Chronic Inflammatory Demyelinating Polyneuropathy (CIDP): Overview, Treatment, and a Case Study.

Chronic inflammatory demyelinating polyneuropathy (CIDP) is an uncommon immune-mediated neuropathy with an often unknown etiology. Patients typically present with gradual muscle weakness, sensory loss, and reduced deep tendon reflexes. Diagnostic challenges persist due to the absence of specific lab findings and definitive criteria. Treatment commonly involves glucocorticoids, IVIG, or plasma exchange, with varied long-term outcomes. We aim to elucidate the diagnostic complexities and treatment modalities associated with chronic CIDP through a comprehensive review of a patient's clinical presentation, diagnostic work-up, and therapeutic interventions. A 70-year-old female with a complex medical history, including dermatomyositis and IgG subclass deficiency, presented with progressive lower extremity weakness and numbness. Initial workup including MRI and CT scans were inconclusive. She was diagnosed with CIDP based on electromyography (EMG)/nerve conduction studies and cerebrospinal fluid (CSF) analysis. Plasma exchange (PLEX) treatment was initiated but led to multifocal cerebral infarcts, complicating her course. Subsequent rounds of PLEX alongside dual antiplatelet therapy showed no adverse neurological events and yielded minimal to moderate improvement in her mobility. The patient was discharged to an inpatient rehabilitation center for continued care. Elevated WBCs and other abnormal lab results were monitored throughout, underscoring the need for a multidisciplinary approach in complex cases like this one. Our comprehensive overview of CIDP and its diagnostic and treatment complexities underscores the challenges clinicians face in both accurate diagnosis and effective management. The multifaceted approach - spanning history-taking, electrodiagnostic studies, and advanced imaging - highlights the necessity for a nuanced, evidence-based practice. The variability in treatment outcomes emphasizes the need for personalized medicine and continuous research to optimize therapeutic strategies. Given the inconclusive nature of some diagnostic tools and the variable treatment responses, there remains a clear need for ongoing study and long-term follow-up to further refine our understanding and management of CIDP.

Bacteremia Associated With a Toothpick Lodged in the Duodenal Wall.

Toothpick ingestion and perforation of the gastrointestinal tract, although a very rare phenomenon, carries a very high mortality risk. Most cases of toothpick ingestion remain unnoticed until very late. The symptoms are often vague, with the most common being abdominal pain. Any obscure case of abdominal pain with bacteremia must be investigated for foreign body ingestion and perforation. A CT scan is the best initial diagnostic modality for toothpick perforation but has low sensitivity with laparoscopy, and endoscopy is the preferred diagnostic tool. No single bacterium is involved in bacteremia due to toothpick ingestion. Surgical or endoscopic removal of the impacted toothpick along with prompt antibiotic therapy leads to excellent outcomes. If left untreated or in case of a late diagnosis, it may lead to life-threatening consequences. Here, we present a case of a 44-year-old man who had ingested a toothpick that pierced through and was lodged in the duodenal wall, leading to bacteremia. The patient was successfully treated and discharged.

A Review of Applications of Artificial Intelligence in Gastroenterology.

Artificial intelligence (AI) is the science that deals with creating 'intelligent machines'. AI has revolutionized medicine because of its application in several fields across medicine like radiology, neurology, ophthalmology, orthopedics and gastroenterology. In this review, we intend to summarize the basics of AI, the application of AI in various gastrointestinal pathologies till date as well as challenges/ problems related to the application of AI in medicine. Literature search using keywords like artificial intelligence, gastroenterology, applications, etc. were used. The literature search was done using Google Scholar, PubMed and ScienceDirect. All the relevant articles were gathered and relevant data were extracted from them. We concluded AI has achieved major feats in the past few decades. It has helped clinicians in diagnosing complex diseases, managing treatments as well as in predicting outcomes, all in all, which helps doctors from all over the globe in dispensing better healthcare services.

Pleural Tuberculosis: A Febrile Presentation Without Respiratory Symptoms.

Tuberculosis (TB) is one of the largest public health crises globally, with pleural TB comprising a large portion of cases. It has a significantly minimal presence within the United States in comparison to the rest of the world. Awareness of its presence and acumen on diagnostics and treatment are essential. Conventional tests are often time consuming, and do not always yield accurate results. We present the case of a patient presenting with fevers but no cough, who eventually found to have large pleural effusion and concluded to have pleural TB without pulmonary parenchymal involvement. He then showed measurable improvement with empiric treatment.

Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback.

BACKGROUND: Due to reimbursement tied in part to patients' perception of their care, hospitals continue to stress obtaining patient feedback and understanding it to plan interventions to improve patients' experience. We demonstrate the use of natural language processing (NLP) to extract meaningful information from patient feedback obtained through Press Ganey surveys. METHODS: The first step was to standardize textual data programmatically using NLP libraries. This included correcting spelling mistakes, converting text to lowercase, and removing words that most likely did not carry useful information. Next, we converted numeric data pertaining to each category based on sentiment and care aspect into charts. We selected care aspect categories where there were more negative comments for more in-depth study. Using NLP, we made tables of most frequently appearing words, adjectives, and bigrams. Comments with frequent words/combinations underwent further study manually to understand factors contributing to negative patient feedback. We then used the positive and negative comments as the training dataset for a neural network to perform sentiment analysis on sentences obtained by splitting mixed reviews. RESULTS: We found that most of the comments were about doctors and nurses, confirming the important role patients ascribed to these two in patient care. "Room," "discharge" and "tests and treatments" were the three categories that had more negative than positive comments. We then tabulated commonly appearing words, adjectives, and two-word combinations. We found that climate control, housekeeping and noise levels in the room, time delays in discharge paperwork, conflicting information about discharge plan, frequent blood draws, and needle sticks were major contributors to negative patient feedback. None of this information was available from numeric data alone. CONCLUSION: NLP is an effective tool to gain insight from raw textual patient feedback to extract meaningful information, making it a powerful tool in processing large amounts of patient feedback efficiently.

Synthetic Cannabinoids: An Emerging Cause of Coagulopathy.

Synthetic cannabinoids-associated coagulopathy cases are on the rise but are usually undetected in clinical settings due to less awareness among physicians and the lack of available literature on the subject. Patient may also not volunteer information about the use of such substances therefore detailed history is important. We present a case of a patient found to have coagulopathy of unclear cause as patient did not report the use of synthetic cannabinoid initially.

Stem Cell Therapies: A Way to Promising Cures.

Stem cells carry the remarkable ability to differentiate into different cell types while retaining the capability to self-replicate and maintain the characteristics of their parent cells, referred to as potency. Stem cells have been studied extensively to better understand human development and organogenesis. Because of advances in stem cell-based therapies, regenerative medicine has seen significant growth. Ophthalmic conditions, some of which are leading causes of blindness worldwide, are being treated with stem cell therapies. Great results have also been obtained in the treatment of oral and maxillofacial defects. Stem-cell-based therapies have great potential in the treatment of chronic medical conditions like diabetes and cardiomyopathy. The unique property of stem cells to migrate towards cancer cells makes them excellent vectors for the transportation of bioactive agents or for targeting cancer cells, both primary and metastatic. While these therapeutic strategies are extremely promising, they are not without limitations. Failure to completely eradicate the tumor and tumor relapse are some of those concerns. Stem cells share some characteristics with cancer stem cells, raising concerns for increasing the risk of cancer occurrence. Ethical concerns due to the fetal origin of stem cells and cost are other major obstacles in the large-scale implementation of such therapies.

A Great Mimicker in Thoracic Spine: Spinal Double Expressor Lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is one of the most common causes of non-Hodgkin's lymphoma (NHL). Some of these DLBCLs can have genetic mutations as well as protein overexpression. The genes involved are MYC, BCL-2 and BCL-6. These are very aggressive and do not respond well to standard chemotherapy regiment. Lymphomas usually show classic signs and symptoms but rarely can present with little or no symptoms or even mimic other disease processes. Here we will present a case where a spinal lymphoma mimicked a hematoma and the patient developed signs and symptoms only after mechanical fall and hitting his back.