Electrophoretic Deposition as a Versatile Low-Cost Tool to Construct a Synthetic Polymeric Solid-Electrolyte Interphase on Silicon Anodes: A Model System Investigation.

The cycling of next-generation, high-capacity silicon (Si) anodes capable of 3579 mAh·g-1 is greatly hindered by the instability of the solid-electrolyte interphase (SEI). The large volume changes of Si during (de)lithiation cause continuous cracking of the SEI and its reconstruction, leading to loss of lithium inventory and extensive consumption of electrolyte. The SEI formed in situ during cell cycling is mostly composed of molecular fragments and oligomers, the structure of which is difficult to tailor. In contrast, ex situ formation of a synthetic SEI provides greater flexibility to deposit long-chain, polymeric, and elastomeric components potentially capable of maintaining integrity against the large ∼350% volume expansion of Si while also enabling electronic passivation of the surface for longer cycling and calendar life. Furthermore, polymers are amenable to structural modifications, and the desired elasticity can be targeted by selection of the SEI polymer feedstock. Herein, electrophoretic deposition (EPD) is used to apply chitosan as a synthetic SEI on model Si thin film electrodes. Comparison of synthetic SEIs obtained without (Si/Chit) and with CH3COOLi (Si/Chit+CH3COOLi) added during EPD is performed to demonstrate a facile route to tuning of the polymer SEI chemistry. Atomic force and scanning electron microscopy reveal that addition of CH3COOLi at EPD assists in conformal deposition of the synthetic SEI. During electrochemical cycling, the Chit+CH3COOLi coating nearly doubles the capacity retention versus the reference bare Si thin film. X-ray photoelectron and Fourier transform infrared spectroscopy reveal that CH3COOLi caps the -NH2 groups of chitosan through amidation during EPD, which suppresses the catalytic reduction of the electrolyte. The presented approach demonstrates and validates EPD as a low-capital route to achieving and chemistry-tuning synthetic SEIs on Si electrodes. More broadly, the method is a promising avenue toward controlled and tailored polymeric SEIs on various conversion-type electrodes with high particle volumetric expansion.

Clinical and Radiological Parameters to Discriminate Tuberculous Peritonitis and Peritoneal Carcinomatosis.

It is challenging to differentiate between tuberculous peritonitis and peritoneal carcinomatosis due to their insidious nature and intersecting symptoms. Computed tomography (CT) is the modality of choice in evaluating diffuse peritoneal disease. We conducted an ambispective analysis of patients suspected as having tuberculous peritonitis or peritoneal tuberculosis between Jan 2020 to Dec 2021. The study aimed to identify the clinical and radiological features differentiating the two entities. We included 44 cases of tuberculous peritonitis and 45 cases of peritoneal carcinomatosis, with a median age of 31.5 (23.5-40) and 52 (46-61) years, respectively (p ≤ 0.001). Fever, past history of tuberculosis, and loss of weight were significantly associated with tuberculous peritonitis (p ≤ 0.001, p = 0.038 and p = 0.001). Pain in the abdomen and history of malignancy were significantly associated with peritoneal carcinomatosis (p = 0.038 and p ≤ 0.001). Ascites was the most common radiological finding. Loculated ascites, splenomegaly and conglomeration of lymph nodes predicted tuberculous peritonitis significantly (p ≤ 0.001, p = 0.010, p = 0.038). Focal liver lesion(s) and nodular omental involvement were significantly associated with peritoneal carcinomatosis (p = 0.011, p = 0.029). The use of clinical features in conjunction with radiological findings provide better diagnostic yields because of overlapping imaging findings.

Chronic stress predisposes to the aggravation of inflammation in autoimmune diseases with focus on rheumatoid arthritis and psoriasis.

The global incidence of autoimmune diseases is on the rise, and many healthcare professionals believe that chronic stress plays a prominent role in both the aggravation and remission of these conditions. It is believed that prolonged exposure to stress is associated with neuroimmune axis malfunction, which eventually dysregulates multiple immunological factors as well as deregulates autoimmune responses that play a central role in various autoimmune diseases, including rheumatoid arthritis and psoriasis. Herein, we performed validation of an 8-week long rat model of chronic unpredictable stress (CUS) which consisted of exposing groups of rats to random stressors daily for 8 weeks. Additionally, we developed a novel rat model combining 8-week long random stressor-induced CUS with CIA-triggered arthritis and IMQ-triggered psoriasis and have successfully used both these models to assess the role of chronic stress in the aggravation of arthritis and psoriasis, respectively. Notably, the 8-week CUS protocol extensively aggravated and prolonged both arthritis and psoriasis condition in the rat model by upregulating the release of different pro-inflammatory cytokines, dysregulation of immune cell responses and oxidative stress system, which were all related to severe inflammation. Further, CUS aggravated macroscopic features and the increase in destruction of joint tissue and epidermal thickness induced by CIA and IMQ, respectively, in rats. In conclusion, this study suggests that exposure to an 8-week long CUS paradigm aggravates the distinctive characteristics of rheumatoid arthritis and psoriasis in rats via amplifying the inflammatory circuits and immune cell responses linked to these autoimmune diseases.

Psychological Analysis for Depression Detection from Social Networking Sites.

Rapid technological advancements are altering people's communication styles. With the growth of the Internet, social networks (Twitter, Facebook, Telegram, and Instagram) have become popular forums for people to share their thoughts, psychological behavior, and emotions. Psychological analysis analyzes text and extracts facts, features, and important information from the opinions of users. Researchers working on psychological analysis rely on social networks for the detection of depression-related behavior and activity. Social networks provide innumerable data on mindsets of a person's onset of depression, such as low sociology and activities such as undergoing medical treatment, a primary emphasis on oneself, and a high rate of activity during the day and night. In this paper, we used five machine learning classifiers-decision trees, K-nearest neighbor, support vector machines, logistic regression, and LSTM-for depression detection in tweets. The dataset is collected in two forms-balanced and imbalanced-where the oversampling of techniques is studied technically. The results show that the LSTM classification model outperforms the other baseline models in the depression detection healthcare approach for both balanced and imbalanced data.

Comparison and performance evaluation of human bio-field visualization algorithm.

Energy is associated with anything and everything around us and can be transferred, transformed but cannot be destroyed. Many existing theories in physics like quantum physics, metaphysics, and electromagnetism give rise to the thought for the existence of an invisible field of bio-energy, in living things. Every living being, at its atomic level, absorbs and releases a good amount of energy, which is not visible through normal eyes but observable to measure through other means. The mentioned energy layer is known as Human Bio-field. Additionally, various studies also clear that measures of such energies can give deeper insights of our wellbeing and health. It also reflect thoughts, emotions, and inter-physiologic, which may affect the functioning of the human body. This article shows the results of the proposed algorithm for the visualisation of human bio-field. Further, the performance of the proposed work is evaluated in terms of accuracy by using existing methods.

CovXmlc: High performance COVID-19 detection on X-ray images using Multi-Model classification.

The Coronavirus Disease 2019 (COVID-19) outbreak has a devastating impact on health and the economy globally, that's why it is critical to diagnose positive cases rapidly. Currently, the most effective test to detect COVID-19 is Reverse Transcription-polymerase chain reaction (RT-PCR) which is time-consuming, expensive and sometimes not accurate. It is found in many studies that, radiology seems promising by extracting features from X-rays. COVID-19 motivates the researchers to undergo the deep learning process to detect the COVID- 19 patient rapidly. This paper has classified the X-rays images into COVID- 19 and normal by using multi-model classification process. This multi-model classification incorporates Support Vector Machine (SVM) in the last layer of VGG16 Convolution network. For synchronization among VGG16 and SVM we have added one more layer of convolution, pool, and dense between VGG16 and SVM. Further, for transformations and discovering the best result, we have used the Radial Basis function. CovXmlc is compared with five existing models using different parameters and metrics. The result shows that our proposed CovXmlc with minimal dataset reached accuracy up to 95% which is significantly higher than the existing ones. Similarly, it also performs better on other metrics such as recall, precision and f-score.

Myositis in a patient with coronavirus disease 2019: A rare presentation.

The world is presently struggling with coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A patient with COVID-19 typically presents with fever, non-productive cough, dyspnea, and myalgia. A 49-year-old female presented with complaints of subacute onset and progressive symmetrical proximal muscle weakness of both upper limbs and lower limbs with no sensory, cranial nerve deficit. She had elevated creatine phosphokinase levels of 906 U/L, an aspartate aminotransferase level of 126 IU/L, a lactate dehydrogenase level of 354 U/L, and an erythrocyte sedimentation rate of 68 mm/1 hr, and magnetic resonance imaging of the pelvis and thigh revealed muscle edema suggestive of myositis. Her reverse transcriptase-polymerase chain reaction result for SARS-CoV-2 was positive. Her evaluation for other causes of myositis was negative. She was managed with intravenous immunoglobulins and supportive care. She showed rapid improvement in symptoms and motor weakness. To our knowledge, this is the first reported case of COVID-19 related disabling myositis in India.

Survey of prevalence of anxiety and depressive symptoms among 1124 healthcare workers during the coronavirus disease 2019 pandemic across India.

BACKGROUND: A prospective study was conducted during the second phase of the coronavirus disease 2019 (COVID-19) pandemic in India to assess the prevalence of anxiety and depressive symptoms among healthcare workers (HCWs) and factors that influence the outcome. METHODS: A self-administered questionnaire was completed by 1124 HCWs during the COVID-19 pandemic (March 30, 2020, to April 2, 2020). Demographic data, questions on COVID-19 and scores of the Hospital Anxiety and Depression Scale were analysed using the chi-square test (Bonferroni correction) and binary logistic regression. RESULTS: The study consists of 1124 HCWs, including 749 doctors, 207 nurses, 135 paramedics, 23 administrators and ten supporting staff members. The prevalence of anxiety and depressive symptoms were reported as 37.2% and 31.4%, respectively. The risk factors for anxiety were female gender (30.6% vs 45.5%), age group (20-35 years) (50.4% vs 61.2%), unmarried (21.2% vs 30.6%) and job profile (nurse) (14.7% vs 26.4%). The protective factor was having service of more than 20 years (23.4% vs 14.8%). The risk factors for depression were age group (20-35 years) (51.3% vs 61.3%) and employed at a primary care hospital (16.2% vs 23.4%). The protective factors were job profile (doctor) (69.9% vs 59.6%) and having service of more than 20 years (22.3% vs 15.5%). CONCLUSION: Approximately one-third of the HCWs reported anxiety and depressive symptoms. The risk factors for anxiety symptoms were female gender, younger age and job profile (nurse) and for depressive symptoms were younger age and working at a primary care hospital. Future research studies should identify strategies for providing a safer and supportive work environment for HCWs to face epidemics/pandemics.

High-Throughput Screening at the Membrane Interface Reveals Inhibitors of Amyloid-ß.

Aggregation and the formation of oligomeric intermediates of amyloid-ß (Aß) at the membrane interface of neuronal cells are implicated in the cellular toxicity and pathology of Alzheimer's disease. Small molecule compounds have been shown to suppress amyloid aggregation and cellular toxicity, but often the presence of a lipid membrane negates their activity. A high-throughput screen of 1800 small molecules was performed to search for membrane active inhibitors, and 21 primary hits were discovered. Through the use of fluorescence-based assays, transmission electron microscopy, and dot blot assays, the initial 21 primary hits were narrowed down to five lead compounds. Nuclear magnetic resonance and circular dichroism experiments were used for further confirmation of amyloid inhibition at the membrane interface and to obtain insights into the secondary structure of amyloid-ß, while size exclusion chromatography was used to characterize the size of Aß species. Lastly, dye-leakage assays allowed us to understand how the addition of the five lead compounds affected amyloid-ß's ability to permeate the lipid bilayer. These results provide insights into small molecules that stabilize small amyloid species in the presence of membranes for the development of tool compounds for deeper investigations of these transient species.

Psychological Health Among Armed Forces Doctors During COVID-19 Pandemic in India.

BACKGROUND: A pandemic poses a significant challenge to the healthcare staff and infrastructure. We studied the prevalence of anxiety and depressive symptoms among armed forces doctors in India during the COVID-19 pandemic and the factors that contribute to these symptoms. METHODS: The study was conducted from March 30, 2020, to April 2, 2020, using a self-administered questionnaire questionnaire using the hospital anxiety and depression scale (HADS), which was sent through Google Forms. Responses were received from 769 respondents. Data were analyzed for demographic details and HADS scores using the chi-square test and backward logistic regression. RESULTS: Anxiety and depressive symptoms were seen in 35.2% and 28.2% of the doctors, respectively. In doctors with anxiety symptoms, significant associations were observed with age (20-35 years, 39.4%, P = 0.01), gender (females, 44.6%, P < 0.001), duration of service (0-10 years, 38%, P = 0.03), and clinical versus non-clinical specialties (non-clinical, 41.3%, P < 0.001) as opposed to marital status, education level, and current department of work.In doctors with depressive symptoms, significant associations were observed with age (P = 0.04), clinical versus non-clinical specialties (P < 0.001), duration of service (0-10 years, 30.1%, P = 0.03), and doctoral degree (P = 0.04) as opposed to gender, marital status, education level, and current working department. CONCLUSION: The study revealed a high prevalence of anxiety and depressive symptoms among armed forces doctors. The main contributing factors are female gender, young age group, non-clinical specialties, and having a doctoral degree.

Durable and self-hydrating tungsten carbide-based composite polymer electrolyte membrane fuel cells.

Proton conductivity of the polymer electrolyte membranes in fuel cells dictates their performance and requires sufficient water management. Here, we report a simple, scalable method to produce well-dispersed transition metal carbide nanoparticles. We demonstrate that these, when added as an additive to the proton exchange Nafion membrane, provide significant enhancement in power density and durability over 100 hours, surpassing both the baseline Nafion and platinum-containing recast Nafion membranes. Focused ion beam/scanning electron microscope tomography reveals the key membrane degradation mechanism. Density functional theory exposes that OH• and H• radicals adsorb more strongly from solution and reactions producing OH• are significantly more endergonic on tungsten carbide than on platinum. Consequently, tungsten carbide may be a promising catalyst in self-hydrating crossover gases while retarding desorption of and capturing free radicals formed at the cathode, resulting in enhanced membrane durability.The proton conductivity of polymer electrolyte membranes in fuel cells dictates their performance, but requires sufficient water management. Here, the authors report a simple method to produce well-dispersed transition metal carbide nanoparticles as additives to enhance the performance of Nafion membranes in fuel cells.

Global regulatory architecture of human, mouse and rat tissue transcriptomes.

BACKGROUND: Predicting molecular responses in human by extrapolating results from model organisms requires a precise understanding of the architecture and regulation of biological mechanisms across species. RESULTS: Here, we present a large-scale comparative analysis of organ and tissue transcriptomes involving the three mammalian species human, mouse and rat. To this end, we created a unique, highly standardized compendium of tissue expression. Representative tissue specific datasets were aggregated from more than 33,900 Affymetrix expression microarrays. For each organism, we created two expression datasets covering over 55 distinct tissue types with curated data from two independent microarray platforms. Principal component analysis (PCA) revealed that the tissue-specific architecture of transcriptomes is highly conserved between human, mouse and rat. Moreover, tissues with related biological function clustered tightly together, even if the underlying data originated from different labs and experimental settings. Overall, the expression variance caused by tissue type was approximately 10 times higher than the variance caused by perturbations or diseases, except for a subset of cancers and chemicals. Pairs of gene orthologs exhibited higher expression correlation between mouse and rat than with human. Finally, we show evidence that tissue expression profiles, if combined with sequence similarity, can improve the correct assignment of functionally related homologs across species. CONCLUSION: The results demonstrate that tissue-specific regulation is the main determinant of transcriptome composition and is highly conserved across mammalian species.

Flow and Particle Dispersion in Lung Acini: Effect of Geometric and Dynamic Parameters During Synchronous Ventilation.

The human lung comprises about 300 million alveoli which are located on bronchioles between the 17th to 24th generations of the acinar tree, with a progressively higher population density in the deeper branches (lower acini). The alveolar size and aspect ratio change with generation number. Due to successive bifurcation, the flow velocity magnitude also decreases as the bronchiole diameter decreases from the upper to lower acini. As a result, fluid dynamic parameters such as Reynolds (Re) and Womersley (α) numbers progressively decrease with increasing generation number. In order to characterize alveolar flow patterns and inhaled particle transport during synchronous ventilation, we have conducted measurements for a range of dimensionless parameters physiologically relevant to the upper acini. Acinar airflow patterns were measured using a simplified in vitro alveolar model consisting of a single transparent elastic truncated sphere (representing the alveolus) mounted over a circular hole on the side of a rigid circular tube (representing the bronchiole). The model alveolus was capable of expanding and contracting in-phase with the oscillatory flow through the bronchiole thereby simulating synchronous ventilation. Realistic breathing conditions were achieved by exercising the model over a range of progressively varying geometric and dynamic parameters to simulate the environment within several generations of the acinar tree. Particle image velocimetry was used to measure the resulting flow patterns. Next, we used the measured flow fields to calculate particle trajectories to obtain particle transport and deposition statistics for massless and finite-size particles under the influence of flow advection and gravity. Our study shows that the geometric parameters (ß and ΔV/V) primarily affect the velocity magnitudes, whereas the dynamic parameters (Re and α) distort the flow symmetry while also altering the velocity magnitudes. Consequently, the dynamic parameters have a greater influence on the particle trajectories and deposition statistics compared to the geometric parameters. The results from this study can benefit pulmonary research into the risk assessment of toxicological inhaled aerosols, and the pharmaceutical industry by providing better insight into the flow patterns and particle transport of inhalable therapeutics in the acini.

Flow and particle dispersion in a pulmonary alveolus--part I: velocity measurements and convective particle transport.

The alveoli are the smallest units of the lung that participate in gas exchange. Although gas transport is governed primarily by diffusion due to the small length scales associated with the acinar region (approximately 500 microm), the transport and deposition of inhaled aerosol particles are influenced by convective airflow patterns. Therefore, understanding alveolar fluid flow and mixing is a necessary first step toward predicting aerosol transport and deposition in the human acinar region. In this study, flow patterns and particle transport have been measured using a simplified in-vitro alveolar model consisting of a single alveolus located on a bronchiole. The model comprises a transparent elastic 5/6 spherical cap (representing the alveolus) mounted over a circular hole on the side of a rigid circular tube (representing the bronchiole). The alveolus is capable of expanding and contracting in phase with the oscillatory flow through the tube. Realistic breathing conditions were achieved by exercising the model at physiologically relevant Reynolds and Womersley numbers. Particle image velocimetry was used to measure the resulting flow patterns in the alveolus. Data were acquired for five cases obtained as combinations of the alveolar-wall motion (nondeforming/oscillating) and the bronchiole flow (none/steady/oscillating). Detailed vector maps at discrete points within a given cycle revealed flow patterns, and transport and mixing of bronchiole fluid into the alveolar cavity. The time-dependent velocity vector fields were integrated over multiple cycles to estimate particle transport into the alveolar cavity and deposition on the alveolar wall. The key outcome of the study is that alveolar-wall motion enhances mixing between the bronchiole and the alveolar fluid. Particle transport and deposition into the alveolar cavity are maximized when the alveolar wall oscillates in tandem with the bronchiole fluid, which is the operating case in the human lung.

Flow and particle dispersion in a pulmonary alveolus--part II: effect of gravity on particle transport.

The acinar region of the human lung comprises about 300x10(6) alveoli, which are responsible for gas exchange between the lung and the blood. As discussed in Part I (Chhabra and Prasad, "Flow and Particle Dispersion in a Pulmonary Alveolus-Part I: Velocity Measurements and Convective Particle Transport," ASME J. Biomech. Eng., 132, p. 051009), the deposition of aerosols in the acinar region can either be detrimental to gas exchange (as in the case of harmful particulate matter) or beneficial (as in the case of inhalable pharmaceuticals). We measured the flow field inside an in-vitro model of a single alveolus mounted on a bronchiole and calculated the transport and deposition of massless particles in Part I. This paper focuses on the transport and deposition of finite-sized particles ranging from 0.25 microm to 4 microm under the combined influence of flow-induced advection (computed from velocity maps obtained by particle image velocimetry) and gravitational settling. Particles were introduced during the first inhalation cycle and their trajectories and deposition statistics were calculated for subsequent cycles for three different particle sizes (0.25 microm, 1 microm, and 4 microm) and three alveolar orientations. The key outcome of the study is that particles or=4 microm is dominated by gravitational settling and shows little effect of fluid advection. Additionally, small and midsize particles deposit at about two-thirds height in the alveolus irrespective of the gravitational orientation whereas the deposition of large particles is governed primarily by the orientation of the gravity vector.

Convective dispersion during steady flow in the conducting airways of the human lung.

The adverse health effects of inhaled particulate matter from the environment depend on its dispersion, transport, and deposition in the human airways. Similarly, precise targeting of deposition sites by pulmonary drug delivery systems also relies on characterizing the dispersion and transport of therapeutic aerosols in the respiratory tract. A variety of mechanisms may contribute to convective dispersion in the lung; simple axial streaming, augmented dispersion, and steady streaming are investigated in this effort. Flow visualization of a bolus during inhalation and exhalation, and dispersion measurements were conducted during steady flow in a three-generational, anatomically accurate in vitro model of the conducting airways to support this goal. Control variables included Reynolds number, flow direction, generation, and branch. Experiments illustrate transport patterns in the lumen cross section and map their relation to dispersion metrics. These results indicate that simple axial streaming, rather than augmented dispersion, is the dominant steady convective dispersion mechanism in symmetric Weibel generations 7-13 during normal respiration. Experimental evidence supports the branching nature of the airways as a possible contributor to steady streaming in the lung.

Secondary velocity fields in the conducting airways of the human lung.

An understanding of flow and dispersion in the human respiratory airways is necessary to assess the toxicological impact of inhaled particulate matter as well as to optimize the design of inhalable pharmaceutical aerosols and their delivery systems. Secondary flows affect dispersion in the lung by mixing solute in the lumen cross section. The goal of this study is to measure and interpret these secondary velocity fields using in vitro lung models. Particle image velocimetry experiments were conducted in a three-generational, anatomically accurate model of the conducting region of the lung. Inspiration and expiration flows were examined under steady and oscillatory flow conditions. Results illustrate secondary flow fields as a function of flow direction, Reynolds number, axial location with respect to the bifurcation junction, generation, branch, phase in the oscillatory cycle, and Womersley number. Critical Dean number for the formation of secondary vortices in the airways, as well as the strength and development length of secondary flow, is characterized. The normalized secondary velocity magnitude was similar on inspiration and expiration and did not vary appreciably with generation or branch. Oscillatory flow fields were not significantly different from corresponding steady flow fields up to a Womersley number of 1 and no instabilities related to shear were detected on flow reversal. These observations were qualitatively interpreted with respect to the simple streaming, augmented dispersion, and steady streaming convective dispersion mechanisms.