Potential for and challenges of menstrual blood as a non-invasive diagnostic specimen: current status and future directions.

Menstrual blood, which is often discarded as a waste product, has emerged as a valuable source of health information. The components of menstrual blood, such as endometrial cells, immune cells, proteins, and microbial signatures, provide insights into health. Studies have shown encouraging results for using menstrual blood to diagnose a variety of conditions, including hormonal imbalances, cervical cancer, endometriosis, chlamydia, diabetes, and other endocrine disorders. This review examines the potential of menstrual blood as a non-invasive diagnostic specimen, exploring its composition, promising applications, and recent advances. This review also discusses challenges to utilizing menstrual blood testing, including ethical considerations, the lack of standardized collection protocols, extensive validation studies, and the societal stigma around menstruation. Overcoming these challenges will open new avenues for personalized medicine and revolutionize healthcare for individuals who menstruate.

Label-free, non-contact determination of resting membrane potential using dielectrophoresis.

Measurement of cellular resting membrane potential (RMP) is important in understanding ion channels and their role in regulation of cell function across a wide range of cell types. However, methods available for the measurement of RMP (including patch clamp, microelectrodes, and potential-sensitive fluorophores) are expensive, slow, open to operator bias, and often result in cell destruction. We present non-contact, label-free membrane potential estimation which uses dielectrophoresis to determine the cytoplasm conductivity slope as a function of medium conductivity. By comparing this to patch clamp data available in the literature, we have demonstratet the accuracy of this approach using seven different cell types, including primary suspension cells (red blood cells, platelets), cultured suspension cells (THP-1), primary adherent cells (chondrocytes, human umbilical mesenchymal stem cells), and adherent (HeLa) and suspension (Jurkat) cancer cell lines. Analysis of the effect of ion channel inhibitors suggests the effects of pharmaceutical agents (TEA on HeLa; DMSO and neuraminidase on red blood cells) can also be measured. Comparison with published values of membrane potential suggest that the differences between our estimates and values recorded by patch clamp are accurate to within published margins of error. The method is low-cost, non-destructive, operator-independent and label-free, and has previously been shown to allow cells to be recovered after measurement.

Efficient state of charge estimation of lithium-ion batteries in electric vehicles using evolutionary intelligence-assisted GLA-CNN-Bi-LSTM deep learning model.

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN-Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

Bibliometric Analysis of Predictors of Altmetric Attention Scores in Orthopedic Research: Investigating Online Visibility.

BACKGROUND: Altmetric Attention Score (AAS) captures online attention received by a research article in addition to traditional bibliometrics. We present a comprehensive bibliometric analysis of high AAS articles and identify predictors of AAS in orthopedics. MATERIALS AND METHODS: The top 30 articles with highest AAS were selected from orthopedic journals using the Dimensions App. Multilevel mixed-effects linear regression was used to address clustering in articles from the same journal, with journals as the leveling variable. RESULTS: A total of 750 articles from 25 journals were included. In the final multivariable model, the funding source (none, industry, government, foundation, university, or multiple), findings (positive, negative, neutral, or not applicable), and the journal's impact factor were significant at P<.05. CONCLUSION: Predictors of AAS are similar to predictors of traditional bibliometrics. Future studies need prospective dynamic data to further elucidate the AAS. [Orthopedics. 20XX;4X(X):XXX-XXX.].

Open-source design of low-cost sensorised elastic actuators for collaborative prosthetics and orthotics.

Collaborative robots, or cobots, have become popular due to their ability to safely operate alongside humans in shared environments. These robots use compliant actuators as a key design element to prevent damage during unintended collisions. In prosthetic and orthotic applications, compliant actuators are crucial for ensuring user safety and comfort. However, most compliant cobots for these applications are excessively expensive and complex to construct. Our study introduces an innovative, cost-effective, and sensorised elastic actuator design tailored for prosthetics and orthotics. The design uses a modular approach and leverages 3D printing technology for rapid customisation, enabling efficient and affordable fabrication. Both hardware and software components are open-source, facilitating unrestricted access for students, researchers, and practitioners. Our design supports impedance and admittance control techniques, enhancing the system's capabilities. Validation results show a standard deviation of 9.67 Nm between calculated and measured torque in impedance control and 0.2563 radians between calculated and measured angles in admittance control. This allows for improved adaptability to varying operational requirements in prosthetics and orthotics. By introducing this educational framework encompassing a low-cost, sensorised elastic actuator design, we aim to address the need for accessible solutions in the field of collaborative robotics for prosthetics and orthotics.

Navigating a Paradigm Shift Venetoclax Treatment Redefines Landscape of Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease characterized by the accumulation of malignant myeloid progenitor hematopoietic cells in the bone marrow and peripheral blood. Recent studies have shown promising results with the use of small molecule inhibitors and targeted therapy in the treatment of patients with AML. One such molecule is venetoclax, which has been approved in AML by the FDA in combination with hypomethylating agents or low-dose cytarabine. We thoroughly searched electronic literature related to venetoclax and its role in AML, using databases such as MEDLINE, PubMed, Google Scholar, and PsychInfo, through April 2024. We applied population, intervention, comparison, and outcome criteria, specifically focusing on studies with a population using venetoclax from review articles and clinical trials. All selected studies were required to be in English, and any study that did not involve the use of venetoclax was excluded. A meticulous literature review was conducted to consolidate the current knowledge and new combination therapies on AML. In our review article, we focused on the latest advances in the treatment of patients with AML. Based on the literature, we recommend that physicians prioritize the use of venetoclax in the management of this deadly disease because it has been shown to significantly impact the course of the disease.

Dynamics of quasi-periodic, bifurcation, sensitivity and three-wave solutions for (n + 1)-dimensional generalized Kadomtsev-Petviashvili equation.

This study endeavors to examine the dynamics of the generalized Kadomtsev-Petviashvili (gKP) equation in (n + 1) dimensions. Based on the comprehensive three-wave methodology and the Hirota's bilinear technique, the gKP equation is meticulously examined. By means of symbolic computation, a number of three-wave solutions are derived. Applying the Lie symmetry approach to the governing equation enables the determination of symmetry reduction, which aids in the reduction of the dimensionality of the said equation. Using symmetry reduction, we obtain the second order differential equation. By means of applying symmetry reduction, the second order differential equation is derived. The second order differential equation undergoes Galilean transformation to obtain a system of first order differential equations. The present study presents an analysis of bifurcation and sensitivity for a given dynamical system. Additionally, when an external force impacts the underlying dynamic system, its behavior resembles quasi-periodic phenomena. The presence of quasi-periodic patterns are identified using chaos detecting tools. These findings represent a novel contribution to the studied equation and significantly advance our understanding of dynamics in nonlinear wave models.

Isabella Barbour 'Ella' Pirrie (1857-1929): Pioneering contributions to British nursing.

Miss Isabella "Ella" Barbour Pirrie (1857-1929) made substantial contributions to nursing through her work in Belfast and Edinburgh. Born to a notable medical family, Pirrie's inclination toward nursing was influenced by her father's profession. She trained at the Liverpool Royal Infirmary and was mentored by Florence Nightingale, whose guidance shaped her nursing philosophy and practices. Notably, Pirrie's role in the Belfast Union Workhouse Infirmary was transformative; she championed the implementation of standardized nursing uniforms, enhancing the professional standing and recognition of nurses. Her efforts led to the establishment of a formal nursing training program in Belfast, despite facing significant resistance and challenges. In 1894, Pirrie moved to Edinburgh to become the First Matron at the Lady Grisell Baillie Memorial Hospital. Her tenure there was marked by significant advancements in nursing education, including the establishment of a community and district nursing department. By the end of her service, over 140 nurses had been trained, with many pursuing international missions. Despite her resignation in 1914 due to health issues, Pirrie continued her work as the superintendent of the Deaconess Rest Home in Edinburgh until her death in 1929. Her legacy is commemorated by a statue at Belfast City Hospital and a plaque at Greyfriars' Charteris Sanctuary, reflecting her profound impact on nursing education and the professionalization of the field.

Dominique-Jean Larrey (1766-1842): The Founder of the Modern Triage System.

Dominique-Jean Larrey was a prominent French surgeon who rose to fame during the age of the Napoleonic Wars. During his service in the French military, he developed dozens of medical innovations. Most important of all were his improvements to the evacuation of the wounded from the battlefield, triage of the wounded, and rapid surgical intervention. His innovations revolutionized military medicine and are still the basis for modern practice today.

An Unusual Case of Takotsubo Cardiomyopathy with Cardiac Arrest: The Importance of Clinical Context in Implanted Cardioverter Defibrillator Candidacy.

Takotsubo syndrome (TTS), also known as stress-induced cardiomyopathy, is characterized by acute heart failure, reversible left ventricular dysfunction, and other complications such as life-threatening arrhythmias. The management of TTS is challenging due to its unpredictable clinical course and the lack of evidence-based treatment recommendations. In this case report, we present a 71-year-old female who developed TTS with ventricular tachycardia (VT) cardiac arrest following septic shock and an exploratory laparotomy for appendicitis. Despite the presence of VT cardiac arrest and a left ventricular ejection fraction of 30-35%, an implanted cardioverter-defibrillator (ICD) was not indicated due to the rapid and satisfactory recovery of the patient's ventricular function. This case highlights the importance of considering the clinical context and the transient nature of TTS in the decision-making process for ICD candidacy.

Enhancing unmanned ground vehicle performance in SAR operations: integrated gesture-control and deep learning framework for optimised victim detection.

In this study, we address the critical need for enhanced situational awareness and victim detection capabilities in Search and Rescue (SAR) operations amidst disasters. Traditional unmanned ground vehicles (UGVs) often struggle in such chaotic environments due to their limited manoeuvrability and the challenge of distinguishing victims from debris. Recognising these gaps, our research introduces a novel technological framework that integrates advanced gesture-recognition with cutting-edge deep learning for camera-based victim identification, specifically designed to empower UGVs in disaster scenarios. At the core of our methodology is the development and implementation of the Meerkat Optimization Algorithm-Stacked Convolutional Neural Network-Bi-Long Short Term Memory-Gated Recurrent Unit (MOA-SConv-Bi-LSTM-GRU) model, which sets a new benchmark for hand gesture detection with its remarkable performance metrics: accuracy, precision, recall, and F1-score all approximately 0.9866. This model enables intuitive, real-time control of UGVs through hand gestures, allowing for precise navigation in confined and obstacle-ridden spaces, which is vital for effective SAR operations. Furthermore, we leverage the capabilities of the latest YOLOv8 deep learning model, trained on specialised datasets to accurately detect human victims under a wide range of challenging conditions, such as varying occlusions, lighting, and perspectives. Our comprehensive testing in simulated emergency scenarios validates the effectiveness of our integrated approach. The system demonstrated exceptional proficiency in navigating through obstructions and rapidly locating victims, even in environments with visual impairments like smoke, clutter, and poor lighting. Our study not only highlights the critical gaps in current SAR response capabilities but also offers a pioneering solution through a synergistic blend of gesture-based control, deep learning, and purpose-built robotics. The key findings underscore the potential of our integrated technological framework to significantly enhance UGV performance in disaster scenarios, thereby optimising life-saving outcomes when time is of the essence. This research paves the way for future advancements in SAR technology, with the promise of more efficient and reliable rescue operations in the face of disaster.

Effects of Nasal Packing on Patients' Post-operative Vital Signs.

Background Since bilateral nasal packing entails nasal and airway obstruction, this practice consequently leads to oral breathing. The resulting hypoxemia may then negatively impact vital signs, including blood pressure (BP), blood oxygen saturation (SpO2), and heart rate (HR). These systemic effects have a detrimental effect on patients. Objective The objective of this study is to observe the effects of bilateral nasal packing on patients' post-operative vital signs. Materials and methods This prospective study was conducted in the department of otolaryngology - head and neck surgery over a six-month period. The study included 83 post-operative patients with nasal surgery, in which bilateral merocele nasal packing was performed. The patients' pulse oximetry, systolic and diastolic BP, and HR were recorded four times the night before and after surgery. A statistical analysis was performed, and the mean values, standard deviation, and range were calculated. A paired sample t-test was also applied. The results are presented in figures and tables. Results The mean age of the participants was 27.65 ± 10.72 years, and 56 (67.5%) were male. Septoplasty was the most common surgery performed, with 63 participants having undergone this procedure (75.9%). When the pre-operative mean values of systolic and diastolic BP, SpO2, and HR were compared with the post-operative mean values, when a bilateral nasal pack was in place, a significant increase was found in all, with a p-value of <0.001 in each. Conclusion Bilateral nasal packing affects patients' vital signs by significantly increasing diastolic and systolic BP and decreasing SpO2. The HR is also significantly increased when packing is in place.

Empowering health care consumers & understanding patients' perspectives on AI integration in oncology and surgery: A perspective.

Introduction: Artificial intelligence (AI) is transforming oncology and surgery by improving diagnostics, personalizing treatments, and enhancing surgical precision. Patients appreciate AI for its potential to provide accurate prognoses and tailored therapies. However, AI's implementation raises ethical concerns, data privacy issues, and the need for transparent communication between patients and health care providers. This study aims to understand patients' perspectives on AI integration in oncology and surgery to foster a balanced and patient-centered approach. Methods: The study utilized a comprehensive literature review and analysis of existing research on AI applications in oncology and surgery. The focus was on examining patient perceptions, ethical considerations, and the potential benefits and risks associated with AI integration. Data was collected from peer-reviewed journals, conference proceedings, and expert opinions to provide a broad understanding of the topic. The perspectives of patients was also emphasized to highlight the nuances of their acceptance and concerns regarding AI in their health care. Results: Patients generally perceive AI in oncology and surgery as beneficial, appreciating its potential for more accurate diagnoses, personalized treatment plans, and improved surgical outcomes. They particularly value AI's role in providing timely and precise diagnostics, which can lead to better prognoses and reduced anxiety. However, concerns about data privacy, ethical implications, and the reliability of AI systems were prevalent. Consequently, trust in AI and health care providers was deemed as a crucial factor for patient acceptance. Additionally, the need for transparent communication and ethical safeguards was also highlighted to address these concerns effectively. Conclusion: The integration of AI in oncology and surgeryholds significant promise for enhancing patient care and outcomes. Patients view AI as a valuable tool that can provide accurate prognoses and personalized treatments. However, addressing ethical concerns, ensuring data privacy, and building trust through transparent communication are essential for successful AI integration. Future initiatives should focus on refining AI algorithms, establishing robust ethical guidelines, and enhancing patient education to harmonize technological advancements with patient-centered care principles.

Weaving Stories into Education: An Urgent Need to Integrate Narrative Medicine into Pakistan's Medical Curriculum.

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Genome-wide analysis and identification of Carotenoid Cleavage Oxygenase (CCO) gene family in coffee (coffee arabica) under abiotic stress.

The coffee industry holds importance, providing livelihoods for millions of farmers globally and playing a vital role in the economies of coffee-producing countries. Environmental conditions such as drought and temperature fluctuations can adversely affect the quality and yield of coffee crops.Carotenoid cleavage oxygenases (CCO) enzymes are essential for coffee plants as they help break down carotenoids contributing to growth and stress resistance. However, knowledge about the CCO gene family in Coffee arabica was limited. In this study identified 21 CCO genes in Coffee arabica (C. arabica) revealing two subfamilies carotenoid cleavage dioxygenases (CCDs) and 9-cis-epoxy carotenoid dioxygenases (NCED) through phylogenic analysis. These subfamilies exhibited distribution patterns in terms of gene structure, domains, and motifs. The 21 CaCCO genes, comprising 5 NCED and 16 CCD genes were found across chromosomes. Promoter sequencing analysis revealed cis-elements that likely interact with plant stress-responsive, growth-related, and phytohormones, like auxin and abscisic acid. A comprehensive genome-wide comparison, between C. arabica and A. thaliana was conducted to understand the characteristics of CCO genes. RTqPCR data indicated that CaNCED5, CaNCED6, CaNCED12, and CaNCED20 are target genes involved in the growth of drought coffee plants leading to increased crop yield, in a conditions, with limited water availability. This reveals the role of coffee CCOs in responding to abiotic stress and identifies potential genes useful for breeding stress-resistant coffee varieties.

Treatment of a resistant case of schizoaffective disorder with lumateperone: A case report.

To this day, there exists skepticism about the reliability and clinical utility of the diagnostic criteria and classification of schizoaffective disorder. In addition, the treatment of schizoaffective disorder, especially of treatment-resistant cases, has been minimally investigated. As a result, formulating official treatment guidelines for schizoaffective disorder has been challenging. We present a case of a 27-year-old female, diagnosed with schizoaffective disorder, bipolar type, for whom, for over 5 years, trials of traditional treatments, to include psychotherapy, pharmacotherapy, and electroconvulsive therapy, were either partially effective or discontinued due to intolerable side effects. The subsequent off-label use of lumateperone led to an adequate response. Lumateperone is an atypical antipsychotic, approved by the Food and Drug Administration for schizophrenia and bipolar depression in adults. Interestingly, it has a similar structure and mechanism of action to paliperidone, the only Food and Drug Administration-approved medication for schizoaffective disorder. Through this case report, as an example of lumateperone's effectiveness and tolerability, as well as a literature review of its pharmacodynamics, we make the case that lumateperone emerges as a promising option for schizoaffective disorder, especially treatment-resistant cases.