Multidisciplinary approach and anesthetic management of a surgical cancer patient with methylene tetrahydrofolate reductase deficiency: a case report and review of the literature

INTRODUCTION

Hyperhomocysteinemia is a known risk factor for myocardial infarction, stroke, peripheral vascular disease, and thrombosis. Elevated plasma homocysteine levels have been demonstrated in patients with recurrent episodes or a single episode of thrombosis. Here we describe the development of cardiovascular disease as a complication of a surgical intervention in a patient with colorectal cancer and hyperhomocysteinemia.

CASE PRESENTATION

A 65-year-old Caucasian man complained of pain and constipation, attributed to previously diagnosed adenocarcinoma (stage IIB) of the hepatic flexure. An anamnestic investigation showed that he had undergone two surgical interventions. During both, he suffered thrombotic postoperative complications, a deep vein thrombosis of the upper extremity after the first operation and retinal vein occlusion after the second. He was diagnosed with hyperhomocysteinemia associated with a homozygous C677T mutation of the gene encoding the enzyme methylenetetrahydrofolate reductase. Our patient was initially treated with folic acid and high-dose B vitamins. On day 7 he underwent a right hemicolectomy. Anesthesia was performed with sevoflurane in 40% O2 and without the use of nitrous oxide. Postoperatively, our patient remained on folic acid and B vitamins and was without immediate or subsequent complications.

CONCLUSIONS

Neoplastic disease and related surgery followed by the administration of chemotherapeutic drugs alter the hemostatic balance in cancer patients. Those suspected of also having a thrombophilic disease require a thorough laboratory diagnostic workup, including a molecular analysis aimed at identifying the genetic mutation responsible for the hyperhomocysteinemia, as indicated. The case described in this report highlights the importance of a multidisciplinary approach that includes expertise in peri-operative anesthesia, surgery, oncology, and hematology.

Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept

Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. In this scenario, objective pain assessment is an open research challenge. This work introduces a framework for automatic pain assessment. The proposed method is based on a wearable biosignal platform to extract quantitative indicators of the patient pain experience, evaluated through a self-assessment report. Two preliminary case studies focused on the simultaneous acquisition of electrocardiography (ECG), electrodermal activity (EDA), and accelerometer signals are illustrated and discussed. The results demonstrate the feasibility of the approach, highlighting the potential of EDA in capturing skin conductance responses (SCR) related to pain events in chronic cancer pain. A weak correlation (R = 0.2) is found between SCR parameters and the standard deviation of the interbeat interval series (SDRR), selected as the Heart Rate Variability index. A statistically significant (p < 0.001) increase in both EDA signal and SDRR is detected in movement with respect to rest conditions (assessed by means of the accelerometer signals) in the case of motion-associated cancer pain, thus reflecting the relationship between motor dynamics, which trigger painful responses, and the subsequent activation of the autonomous nervous system. With the objective of integrating parameters obtained from biosignals to establish pain signatures within different clinical scenarios, the proposed framework proves to be a promising research approach to define pain signatures in different clinical contexts.

Crossing the AI Chasm in Neurocritical Care

Despite the growing interest in possible applications of computer science and artificial intelligence (AI) in the field of neurocritical care (neuro-ICU), widespread clinical applications are still missing. In neuro-ICU, the collection and analysis in real time of large datasets can play a crucial role in advancing this medical field and improving personalized patient care. For example, AI algorithms can detect subtle changes in brain activity or vital signs, alerting clinicians to potentially life-threatening conditions and facilitating rapid intervention. Consequently, data-driven AI and predictive analytics can greatly enhance medical decision making, diagnosis, and treatment, ultimately leading to better outcomes for patients. Nevertheless, there is a significant disparity between the current capabilities of AI systems and the potential benefits and applications that could be achieved with more advanced AI technologies. This gap is usually indicated as the AI chasm. In this paper, the underlying causes of the AI chasm in neuro-ICU are analyzed, along with proposed recommendations for utilizing AI to attain a competitive edge, foster innovation, and enhance patient outcomes. To bridge the AI divide in neurocritical care, it is crucial to foster collaboration among researchers, clinicians, and policymakers, with a focus on specific use cases. Additionally, strategic investments in AI technology, education and training, and infrastructure are needed to unlock the potential of AI technology. Before implementing a technology in patient care, it is essential to conduct thorough studies and establish clinical validation in real-world environments to ensure its effectiveness and safety. Finally, the development of ethical and regulatory frameworks is mandatory to ensure the secure and efficient deployment of AI technology throughout the process.

The Experiments on Electrical Anesthesia in Italy in the Second Half of the Nineteenth Century. A Dispute Between a Fearless Surgeon Patriot and a Positivist Researcher

Electric anesthesia is the anesthesia, usually general anesthesia, produced by the application of an electrical current. This fascinating issue of the anesthesia history was made possible thanks to the pioneering experiments on electrotherapy and electrophysiology performed by two researchers: the neurologist Guillaume Duchenne (1806-1875) and the biologist Stéphane Leduc (1853-1939). The aim of this study is the review of the dispute between two Italian scientists on the effectiveness of electric anesthesia in the second half of the 19th century. One of the two contenders was Rodolfo Rodolfi (1827-1896), an Italian surgeon and patriot who took part in the First Italian War of Independence of 1848, whereas the other protagonist of the dispute was the positivist Plinio Schivardi (1833-1908), a pupil of Duchenne who brought to Italy his knowledge of electrotherapy, collecting these experiences in the Theoretical Practical Manual of Electrotherapy, the first book on the subject written in Italian.

The intercalatus nucleus of Staderini

Rutilio Staderini was one of the leading Italian anatomists of the twentieth century, together with some scientists, such as Giulio Chiarugi, Giovanni Vitali, and others. He was also a member of a new generation of anatomists. They had continued the tradition of the most famous Italian scientists, which started from the Renaissance up until the nineteenth century. Although he carried out important studies of neuroanatomy and comparative anatomy, as well as embryology, his name is rarely remembered by most medical historians. His name is linked to the nucleus he discovered: the Staderini nucleus or intercalated nucleus, a collection of nerve cells in the medulla oblongata located lateral to the hypoglossal nucleus. This article focuses on the biography of the neuroanatomist as well as the nucleus that carries his name and his other research, especially on comparative anatomy and embryology.

Dissecting the link between PD-1/PD-L1-based immunotherapy and cancer pain: mechanisms, research implications, and artificial intelligence perspectives

Cancer-related pain represents one of the most common complaints of cancer patients especially for those with advanced-stage of disease and/or bone metastases. More effective therapeutic strategies are needed not only to improve the survival of cancer patients but also to relieve cancer-related pain. In the last decade, immune checkpoint inhibitor (ICI)-based immunotherapy targeting programmed cell death-1 (PD-1) and its ligand 1 (PD-L1) has revolutionized cancer care. Beyond its anticancer role, PD-1/PD-L1 axis pathway is involved in many other physiological processes. PD-L1 expression is found in both malignant tissues and normal tissues including the dorsal root ganglion, and spinal cord. Through its interaction with PD-1, PD-L1 can modulate neuron excitability, leading to the suppression of inflammatory, neuropathic, and bone cancer pain. Therefore, since the intricate relationship between immunotherapy and pain should be largely dissected, this comprehensive review explores the complex relationship between PD-1/PD-L1-based immunotherapy and cancer-related pain. It delves into the potential mechanisms through which PD-1/PD-L1 immunotherapy might modulate pain pathways, including neuroinflammation, neuromodulation, opioid mechanisms, and bone processes. Understanding these mechanisms is crucial for developing future research directions in order to optimize pain management strategies in cancer patients. Finally, this article discusses the role of artificial intelligence (AI) in advancing research and clinical practice in this context. AI-based strategies, such as analyzing large datasets and creating predictive models, can identify patterns and correlations between PD-1/PD-L1 immunotherapy and pain. These tools can assist healthcare providers in tailoring treatment plans and pain management strategies to individual patients, ultimately improving outcomes and quality of life for those undergoing PD-1/PD-L1-based immunotherapy.

THE “NEAPOLITAN SCHOOL OF ELECTROTHERAPY” BETWEEN THE LATE NINETEENTH AND THE FIRST DECADES OF THE TWENTIETH CENTURY

Electrotherapy is the use of electrical energy as a medical treatment. In the nineteenth century, the "golden age" of electrotherapy, the development of this discipline was part of a historical-scientific context characterized by the affirmation of neurology as an autonomous branch and, finally, detached from psychiatry. After a period of limited scientific interest and development, in the second half of the 20th century, electrotherapy underwent a revival. Nowadays, the use of electrotherapy has been researched and accepted in various fields of medicine, including but not limited to rehabilitation, neurology, pain management, and oncology. From its first applications, electrotherapy joined neurology which used it for both diagnostic and therapeutic purposes. In Italy, several scientists carried out experiments on the subject, and an important contribution to the development of the discipline was provided by the "Neapolitan school of electrotherapy". This improvement was made above all by Francesco Vizioli (1834- 1899) and his pupil Francesco Paolo Sgobbo (1860-1936). Despite these premises, however, the decline of electrotherapy as an autonomous science soon came. Meanwhile, radiology, associated initially with electrotherapy, developed rapidly. When Mario Bertolotti (1876- 1957), former professor of Radiology at the University of Turin and one of the founders of Italian radiology, succeeded Sgobbo in 1935, the name (and the discipline) "electrotherapy" was deleted from the diction of the new chair, and from that of the department, which was indicated only as "Radiology". Radiodiagnostic devices, supplies, and roentgen therapy equipment replaced the numerous devices used for electrotherapy. This manuscript is focused on the Neapolitan school of electrotherapy from the late nineteenth century to the beginning of the twentieth. The work of the leading figures who have given the greatest impetus to the study and application of electrotherapy is described. Finally, the electrotherapy devices used are briefly illustrated.

Maria Montessori (1870-1952): Women's emancipation, pedagogy and extra verbal communication

Maria Montessori is one of the most well-known women in Italian history. Although she was the first woman who graduated in medicine in Italy, she is mostly known as an educator. Her teaching method--the Montessori Method- is still used worldwide--Because she could not speak English during the imprisonment in India, there was a big obstacle for her communication with children. However, the need to adopt a non-verbal communication, led her to a sensational discovery: children use an innate and universal language. This language, made of gestures and mimic, is called extra verbal communication.

Recent Advances and Perspectives in Anesthesiology: Towards Artificial Intelligence-Based Applications

In recent years, the field of anesthesiology has seen remarkable advancements in patient safety, comfort, and outcomes [...].

Ethical issues in pain and palliation

PURPOSE OF REVIEW

Increased public awareness of ethical issues in pain and palliative care, along with patient advocacy groups, put pressure on healthcare systems and professionals to address these concerns.Our aim is to review the ethics dilemmas concerning palliative care in ICU, artificial intelligence applications in pain therapy and palliative care, and the opioids epidemics.

RECENT FINDINGS

In this focus review, we highlighted state of the art papers that were published in the last 18 months, on ethical issues in palliative care within the ICU, artificial intelligence trajectories, and how opioids epidemics has impacted pain management practices (see Visual Abstract).

SUMMARY

Palliative care in the ICU should involve a multidisciplinary team, to mitigate patients suffering and futility. Providing spiritual support in the ICU is an important aspect of holistic patient care too.Increasingly sophisticated tools for diagnosing and treating pain, as those involving artificial intelligence, might favour disparities in access, cause informed consent problems, and surely, they need prudence and reproducibility.Pain clinicians worldwide continue to face the ethical dilemma of prescribing opioids for patients with chronic noncancer pain. Balancing the need for effective pain relief with the risk of opioid misuse, addiction, and overdose is a very controversial task.

Employing the Artificial Intelligence Object Detection Tool YOLOv8 for Real-Time Pain Detection: A Feasibility Study

INTRODUCTION

Effective pain management is crucial for patient care, impacting comfort, recovery, and overall well-being. Traditional subjective pain assessment methods can be challenging, particularly in specific patient populations. This research explores an alternative approach using computer vision (CV) to detect pain through facial expressions.

METHODS

The study implements the YOLOv8 real-time object detection model to analyze facial expressions indicative of pain. Given four pain datasets, a dataset of pain-expressing faces was compiled, and each image was carefully labeled based on the presence of pain-associated Action Units (AUs). The labeling distinguished between two classes: pain and no pain. The pain category included specific AUs (AU4, AU6, AU7, AU9, AU10, and AU43) following the Prkachin and Solomon Pain Intensity (PSPI) scoring method. Images showing these AUs with a PSPI score above 2 were labeled as expressing pain. The manual labeling process utilized an open-source tool, makesense.ai, to ensure precise annotation. The dataset was then split into training and testing subsets, each containing a mix of pain and no-pain images. The YOLOv8 model underwent iterative training over 10 epochs. The model's performance was validated using precision, recall, and mean Average Precision (mAP) metrics, and F1 score.

RESULTS

When considering all classes collectively, our model attained a mAP of 0.893 at a threshold of 0.5. The precision for "pain" and "nopain" detection was 0.868 and 0.919, respectively. F1 scores for the classes "pain", "nopain", and "all classes" reached a peak value of 0.80. Finally, the model was tested on the Delaware dataset and in a real-world scenario.

DISCUSSION

Despite limitations, this study highlights the promise of using real-time computer vision models for pain detection, with potential applications in clinical settings. Future research will focus on evaluating the model's generalizability across diverse clinical scenarios and its integration into clinical workflows to improve patient care.

[Prof. Giuseppe Moscati (1880-1927). A special issue on his scientific production]

Professor Giuseppe Moscati (1880-1927) was canonized by the Catholic Church in 1987. This act recognized the extraordinary qualities of a doctor who, considering his work as a "sublime mission", dedicated his life to helping the sick. The fame of the "Holy doctor" or "doctor of the poor", as people called him, was soon widespread and, immediately after his death, made him the subject of several biographies. These treatises, mainly written by religious hagiographers, have little analyzed Moscati as a doctor and scientist, or, in rare cases, privileged the description of his medical career, touching only marginally salient aspects of his work as a scientist and researcher. The purpose of this paper is to provide an insight into the figure of Moscati as a scientist, contextualizing his research activities in the scientific fields of biochemistry and physiology, which were flourishing in the early twentieth century. By a more careful analysis, his scientific production appears original for the resource-poor context and the innovative research method. The Italian scientist, combining physiological chemistry and biochemistry at the clinic, anticipated the birth of modern branches as the "Laboratory Medicine" and the "Clinical Pathology". In particular, he carried out studies on diabetes, storage diseases, metabolism, toxicology, pathology, nephrology and occupational diseases. Moreover, he anticipated the concept of modern laboratory in the oncological field as means for the characterization of the "nature" of neoplasms and related therapeutic approaches. Therefore, even if Moscati was rarely mentioned by the sources of the history of medicine, it is safe to assume that the fame of the Saint has paradoxically eclipsed that of the scientist, preventing him from receiving the necessary reward.

The Illness and Death of Enrico Caruso (1873-1921): A Medical Chorus Out of Tune?

The Italian opera singer Enrico Caruso is considered by many people the most famous opera singer of all time or "The Matchless Singer" for his unique and suggestive vocal timber. Although a man of humble origins, he managed to rise from poverty, thanks to his extraordinary intelligence and determination. From his debut in 1895 in Naples, until December 24, 1920, the tenor had a brilliant career with many performances and over 500 songs in his repertoire. This intense lifestyle went on until 1919, when the fortune that had always accompanied him began to fade and he entered a fast "descending parable." In this study, we analyze Caruso's medical history during his last year of life: Through the study of the newspapers from the period and the statements reported on the tenor's many biographies, we tried to offer a detailed evaluation of the complex pathogenic chain of events that led to his death, impeding him from keeping to alleviate the heart-breaking nostalgia of many emigrants that felt in his singing the warmth of a too distant land.

"Emerging Topics in Pain Medicine": Advancing Research and Patient-Centered Health Strategies

Pain, in all its various forms and manifestations, impacts the lives of millions worldwide [...].

Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain

BACKGROUND

Tailoring effective strategies for cancer pain management requires a careful analysis of multiple factors that influence pain phenomena and, ultimately, guide the therapy. While there is a wealth of research on automatic pain assessment (APA), its integration with clinical data remains inadequately explored. This study aimed to address the potential correlations between subjective and APA-derived objectives variables in a cohort of cancer patients.

METHODS

A multidimensional statistical approach was employed. Demographic, clinical, and pain-related variables were examined. Objective measures included electrodermal activity (EDA) and electrocardiogram (ECG) signals. Sensitivity analysis, multiple factorial analysis (MFA), hierarchical clustering on principal components (HCPC), and multivariable regression were used for data analysis.

RESULTS

The study analyzed data from 64 cancer patients. MFA revealed correlations between pain intensity, type, Eastern Cooperative Oncology Group Performance status (ECOG), opioids, and metastases. Clustering identified three distinct patient groups based on pain characteristics, treatments, and ECOG. Multivariable regression analysis showed associations between pain intensity, ECOG, type of breakthrough cancer pain, and opioid dosages. The analyses failed to find a correlation between subjective and objective pain variables.

CONCLUSIONS

The reported pain perception is unrelated to the objective variables of APA. An in-depth investigation of APA is required to understand the variables to be studied, the operational modalities, and above all, strategies for appropriate integration with data obtained from self-reporting.

TRIAL REGISTRATION

This study is registered with ClinicalTrials.gov, number (NCT04726228), registered 27 January 2021, https://classic.

CLINICALTRIALS

gov/ct2/show/NCT04726228?term=nct04726228&draw=2&rank=1.