frequency (T-F) spectrum classification of de-noised ECG data for the automatic screening of OSA patients using deep convolutional neural networks (DCNNs). At first, a filter-fusion algorithm is used to eliminate the artifacts from the raw ECG data. Stock-well transform (S-T) is employed to change filtered time-domain ECG into T-F spectrums. To discriminate between apnea and normal ECG signals, the obtained T-F spectrums are categorized using benchmark Alex-Net and Squeeze-Net, along with a less complex DCNN. The superiority of the presented system is measured by computing the sensitivity, specificity, accuracy, negative predicted value, precision, F1-score, and Fowlkes-Mallows index. The results of comparing all three utilized DCNNs reveal that the proposed DCNN requires fewer learning parameters and provides higher accuracy. An average accuracy of 95.31% is yielded using the proposed system. The presented deep learning system is lightweight and faster than Alex-Net and Squeeze-Net as it utilizes fewer learnable parameters, making it simple and reliable.

Association of Serum Proteins Electrophoretic Pattern and Serum Hormones in Women with Spontaneous Pregnancy Loss.

Background: Pregnancy is the state of carrying a developing embryo or fetus within a female body. Once pregnancy is established, a range of endocrinological events appear in its maintenance, finally helping in the successful pregnancy. The complications which are usually observed in pregnancy are gestational diabetes, preeclampsia, preterm labor, and spontaneous pregnancy loss or miscarriage, while 10%-15% of clinically recognized pregnancies terminate into spontaneous miscarriage. Thus, many attempts have been made by different researchers for the diagnosis of high-risk pregnancy on altered protein pattern using placental villous tissue or follicular fluid, but these are difficult to obtain and results of different studies are not constant. Aim: This study was designed to identify the association (if any) among serum protein(s) electrophoretic pattern and different serum hormones in normal pregnant women (controls) and gestational age-matched women with spontaneous pregnancy loss (cases). Materials and Methods: This study was carried out for 1½ year from October 2018 to March 2020 and included 120 participants (60 normal pregnant women and 60 women with spontaneous pregnancy loss) between 20 and 45 years of age with no mean age difference. The electrophoresis of serum was carried out using slab gel electrophoretic unit and serum thyroid-stimulating hormone (TSH), total tri-iodothyronine (TT3), total thyroxine (TT4), prolactin, and beta human chorionic gonadotropin (ß-hCG) levels were analyzed using TSOSH AIA analyzer at Adesh University, Bathinda. Results: Significant variations in the expression of proteins with molecular weight around ~150 kDa, ~50 kDa, and ~25 kDa were observed in normal pregnant women and women with spontaneous pregnancy loss. However, the protein band of ~50 kDa was found to be highly expressed in the serum of 1st and 2nd trimester women experiencing spontaneous pregnancy loss. Therefore, selected protein band of ~50 kDa was further processed by ECI-mass spectrophotometry QUAD time of flight and 365 different proteins were found, out of these; 34 proteins were found to be unidentified protein products (Verified using NCBI data base). Further, TT3, total proteins, ß-hCG, and prolactin level were found to be low, whereas, TSH was found to be high in women experiencing spontaneous pregnancy loss. However, difference in the level of ß-hCG in the 1st trimester and TT4 among normal pregnant women and women with spontaneous pregnancy loss was observed to be statistically insignificant. Conclusion: This study indicated that the evaluation of serum protein variations along with hormonal profile may provide valuable information about high-risk pregnancy. Moreover, the differential expression of proteins in women with spontaneous pregnancy loss can be further explored to develop potential biomarker for the early identification of high-risk pregnancy and appropriate preventive measure.

Comparison of genicular nerve block with adductor canal block for postoperative pain management in patients undergoing arthroscopic knee ligament reconstruction: A randomised controlled trial.

Background and Aims: Genicular nerve block (GNB) is beneficial in early ambulation and faster patient discharge since it selectively blocks articular branches and is motor-sparing. This study aimed to compare the analgesic efficacy of ultrasound (US)-guided GNB with adductor canal block (ACB) in patients undergoing arthroscopic anterior cruciate ligament reconstruction (ACLR). Methods: This randomised, double-blind study was conducted on 38 adults undergoing arthroscopic ACLR. Patients in Group GNB (n = 19) received US-guided GNB with 3 ml of 0.25% bupivacaine and 2 mg dexamethasone. Patients in Group ACB (n = 19) received US-guided ACB with 20 ml of 0.25% bupivacaine with 6 mg dexamethasone. Postoperative rescue analgesia was provided by intravenous Patient Controlled Analgesia (PCA) with morphine. The primary outcome was Numerical Rating Scale (NRS) pain scores over 24 h. The secondary outcome was the duration of analgesia and 24-h morphine consumption. The Chi-square test was used to test the statistical significance between categorical variables. Independent t-test or Mann-Whitney U test was used to compare continuous variables. Results: NRS scores at rest and physical activity at 24 h were similar in both the groups (P = 0.429 and P = 0.101, respectively). The mean time to rescue analgesia was comparable in both groups (Group GNB: 820.79 [483.65] min [95% confidence interval {CI}: 603.31-1038.27] and Group ACB: 858.95 [460.06] min [95% CI: 652.08, 1065.82], P = 0.805), and the mean 24-h morphine consumption was also comparable in both groups (P = 1.000). Conclusion: US-guided GNB has an analgesic efficacy similar to US-guided ACB for patients undergoing arthroscopic ACLR.

Development of an EN8 Steel Stepped Rotor by a Novel Engraving Milling Technique.

The rotor or impeller is a rotational and key part of a pump and compressor. This article presents the detailed development process of a rotor of small size constructed from an EN8 steel cylindrical blank using a novel technique based on a computer numerical control engraving milling machine (CNC-EMM) equipped with a 4 mm tungsten carbide end mill cutter. We fabricated a total of twenty-eight stepped rotors following the Box-Behnken Design (BBD) DoE technique at fourteen distinct combinations of CNC-EMM variable parameters, namely rotational speed, feed, and plunge feed. Average roughness 'Ra', an important surface quality indicator, has been considered and presented in this article, as a quality measure for the fabricated rotors. Feed and plunge feed have been identified as the most influencing variable parameters as per an analysis of variance (ANOVA) test. The lowest average roughness value obtained by this process for the rotor blade was 0.11 µm. A micrograph obtained from a field-emission scanning electron microscope (FE-SEM) showed a uniform and accurate tooth profile along with burr formation at corner edges. This study claims to establish engraving milling as a viable alternative to other manufacturing processes used for rotor blades. The findings of this study are useful to scholars, engineers, and researchers who are exploring new ways to fabricate mechanical parts and components.

Detection of SARS-CoV-2-specific mucosal antibodies in saliva following concomitant COVID-19 and influenza vaccination in the ComFluCOV trial.

The ComFluCOV trial randomized 679 participants to receive an age-appropriate influenza vaccine, or placebo, alongside their second COVID-19 vaccine. Concomitant administration was shown to be safe, and to preserve systemic immune responses to both vaccines. Here we report on a secondary outcome of the trial investigating SARS-CoV-2-specific mucosal antibody responses. Anti-spike IgG and IgA levels in saliva were measured with in-house ELISAs. Concomitant administration of an influenza vaccine did not affect salivary anti-spike IgG positivity rates to Pfizer/BioNTech BNT162b2 (99.1 cf. 95.6%), or AstraZeneca ChAdOx1 (67.8% cf. 64.9%), at 3-weeks post-vaccination relative to placebo. Furthermore, saliva IgG positively correlated with serum titres highlighting the potential utility of saliva for assessing differences in immunogenicity in future vaccine studies. Mucosal IgA was not detected in response to either COVID-19 vaccine, reinforcing the need for novel vaccines capable of inducing sterilising immunity or otherwise reducing transmission. The trial is registered as ISRCTN 14391248.

Water quality monitoring and modeling for an urban storm drainage channel in Thane, India.

The absence of a sewer system and inadequate wastewater treatment plants results in a discharge of untreated wastewater to the urban drainage channels and pollutes receiving waters. Field visits were carried out to observe water quality parameters such as dissolved oxygen (DO), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) in an urban drainage system (Kolshet drain) in Thane City, Mumbai Metropolitan Region, India. Dye-tracing studies using rhodamine WT dye were used for computing the velocity, discharge, and dispersion coefficient of the drain. The data analysis shows that the BOD and COD values in the drain are higher than the permissible limits (30 mg L-1 for BOD and 250 mg L-1 for COD), which is not suitable for disposal to any receiving water body. Also, the DO was less than the permissible limit of a minimum of 3 mg L-1 (for the survival of aquatic life). It is seen that the higher BOD load significantly reduced the DO throughout the drain. The Water Quality Analysis Simulation Program (WASP 8.32, 2019) developed by the US Environmental Protection Agency (USEPA) has been used for the simulation of the DO and BOD in the drainage channel. The model simulates an appropriate estimate of the expected variation of DO and BOD at points of interest. The modeling for the Kolshet drain is expected to enable better estimates of the wastewater parameters and the pollution transport in the drain for planning purposes.

Engineering the ADDobody protein scaffold for generation of high-avidity ADDomer super-binders.

Adenovirus-derived nanoparticles (ADDomer) comprise 60 copies of adenovirus penton base protein (PBP). ADDomer is thermostable, rendering the storage, transport, and deployment of ADDomer-based therapeutics independent of a cold chain. To expand the scope of ADDomers for new applications, we engineered ADDobodies, representing PBP crown domain, genetically separated from PBP multimerization domain. We inserted heterologous sequences into hyper-variable loops, resulting in monomeric, thermostable ADDobodies expressed at high yields in Escherichia coli. The X-ray structure of an ADDobody prototype validated our design. ADDobodies can be used in ribosome display experiments to select a specific binder against a target, with an enrichment factor of ∼104-fold per round. ADDobodies can be re-converted into ADDomers by genetically reconnecting the selected ADDobody with the PBP multimerization domain from a different species, giving rise to a multivalent nanoparticle, called Chimera, confirmed by a 2.2 Å electron cryo-microscopy structure. Chimera comprises 60 binding sites, resulting in ultra-high, picomolar avidity to the target.

Remote photoplethysmography-based human vital sign prediction using cyclical algorithm.

This article aims to predict vital signs like heart rate (HR), respiration rate, and arterial oxygen saturation using ambient light video, eliminating chronic distortions through improved frame quality with BER estimation. The study employs the cascade residual CNN-FPNR technique for preprocessing and SNR enhancement using energy variance maximization. The image cascade network (ICNet) facilitates segmentation, achieving strong segmentation in low-light ambient videos. Remote photoplethysmography (iPPG) enables noncontact vital sign monitoring, predicting HR and respiratory rate (RR). An innovative noninvasive temperature and cyclical algorithm, incorporating principal component analysis and fast Fourier transform, evaluate patient HR and RR. To address challenges related to involuntary movements, a dynamic time-warping-based optimization method is used for precise region selection. The study introduces an intensity variance-based threshold analysis for arterial oxygen saturation level determination. Ultimately, the support vector machine (SVM) classification technique evaluates the ground truth, showcasing the system's promising potential for remote and accurate vital sign assessment.

In vitro generated antibodies guide thermostable ADDomer nanoparticle design for nasal vaccination and passive immunization against SARS-CoV-2.

Background: Due to COVID-19, pandemic preparedness emerges as a key imperative, necessitating new approaches to accelerate development of reagents against infectious pathogens. Methods: Here, we developed an integrated approach combining synthetic, computational and structural methods with in vitro antibody selection and in vivo immunization to design, produce and validate nature-inspired nanoparticle-based reagents against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Results: Our approach resulted in two innovations: (i) a thermostable nasal vaccine called ADDoCoV, displaying multiple copies of a SARS-CoV-2 receptor binding motif derived epitope and (ii) a multivalent nanoparticle superbinder, called Gigabody, against SARS-CoV-2 including immune-evasive variants of concern (VOCs). In vitro generated neutralizing nanobodies and electron cryo-microscopy established authenticity and accessibility of epitopes displayed by ADDoCoV. Gigabody comprising multimerized nanobodies prevented SARS-CoV-2 virion attachment with picomolar EC50. Vaccinating mice resulted in antibodies cross-reacting with VOCs including Delta and Omicron. Conclusion: Our study elucidates Adenovirus-derived dodecamer (ADDomer)-based nanoparticles for use in active and passive immunization and provides a blueprint for crafting reagents to combat respiratory viral infections.

Block sparsity promoting algorithm for efficient construction of cluster expansion models for multicomponent alloys.

Multicomponent alloys are gaining significance as drivers of technological breakthroughs especially in structural and energy storage materials. The vast configuration space of these materials prohibit computational modeling using first-principles based methods alone. The cluster expansion (CE) method is the most widely used tool for modeling configurational disorder in alloys. CE relies on machine learning algorithms to train Hamiltonians and uses first-principles calculated data as training sets. In this paper we present a new compressive sensing-based algorithm for the efficient construction of CE Hamiltonians of multicomponent alloys. Our algorithm constructs highly sparse and physically reasonable models from a carefully selected small training set of alloy structures. Compared to conventional fitting algorithms, the algorithm achieves more than 50% reduction in the training set size. The resultant sparse models can sample the configuration space at least 3 × faster. We demonstrate this algorithm on 4 different alloy systems, namely Ag-Au, Ag-Au-Cu, Ag-Au-Cu-Pd and (Ge,Sn)(S,Se,Te).The sparse CE models for these alloys can rapidly reproduce known ground state orderings and order-disorder transitions. Our method can truly enable high-throughput multicomponent alloy thermodynamics by reducing the cost associated with model construction and configuration sampling.

Laser-Based Manufacturing of Ceramics: A Review.

Ceramics are widely used in microelectronics, semiconductor manufacturing, medical devices, aerospace, and aviation, cutting tools, precision optics, MEMS and NEMS devices, insulating components, and ceramic molds. But the fabrication and machining of the ceramic-based materials by conventional processes are always difficult due to their higher hardness and mechanical properties. Therefore, advanced manufacturing techniques are being preferred for these advanced materials, and out of that, laser-based processes are widely used. The benefits of laser fabrication and machining of ceramics include high precision, reduced thermal damage, non-contact processing, and the ability to work with complex geometries. Laser technology continues to advance, enabling even more intricate and diverse applications for ceramics in a wide range of industries. This paper explains various laser based ceramic processing techniques, such as selective laser sintering and melting, and laser machining techniques, such as laser drilling, etc. Identifying and optimizing the process parameters that influence the output quality of laser processed parts is the key technique to improving the quality, which is also focused on in this paper. It aims to facilitate the researchers by providing knowledge on laser-based manufacturing of ceramics and their composites to establish the field further.

Ultrasound-guided anterior suprascapular nerve block versus interscalene brachial plexus block for arthroscopic shoulder surgery: A randomised controlled study.

Background and Aims: The interscalene brachial plexus block (ISB) affects the phrenic nerve, resulting in hemi-diaphragmatic paresis (HDP) and, possibly, respiratory distress. Suprascapular nerve block via an anterior approach (SSB-A) is performed more distally at the level of the trunk of the brachial plexus and, thus, may spare the phrenic nerve. This study compares the analgesic efficacy and decline of hemi-diaphragmatic excursion (HDE) following ultrasound (US)-guided SSB-A versus ISB for arthroscopic shoulder surgery. Methods: This study was conducted on 60 adult participants undergoing arthroscopic shoulder surgery under general anaesthesia. Both US-guided SSB-A (n = 30) and ISB (n = 30) were performed with a combination of 10 ml bupivacaine (0.5%) and 4 mg dexamethasone. The primary objective was to compare the duration of analgesia (time to first rescue analgesia), and secondary objectives were to compare 24-h postoperative numerical rating scale (NRS) scores, 24-h morphine consumption and post block change in HDE, and pulmonary function tests (PFTs) between the two groups. For analysing intergroup differences of NRS, HDE and PFT; Pearson's Chi-squared test or Fisher's exact test, unpaired t test, and Mann-Whitney U test were used. For intragroup differences, paired t test was used. A P value <0.05 was considered significant. Results: The duration of analgesia (mean ± Standard Deviation) was similar in two groups (SSB-A = 1,345 ± 182 min, ISB = 1,375 ± 156 min; P = 0.8). The reduction in HDE was significantly greater in the ISB group (44%) than in the SSB-A group (10%). Pulmonary function was better preserved in the SSB-A group. Conclusion: Compared to ISB, SSB-A has a similar analgesic efficacy for arthroscopic shoulder surgeries, but it is superior in preserving diaphragmatic function and pulmonary function.

INSOMNet: Automated insomnia detection using scalogram and deep neural networks with ECG signals.

Sleep is a natural state of rest for the body and mind. It is essential for a human's physical and mental health because it helps the body restore itself. Insomnia is a sleep disorder that causes difficulty falling asleep or staying asleep and can lead to several health problems. Conventional sleep monitoring and insomnia detection systems are expensive, laborious, and time-consuming. This is the first study that integrates an electrocardiogram (ECG) scalogram with a convolutional neural network (CNN) to develop a model for the accurate measurement of the quality of sleep in identifying insomnia. Continuous wavelet transform has been employed to convert 1-D time-domain ECG signals into 2-D scalograms. Obtained scalograms are fed to AlexNet, MobileNetV2, VGG16, and newly developed CNN for automated detection of insomnia. The proposed INSOMNet system is validated on the cyclic alternating pattern (CAP) and sleep disorder research center (SDRC) datasets. Six performance measures, accuracy (ACC), false omission rate (FOR), sensitivity (SEN), false discovery rate (FDR), specificity (SPE), and threat score (TS), have been calculated to evaluate the developed model. Our developed system attained the classifications ACC of 98.91%, 98.68%, FOR of 1.5, 0.66, SEN of 98.94%, 99.31%, FDR of 0.80, 2.00, SPE of 98.87%, 98.08%, and TS 0.98, 0.97 on CAP and SDRC datasets, respectively. The developed model is less complex and more accurate than transfer-learning networks. The prototype is ready to be tested with a huge dataset from diverse centers.

Endoscopic Ultrasound Guided Shear Wave Elastography Is Safe With High Feasibility and Reproducibility When Used in the Pancreas: Findings From a Prospective Cohort.

OBJECTIVES: The aim of this study was to assess the safety, feasibility, and reproducibility of endoscopic ultrasound shear wave elastography (EUS-SWE) in the pancreas. METHODS: This is a prospective registry of consecutive patients undergoing clinically indicated EUS. Ten readings of SWE velocities (Vs [distance/time, m/s]) were obtained in the head (HOP), body, and tail of pancreas to quantify tissue stiffness. Each Vs score was accompanied by a reliability measurement VsN (%) with VsN >50% considered reliable. Safety was evaluated by perioperative complications rate. Feasibility was determined by technical success of obtaining measurements. Reproducibility was evaluated using intraclass correlation coefficient analysis. RESULTS: Total of 3320 EUS-SWE measurements were performed on 117 patients without perioperative complications. Measurement success rate was 100% across all locations. Reliable measurements were more common in the HOP (953/1120 [85.1%]) followed by body (853/1130 [75.5%]) and tail of pancreas (687/1070 [64.2%]) (P < 0.001). The analysis showed good reproducibility in all locations (intraclass correlation coefficient range, 0.80-0.89). CONCLUSIONS: Endoscopic ultrasound-SWE is safe, has 100% technical success rate, and is highly reproducible when used in the pancreas. Our study suggests that SWE measurements in the HOP offer the highest reliability, likely because of large study area and less respiratory artifact.