Navigating the Incidence of Postoperative Arrhythmia and Hospitalization Length: The Role of Amiodarone and Other Antiarrhythmics in Prophylaxis.

Antiarrhythmic drugs play a pivotal role in managing and preventing arrhythmias. Amiodarone, classified as a class III antiarrhythmic, has been used prophylactically to effectively prevent atrial fibrillation postoperatively in cardiac surgeries. However, there is a lack of consensus on the use of amiodarone and other antiarrhythmic drugs as prophylaxis to reduce the occurrence of all types of postoperative arrhythmias in cardiac and non-cardiac surgeries. A comprehensive PubMed query yielded 614 relevant papers, of which 52 clinical trials were analyzed. The data collection included the class of antiarrhythmics, timing or method of drug administration, surgery type, type of arrhythmia and its incidence, and hospitalization length. Statistical analyses focused on prophylactic antiarrhythmics and their respective reductions in postoperative arrhythmias and hospitalization length. Prophylactic amiodarone alone compared to placebo demonstrated a significant reduction in postoperative arrhythmia incidence in cardiac and non-cardiac surgeries (24.01%, p<0.0001), and it was the only treatment group to significantly reduce hospitalization length versus placebo (p = 0.0441). Prophylactic use of class 4 antiarrhythmics versus placebo also demonstrated a significant reduction in postoperative arrhythmia incidence (28.01%, p<0.0001), and while there was no significant statistical reduction compared to amiodarone (4%, p=0.9941), a lack of abundant data provides a case for further research on the prophylactic use of class 4 antiarrhythmics for this indication. Amiodarone prophylaxis remains a prime cornerstone of therapy in reducing postoperative arrhythmia incidence and hospitalization length. Emerging data suggests a need for a broader exploration of alternative antiarrhythmic agents and combination therapies, particularly class 4 antiarrhythmics, in both cardiac and non-cardiac surgeries. This meta-analysis depicts the effectiveness of amiodarone, among other antiarrhythmics, in postoperative arrhythmia incidence and hospitalization length reduction in cardiac and non-cardiac surgeries.

Transcatheter Aortic Valve Replacement (TAVR) Versus Surgical Aortic Valve Replacement (SAVR): A Review on the Length of Stay, Cost, Comorbidities, and Procedural Complications.

This review provides an in-depth analysis of the effect of length of stay (LOS), comorbidities, and procedural complications on the cost-effectiveness of transcatheter aortic valve replacement (TAVR) in comparison to surgical aortic valve replacement (SAVR). We found that the average LOS was shorter for patients undergoing TAVR, contributing to lower average costs associated with the procedure, although the LOS varied between patients due to the severity of illness and comorbidities present. TAVR has also been found to improve the quality of life for patients receiving aortic valve replacement compared to SAVR. Although TAVR has a lower rate of most post-operative complications caused by SAVR, such as bleeding and cardiac complications, TAVR shows an increased rate of permanent pacemaker (PPM) implantation due to mechanical trauma on the heart's conduction system. In addition, our findings suggest that the cost-effectiveness of each procedure varies based on the types of valve, the patient history of other medical conditions, and the procedural methods. Our findings show that TAVR is preferred over SAVR in terms of cost-effectiveness across a variety of patients with other coexisting medical conditions, including cancer, advanced kidney disease, cirrhosis, diabetes mellitus, and bundle branch block. TAVR also appears to be superior to SAVR with fewer post-operative complications. However, TAVR appears to have a higher rate of PPM implantation rates as compared to SAVR. The comorbidities of the valve recipient must be considered when deciding whether to use TAVR or SAVR as cost-effectiveness varies with the patient background.

Advancing Breast Cancer Research Through Collaborative Computing: Harnessing Google Colab for Innovation.

This investigation explores the potential efficacy of machine learning algorithms (MLAs), particularly convolutional neural networks (CNNs), in distinguishing between benign and malignant breast cancer tissue through the analysis of 1000 breast cancer images gathered from Kaggle.com, a domain of publicly accessible data. The dataset was meticulously partitioned into training, validation, and testing sets to facilitate model development and evaluation. Our results reveal promising outcomes, with the developed model achieving notable precision (92%), recall (92%), accuracy (92%), sensitivity (89%), specificity (96%), an F1 score of 0.92, and an area under the curve (AUC) of 0.944. These metrics underscore the model's ability to accurately identify malignant breast cancer images. Because of limitations such as sample size and potential variations in image quality, further research, data collection, and integration of theoretical models in a real-world clinical setting are needed to expand the reliability and generalizability of these MLAs. Nonetheless, this study serves to highlight the potential use of artificial intelligence models as supporting tools for physicians to utilize in breast cancer detection.

Expression of E-cadherin and N-cadherin in Epithelial-to-Mesenchymal Transition of Osteosarcoma: A Systematic Review.

Osteosarcoma (OS) is a debilitating cancer of the bone that commonly afflicts the young and old. This may be de novo or associated with tumorigenic syndromes. However, many molecular mechanisms are still being uncovered and may offer greater avenues for screening and therapy. Cadherins, including E-cadherin and N-cadherin/vimentin, are involved in epithelial-to-mesenchymal transmission (EMT), which is key for tumor invasion. A study reviewing the relationship between OS and cadherins might elucidate a potential target for therapy and screening. A robust literature review was conducted by searching PubMed with the keywords "osteosarcoma", "cadherin", "e-cadherin" and "n-cadherin". Of a preliminary 266 papers, 25 were included in the final review. Review articles and those without primary data were excluded. Loss of E-cadherin is noted in metastatic cell lines of osteosarcoma. Overexpression of E-cadherin or knockout of N-cadherin/vimentin results in loss of metastatic potential. There are several methods of gene knockout, including CRISPR-Cas9 gene editing, viral vector insertion with micro RNA complementary to long noncoding RNA within gene segments, or proteomic editing. Screening for EMT and genetic treatment of EMT is a possible avenue for the treatment of refractory osteosarcoma. Several studies were conducted ex vivo. Further testing involving in vitro therapy is necessary to validate these methods. Limitations of this study involve a lack of in vivo trials to validate methods.

Deep Learning Classification of Tuberculosis Chest X-rays.

Background Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis. It primarily affects the lungs but can also affect other organs, such as the kidneys, bones, and brain. TB is transmitted through the air when an infected individual coughs, sneezes, or speaks, releasing tiny droplets containing the bacteria. Despite significant efforts to combat TB, challenges such as drug resistance, co-infection with human immunodeficiency virus (HIV), and limited resources in high-burden settings continue to pose obstacles to its eradication. TB remains a significant global health challenge, necessitating accurate and timely detection for effective management.  Methods This study aimed to develop a TB detection model using chest X-ray images obtained from Kaggle.com, utilizing Google's Collaboration Platform. Over 1196 chest X-ray images, comprising both TB-positive and normal cases, were employed for model development. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. Results The model achieved an average precision of 0.934, with precision and recall values of 94.1% each, indicating its high accuracy in classifying TB-positive and normal cases. Sensitivity and specificity values were calculated as 96.85% and 91.49%, respectively. The F1 score was also calculated to be 0.941. The overall accuracy of the model was found to be 94%.  Conclusion These results highlight the potential of machine learning algorithms for TB detection using chest X-ray images. Further validation studies and research efforts are needed to assess the model's generalizability and integration into clinical practice, ultimately facilitating early detection and improved management of TB.

Exploring the Surgical Outcomes of Pancreatic Cancer Resections Performed in Low- Versus High-Volume Centers.

Introduction Pancreatic cancer resections comprise a class of complex surgical operations with a high postoperative morbidity rate. Due to the complicated nature of pancreatic resection, individuals who undergo this procedure are advised to visit a high-volume medical center that performs such pancreatic surgeries frequently. However, this specialized treatment option may not be available for uninsured patients or patients with other socioeconomic limitations that may restrict their access to these facilities. To gain a better understanding of the impact of healthcare disparities on surgical outcomes, we aimed to explore if there were significant differences in mortality rate post-pancreatic resection between high- and low-volume hospitals within San Bernardino, Riverside, Los Angeles, and Orange Counties. Methods We utilized the California Health and Human Services Agency (CHHS) California Hospital Inpatient Mortality Rates and Quality Ratings public dataset to compare risk-adjusted mortality rates (RA-MR) of pancreatic cancer resections procedures. We focused on procedures performed in hospitals within San Bernardino, Riverside, Los Angeles, and Orange County from 2012 to 2015. To assess post-resection outcomes in relation to hospital volume, we utilized an independent T-test (significance level was set equal to 0.05) to determine if there is a statistically significant difference in RA-MR after pancreatic resection between high- and low-volume hospitals. Results During the 2012-2015 study period, 57 hospitals across San Bernardino, Riverside, Orange, and Los Angeles Counties were identified to perform a total of 6,204 pancreatic resection procedures. The low-volume hospital group (N=2,539) was associated with a higher RA-MR of M=4.45 (SD=11.86). By comparison, the high-volume hospital group (N=3,665) was associated with a lower RA-MR of M=1.72 (SD=2.61). Conclusion Pancreatic resection surgeries performed at low-volume hospitals resulted in a significantly higher RA-MR compared to procedures done at high-volume hospitals in California.

An Analysis of the Usage of Retinal Imaging Technology in the Detection of Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) is a disease that worsens the central vision of numerous individuals across the globe. Ensuring that patients are diagnosed accurately and that their symptoms are carefully monitored is essential to ensure that adequate care is delivered. To accomplish this objective, retinal imaging technology is necessary to assess the pathophysiology that is required to give an accurate diagnosis of AMD. The purpose of this review is to assess the ability of various retinal imaging technologies such as optical coherence tomography (OCT), color fundus retinal photography, fluorescein angiography, and fundus photography. The statistical methods that were conducted yielded results that suggested that using OCT in conjunction with other imaging technologies results in a higher detection of symptoms among patients that have AMD. Further investigation should be conducted to ascertain the validity of the conclusions that were stated within the review.

Utilizing Deep Learning for X-ray Imaging: Detecting and Classifying Degenerative Spinal Conditions.

Background Degenerative spinal conditions (DSCs) involve a diverse set of pathologies that significantly impact health and quality of life, affecting many individuals at least once during their lifetime. Treatment approaches are varied and complex, reflecting the intricacy of spinal anatomy and kinetics. Diagnosis and management pose challenges, with the accurate detection of lesions further complicated by age-related degeneration and surgical implants. Technological advancements, particularly in artificial intelligence (AI) and deep learning, have demonstrated the potential to enhance detection of spinal lesions. Despite challenges in dataset creation and integration into clinical settings, further research holds promise for improved patient outcomes. Methods This study aimed to develop a DSC detection and classification model using a Kaggle dataset of 967 spinal X-ray images at the Department of Neurosurgery of Arrowhead Regional Medical Center, Colton, California, USA. Our entire workflow, including data preprocessing, training, validation, and testing, was performed by utilizing an online-cloud based AI platform. The model's performance was evaluated based on its ability to accurately classify certain DSCs (osteophytes, spinal implants, and foraminal stenosis) and distinguish these from normal X-rays. Evaluation metrics, including accuracy, precision, recall, and confusion matrix, were calculated.  Results The model achieved an average precision of 0.88, with precision and recall values of 87% and 83.3%, respectively, indicating its high accuracy in classifying DSCs and distinguishing these from normal cases. Sensitivity and specificity values were calculated as 94.12% and 96.68%, respectively. The overall accuracy of the model was calculated to be 89%.  Conclusion These findings indicate the utility of deep learning algorithms in enhancing early DSC detection and screening. Our platform is a cost-effective tool that demonstrates robust performance given a heterogeneous dataset. However, additional validation studies are required to evaluate the model's generalizability across different populations and optimize its seamless integration into various types of clinical practice.

Deep Learning Classification of Drusen, Choroidal Neovascularization, and Diabetic Macular Edema in Optical Coherence Tomography (OCT) Images.

Background Age-related macular degeneration (AMD), diabetic retinopathy (DR), drusen, choroidal neovascularization (CNV), and diabetic macular edema (DME) are significant causes of visual impairment globally. Optical coherence tomography (OCT) imaging has emerged as a valuable diagnostic tool for these ocular conditions. However, subjective interpretation and inter-observer variability highlight the need for standardized diagnostic approaches. Methods This study aimed to develop a robust deep learning model using artificial intelligence (AI) techniques for the automated detection of drusen, CNV, and DME in OCT images. A diverse dataset of 1,528 OCT images from Kaggle.com was used for model training. The performance metrics, including precision, recall, sensitivity, specificity, F1 score, and overall accuracy, were assessed to evaluate the model's effectiveness. Results The developed model achieved high precision (0.99), recall (0.962), sensitivity (0.985), specificity (0.987), F1 score (0.971), and overall accuracy (0.987) in classifying diseased and healthy OCT images. These results demonstrate the efficacy and efficiency of the model in distinguishing between retinal pathologies. Conclusion The study concludes that the developed deep learning model using AI techniques is highly effective in the automated detection of drusen, CNV, and DME in OCT images. Further validation studies and research efforts are necessary to evaluate the generalizability and integration of the model into clinical practice. Collaboration between clinicians, policymakers, and researchers is essential for advancing diagnostic tools and management strategies for AMD and DR. Integrating this technology into clinical workflows can positively impact patient care, particularly in settings with limited access to ophthalmologists. Future research should focus on collecting independent datasets, addressing potential biases, and assessing real-world effectiveness. Overall, the use of machine learning algorithms in conjunction with OCT imaging holds great potential for improving the detection and management of drusen, CNV, and DME, leading to enhanced patient outcomes and vision preservation.

An Investigation on the Efficacy of Glucagon-Like Peptide 1 Receptor Agonists Drugs in Reducing Urine Albumin-to-Creatinine Ratio in Patients With Type 2 Diabetes: A Potential Treatment for Diabetic Nephropathy.

As diabetes mellitus becomes increasingly prevalent globally, so does diabetic nephropathy, a complication leading to one of the world's leading causes of end-stage renal disease (ESRD). Current research has linked an increase in the urine albumin-to-creatinine ratio (UACR), a marker for kidney damage, to a greater risk of adverse renal outcomes and ESRD in patients with diabetes. Of the diabetes medications studied and implemented in clinical settings, glucagon-like peptide-1 receptor agonist (GLP1-RA) drugs have been shown to not only help control HbA1c in diabetes but have also demonstrated numerous cardiovascular, hepatic, and renal benefits. The objective of our study was to assess the efficacy of GLP1-RA drugs in reducing UACR in patients with type 2 diabetes mellitus (T2 DM) to determine if GLP1-RAs could be used to provide renoprotection in diabetic nephropathy in addition to their glucose-lowering effects. Upon a comprehensive review of the literature, we conducted a statistical analysis to determine the efficacy of GLP1-RA monotherapy and combination therapy in reducing UACR in comparison to placebo and insulin glargine. Of the studies analyzed, GLP1-RAs exhibited a statistically significant effect in reducing UACR in comparison to a placebo but not in comparison to insulin glargine. GLP1-RA combination therapy (GLP1-RA used with either insulin glargine, metformin, or dapagliflozin) did not exhibit statistically significant UACR reductions in comparison with insulin glargine. However, GLP1-RA combination therapy showed a trend suggestive of being more effective than insulin glargine in reducing UACR, but due to the limited literature studying this treatment method, further studies in a more focused group of patients with diabetic nephropathy may produce stronger and more definitive results. GLP1-RA monotherapy or combination therapy has been determined to be an effective method for reducing UACR and decreasing the incidence of adverse renal outcomes associated with diabetic kidney disease. GLP1-RA therapy could serve as an alternative treatment in diabetic nephropathy to insulin glargine, which carries a higher risk of hypoglycemia and unintentional weight gain while potentially being less cost-effective.

Exploring the Role of External Beam Radiation Therapy in Osteosarcoma Treatment: Impact of Diagnostic Imaging Delays and Innovative Techniques.

Osteosarcomas are a type of bone cancer that typically affect young adults, often in the bones of the arms and legs. To treat osteosarcoma, doctors typically use a combination of chemotherapy, radiotherapy, and surgery, with External Beam Radiation Therapy (EBRT) being the most commonly used form of radiotherapy. EBRT involves directing high-energy photons, X-rays, gamma rays, protons, and electrons at the tumor to induce cancer cell death. Additionally, healthcare providers use imaging techniques to monitor treatment success. This literature review aims to explore the relationship between osteosarcomas and EBRT, investigate the impact of the delayed diagnosis on survival rates, and examine the effectiveness of innovative uses of EBRT for treating osteosarcomas in unusual locations using comprehensive diagnostic techniques. To achieve these objectives, the review examines case studies and literary analyses and categorizes them based on the delay between symptom onset and diagnosis. The null hypothesis is that the presence or absence of a delay in diagnosis does not significantly impact outcomes for the "Delay" category. A lack of delay results in a more favorable outcome in the "Lack of Delay" category. However, the data and statistical results suggest that additional follow-up care in patients with rare or commonly recurring cancers could benefit outcomes. It is important to note that due to the rarity of osteosarcoma with EBRT, the small sample size in the studies warrants further investigation. Interestingly, many patients presented with head and neck tumors despite the most common location of osteosarcoma being in the long bones.

A Systematic Review of the Usage of Lidocaine in Hip Replacement Surgery.

Hip replacement procedures, professionally known as hip arthroplasty, are one of the most common orthopedic procedures. Due to the variation in this procedure, the use and types of anesthetics differ. One such commonly used anesthetic is lidocaine. Since there are currently no standardized or general procedures for the application of lidocaine for perioperative hip arthroplasty procedures, this review aims to delve into this topic. A literature review surrounding the key terms "hip replacement" and "lidocaine" was performed on PubMed. After reviewing 24 randomized control trials, statistical analyses between groups that had no lidocaine versus groups that did were performed. The results showed that there was no statistical significance between various age groups and the use of lidocaine. One percent (1%) and 2% injected into the lumbar region were the most commonly reported doses of lidocaine, with 2% often being the first test dose. Other conclusions were that lidocaine was used for general anesthesia for individuals that underwent hip arthroplasty due to an underlying condition (cauda equina syndrome, ankylosing spondylitis, etc.). Lidocaine was also used for postoperative pain relief, which is a potential concern from its addictive qualities. This investigation outlines the current stance and usage of lidocaine in perioperative hip arthroplasty while noting its limitations.

A Review of the Cost Effectiveness of Transcatheter Aortic Valve Replacement (TAVR) Versus Surgical Aortic Valve Replacement (SAVR).

The cost of transcatheter aortic valve replacement (TAVR) has been studied in the context of high-risk or specific comorbidity populations; this paper provides a comprehensive overview of broader patient populations' outcomes and costs with TAVR in comparison to surgical aortic valve replacement (SAVR). In the past, SAVR had been the more cost-effective option than TAVR, but in recent years, TAVR has been becoming more cost-effective.Though the cost of TAVR can vary due to several factors the major focus of this review will focus on the surgical technique, medicare reimbursements, insertion point, and varying risk populations. In conclusion, the price of TAVR is declining as more cost-efficient valves arrive on the market. Climbing healthcare costs play a significant role in clinical decisions when deciding on which procedures are most cost-effective for the patient and healthcare system. The declining price of TAVR could lead to the preference of TAVR over SAVR for both low-risk and high-risk aortic stenosis patients.

Development of a Machine Learning-Based Model for Accurate Detection and Classification of Cervical Spine Fractures Using CT Imaging.

Cervical spine fractures represent a significant healthcare challenge, necessitating accurate detection for appropriate management and improved patient outcomes. This study aims to develop a machine learning-based model utilizing a computed tomography (CT) image dataset to detect and classify cervical spine fractures. Leveraging a large dataset of 4,050 CT images obtained from the Radiological Society of North America (RSNA) Cervical Spine Fracture dataset, we evaluate the potential of machine learning and deep learning algorithms in achieving accurate and reliable cervical spine fracture detection. The model demonstrates outstanding performance, achieving an average precision of 1 and 100% precision, recall, sensitivity, specificity, and accuracy values. These exceptional results highlight the potential of machine learning algorithms to enhance clinical decision-making and facilitate prompt treatment initiation for cervical spine fractures. However, further research and validation efforts are warranted to assess the model's generalizability across diverse populations and real-world clinical settings, ultimately contributing to improved patient outcomes in cervical spine fracture cases.

An Analysis of Lidocaine Usage in the Treatment of Squamous Cell Carcinoma.

Squamous cell carcinoma (SCC) is a form of skin cancer that can be treated using a procedure known as Mohs surgery. Mohs surgery is a safe and effective procedure for eliminating SCC. This surgery requires the usage of an analgesic known as lidocaine. Additional anesthetics were also reported to be necessary for this procedure to be conducted in a manner that significantly minimizes patient harm. According to the review, it was found that SCC was treated with lidocaine as a topical analgesic outside of Mohs surgery. This review analyzes the usage of lidocaine in the treatment of SCC. It was also discovered that lidocaine, as an agent, has the potential to slow the progression of SCC, but more research is needed to see if this is truly the case. On average, it was reported that the concentration of lidocaine used in the in vivo studies was significantly higher than that in the in vitro investigations. Further exploration may be needed to verify the conclusions that were based on the analysis of the papers within the review.

Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology.

This study explores the application of machine learning and deep learning algorithms to facilitate the accurate diagnosis of melanoma, a type of malignant skin cancer, and benign nevi. Leveraging a dataset of 793 dermatological images from the Kaggle online platform (Google LLC, Mountain View, California, United States), we developed a model that can accurately differentiate between these lesions based on their distinctive features. The dataset was divided into training (80%), validation (10%), and testing (10%) sets to optimize model performance and ensure its generalizability. Our findings demonstrate the potential of machine learning algorithms in enhancing the efficiency and accuracy of melanoma and nevi detection, with the developed model exhibiting robust performance metrics. Nonetheless, limitations exist due to the potential lack of comprehensive representation of melanoma and nevi cases in the dataset, and variations in image quality and acquisition methods, which may influence the model's performance in real-world clinical settings. Therefore, further research, validation studies, and integration into clinical practice are necessary to ensure the reliability and generalizability of these models. This study underscores the promise of artificial intelligence in advancing dermatologic diagnostics, aiming to improve patient outcomes by supporting early detection and treatment initiation for melanoma.

A Systematic Review of the Usage of Rotigotine During Early and Advanced Stage Parkinson's.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that occurs in old age due to a decrease in dopamine, which causes nerve cell destruction. This disease is difficult to diagnose since its symptoms are similar to those of the aging process. Those with PD have impaired motor control and function, dyskinesia, and tremors. To treat PD, drugs that enhance the amount of dopamine given to the brain are administered to alleviate symptoms. This inquiry examines the prescription of rotigotine to achieve this objective. The primary objective of this review is to examine the usage of rotigotine in both the late and early stages of PD. The statistical model utilized in the review found that there was not a significant difference in the dosage of rotigotine prescribed to late and early-stage PD patients, however, there were some confounding variables that may have skewed this result; therefore, further research is necessary to validate or nullify this hypothesis.

An Analysis of the Use of Proparacaine in Cataract Surgery.

A cataract is the primary cause of preventable blindness and is characterized by a congenital, developmental, or acquired opacity of the human lens. Cataracts are predominantly treated through surgical procedures utilizing a combination of anesthetic agents such as proparacaine to reduce patient discomfort. Proparacaine is used to inhibit voltage-gated sodium channels on neuronal membranes to prevent signal propagation and pain signaling in the patient. Current clinical standards call for the utilization of 0.5% proparacaine when used for local anesthesia in cataract surgeries. In this review, the authors extracted the reported application site and concentrations of proparacaine in conjunction with various combination agents to accurately describe its usage in cataract surgery. It was found that most surgeons adhered to the standard concentrations of proparacaine and generally used tropicamide, an eye dilator, as a combination agent in cataract surgery. Additionally, surgeons preferred anesthetic application to the retrobulbar block. The authors find that although surgeons are following standard protocol, adjustments for lowering the standard dose of proparacaine could prove beneficial in preventing proparacaine toxicity. Furthermore, the authors find that more research can be conducted in the future examining other combination agents for use with proparacaine to improve patient outcomes.

An Investigation on the Preconditions and Diagnosis Methods for Alien Hand Syndrome.

Although it is not a very common condition, people who have suffered from neuro-damage or neuro-diseases are at risk for developing a condition known as Alien hand syndrome (AHS). Individuals who have this condition are unable to control the movement of their hands for certain brief intervals of time. In order to improve upon the treatment of individuals with AHS, it is important that signs and symptoms of the disease are identified as soon as possible. The purpose of this investigation is to catalog the data regarding the pre-existing conditions and the method of diagnosis for AHS. Within the review, it was revealed that stroke was the most common pre-existing condition for the disease. Therefore, physicians who have stroke patients within their care should carefully monitor their condition in case they do develop AHS. Additionally, it was found that using an MRI machine was the most common method of diagnosing a patient with AHS. This was most likely because MRI scans provide the most information about a patient's brain functionality which can be used to deduce if an individual has AHS.

Assessing the Efficacy of Alkylating Agent Regimens in the Treatment of Infantile Malignant Osteopetrosis: Cyclophosphamide, Busulfan, or Thiotepa.

Infantile malignant osteopetrosis is a debilitating disease that requires total bone marrow irradiation and transplant procedures for patients to survive. The major complication of this procedure is graft vs host disease (GVHD), followed by infections and end organ toxicity. Therefore, current research efforts into treatment mainly aim to reduce GVHD while limiting infections and organ toxicity. Different regimens of alkylating agents have been used to try to reduce GVHD. The most common regimen is cyclophosphamide (Cy) with busulfan (Bu), followed by Cy with Bu and thiotepa (Thio). This meta-analysis aimed to evaluate the efficacy of different treatments by comparing mortality and morbidity causes and rates across groups. The mean one-year survival rate for the Cy, Bu, Thio regimen studies in the human leukocyte antigen (HLA) unmatched group (45.01%) was statistically lower than the one-year survival rate for the studies using just a Cy, Bu regimen (70.8%) in the HLA unmatched studies (p<0.00142). The one-year survival in the studies which had HLA-matched donors was 80.56%, which is statistically higher (p<0.001) than the one-year survival in the HLA-unmatched studies (53.96%), indicating a benefit of finding HLA-matched donors. It seems that price and availability could be a factor in the widespread use of Cy.