Traumatic Brain Injury as an Independent Predictor of Futility in the Early Resuscitation of Patients in Hemorrhagic Shock.

This review explores the concept of futility timeouts and the use of traumatic brain injury (TBI) as an independent predictor of the futility of resuscitation efforts in severely bleeding trauma patients. The national blood supply shortage has been exacerbated by the lingering influence of the COVID-19 pandemic on the number of blood donors available, as well as by the adoption of balanced hemostatic resuscitation protocols (such as the increasing use of 1:1:1 packed red blood cells, plasma, and platelets) with and without early whole blood resuscitation. This has underscored the urgent need for reliable predictors of futile resuscitation (FR). As a result, clinical, radiologic, and laboratory bedside markers have emerged which can accurately predict FR in patients with severe trauma-induced hemorrhage, such as the Suspension of Transfusion and Other Procedures (STOP) criteria. However, the STOP criteria do not include markers for TBI severity or transfusion cut points despite these patients requiring large quantities of blood components in the STOP criteria validation cohort. Yet, guidelines for neuroprognosticating patients with TBI can require up to 72 h, which makes them less useful in the minutes and hours following initial presentation. We examine the impact of TBI on bleeding trauma patients, with a focus on those with coagulopathies associated with TBI. This review categorizes TBI into isolated TBI (iTBI), hemorrhagic isolated TBI (hiTBI), and polytraumatic TBI (ptTBI). Through an analysis of bedside parameters (such as the proposed STOP criteria), coagulation assays, markers for TBI severity, and transfusion cut points as markers of futilty, we suggest amendments to current guidelines and the development of more precise algorithms that incorporate prognostic indicators of severe TBI as an independent parameter for the early prediction of FR so as to optimize blood product allocation.

LMCrot: an enhanced protein crotonylation site predictor by leveraging an interpretable window-level embedding from a transformer-based protein language model.

MOTIVATION: Recent advancements in natural language processing have highlighted the effectiveness of global contextualized representations from protein language models (pLMs) in numerous downstream tasks. Nonetheless, strategies to encode the site-of-interest leveraging pLMs for per-residue prediction tasks, such as crotonylation (Kcr) prediction, remain largely uncharted. RESULTS: Herein, we adopt a range of approaches for utilizing pLMs by experimenting with different input sequence types (full-length protein sequence versus window sequence), assessing the implications of utilizing per-residue embedding of the site-of-interest as well as embeddings of window residues centered around it. Building upon these insights, we developed a novel residual ConvBiLSTM network designed to process window-level embeddings of the site-of-interest generated by the ProtT5-XL-UniRef50 pLM using full-length sequences as input. This model, termed T5ResConvBiLSTM, surpasses existing state-of-the-art Kcr predictors in performance across three diverse datasets. To validate our approach of utilizing full sequence-based window-level embeddings, we also delved into the interpretability of ProtT5-derived embedding tensors in two ways: firstly, by scrutinizing the attention weights obtained from the transformer's encoder block; and secondly, by computing SHAP values for these tensors, providing a model-agnostic interpretation of the prediction results. Additionally, we enhance the latent representation of ProtT5 by incorporating two additional local representations, one derived from amino acid properties and the other from supervised embedding layer, through an intermediate fusion stacked generalization approach, using an n-mer window sequence (or, peptide/fragment). The resultant stacked model, dubbed LMCrot, exhibits a more pronounced improvement in predictive performance across the tested datasets. AVAILABILITY AND IMPLEMENTATION: LMCrot is publicly available at https://github.com/KCLabMTU/LMCrot.

Integrating Embeddings from Multiple Protein Language Models to Improve Protein O-GlcNAc Site Prediction.

O-linked ß-N-acetylglucosamine (O-GlcNAc) is a distinct monosaccharide modification of serine (S) or threonine (T) residues of nucleocytoplasmic and mitochondrial proteins. O-GlcNAc modification (i.e., O-GlcNAcylation) is involved in the regulation of diverse cellular processes, including transcription, epigenetic modifications, and cell signaling. Despite the great progress in experimentally mapping O-GlcNAc sites, there is an unmet need to develop robust prediction tools that can effectively locate the presence of O-GlcNAc sites in protein sequences of interest. In this work, we performed a comprehensive evaluation of a framework for prediction of protein O-GlcNAc sites using embeddings from pre-trained protein language models. In particular, we compared the performance of three protein sequence-based large protein language models (pLMs), Ankh, ESM-2, and ProtT5, for prediction of O-GlcNAc sites and also evaluated various ensemble strategies to integrate embeddings from these protein language models. Upon investigation, the decision-level fusion approach that integrates the decisions of the three embedding models, which we call LM-OGlcNAc-Site, outperformed the models trained on these individual language models as well as other fusion approaches and other existing predictors in almost all of the parameters evaluated. The precise prediction of O-GlcNAc sites will facilitate the probing of O-GlcNAc site-specific functions of proteins in physiology and diseases. Moreover, these findings also indicate the effectiveness of combined uses of multiple protein language models in post-translational modification prediction and open exciting avenues for further research and exploration in other protein downstream tasks. LM-OGlcNAc-Site's web server and source code are publicly available to the community.

Type B Lactic Acidosis in a Patient with Mantle Cell Lymphoma.

Type B lactic acidosis is an uncommon medical emergency in which acid production overwhelms hepatic clearance. This specific etiology of lactic acidosis occurs without organ hypoperfusion and has been most commonly described in patients with hematologic malignancies but also in patients with solid tumors. The mechanism by which cancer cells switch their glucose metabolism toward increasingly anaerobic glycolytic phenotypes has been described as the "Warburg effect." Without treating the underlying malignancy, the prognosis for patients diagnosed with malignancy-related type B lactic acidosis is extremely poor. Here, we present a case of a 66-year-old male who was diagnosed with type B lactic acidosis secondary to mantle cell lymphoma. Bicarbonate drip was started to correct the lactic acidosis. The patient was also immediately treated with rituximab chemotherapy combined with rasburicase to avoid the hyperuricemia associated with tumor lysis syndrome. He responded to the early treatment and was discharged with normal renal function. Type B lactic acidosis secondary to hematologic malignancy is important to recognize. In order to successfully treat this syndrome, early diagnosis and simultaneous treatment of the imbalance of lactic acid levels and the underlying malignancy are necessary.

LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model.

Phosphorylation is one of the most important post-translational modifications and plays a pivotal role in various cellular processes. Although there exist several computational tools to predict phosphorylation sites, existing tools have not yet harnessed the knowledge distilled by pretrained protein language models. Herein, we present a novel deep learning-based approach called LMPhosSite for the general phosphorylation site prediction that integrates embeddings from the local window sequence and the contextualized embedding obtained using global (overall) protein sequence from a pretrained protein language model to improve the prediction performance. Thus, the LMPhosSite consists of two base-models: one for capturing effective local representation and the other for capturing global per-residue contextualized embedding from a pretrained protein language model. The output of these base-models is integrated using a score-level fusion approach. LMPhosSite achieves a precision, recall, Matthew's correlation coefficient, and F1-score of 38.78%, 67.12%, 0.390, and 49.15%, for the combined serine and threonine independent test data set and 34.90%, 62.03%, 0.298, and 44.67%, respectively, for the tyrosine independent test data set, which is better than the compared approaches. These results demonstrate that LMPhosSite is a robust computational tool for the prediction of the general phosphorylation sites in proteins.

Alpha-lipoic acid improved anemia, erythropoietin resistance, maintained glycemic control, and reduced cardiovascular risk in diabetic patients on hemodialysis: a multi-center prospective randomized controlled study.

OBJECTIVE: To investigate the impact of alpha-lipoic acid (ALA) on inflammation, oxidative stress, anemia, and glycemic parameters and their association with cardiovascular risk in diabetic patients on hemodialysis. PATIENTS AND METHODS: In this multi-center, randomized, controlled study, 60 diabetic patients on hemodialysis were randomized into control group (n=30) which received Epoetin-alpha plus insulin therapy, and alpha-lipoic acid group (n=30) which received the same treatment plus alpha-lipoic acid (ALA) 600 mg once daily. Serum levels of high sensitivity C-reactive protein (hs-CRP), tumor necrosis factor-alpha (TNF-α), 8-hydroxy-2'-deoxyguanosine (8-OHdG), creatinine, urea, blood urea nitrogen (BUN), hemoglobin (Hb), iron parameters, fasting blood glucose (FBG), glycated hemoglobin (HbA1c), and fructosamine were measured at baseline and six months after intervention. The ankle-brachial index (ABI) was used to evaluate the clinical outcome. Erythropoietin resistance index (ERI), the weekly cost of Epoetin-alpha doses, and the total cost were calculated. RESULTS: The two groups were statistically similar at baseline. After the intervention, as compared to the control group, ALA group showed significant reductions in serum levels of hs-CRP, TNF-α, 8-OHdG (p<0.001), urea, and BUN (p=0.029) with significant elevations in Hb concentration (p<0.001), serum iron (p=0.037) and transferrin saturation (p<0.001). ALA group showed a significant decline in FBG (p=0.004), HbA1c (p<0.001), fructosamine (p=0.005), ERI (p<0.001), weekly doses, and the weekly cost of Epoetin-alpha, and the total cost (p<0.001). ALA provided a cardio-protective effect, whereas the percentage of patients with acceptable ABI (0.9-1) was significantly higher in ALA group than in the control group (p=0.024), and those with abnormally low ABI (<0.9) were lower in the ALA group. CONCLUSIONS: Due to its efficacy and safety, alpha-lipoic acid represents a pharmaco-economic supplement for diabetic patients on hemodialysis. Further trials are needed for complete evaluation of ALA effects.

Use of Thromboelastography and Rotational Thromboelastometry in Otolaryngology: A Narrative Review.

In the field of otolaryngology-head and neck surgery (ENT), coagulopathies present unique diagnostic and therapeutic challenges. In both hyper- and hypocoagulable patients, management of coagulopathies requires intricate attention to the nature of hemostatic competence. Common coagulation tests (CCTs) offer only a snapshot of hemostatic competence and do not provide a clear insight into the patient's real-time hemostatic condition. Viscoelastic tests (VETs) offer a holistic and concurrent picture of the coagulation process. Although VETs have found prominent utilization in hepatic transplants, obstetrics, and emergent surgical settings, they have not been fully adopted in the realm of otolaryngology. The objective of this manuscript is to provide an overview of the literature evaluating the current utilization and possible future uses of VETs in the field of otolaryngology. The authors performed a comprehensive literature search of the utilization of VETs in otolaryngology and identified applicable studies that included descriptions of viscoelastic testing. Twenty-five studies were identified in this search, spanning topics from head and neck oncology, microvascular free flap reconstruction, obstructive sleep apnea, adenotonsillectomy, facial trauma, and epistaxis. The applicability of VETs has been demonstrated in head and neck oncology and microvascular free flap management, although their pervasiveness in practice is limited. Underutilization of VETs in the field of otolaryngology may be due to a lack of familiarity of the tests amongst practitioners. Instead, most otolaryngologists continue to rely on CCTs, including PT, PTT, INR, CBC, fibrinogen levels, and thrombin time. Learning to perform, interpret, and skillfully employ VETs in clinical and operative practice can greatly improve the management of coagulopathic patients who are at increased risk of bleeding or thrombosis.

Viscoelastic Hemostatic Assays: A Primer on Legacy and New Generation Devices.

Viscoelastic hemostatic assay (VHAs) are whole blood point-of-care tests that have become an essential method for assaying hemostatic competence in liver transplantation, cardiac surgery, and most recently, trauma surgery involving hemorrhagic shock. It has taken more than three-quarters of a century of research and clinical application for this technology to become mainstream in these three clinical areas. Within the last decade, the cup and pin legacy devices, such as thromboelastography (TEG® 5000) and rotational thromboelastometry (ROTEM® delta), have been supplanted not only by cartridge systems (TEG® 6S and ROTEM® sigma), but also by more portable point-of-care bedside testing iterations of these legacy devices (e.g., Sonoclot®, Quantra®, and ClotPro®). Here, the legacy and new generation VHAs are compared on the basis of their unique hemostatic parameters that define contributions of coagulation factors, fibrinogen/fibrin, platelets, and clot lysis as related to the lifespan of a clot. In conclusion, we offer a brief discussion on the meteoric adoption of VHAs across the medical and surgical specialties to address COVID-19-associated coagulopathy.

Adaptive dynamic feedback control of parallel robots with unknown kinematic and dynamic properties.

Uncertainties in dynamic and kinematic parameters are unavoidable components in the control of robotic manipulators. Although calibration is a well-known method to reject this issue, it is time-consuming, some parameters may be altered slowly, and therefore, it is not applicable to some special cases such as deployable cable-driven robots. This paper addresses an adaptive dynamic feedback controller in which the adaptation laws together with new states could remedy these shortcomings and may be appropriately used in deployable cable-driven robots. For this purpose, the Jacobian matrix and its determinant are expressed in regressor form. Additionally, a non-singular sliding surface is considered for the trajectory tracking error. The fast finite-time feasible trajectory tracking is ensured by Lyapunov direct method using an appropriate design of adaptation laws of unknown parameters together with dynamical matrices in the presence of external disturbance. A 4RPR (revolute-prismatic-revolute) redundant rigid body and a fully actuated 3-DOF cable-driven robot are considered to verify the proposed method and also compare the results with state-of-art by simulation and experiment.

Applications of Haptic Technology, Virtual Reality, and Artificial Intelligence in Medical Training During the COVID-19 Pandemic.

This paper examines how haptic technology, virtual reality, and artificial intelligence help to reduce the physical contact in medical training during the COVID-19 Pandemic. Notably, any mistake made by the trainees during the education process might lead to undesired complications for the patient. Therefore, training of the medical skills to the trainees have always been a challenging issue for the expert surgeons, and this is even more challenging in pandemics. The current method of surgery training needs the novice surgeons to attend some courses, watch some procedure, and conduct their initial operations under the direct supervision of an expert surgeon. Owing to the requirement of physical contact in this method of medical training, the involved people including the novice and expert surgeons confront a potential risk of infection to the virus. This survey paper reviews recent technological breakthroughs along with new areas in which assistive technologies might provide a viable solution to reduce the physical contact in the medical institutes during the COVID-19 pandemic and similar crises.

A Review of the Neutrophil Extracellular Traps (NETs) from Cow, Sheep and Goat Models.

This review provides insight into the importance of understanding NETosis in cows, sheep, and goats in light of the importance to their health, welfare and use as animal models. Neutrophils are essential to innate immunity, pathogen infection, and inflammatory diseases. The relevance of NETosis as a conserved innate immune response mechanism and the translational implications for public health are presented. Increased understanding of NETosis in ruminants will contribute to the prediction of pathologies and design of strategic interventions targeting NETs. This will help to control pathogens such as coronaviruses and inflammatory diseases such as mastitis that impact all mammals, including humans. Definition of unique attributes of NETosis in ruminants, in comparison to what has been observed in humans, has significant translational implications for one health and global food security, and thus warrants further study.

Easy way to treat female stress urinary incontinence with bulking agents (with video).

Since the recent controversy about vaginal mesh implants, surgeons should use safe and effective devices and procedures to treat female stress urinary incontinence (SUI). We propose to describe the noninvasive and ambulatory technique of the urethral bulking procedure. Bulking agents are a simple, minimally invasive technique that can be offered in the treatment of female SUI.

RF-NR: Random Forest Based Approach for Improved Classification of Nuclear Receptors.

The Nuclear Receptor (NR) superfamily plays an important role in key biological, developmental, and physiological processes. Developing a method for the classification of NR proteins is an important step towards understanding the structure and functions of the newly discovered NR protein. The recent studies on NR classification are either unable to achieve optimum accuracy or are not designed for all the known NR subfamilies. In this study, we developed RF-NR, which is a Random Forest based approach for improved classification of nuclear receptors. The RF-NR can predict whether a query protein sequence belongs to one of the eight NR subfamilies or it is a non-NR sequence. The RF-NR uses spectrum-like features namely: Amino Acid Composition, Di-peptide Composition, and Tripeptide Composition. Benchmarking on two independent datasets with varying sequence redundancy reduction criteria, the RF-NR achieves better (or comparable) accuracy than other existing methods. The added advantage of our approach is that we can also obtain biological insights about the important features that are required to classify NR subfamilies. RF-NR is freely available at http://bcb.ncat.edu/RF_NR.

Enantioselective synthesis of (S)-naproxen using immobilized lipase on chitosan beads.

S-naproxen by enantioselective hydrolysis of racemic naproxen methyl ester was produced using immobilized lipase. The lipase enzyme was immobilized on chitosan beads, activated chitosan beads by glutaraldehyde, and Amberlite XAD7. In order to find an appropriate support for the hydrolysis reaction of racemic naproxen methyl ester, the conversion and enantioselectivity for all carriers were compared. In addition, effects of the volumetric ratio of two phases in different organic solvents, addition of cosolvent and surfactant, optimum pH and temperature, reusability, and inhibitory effect of methanol were investigated. The optimum volumetric ratio of two phases was defined as 3:2 of aqueous phase to organic phase. Various water miscible and water immiscible solvents were examined. Finally, isooctane was chosen as an organic solvent, while 2-ethoxyethanol was added as a cosolvent in the organic phase of the reaction mixture. The optimum reaction conditions were determined to be 35 °C, pH 7, and 24 h. Addition of Tween-80 in the organic phase increased the accessibility of immobilized enzyme to the reactant. The optimum organic phase compositions using a volumetric ratio of 2-ethoxyethanol, isooctane and Tween-80 were 3:7 and 0.1% (v/v/v), respectively. The best conversion and enantioselectivity of immobilized enzyme using chitosan beads activated by glutaraldehyde were 0.45 and 185, respectively.

CNN-BLPred: a Convolutional neural network based predictor for ß-Lactamases (BL) and their classes.

BACKGROUND: The ß-Lactamase (BL) enzyme family is an important class of enzymes that plays a key role in bacterial resistance to antibiotics. As the newly identified number of BL enzymes is increasing daily, it is imperative to develop a computational tool to classify the newly identified BL enzymes into one of its classes. There are two types of classification of BL enzymes: Molecular Classification and Functional Classification. Existing computational methods only address Molecular Classification and the performance of these existing methods is unsatisfactory. RESULTS: We addressed the unsatisfactory performance of the existing methods by implementing a Deep Learning approach called Convolutional Neural Network (CNN). We developed CNN-BLPred, an approach for the classification of BL proteins. The CNN-BLPred uses Gradient Boosted Feature Selection (GBFS) in order to select the ideal feature set for each BL classification. Based on the rigorous benchmarking of CCN-BLPred using both leave-one-out cross-validation and independent test sets, CCN-BLPred performed better than the other existing algorithms. Compared with other architectures of CNN, Recurrent Neural Network, and Random Forest, the simple CNN architecture with only one convolutional layer performs the best. After feature extraction, we were able to remove ~95% of the 10,912 features using Gradient Boosted Trees. During 10-fold cross validation, we increased the accuracy of the classic BL predictions by 7%. We also increased the accuracy of Class A, Class B, Class C, and Class D performance by an average of 25.64%. The independent test results followed a similar trend. CONCLUSIONS: We implemented a deep learning algorithm known as Convolutional Neural Network (CNN) to develop a classifier for BL classification. Combined with feature selection on an exhaustive feature set and using balancing method such as Random Oversampling (ROS), Random Undersampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE), CNN-BLPred performs significantly better than existing algorithms for BL classification.

RF-Hydroxysite: a random forest based predictor for hydroxylation sites.

Protein hydroxylation is an emerging posttranslational modification involved in both normal cellular processes and a growing number of pathological states, including several cancers. Protein hydroxylation is mediated by members of the hydroxylase family of enzymes, which catalyze the conversion of an alkyne group at select lysine or proline residues on their target substrates to a hydroxyl. Traditionally, hydroxylation has been identified using expensive and time-consuming experimental methods, such as tandem mass spectrometry. Therefore, to facilitate identification of putative hydroxylation sites and to complement existing experimental approaches, computational methods designed to predict the hydroxylation sites in protein sequences have recently been developed. Building on these efforts, we have developed a new method, termed RF-hydroxysite, that uses random forest to identify putative hydroxylysine and hydroxyproline residues in proteins using only the primary amino acid sequence as input. RF-Hydroxysite integrates features previously shown to contribute to hydroxylation site prediction with several new features that we found to augment the performance remarkably. These include features that capture physicochemical, structural, sequence-order and evolutionary information from the protein sequences. The features used in the final model were selected based on their contribution to the prediction. Physicochemical information was found to contribute the most to the model. The present study also sheds light on the contribution of evolutionary, sequence order, and protein disordered region information to hydroxylation site prediction. The web server for RF-hydroxysite is available online at .

RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest.

Protein phosphorylation is one of the most widespread regulatory mechanisms in eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an important problem in the field of bioinformatics. Here, we report a new method, termed Random Forest-based Phosphosite predictor 2.0 (RF-Phos 2.0), to predict phosphorylation sites given only the primary amino acid sequence of a protein as input. RF-Phos 2.0, which uses random forest with sequence and structural features, is able to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation and an independent dataset, RF-Phos 2.0 compares favorably to other popular mammalian phosphosite prediction methods, such as PhosphoSVM, GPS2.1, and Musite.

[Preliminary results for ambulatory surgery for vaginal prolapse]. / Résultats préliminaires de la prise en charge ambulatoire du prolapsus vaginal.

OBJECTIVE, PATIENTS AND METHODS: From a study of 65 patients with vaginal prolapse, we studied 56 patients treated by the Elevate technic during an ambulatory hospitalisation. RESULTS: Seven patients failed to the ambulatory protocol and needed to stay. Three of them for urinary retention. Eighty-eight percent of the patients were satisfied by this protocol. CONCLUSION: This study shows the feasibility of prolapse surgery in an ambulatory settings.

[Tissue effect of Greenlight(®) laser on uterine fibroids]. / Évaluation des effets tissulaires du laser Greenlight(®) pour la prise en charge des fibromes utérins.

OBJECTIVE: We evaluated the efficacy and the safety parameters for Greenlight(®) laser to vaporise myoma. MATERIALS AND METHODS: We studied 6 utero after hysterectomies for myoma and used the Greenlight(®) laser with different level of power and duration on myoma and normal myometer. We compared the tissue effect with the monopolar and bipolar resection. We studied the tissue effect by histological exam. RESULTS: The Greenlight(®) laser is able to vaporize myoma with a low side effect on normal myometer of 85µm (199µm with bipolar and 254µm with monopolar). CONCLUSION: The laser Greenlight(®) is efficient to vaporize myoma in vitro and presents some safety parameter. This study could lead to a clinical prospective study to demonstrate its ability to treat symptomatic myoma.