Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of tortoise-shell glue on rat oligoasthenospermia model

To the Editor: Infertility is a public health concern in modern society. Oligoasthenospermia (OA) is one of the leading causes of male infertility that impacts about 5% of reproductive-aged couples worldwide. The etiology and pathogenesis of OA remain poorly understood. The characteristics of OA include a reduction in the total number (<33 10 per ejaculate or <12 10/mL) or concentration of spermatozoa, and low or nil sperm motility (<32%). Traditional Chinese medicine (TCM) has a long history of treating male infertility by using drugs derived from natural sources, such as herbal or animal tissue-derived materials. Tortoise-shell glue (TSG), a classic TCM, is prominently used as a drug or supplement on multiple health conditions to replenish “Qi”, which is considered to be the energy source of the human body in the TCM theory. Amitochondrion, a cell’s “powerhouse,” is essential for cellular signaling and plays a role similar to that of “Qi” in TCM. The mid piece of sperm is packed with mitochondria; therefore, mitochondrial dysfunction may play a vital causative role in inadequate sperm mobility and male infertility. Mitochondria not only provide the spermatozoa the necessary energy for successful fertilization but are also closely associated with the viability of the spermatozoa via regulating the permeability by the mitochondrial permeability transition pore (mPTP), a transmembrane protein located in the inner membrane of mitochondria. Normally, it remains closed, but the opening of mPTP allows large molecules to pass through the mitochondrial membrane. Malfunction of mPTP leads to cell apoptosis, and in the case of OA, it results in a reduced sperm number and mobility. In the current study, we aimed to evaluate the therapeutic effects of TSG on spermatogenesis and reproductive hormones, and also investigate the potential protective effects of TSG on mitochondrial functions.
Diabetic Ketoacidosis with Lower-than-anticipated Glucose Levels, and Recalcitrant Metabolic Acidosis Requiring Rescue Hemodialysis in a Patient of COVID-19 Infection

Abstract Coronavirus disease-2019 (COVID-19) has stood out as a disease of great medical interest, influencing disease evolution, and severity of diabetes mellitus. The intersection of COVID-19 infection and diabetes mellitus has unmasked inflammation and critical metabolic disturbances. We deliberate the case of a young woman, with type 2 diabetes mellitus, who was hospitalized for COVID-19 infection. Work-up revealed diabetic ketoacidosis (DKA) with lower-than-anticipated glucose levels, and acute metabolic acidosis. Refractoriness of metabolic acidosis to standard treatment required hemodialysis as a salvage therapy. How to cite this article Vadi S, Bajpe S, Kulkarni N. Diabetic Ketoacidosis with Lower-than-anticipated Glucose Levels, and Recalcitrant Metabolic Acidosis Requiring Rescue Hemodialysis in a Patient of COVID-19 Infection. Indian J Crit Care Med 2022;26(6):752–754.
Dry Needling Produces Mild Injuries Irrespective to Muscle Stiffness and Tension in Ex Vivo Mice Muscles

Numerous studies have suggested that the myofascial trigger points are responsible for most of the myofascial pain syndrome, so it seems reasonable that its destruction is a good therapeutic solution. The effectiveness of dry needling (DN) has been confirmed in muscles with myofascial trigger points, hypertonicity, and spasticity. The objective of this study is to analyze the need of repetitive punctures on muscles in different situations. The levator auris longus (LAL) muscle and gastrocnemius muscle from adult male Swiss mice were dissected and maintained alive, while being submerged in an oxygenated Ringer's solution. DN was evaluated under four animal models, mimicking the human condition: normal healthy muscles, muscle fibers with contraction knots, muscles submerged in a depolarizing Ringer solution (KCl-CaCl2), and muscles submerged in Ringer solution with formalin. Thereafter, samples were evaluated with optical microscopy (LAL) and scanning electron microscopy (gastrocnemius). Healthy muscles allowed the penetration of needles between fibers with minimal injuries. In muscles with contraction knots, the needle separated many muscle fibers, and several others were injured, while blood vessels and intramuscular nerves were mostly not injured. Muscles submerged in a depolarizing solution inducing sustained contraction showed more injured muscular fibers and several muscle fibers separated by the needle. Finally, the muscles submerged in Ringer solution with formalin showed a few number of injured muscular fibers and abundant muscle fibers separated by the needle. Scanning electron microscopy images confirm the optical analyses. In summary, dry needling is a technique that causes mild injury irrespective of the muscle tone.
Manufacturing artificial bone allografts: a perspective

ABSTRACT Bone grafts have traditionally come from four sources: the patients’ own tissue (autograft), tissue from a living or cadaveric human donor (allograft), animal donors (xenograft) and synthetic artificial biomaterials (ceramics, cement, polymers, and metal). However, all of these have advantages and drawbacks. The most commercially successful bone grafts so far are allografts, which hold 57% of the current bone graft market; however, disease transmission and scarcity are still significant drawbacks limiting their use. Tissue-engineered grafts have great potential, in which human stem cells and synthetical biomaterials are combined to produce bone-like tissue in vitro, but this is yet to be approved for widespread clinical practice. It is hypothesised that artificial bone allografts can be mass-manufactured to replace conventional bone allografts through refined bone tissue engineering prior to decellularisation. This review article aims to review current literature on (1) conventional bone allograft preparation; (2) bone tissue engineering including the use of synthetic biomaterials as bone graft substitute scaffolds, combined with osteogenic stem cells in vitro; (3) potential artificial allograft manufacturing processes, including mass production of engineered bone tissue, osteogenic enhancement, decellularisation, sterilisation and safety assurance for regulatory approval. From these assessments, a practical route map for mass production of artificial allografts for clinical use is proposed.
History, progress and future challenges of artificial blood vessels: a narrative review

ABSTRACT Cardiovascular disease serves as the leading cause of death worldwide, with stenosis, occlusion, or severe dysfunction of blood vessels being its pathophysiological mechanism. Vascular replacement is the preferred surgical option for treating obstructed vascular structures. Due to the limited availability of healthy autologous vessels as well as the incidence of postoperative complications, there is an increasing demand for artificial blood vessels. From synthetic to natural, or a mixture of these components, numerous materials have been used to prepare artificial vascular grafts. Although synthetic grafts are more appropriate for use in medium to large-diameter vessels, they fail when replacing small-diameter vessels. Tissue-engineered vascular grafts are very likely to be an ideal alternative to autologous grafts in small-diameter vessels and are worthy of further investigation. However, a multitude of problems remain that must be resolved before they can be used in biomedical applications. Accordingly, this review attempts to describe these problems and provide a discussion of the generation of artificial blood vessels. In addition, we deliberate on current state-of-the-art technologies for creating artificial blood vessels, including advances in materials, fabrication techniques, various methods of surface modification, as well as preclinical and clinical applications. Furthermore, the evaluation of grafts both in vivo and in vitro, mechanical properties, challenges, and directions for further research are also discussed.
Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5–15 years to 1–2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.
Optimisation of a Mouse Model of Cerebral Ischemia-Reperfusion to Address Issues of Survival and Model Reproducibility and Consistency

Middle cerebral artery occlusion (MCAO) induced brain ischemia-reperfusion model in Mice is essential for understanding the pathology of stroke and investigating potential treatments, in which a variety of methods may be employed to block the middle cerebral artery (MCA), the most common being through the insertion of a monofilament; however, in vivo ischemia-reperfusion models are associated, particularly in mice, with high variability in lesion volume and high mortality. We aimed to optimise a mouse model of cerebral ischemia-reperfusion, addressing issues of mouse survival, model reproducibility, and consistency. The model was optimised in two ways: first, insert the monofilament directly through the internal carotid artery rather than through the external or common carotid artery, and second, by extending the length of the silicone coating on the monofilament, the length of the silicone coating enables embolization of the beginning of the middle cerebral artery, as well as the anterior cerebral artery and part of the posterior communicating artery. Results: We assessed various parameters, including blood flow changes in the middle cerebral artery, stability of the infarct area, correlation between infarct volume percentages and neurological deficit scores, mortality, weight changes, and wellbeing. We found that optimisation of the surgical procedure may improve mouse wellbeing and reduce mortality, through reduced weight loss and decrease the variability. In conclusion, we suggest that the optimisation of the model is superior for the study of both short and long-term outcomes of ischemic stroke. These results have considerable implications on stroke model selection for researchers.
Systematic review of women's knowledge, attitude, and practice towards breast cancer

The present study aims to systematically review the women's knowledge, attitude, and practice (KAP) of breast cancer (BC) screening methods to get enough information for policymakers to orient the screening strategies. All English KAP studies on BC screening methods in five databases up to January 2021 were included. The quality of the final articles was assessed using the STROBE checklist. The qualitative synthesis was performed. Out of 5574 retrieved articles, 28 were included. About 64% of the articles were of high quality. Overall, there were poor knowledge, negative attitude, and low practice between women. The educational programs and cultural plans can encourage regular screening. Women's excessive optimism to their BC risk should be eliminated by focusing on the risk of the disease, more.
Game Engine Comparative Anatomy

Video game developers use game engines as a tool to manage complex aspects of game development. While engines play a big role in the success of games, to the best of our knowledge, they are often devel-oped in isolation, in a closed-source manner, without architectural dis-cussions, comparison, and collaboration among projects. In this work in progress, we compare the call graphs of two open-source engines: Godot 3.4. 4 and Urho3D 1.8. While static analysis tools could provide us with a general picture without precise call graph paths, the use of a proﬁler such as Callgrind allows us to also view the call order and frequency. These graphs give us insight into the engines’ designs. We showed that, by using Callgrind, we can obtain a high-level view of an engine’s architecture, which can be used to understand it. In future work, we intend to apply both dynamic and static analysis to other open-source engines to understand architectural patterns and their impact on aspects such as performance and maintenance.
Bayesian Tensor Factorized Vector Autoregressive Models for Inferring Granger Causality Patterns from High-Dimensional Multi-subject Panel Neuroimaging Data

Understanding the dynamics of functional brain connectivity patterns using noninvasive neuroimaging techniques is an important focus in human neuroscience. Vector autoregressive (VAR) processes and Granger causality analysis (GCA) have been extensively used for this purpose. While high-resolution multi-subject neuroimaging data are routinely collected now-a-days, the statistics literature on VAR models has remained heavily focused on small-to-moderate dimensional problems and single-subject data. Motivated by these issues, we develop a novel Bayesian random effects panel VAR model for multi-subject high-dimensional neuroimaging data. We begin with a single-subject model that structures the VAR coefficients as a three-way tensor, then reduces the dimensions by applying a Tucker tensor decomposition. A novel sparsity-inducing shrinkage prior allows data-adaptive rank and lag selection. We then extend the approach to a novel random effects model for multi-subject data that carefully avoids the dimensions getting exploded with the number of subjects but also flexibly accommodates subject-specific heterogeneity. We design a Markov chain Monte Carlo algorithm for posterior computation. Finally, GCA with posterior false discovery control is performed on the posterior samples. The method shows excellent empirical performance in simulation experiments. Applied to our motivating functional magnetic resonance imaging study, the approach allows the directional connectivity of human brain networks to be studied in fine detail, revealing meaningful but previously unsubstantiated cortical connectivity patterns.
Identifying Orientation-specific Lipid-protein Fingerprints using Deep Learning

Improved understanding of the relation between the behavior of RAS and RAF proteins and the local lipid environment in the cell membrane is critical for getting insights into the mechanisms underlying cancer formation. In this work, we employ deep learning (DL) to learn this relationship by predicting protein orientational states of RAS and RAS-RAF protein complexes with respect to the lipid membrane based on the lipid densities around the protein domains from coarse-grained (CG) molecular dynamics (MD) simulations. Our DL model can predict six protein states with an overall accuracy of over 80%. The findings of this work offer new insights into how the proteins modulate the lipid environment, which in turn may assist designing novel therapies to regulate such interactions in the mechanisms associated with cancer development.
Probing the initial state of inflation: analytical structure of cosmological correlators

We study the analytic structure of in-in correlation functions in a deSitter background. The aim of this study is to probe the initial conditions for inflation through the features of correlation functions of the field fluctuations, and understand precisely how an in-in correlator responds to particles in the initial state. We emphasize that the choice of vacuua and the corresponding particle interpretation for these fluctuations is flexible, and we clarify the role of this choice at the level of calculations and their diagrammatic interpretation. We consider several possibilities aside from the standard Bunch Davies (BD) vacuum prescription for the initial state, and trace the change in pole structure as one begins adding excitations; starting from just a single particle, to highly excited states and special cases such as a coherent state. We also go beyond the pole structure of the bispectrum, and calculate the 4 point classical and quantum correlators. We illustrate — with the example of coherent states — the subtleties in concluding a Bunch Davies initial state from the absence of physical poles in the bispectrum, which is interesting in light of some recent literature. Initial states with a finite number of excitations are plagued with disconnected diagrams isolated in phase space, and we highlight their implications on the observation of these signals, and how the situation changes as one begins to excite more and more particles. We also comment about the implications of various initial conditions on the squeezed limit of the bispectrum. These new pole structures are a direct consequence of mixing of positive and negative frequency modes which is a characteristic of curved spacetimes; in particular, we see in detail how particles in an initial state replicate mode mixing structures. This study aims to clarify the missing details that link quantum and classical initial conditions, and sharpen our understanding of in-in correlators in inflation.
MorphoActivation: Generalizing ReLU activation function by mathematical morphology

This paper analyses both nonlinear activation functions and spatial max-pooling for Deep Convolutional Neural Networks (DCNNs) by means of the algebraic basis of mathematical morphology. Additionally, a general family of activation functions is proposed by considering both max-pooling and nonlinear operators in the context of morphological representations. Experimental section validates the goodness of our approach on classical benchmarks for supervised learning by DCNN.
Maximum Relative Divergence Principle for Grading Functions on Power Sets

The concept of Relative Divergence of one Grading Function from another on a given set is extended here from totally ordered chains to subset inclusion-ordered power set of a ﬁnite event space. In particular, using general (non-additive) measures as grading functions, Shannon’s Entropy concept is extended to such sets. Based on that, Maximum Relative Divergence Principle is introduced as a tool for determining the ”most reasonable” grading function and used in applications where that function is supposed to be ”element-additive” or ”cardinality-dependent” under application-speciﬁc linear constraints.
On the Existence and Stability of Modified Maxwell Steklov Eigenvalues

In recent decades qualitative inverse scattering methods with eigenvalues as target signatures received much attention. To understand those methods a knowledge on the properties of the related eigenvalue problems is essential. However, even the existence of eigenvalues for such (nonselfadjoint) problems is a challenging question and existing results for absorbing media are usually established under unrealistic assumptions or a smoothing of the eigenvalue problem. We present a technique to prove the existence of infinitely many eigenvalues for such problems under realistic assumptions. In particular we consider the class of scalar and modified Maxwell nonselfadjoint Steklov eigenvalue problems. In addition, we present stability results for the eigenvalues with respect to changes in the material parameters. In distinction to existing results the analysis of the present article requires only minimal regularity assumptions. By that we mean that the regularity of the domain is not required to be better than Lipschitz, and the material coefficients are only assumed to be piece-wise $W^{1,\infty}$. Also the stability estimates for eigenvalues are obtained solely in $L^p$-norms ($p<\infty$) of the material perturbations.
Subgraph Frequency Distribution Estimation using Graph Neural Networks

Small subgraphs (graphlets) are important features to describe fundamental units of a large network. The calculation of the subgraph frequency distributions has a wide application in multiple domains including biology and engineering. Unfortunately due to the inherent complexity of this task, most of the existing methods are computationally intensive and inefficient. In this work, we propose GNNS, a novel representational learning framework that utilizes graph neural networks to sample subgraphs efficiently for estimating their frequency distribution. Our framework includes an inference model and a generative model that learns hierarchical embeddings of nodes, subgraphs, and graph types. With the learned model and embeddings, subgraphs are sampled in a highly scalable and parallel way and the frequency distribution estimation is then performed based on these sampled subgraphs. Eventually, our methods achieve comparable accuracy and a significant speedup by three orders of magnitude compared to existing methods.
PR-DARTS: Pruning-Based Differentiable Architecture Search

The deployment of Convolutional Neural Networks (CNNs) on edge devices is hindered by the substantial gap between performance requirements and available processing power. While recent research has made large strides in developing network pruning methods for reducing the computing overhead of CNNs, there remains considerable accuracy loss, especially at high pruning ratios. Questioning that the architectures designed for non-pruned networks might not be effective for pruned networks, we propose to search architectures for pruning methods by defining a new search space and a novel search objective. To improve the generalization of the pruned networks, we propose two novel PrunedConv and PrunedLinear operations. Specifically, these operations mitigate the problem of unstable gradients by regularizing the objective function of the pruned networks. The proposed search objective enables us to train architecture parameters regarding the pruned weight elements. Quantitative analyses demonstrate that our searched architectures outperform those used in the state-of-the-art pruning networks on CIFAR-10 and ImageNet. In terms of hardware effectiveness, PR-DARTS increases MobileNet-v2’s accuracy from 73.44% to 81.35% (+7.91% improvement) and runs 3.87 × faster.