Accelerating the discovery of antifungal peptides using deep temporal convolutional networks.

The application of machine intelligence in biological sciences has led to the development of several automated tools, thus enabling rapid drug discovery. Adding to this development is the ongoing COVID-19 pandemic, due to which researchers working in the field of artificial intelligence have acquired an active interest in finding machine learning-guided solutions for diseases like mucormycosis, which has emerged as an important post-COVID-19 fungal complication, especially in immunocompromised patients. On these lines, we have proposed a temporal convolutional network-based binary classification approach to discover new antifungal molecules in the proteome of plants and animals to accelerate the development of antifungal medications. Although these biomolecules, known as antifungal peptides (AFPs), are part of an organism's intrinsic host defense mechanism, their identification and discovery by traditional biochemical procedures is arduous. Also, the absence of a large dataset on AFPs is also a considerable impediment in building a robust automated classifier. To this end, we have employed the transfer learning technique to pre-train our model on antibacterial peptides. Subsequently, we have built a classifier that predicts AFPs with accuracy and precision of 94%. Our classifier outperforms several state-of-the-art models by a considerable margin. The results of its performance were proven as statistically significant using the Kruskal-Wallis H test, followed by a post hoc analysis performed using the Tukey honestly significant difference (HSD) test. Furthermore, we identified potent AFPs in representative animal (Histatin) and plant (Snakin) proteins using our model. We also built and deployed a web app that is freely available at https://tcn-afppred.anvil.app/ for the identification of AFPs in protein sequences.

Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM.

Fungal infections or mycosis cause a wide range of diseases in humans and animals. The incidences of community acquired; nosocomial fungal infections have increased dramatically after the emergence of COVID-19 pandemic. The increase in number of patients with immunodeficiency / immunosuppression related diseases, resistance to existing antifungal compounds and availability of limited therapeutic options has triggered the search for alternative antifungal molecules. In this direction, antifungal peptides (AFPs) have received a lot of interest as an alternative to currently available antifungal drugs. Although the AFPs are produced by diverse population of living organisms, identifying effective AFPs from natural sources is time-consuming and expensive. Therefore, there is a need to develop a robust in silico model capable of identifying novel AFPs in protein sequences. In this paper, we propose Deep-AFPpred, a deep learning classifier that can identify AFPs in protein sequences. We developed Deep-AFPpred using the concept of transfer learning with 1DCNN-BiLSTM deep learning algorithm. The findings reveal that Deep-AFPpred beats other state-of-the-art AFP classifiers by a wide margin and achieved approximately 96% and 94% precision on validation and test data, respectively. Based on the proposed approach, an online prediction server is created and made publicly available at https://afppred.anvil.app/. Using this server, one can identify novel AFPs in protein sequences and the results are provided as a report that includes predicted peptides, their physicochemical properties and motifs. By utilizing this model, we identified AFPs in different proteins, which can be chemically synthesized in lab and experimentally validated for their antifungal activity.

StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides.

Due to the rapid emergence of multi-drug resistant (MDR) bacteria, existing antibiotics are becoming ineffective. So, researchers are looking for alternatives in the form of antibacterial peptides (ABPs) based medicines. The discovery of novel ABPs using wet-lab experiments is time-consuming and expensive. Many machine learning models have been proposed to search for new ABPs, but there is still scope to develop a robust model that has high accuracy and precision. In this work, we present StaBle-ABPpred, a stacked ensemble technique-based deep learning classifier that uses bidirectional long-short term memory (biLSTM) and attention mechanism at base-level and an ensemble of random forest, gradient boosting and logistic regression at meta-level to classify peptides as antibacterial or otherwise. The performance of our model has been compared with several state-of-the-art classifiers, and results were subjected to analysis of variance (ANOVA) test and its post hoc analysis, which proves that our model performs better than existing classifiers. Furthermore, a web app has been developed and deployed at https://stable-abppred.anvil.app to identify novel ABPs in protein sequences. Using this app, we identified novel ABPs in all the proteins of the Streptococcus phage T12 genome. These ABPs have shown amino acid similarities with experimentally tested antimicrobial peptides (AMPs) of other organisms. Hence, they could be chemically synthesized and experimentally validated for their activity against different bacteria. The model and app developed in this work can be further utilized to explore the protein diversity for identifying novel ABPs with broad-spectrum activity, especially against MDR bacterial pathogens.

Benefits of Rib Fixation Utilizing Low-Cost Materials: A Randomized Pilot Trial.

INTRODUCTION: Surgical stabilization of rib fractures (SSRF) using standard rib plating systems has become a norm in developed countries. However, the procedure has not garnered much interest in low-middle-income countries, primarily because of the cost. METHODS: This was a single-center pilot randomized trial. Patients with severe rib fractures were randomized into two groups: SSRF and nonoperative management. SSRF arm patients underwent surgical fixation in addition to the tenets of nonoperative management. Low-cost materials like stainless steel wires and braided polyester sutures were used for fracture fixation. The primary outcome was to assess the duration of hospital stay. RESULTS: Twenty-two patients were randomized, 11 in each arm. Per-protocol analysis showed that the SSRF arm had significantly reduced duration of hospital stay (22.6 ± 19.1 d versus 7.9 ± 5.7 d, P value 0.031), serial pain scores at 48 h and 5 d (median score 5, IQR (3-6) versus median score 7, IQR (6.5-8), P value 0.004 at 48 h and median score 2 IQR (2-3) versus median score 7 IQR (4.5-7) P value 0.0005 at 5 d), significantly reduced need for injectable opioids (9.9 ± 3.8 mg versus 4.4 ± 3.4 mg, P value 0.003) and significantly more ventilator-free days (19.9 ± 8.7 d versus 26.4 ± 3.2 d, P value 0.04). There were no statistically significant differences in the total duration of ICU stay (median number of days 2, IQR 1-4.5 versus median number of days 7, IQR 1-14, P value 0.958), need for tracheostomy (36.4% versus 0%, P value 0.155), and pulmonary and pleural complications. CONCLUSIONS: SSRF with low-cost materials may provide benefits similar to standard rib plating systems and can be used safely in resource-poor settings.

An investigation of the environmental implications of bioplastics: Recent advancements on the development of environmentally friendly bioplastics solutions.

The production and utilization of plastics may prove beneficial, but the environmental impact suggests the opposite. The single-use plastics (SUP) and conventional plastics are harmful to the environment and need prompt disposal. Bioplastics are increasingly being considered as a viable alternative to conventional plastics due to their potential to alleviate environmental concerns such as greenhouse gas emissions and pollution. However, the previous reviews revealed a lack of consistency in the methodologies used in the Life Cycle Assessments (LCAs), making it difficult to compare the results across studies. The current study provides a systematic review of LCAs that assess the environmental impact of bioplastics. The different mechanical characteristics of bio plastics, like tensile strength, Young's modulus, flexural modulus, and elongation at break are reviewed which suggest that bio plastics are comparatively much better than synthetic plastics. Bioplastics have more efficient mechanical properties compared to synthetic plastics which signifies that bioplastics are more sustainable and reliable than synthetic plastics. The key challenges in bioplastic adoption and production include competition with food production for feedstock, high production costs, uncertainty in end-of-life management, limited biodegradability, lack of standardization, and technical performance limitations. Addressing these challenges requires collaboration among stakeholders to drive innovation, reduce costs, improve end-of-life management, and promote awareness and education. Overall, the study suggests that while bioplastics have the potential to reduce environmental impact, further research is needed to better understand their life cycle and optimize their end-of-life (EoL) management and production to maximize their environmental benefits.

Unveiling the potential of membrane in climate change mitigation and environmental resilience in ecosystem.

Carbon capture technologies are becoming increasingly crucial in addressing global climate change issues by lowering CO2 emissions from industrial and power generation activities. Post-combustion carbon capture, which uses membranes instead of adsorbents, has emerged as one of promising and environmentally friendly approaches among these technologies. The operation of membrane technology is based on the premise of selectively separating CO2 from flue gas emissions. This provides a number of different benefits, including improved energy efficiency and decreased costs of operation. Because of its adaptability to changing conditions and its low impact on the surrounding ecosystem, it is an appealing choice for a diverse array of uses. However, there are still issues to be resolved, such as those pertaining to establishing a high selectivity, membrane degradation, and the costs of the necessary materials. In this article, we evaluate and explore the prospective applications and roles of membrane technologies to control climate change by post-combustion carbon capturing. The primary proposition suggests that the utilization of membrane-based carbon capture has the potential to make a substantial impact in mitigating CO2 emissions originating from industrial and power production activities. This is due to its heightened ability to selectively absorb carbon, better efficiency in energy consumption, and its flexibility to various applications. The forthcoming challenges and potential associated with the application of membranes in post-carbon capture are also discussed.

A narrative review on therapeutic potential of naringenin in colorectal cancer: Focusing on molecular and biochemical processes.

Colorectal cancer (CRC) is a common and highly metastatic cancer affecting people worldwide. Drug resistance and unwanted side effects are some of the limitations of current treatments for CRC. Naringenin (NAR) is a naturally occurring compound found in abundance in various citrus fruits such as oranges, grapefruits, and tomatoes. It possesses a diverse range of pharmacological and biological properties that are beneficial for human health. Numerous studies have highlighted its antioxidant, anticancer, and anti-inflammatory activities, making it a subject of interest in scientific research. This review provides a comprehensive overview of the effects of NAR on CRC. The study's findings indicated that NAR: (1) interacts with estrogen receptors, (2) regulates the expression of genes related to the p53 signaling pathway, (3) promotes apoptosis by increasing the expression of proapoptotic genes (Bax, caspase9, and p53) and downregulation of the antiapoptotic gene Bcl2, (4) inhibits the activity of enzymes involved in cell survival and proliferation, (5) decreases cyclin D1 levels, (6) reduces the expression of cyclin-dependent kinases (Cdk4, Cdk6, and Cdk7) and antiapoptotic genes (Bcl2, x-IAP, and c-IAP-2) in CRC cells. In vitro CDK2 binding assay was also performed, showing that the NAR derivatives had better inhibitory activities on CDK2 than NAR. Based on the findings of this study, NAR is a potential therapeutic agent for CRC. Additional pharmacology and pharmacokinetics studies are required to fully elucidate the mechanisms of action of NAR and establish the most suitable dose for subsequent clinical investigations.

Nucleic acid-based vaccine for ovarian cancer cells; bench to bedside.

Ovarian cancer continues to be a difficult medical issue that affects millions of individuals worldwide. Important platforms for cancer immunotherapy include checkpoint inhibitors, chimeric antigen receptor T cells, bispecific antibodies, cancer vaccines, and other cell-based treatments. To avoid numerous infectious illnesses, conventional vaccinations based on synthetic peptides, recombinant subunit vaccines, and live attenuated and inactivated pathogens are frequently utilized. Vaccine manufacturing processes, however, are not entirely safe and carry a significant danger of contaminating living microorganisms. As a result, the creation of substitute vaccinations is required for both viral and noninfectious illnesses, including cancer. Recently, there has been testing of nucleic acid vaccines, or NAVs, as a cancer therapeutic. Tumor antigens (TAs) are genetically encoded by DNA and mRNA vaccines, which the host uses to trigger immune responses against ovarian cancer cells that exhibit the TAs. Despite being straightforward, safe, and easy to produce, NAVs are not currently thought to be an ideal replacement for peptide vaccines. Some obstacles to this strategy include selecting the appropriate therapeutic agents (TAs), inadequate immunogenicity, and the immunosuppressive characteristic of ovarian cancer. We focus on strategies that have been employed to increase NAVs' effectiveness in the fight against ovarian cancer in this review.

Deep Proteomics Network and Machine Learning Analysis of Human Cerebrospinal Fluid in Japanese Encephalitis Virus Infection.

Japanese encephalitis virus is a leading cause of neurological infection in the Asia-Pacific region with no means of detection in more remote areas. We aimed to test the hypothesis of a Japanese encephalitis (JE) protein signature in human cerebrospinal fluid (CSF) that could be harnessed in a rapid diagnostic test (RDT), contribute to understanding the host response and predict outcome during infection. Liquid chromatography and tandem mass spectrometry (LC-MS/MS), using extensive offline fractionation and tandem mass tag labeling (TMT), enabled comparison of the deep CSF proteome in JE vs other confirmed neurological infections (non-JE). Verification was performed using data-independent acquisition (DIA) LC-MS/MS. 5,070 proteins were identified, including 4,805 human proteins and 265 pathogen proteins. Feature selection and predictive modeling using TMT analysis of 147 patient samples enabled the development of a nine-protein JE diagnostic signature. This was tested using DIA analysis of an independent group of 16 patient samples, demonstrating 82% accuracy. Ultimately, validation in a larger group of patients and different locations could help refine the list to 2-3 proteins for an RDT. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD034789 and 10.6019/PXD034789.

AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom.

With advancements in genomics, there has been substantial reduction in the cost and time of genome sequencing and has resulted in lot of data in genome databases. Antimicrobial host defense proteins provide protection against invading microbes. But confirming the antimicrobial function of host proteins by wet-lab experiments is expensive and time consuming. Therefore, there is a need to develop an in silico tool to identify the antimicrobial function of proteins. In the current study, we developed a model AniAMPpred by considering all the available antimicrobial peptides (AMPs) of length $\in $[10 200] from the animal kingdom. The model utilizes a support vector machine algorithm with deep learning-based features and identifies probable antimicrobial proteins (PAPs) in the genome of animals. The results show that our proposed model outperforms other state-of-the-art classifiers, has very high confidence in its predictions, is not biased and can classify both AMPs and non-AMPs for a diverse peptide length with high accuracy. By utilizing AniAMPpred, we identified 436 PAPs in the genome of Helobdella robusta. To further confirm the functional activity of PAPs, we performed BLAST analysis against known AMPs. On detailed analysis of five selected PAPs, we could observe their similarity with antimicrobial proteins of several animal species. Thus, our proposed model can help the researchers identify PAPs in the genome of animals and provide insight into the functional identity of different proteins. An online prediction server is also developed based on the proposed approach, which is freely accessible at https://aniamppred.anvil.app/.

Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec.

The overuse of antibiotics has led to emergence of antimicrobial resistance, and as a result, antibacterial peptides (ABPs) are receiving significant attention as an alternative. Identification of effective ABPs in lab from natural sources is a cost-intensive and time-consuming process. Therefore, there is a need for the development of in silico models, which can identify novel ABPs in protein sequences for chemical synthesis and testing. In this study, we propose a deep learning classifier named Deep-ABPpred that can identify ABPs in protein sequences. We developed Deep-ABPpred using bidirectional long short-term memory algorithm with amino acid level features from word2vec. The results show that Deep-ABPpred outperforms other state-of-the-art ABP classifiers on both test and independent datasets. Our proposed model achieved the precision of approximately 97 and 94% on test dataset and independent dataset, respectively. The high precision suggests applicability of Deep-ABPpred in proposing novel ABPs for synthesis and experimentation. By utilizing Deep-ABPpred, we identified ABPs in the tail protein sequences of Streptococcus bacteriophages, chemically synthesized identified peptides in lab and tested their activity in vitro. These ABPs showed potent antibacterial activity against selected Gram-positive and Gram-negative bacteria, which confirms the capability of Deep-ABPpred in identifying novel ABPs in protein sequences. Based on the proposed approach, an online prediction server is also developed, which is freely accessible at https://abppred.anvil.app/. This web server takes the protein sequence as input and provides ABPs with high probability (>0.95) as output.

Range and strength of mechanical interactions of force dipoles in elastic fiber networks.

Mechanical forces generated by myosin II molecular motors drive diverse cellular processes, most notably shape change, division and locomotion. These forces may be transmitted over long range through the cytoskeletal medium - a disordered, viscoelastic network of biopolymers. The resulting cell size scale force chains can in principle mediate mechanical interactions between distant actomyosin units, leading to self-organized structural order in the cell cytoskeleton. Inspired by such force transmission through elastic structures in the cytoskeleton, we consider a percolated fiber lattice network, where fibers are represented as linear elastic elements that can both bend and stretch, and the contractile activity of myosin motors is represented by force dipoles. Then, by using a variety of metrics, we show how two such contractile force dipoles interact with each other through their mutual mechanical deformations of the elastic fiber network. As a prelude to two-dipole interactions, we quantify how forces propagate through the network from a single anisotropic force dipole by analyzing clusters of nodes connected by highly strained bonds, as well as through the decay rate of strain energy with distance from a force dipole. We show that predominant fiber bending screens out force propagation, resulting in reduced and strongly network configuration-dependent dipole interactions. On the other hand, stretching-dominated networks support longer-ranged inter-dipole interactions that recapitulate the predictions of linear elasticity theory. By characterizing the differences between tensile and compressive force propagation in the fiber network, we show how inter-dipole interaction depends on the dipoles' mutual separation and orientation. The resulting elastic interaction energy may mediate a force between multiple distant dipoles, leading to their self-organization into ordered configurations. This provides a potential pathway for active mechanical force-driven structural order in elastic biopolymer networks.

Central Atlantoaxial Dislocation: Presenting Symptoms, Diagnostic Parameters, and Surgical Treatment from Reports on 15 Surgically Treated Patients.

AIM: This chapter reviews the clinical entity of central or axial atlantoaxial instability (CAAD). MATERIAL AND METHODS: From January 2018 to November 2020, 15 patients were identified as having CAAD, wherein there was no atlantoaxial instability when analyzed by conventional radiological parameters and wherein there was no evidence of neural or dural compression due to the odontoid process. The patients were identified as having atlantoaxial instability on the basis of the alignment of facets on lateral profile imaging and a range of telltale clinical and radiological indicators. The clinical statuses of the patients were recorded both before and after surgical treatment by using the specially designed Goel symptom severity index and visual analog scale (VAS) scores. All patients were treated via atlantoaxial fixation. RESULTS: There were six men and nine women ranging in age from 18 to 45 years (average: 37 years). The presenting clinical symptoms were relatively subtle and long-standing. Apart from symptoms that are generally related to neural compromise at the craniovertebral junction, a range of nonspecific cranial and spinal symptoms were prominent. The follow-up time after surgery ranged from 6 to 34 months. All patients showed early postoperative and sustained clinical recovery. CONCLUSIONS: The correct diagnosis and appropriate surgical treatment of CAAD can provide an opportunity for quick and lasting clinical recovery.

COVID-associated rhinocerebral mucormycosis: a retrospective analysis of presentation and outcomes.

OBJECTIVES: To comprehensively analyse the disease presentation and mortality of COVID-associated rhino-orbito-cerebral mucormycosis. METHODS: A retrospective analysis of the demographics, clinical and radiographic findings was performed. A binary logistic regression analysis was performed to examine the survival of patients with mucormycosis from hypothesised predictors. RESULTS: A total of 202 patients were included in this study. Statistical significance was demonstrated in the predilection to the male gender, recent history of SARS-COV-2, history of use of corticosteroid and hyperglycemia in this cohort of CAM. The mortality rate was 18.31%. Advanced age, raised HbA1c and intra-orbital extension were found to be predictors adversely affecting survival. CONCLUSION: Early diagnosis, aggressive surgical therapy, early and appropriate medical therapy can help improve outcomes. LEVEL OF EVIDENCE: Level 4.

Estimation of number of unmanned aerial vehicles in a scene utilizing acoustic signatures and machine learning.

With the exponential growth in unmanned aerial vehicle (UAV)-based applications, there is a need to ensure safe and secure operations. From a security perspective, detecting and localizing intruder UAVs is still a challenge. It is even more challenging to accurately estimate the number of intruder UAVs on the scene. In this work, we propose a simple acoustic-based technique to detect and estimate the number of UAVs. Our method utilizes acoustic signals generated from the motion of UAV motors and propellers. Acoustic signals are captured by flying an arbitrary number of ten UAVs in different combinations in an indoor setting. The recorded acoustic signals are trimmed, processed, and arranged to create an UAV audio dataset. The UAV audio dataset is subjected to time-frequency transformations to generate audio spectrogram images. The generated spectrogram images are then fed to a custom lightweight convolutional neural network (CNN) architecture to estimate the number of UAVs in the scene. Following training, the proposed model achieves an average test accuracy of 93.33% as compared to state-of-the-art benchmark models. Furthermore, the deployment feasibility of the proposed model is validated by running inference time calculations on edge computing devices, such as the Raspberry Pi 4, NVIDIA Jetson Nano, and NVIDIA Jetson AGX Xavier.

New 5,5-(1,4-Phenylenebis(methyleneoxy)diisophthalic Acid Appended Zn(II) and Cd(II) MOFs as Potent Photocatalysts for Nitrophenols.

Metal-organic frameworks (MOFs) are peculiar multimodal materials that find photocatalytic applications for the decomposition of lethal molecules present in the wastewater. In this investigation, two new d10-configuration-based MOFs, [Zn2(L)(H2O)(bbi)] (1) and [Cd2(L)(bbi)] (2) (5,5-(1,4-phenylenebis(methyleneoxy)diisophthalic acid (H2L) and 1,1'-(1,4-butanediyl)bis(imidazole) (bbi)), have been synthesized and characterized. The MOF 1 displayed a (4,6)-connected (3.43.52)(32.44.52.66.7) network topology, while 2 had a (3,10)-connected network with a Schläfli symbol of (410.511.622.72)(43)2. These MOFs have been employed as photocatalysts to photodegrade nitrophenolic compounds, especially p-nitrophenol (PNP). The photocatalysis studies reveal that 1 displayed relatively better photocatalytic performance than 2. Further, the photocatalytic efficacy of 1 has been assessed by altering the initial PNP concentration and photocatalyst dosage, which suggest that at 80 ppm PNP concentration and at its 50 mg concentration the MOF 1 can photo-decompose around 90.01% of PNP in 50 min. Further, radical scavenging experiments reveal that holes present over 1 and ·OH radicals collectively catalyze the photodecomposition of PNP. In addition, utilizing density of states (DOS) calculations and Hirshfeld surface analyses, a plausible photocatalysis mechanism for nitrophenol degradation has been postulated.

A randomized-controlled trial comparing 20% albumin to plasmalyte in patients with cirrhosis and sepsis-induced hypotension [ALPS trial].

BACKGROUND & AIMS: The choice of resuscitation fluid in patients with cirrhosis and sepsis-induced hypotension is unclear. 5% albumin was superior to normal saline in the FRISC study. We compared the efficacy and safety of 20% albumin, which has greater oncotic properties, to plasmalyte in reversing sepsis-induced hypotension. METHODS: Critically ill patients with cirrhosis underwent open-label randomization to receive either 20% albumin (0.5-1.0 g/kg over 3 hours; n = 50) or plasmalyte (30 ml/kg over 3 hours, n = 50). The primary endpoint of the study was the attainment of mean arterial pressure (MAP) above 65 mmHg at 3 hours. RESULTS: Baseline characteristics were comparable in albumin and plasmalyte groups; arterial lactate (6.16±3.18 mmol/L vs. 6.38±4.77 mmol/L; p = 0.78), MAP (51.4±6.52 mmHg vs. 49.9±4.45 mmHg; p = 0.17) and SOFA score (10.8±2.96 vs. 11.1±4.2; p = 0.68), respectively. Most patients were alcoholics (39%) and had pneumonia (40%). In the intention-to-treat analysis, albumin was superior to plasmalyte in achieving the primary endpoint (62% vs. 22%; p <0.001). A faster decline in arterial lactate (p = 0.03), a reduced need for dialysis (48% vs. 62%; p = 0.16), and a longer time to initiation of dialysis (in hours) (68.13±47.79 vs. 99.7± 63.4; p = 0.06) were seen with albumin. However, the 28-day mortality rate was not different (58% vs. 62%, p = 0.57) and treatment had to be discontinued in 11 (22%) patients in the albumin group due to adverse effects compared to no discontinuations in the plasmalyte group. CONCLUSION: In patients with cirrhosis and sepsis-induced hypotension, 20% albumin leads to a faster improvement in hemodynamics and lactate clearance than plasmalyte, while 28-day survival was similar. However, patients on 20% albumin need to be closely monitored as it was more often associated with pulmonary complications. CLINICAL TRIAL REGISTRATION: NCT02721238. LAY SUMMARY: The current randomized-controlled trial performed in critically ill patients with cirrhosis and sepsis-induced hypotension highlights that 20% albumin restores arterial pressure more quickly but causes more pulmonary complications than plasmalyte. The impact on renal functions was also modest. These effects did not result in improvement in survival at 28 days. Plasmalyte is safer and well-tolerated and can be considered for volume resuscitation in patients with cirrhosis and sepsis-induced hypotension.

Economic Evaluation of Nutrition-Sensitive Agricultural Interventions to Increase Maternal and Child Dietary Diversity and Nutritional Status in Rural Odisha, India.

BACKGROUND: Economic evaluations of nutrition-sensitive agriculture (NSA) interventions are scarce, limiting assessment of their potential affordability and scalability. OBJECTIVES: We conducted cost-consequence analyses of 3 participatory video-based interventions of fortnightly women's group meetings using the following platforms: 1) NSA videos; 2) NSA and nutrition-specific videos; or 3) NSA videos with a nutrition-specific participatory learning and action (PLA) cycle. METHODS: Interventions were tested in a 32-mo, 4-arm cluster-randomized controlled trial, Upscaling Participatory Action and Videos for Agriculture and Nutrition (UPAVAN) in the Keonjhar district, Odisha, India. Impacts were evaluated in children aged 0-23 mo and their mothers. We estimated program costs using data collected prospectively from expenditure records of implementing and technical partners and societal costs using expenditure assessment data collected from households with a child aged 0-23 mo and key informant interviews. Costs were adjusted for inflation, discounted, and converted to 2019 US$. RESULTS: Total program costs of each intervention ranged from US$272,121 to US$386,907. Program costs per pregnant woman or mother of a child aged 0-23 mo were US$62 for NSA videos, US$84 for NSA and nutrition-specific videos, and US$78 for NSA videos with PLA (societal costs: US$125, US$143, and US$122, respectively). Substantial shares of total costs were attributable to development and delivery of the videos and PLA (52-69%) and quality assurance (25-41%). Relative to control, minimum dietary diversity was higher in the children who underwent the interventions incorporating nutrition-specific videos and PLA (adjusted RRs: 1.19 and 1.27; 95% CIs: 1.03-1.37 and 1.11, 1.46, respectively). Relative to control, minimum dietary diversity in mothers was higher in those who underwent NSA video (1.21 [1.01, 1.45]) and NSA with PLA (1.30 [1.10, 1.53]) interventions. CONCLUSION: NSA videos with PLA can increase both maternal and child dietary diversity and have the lowest cost per unit increase in diet diversity. Building on investments made in developing UPAVAN, cost-efficiency at scale could be increased with less intensive monitoring, reduced startup costs, and integration within existing government programs. This trial was registered at clinicaltrials.gov as ISRCTN65922679.

Protocolized approach saves the limb in peripheral arterial injury: A decade experience.

PURPOSE: Outcomes of peripheral arterial injury (PAI) depend on various factors, such as warm ischemia time and concomitant injuries. Suboptimal prehospital care may lead to delayed presentation, and a lack of dedicated trauma system may lead to poorer outcome. Also, there are few reports of these outcomes. The aim of this study was to review our experience of PAI management for more than a decade, and identify the predictors of limb loss in these patients. METHODS: This is a retrospective analysis of prospectively maintained database of trauma admissions at a level I trauma center from January 2008 to December 2019. Patients with acute upper limb arterial injuries or lower limb arterial injuries at or above the level of popliteal artery were included. Association of limb loss with ischemia time, mechanism of injury and concomitant injuries was studied using multiple logistic regressions. Statistical analysis was performed using STATA version 15.0 (Stata Corp LLC, Texas). RESULTS: Out of 716 patients with PAI, the majority (92%) were young males. Blunt trauma was the most common mechanism of injury. Median ischemia time was 4 h (interquartile range 2-7 h). Brachial artery (28%) was the most common injured vessel followed by popliteal artery (18%) and femoral artery (17%). Limb salvage rate was 78%. Out of them, 158 (22%) patients needed amputation, and 53 (7%) had undergone primary amputation. The majority (86%) of patients who required primary or secondary amputations had blunt trauma. On multivariate analysis, blunt trauma, ischemia time more than 6 h and concomitant venous, skeletal, and soft tissue injuries were associated with higher odds of amputation. CONCLUSION: Over all limb salvage rates was 78% in our series. Blunt mechanism of injury and associated skeletal and soft tissue injury, ischemia time more than 6 h portend a poor prognosis. Injury prevention, robust prehospital care, and rapid referral to specialized trauma center are few efficient measures, which can decrease the morbidity associated with vascular injury.

Early prediction of COVID-19 using ensemble of transfer learning.

In the wake of the COVID-19 outbreak, automated disease detection has become a crucial part of medical science given the infectious nature of the coronavirus. This research aims to introduce a deep ensemble framework of transfer learning models for early prediction of COVID-19 from the respective chest X-ray images of the patients. The dataset used in this research was taken from the Kaggle repository having two classes-COVID-19 Positive and COVID-19 Negative. The proposed model achieved high accuracy on the test sample with minimum false positive prediction. It can assist doctors and technicians with early detection of COVID-19 infection. The patient's health can further be monitored remotely with the help of connected devices with the Internet, which may be termed as the Internet of Medical Things (IoMT). The proposed IoMT-based solution for the automatic detection of COVID-19 can be a significant step toward fighting the pandemic.