Transcarotid Arterial Revascularization of Symptomatic Internal Carotid Artery Disease: A Systematic Review and Study-Level Meta-Analysis.

BACKGROUND: Transcarotid artery revascularization (TCAR) is an interventional therapy for symptomatic internal carotid artery disease. Currently, the utilization of TCAR is contentious due to limited evidence. In this study, we evaluate the safety and efficacy of TCAR in patients with symptomatic internal carotid artery disease compared with carotid endarterectomy (CEA) and carotid artery stenting (CAS). METHODS: A systematic review was conducted, spanning from January 2000 to February 2023, encompassing studies that used TCAR for the treatment of symptomatic internal carotid artery disease. The primary outcomes included a 30-day stroke or transient ischemic attack, myocardial infarction, and mortality. Secondary outcomes comprised cranial nerve injury and major bleeding. Pooled odds ratios (ORs) for each outcome were calculated to compare TCAR with CEA and CAS. Furthermore, subgroup analyses were performed based on age and degree of stenosis. In addition, a sensitivity analysis was conducted by excluding the vascular quality initiative registry population. RESULTS: A total of 7 studies involving 24 246 patients were analyzed. Within this patient cohort, 4771 individuals underwent TCAR, 12 350 underwent CEA, and 7125 patients underwent CAS. Compared with CAS, TCAR was associated with a similar rate of stroke or transient ischemic attack (OR, 0.77 [95% CI, 0.33-1.82]) and myocardial infarction (OR, 1.29 [95% CI, 0.83-2.01]) but lower mortality (OR, 0.42 [95% CI, 0.22-0.81]). Compared with CEA, TCAR was associated with a higher rate of stroke or transient ischemic attack (OR, 1.26 [95% CI, 1.03-1.54]) but similar rates of myocardial infarction (OR, 0.9 [95% CI, 0.64-1.38]) and mortality (OR, 1.35 [95% CI, 0.87-2.10]). CONCLUSIONS: Although CEA has traditionally been considered superior to stenting for symptomatic carotid stenosis, TCAR may have some advantages over CAS. Prospective randomized trials comparing the 3 modalities are needed.

Skin and Syntax: Large Language Models in Dermatopathology.

This literature review introduces the integration of Large Language Models (LLMs) in the field of dermatopathology, outlining their potential benefits, challenges, and prospects. It discusses the changing landscape of dermatopathology with the emergence of LLMs. The potential advantages of LLMs include a streamlined generation of pathology reports, the ability to learn and provide up-to-date information, and simplified patient education. Existing instances of LLMs encompass diagnostic support, research acceleration, and trainee education. Challenges involve biases, data privacy and quality, and establishing a balance between AI and dermatopathological expertise. Prospects include the integration of LLMs with other AI technologies to improve diagnostics and the improvement of multimodal LLMs that can handle both text and image input. Our implementation guidelines highlight the importance of model transparency and interpretability, data quality, and continuous oversight. The transformative potential of LLMs in dermatopathology is underscored, with an emphasis on a dynamic collaboration between artificial intelligence (AI) experts (technical specialists) and dermatopathologists (clinicians) for improved patient outcomes.

Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events.

Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is possible that any medication can influence other drugs' effectiveness. The drug-to-drug interactions are detected based on the interactions of chemical substructures, targets, pathways, and enzymes; therefore, machine learning (ML) and deep learning (DL) techniques are used to find the associated DDI events. The DL model, i.e., Convolutional Neural Network (CNN), is used to analyze the DDI. DDI is based on the 65 different drug-associated events, which is present in the drug bank database. Our model uses the inputs, which are chemical structures (i.e., smiles of drugs), enzymes, pathways, and the target of the drug. Therefore, for the multi-model CNN, we use several layers, activation functions, and features of drugs to achieve better accuracy as compared to traditional prediction algorithms. We perform different experiments on various hyperparameters. We have also carried out experiments on various iterations of drug features in different sets. Our Multi-Modal Convolutional Neural Network - Drug to Drug Interaction (MCNN-DDI) model achieved an accuracy of 90.00% and an AUPR of 94.78%. The results showed that a combination of the drug's features (i.e., chemical substructure, target, and enzyme) performs better in DDIs-associated events prediction than other features.

FLAIR signal intensity ratio predicts small subcortical infarct early neurologic deterioration: a cross-sectional study.

PURPOSE: Prior studies have used the fluid-attenuated inversion recovery sequence signal intensity ratio (FLAIR-SIR) to predict those with an incomplete infarct that may safely receive acute thrombolytics. Clinical early neurologic deterioration (END) of small subcortical infarcts (SSIs) is suspected to occur due to delayed infarct completion. We aimed to understand if a lower FLAIR-SIR, suggestive of an incomplete infarct, would have a higher likelihood of SSI-related END. METHODS: A cross-sectional retrospective study was performed of those with an acute SSI (anterior or posterior circulation) without significant parent vessel steno-occlusive disease. END was defined as a new or worsened disabling neurologic deficit during the index hospitalization. Standard-of-care brain MRIs were reviewed from the hospitalization, and a FLAIR-SIR cutoff of ≤ 1.15 was used based on prior studies. Adjusted logistic regression models were used for analysis. RESULTS: We identified 252 patients meeting inclusion criteria: median (IQR) age 68 (12) years, 38.5% (97/252) female, and 11% (28/252) with END. Tobacco use was more common in those without END (32%) compared with END (55%, p = 0.03). In adjusted analyses, a FLAIR-SIR cutoff of ≤ 1.15 yielded an odds ratio of 2.8 (95% CI 1.23-6.13, p = 0.012) of early neurological deterioration. CONCLUSION: Those with a FLAIR-SIR ≤ 1.15 are nearly threefold more likely to develop SSI-related END.

An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma.

When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews's correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.

Off-Label Use of Baricitinib in Dermatology.

The current US Food and Drug Administration (FDA) indications for baricitinib include alopecia areata, rheumatoid arthritis, and COVID-19. However, increasing evidence indicates that baricitinib is effective in treating a variety of dermatological conditions. This review article comprehensively presents the available literature on this topic and will be of interest to practitioners in the field. These disorders may be broadly classified as connective tissue diseases, eczematous dermatoses, alopecias, vascular disorders, granulomatous diseases, neutrophilic dermatoses, vitiligo, psoriasis, lichenoid disorders, and other miscellaneous disorders. Shah A, Yumeen S, Qureshi A, et al. Off-label use of baricitinib in dermatology. J Drugs Dermatol. 2023;22(8):795-801. doi:10.36849/JDD.7360.

Borderzone Infarcts and Recurrent Cerebrovascular Events in Symptomatic Intracranial Arterial Stenosis: A Systematic Review and Meta-Analysis.

BACKGROUND AND PURPOSE: Intracranial arterial stenosis (ICAS)-related stroke occurs due to three primary mechanisms with distinct infarct patterns: (1) borderzone infarcts (BZI) due to impaired distal perfusion, (2) territorial infarcts due to distal plaque/thrombus embolization, and (3) plaque progression occluding perforators. The objective of the systematic review is to determine whether BZI secondary to ICAS is associated with a higher risk of recurrent stroke or neurological deterioration. METHODS: As part of this registered systematic review (CRD42021265230), a comprehensive search was performed to identify relevant papers and conference abstracts (with ≥20 patients) reporting initial infarct patterns and recurrence rates in patients with symptomatic ICAS. Subgroup analyses were performed for studies including any BZI versus isolated BZI and those excluding posterior circulation stroke. The study outcome included neurological deterioration or recurrent stroke during follow-up. For all outcome events, corresponding risk ratios (RRs) and 95% confidence intervals (95% CI) were calculated. RESULTS: A literature search yielded 4,478 records with 32 selected during the title/abstract triage for full text; 11 met inclusion criteria and 8 studies were included in the analysis (n=1,219 patients; 341 with BZI). The meta-analysis demonstrated that the RR of outcome in the BZI group compared to the no BZI group was 2.10 (95% CI 1.52-2.90). Limiting the analysis to studies including any BZI, the RR was 2.10 (95% CI 1.38-3.18). For isolated BZI, RR was 2.59 (95% CI 1.24-5.41). RR was 2.96 (95% CI 1.71-5.12) for studies only including anterior circulation stroke patients. CONCLUSION: This systematic review and meta-analysis suggests that the presence of BZI secondary to ICAS may be an imaging biomarker that predicts neurological deterioration and/or stroke recurrence.

EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma.

The most common cause of mortality and disability globally right now is cholangiocarcinoma, one of the worst forms of cancer that may affect people. When cholangiocarcinoma develops, the DNA of the bile duct cells is altered. Cholangiocarcinoma claims the lives of about 7000 individuals annually. Women pass away less often than men. Asians have the greatest fatality rate. Following Whites (20%) and Asians (22%), African Americans (45%) saw the greatest increase in cholangiocarcinoma mortality between 2021 and 2022. For instance, 60-70% of cholangiocarcinoma patients have local infiltration or distant metastases, which makes them unable to receive a curative surgical procedure. Across the board, the median survival time is less than a year. Many researchers work hard to detect cholangiocarcinoma, but this is after the appearance of symptoms, which is late detection. If cholangiocarcinoma progression is detected at an earlier stage, then it will help doctors and patients in treatment. Therefore, an ensemble deep learning model (EDLM), which consists of three deep learning algorithms-long short-term model (LSTM), gated recurrent units (GRUs), and bi-directional LSTM (BLSTM)-is developed for the early identification of cholangiocarcinoma. Several tests are presented, such as a 10-fold cross-validation test (10-FCVT), an independent set test (IST), and a self-consistency test (SCT). Several statistical techniques are used to evaluate the proposed model, such as accuracy (Acc), sensitivity (Sn), specificity (Sp), and Matthew's correlation coefficient (MCC). There are 672 mutations in 45 distinct cholangiocarcinoma genes among the 516 human samples included in the proposed study. The IST has the highest Acc at 98%, outperforming all other validation approaches.

When Surgery Won't Cut It: Successful Management of Eruptive Squamous Atypia With Combination Medical Therapy.

The termeruptive squamous atypia(ESA) is used to describe squamous proliferations that do not present with high-grade histologic features and for which surgical management may exacerbate the condition. Non-surgical management of ESA with radiation, local or systemic chemotherapy, retinoids, or immunotherapy have been reported with variable success. In contrast, combination treatment with retinoids, immunomodulatory or chemotherapeutic agents may result in a more durable response. We report a case of recalcitrant ESA of the lower extremities where complete clinical remission was induced with triple combination medical management with intralesional 5-fluorouracil, field treatment with topical 5-fluorouracil and imiquimod, and oral acitretin. Our case adds to the literature supporting combination medical therapy for challenging cases of ESA.

Deep learning ensemble 2D CNN approach towards the detection of lung cancer.

In recent times, deep learning has emerged as a great resource to help research in medical sciences. A lot of work has been done with the help of computer science to expose and predict different diseases in human beings. This research uses the Deep Learning algorithm Convolutional Neural Network (CNN) to detect a Lung Nodule, which can be cancerous, from different CT Scan images given to the model. For this work, an Ensemble approach has been developed to address the issue of Lung Nodule Detection. Instead of using only one Deep Learning model, we combined the performance of two or more CNNs so they could perform and predict the outcome with more accuracy. The LUNA 16 Grand challenge dataset has been utilized, which is available online on their website. The dataset consists of a CT scan with annotations that better understand the data and information about each CT scan. Deep Learning works the same way our brain neurons work; therefore, deep learning is based on Artificial Neural Networks. An extensive CT scan dataset is collected to train the deep learning model. CNNs are prepared using the data set to classify cancerous and non-cancerous images. A set of training, validation, and testing datasets is developed, which is used by our Deep Ensemble 2D CNN. Deep Ensemble 2D CNN consists of three different CNNs with different layers, kernels, and pooling techniques. Our Deep Ensemble 2D CNN gave us a great result with 95% combined accuracy, which is higher than the baseline method.

Association of hemoglobin trend and outcomes in aneurysmal subarachnoid hemorrhage: A single center cohort study.

BACKGROUND: Anemia has been linked to delayed cerebral ischemia (DCI) and worse outcome in patients with aneurysmal subarachnoid hemorrhage (aSAH). However, the association of hemoglobin (Hb) trend and outcomes is not well studied. We investigated predictors of Hb trend and its association with outcomes in patients with aSAH. Our hypothesis was that a negative Hb trend is associated with poorer outcomes independent of Hb values. METHODS: We conducted a retrospective study of a prospectively collected cohort of consecutive patients with aSAH who were admitted to an academic center (2016-2021). We tested the association of Hb trend and values with measures including DCI and poor functional outcome defined as modified Rankin scale 4-6 at 3 months after discharge. Multiple linear regression analysis was used to identify factors associated with Hb difference from admission to discharge. RESULTS: We included 310 patients with confirmed aneurysmal etiology (mean age 57 years, SD13.6; 62 % female). Greater Hb decrement from admission to discharge was independently associated with higher likelihood of both DCI (OR 1.28 per 1 g/dl decrease in Hb, 95 % CI 1.08-1.47; p = 0.003) and poor functional outcome (OR 1.27 per 1 g/dl decrease in Hb, 1.03-1.53; p = 0.026) independent of any absolute Hb values. Predictors of Hb decrement from admission to discharge were hospital length of stay, Hunt and Hess grades, female sex and age. CONCLUSION: Greater Hb decrement can be associated with higher likelihood of DCI and poor functional outcome in aSAH. More evidence is needed to use Hb trend to guide transfusion threshold in aSAH patients.

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations.

Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocarcinoma starts in secretary cells. Breast adenocarcinoma is the most common of all cancers that occur in women. According to a survey within the United States of America, there are more than 282,000 breast adenocarcinoma patients registered each 12 months, and most of them are women. Recognition of cancer in its early stages saves many lives. A proposed framework is developed for the early detection of breast adenocarcinoma using an ensemble learning technique with multiple deep learning algorithms, specifically: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bi-directional LSTM. There are 99 types of driver genes involved in breast adenocarcinoma. This study uses a dataset of 4127 samples including men and women taken from more than 12 cohorts of cancer detection institutes. The dataset encompasses a total of 6170 mutations that occur in 99 genes. On these gene sequences, different algorithms are applied for feature extraction. Three types of testing techniques including independent set testing, self-consistency testing, and a 10-fold cross-validation test is applied to validate and test the learning approaches. Subsequently, multiple deep learning approaches such as LSTM, GRU, and bi-directional LSTM algorithms are applied. Several evaluation metrics are enumerated for the validation of results including accuracy, sensitivity, specificity, Mathew's correlation coefficient, area under the curve, training loss, precision, recall, F1 score, and Cohen's kappa while the values obtained are 99.57, 99.50, 99.63, 0.99, 1.0, 0.2027, 99.57, 99.57, 99.57, and 99.14 respectively.